Projects
| Project | Abstract | Prime Company | Domain | Tags | Full text |
|---|---|---|---|---|---|
| 3DCTRL | 3DCTRL project aims to evaluate cloud correction methodologies in Copernicus Sentinel-4, Sentinel-5 and Sentinel-5p trace gas retrieval schemes and to explore ways to improve handling of realistic clouds in the retrievals of atmospheric species. [...] | ARISTOTLE UNIV. OF THESSALONIKI (GR) | Science | atmosphere, atmosphere science cluster, clouds, permanently open call, science, Sentinel-5P, TROPOMI | 3DCTRL project aims to evaluate cloud correction methodologies in Copernicus Sentinel-4, Sentinel-5 and Sentinel-5p trace gas retrieval schemes and to explore ways to improve handling of realistic clouds in the retrievals of atmospheric species. Cloud shadow fraction, cloud top height, cloud optical depth, solar zenith and viewing angles, were identified as the metrics being the most important in identifying 3D cloud impacts on NO2 TVCD retrievals. For a solar zenith angle less than about 40° the synthetic data show that the NO2 TVCD bias is typically below 10%. For larger solar zenith angles both synthetic and observational data often show NO2 TVCD bias on the order of tens of %. In 3DCTRL, fast retrieval algorithms for 3D cloudy scenes will be designed. Very promising is a retrieval algorithm based on a linearized one-dimensional radiative transfer model, in which the direct beam and its derivative with respect to the total column are computed in a three-dimensional atmosphere. The performance of new methods for cloud correction will be evaluated against the present operational products and independent measurements. 3DCTRL project has the following main objectives: (a) Generate synthetic reference datasets in which true cloud properties including their 3D structure and vertical distribution are known by means of 3D radiative transfer simulations, realistic synthetic data of cloud properties will be obtained from large-eddy simulation (LES) model (b) Explore ways to improve the handling of realistic clouds in trace gas retrievals, specifically for NO2 (c) Testing and evaluation of improved approaches for cloud correction by application on synthetic and real TROPOMI-S5P data |
| A MARKETPLACE FOR SATELLITE IMAGE TASKING | A key indication of the performance of earth observation is how easy, quick and affordable it is to collect fresh imagery of a location. This underpins the viability of value-added services and the credibility of the sector right across markets. [...] | GEOCENTO LIMITED (GB) | Enterprise | generic platform service, permanently open call | A key indication of the performance of earth observation is how easy, quick and affordable it is to collect fresh imagery of a location. This underpins the viability of value-added services and the credibility of the sector right across markets. It is our contention that fresh image collection rates poorly in this regard, particularly in relation to very high resolution optical imagery. If a customer wishes to obtain a fresh image of their area of interest, they have to select a supplier (typically based on reputation rather than informed knowledge), select a service (which can be complex and not necessarily a great fit to requirements), wait for a feasibility report, and then pay a supplier before knowing if the collection will be successful. To address this, we are developing EarthImages-on-Demand – a commercial service that drives standardised requests for very high resolution optical image collection to the network of imaging suppliers for cooperative fulfilment. This turns the current image collection protocol around 180 degrees in favour of customers by allowing them to specify what imagery they want, where and when and the price they are prepared to pay (via a simply interface), and then have the money released to the first supplier that delivers to specification within the requested time window. EarthImages-on-Demand will benefit the whole sector by creating a dynamic between demand and supply, encouraging competition for image collection (driving standards in delivery time and pricing) and supporting scalability through standardisation of imaging requests. We are looking to exploit these benefits by identifying and stimulating demand for the service in key markets, starting with some pilot projects, thus ultimately benefitting suppliers as well as customers through increased demand for image collection. |
| AALM4INFRAM: ARCTIC ACTIVE LAYER MONITORING FOR INFRASTRUCTURE MANAGEMENT | This project will use various InSAR based approaches to characterize changes in land subsidence rates due to permafrost melting in Greenland and assess the impact such changes are having on critical infrastructure in the region. | GAMMA REMOTE SENSING AG (CH) | Digital Platform Services | climate, land, permanently open call, SAR, snow and ice | This project will use various InSAR based approaches to characterize changes in land subsidence rates due to permafrost melting in Greenland and assess the impact such changes are having on critical infrastructure in the region. |
| Advanced Sentinel-1 analysis ready data for Africa | Historically for land application, synthetic aperture radar (SAR) satellite imagery has often been seen only as as complement to optical remote sensing in cloud covered areas.
There are several reasons for this: 1) the threshold of [...] |
NORTHERN RESEARCH INSTITUTE (NORUT) (NO) | Sustainable Development | permanently open call, SAR | Historically for land application, synthetic aperture radar (SAR) satellite imagery has often been seen only as as complement to optical remote sensing in cloud covered areas. There are several reasons for this: 1) the threshold of interpretation and understanding of SAR imagery is often perceived as very high to an untrained user, 2) the human capacity and technical capability in pre-processing SAR data has been out of reach without adequate, often expensive software, and technically-trained staff and 3) the availability of data has been too sparse and expensive for being used operationally for applications other than in (sub)-polar regions. This has especially been the case in developing countries. The Copernicus program, specifically the Sentinel-1A/B (S1) satellites, and recent international efforts opened for a new era of operational SAR application, data access and processing and overcome the challenges 2 and 3 above. Satellite open data cubes (ODC) are currently developed in several countries, including in Africa, with the aim to provide analysis ready data (ARD) from both optical and SAR sensors. The combination of both optical and SAR generally improves the application results. However, for SAR data these ARD efforts generally aim to provide only pre-processed, i.e. radiometric, terrain and slope corrected and georeferenced, single SAR scenes or, at the best, yearly mosaics with questionable consistency and reduce little the subjective reluctance of using SAR data operationally. The purely vast amount of single scenes therefore needs further processing in order to reduce the amount of data as well as to make the data more attractive and easier to interpret for untrained users. This project is intended to overcome user reluctance to integrate SAR data into their EO monitoring and assessment activities by making advanced SAR products available as Analysis Ready Data and demonstrate the possibilities of processing and integrating these data with conventional EO data in a cloud environment. The primary focus will be users in developing countries so the demonstration activities will explicitly take into account issues such as bandwidth constraints. |
| AI and EO as Innovative Methods for Monitoring West Nile Virus Spread (AIDEO) | AI and EO as Innovative Methods for Monitoring West Nile Virus Spread (AIDEO) is being developed by the Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise “G. Caporale”, a veterinary public health institution that has an established [...] | Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise “G. Caporale” (IT) | Digital Platform Services | artificial intelligence, enterprise, health, permanently open call | AI and EO as Innovative Methods for Monitoring West Nile Virus Spread (AIDEO) is being developed by the Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise “G. Caporale”, a veterinary public health institution that has an established international track record in the surveillance, diagnosis, epidemiology, modelling, molecular epidemiology of Vector Borne Diseases (VBDs), AImageLab, that is a research laboratory of the Dipartimento di Ingegneria “Enzo Ferrari” at the University of Modena and Reggio Emilia with extensive experience in Computer Vision, Pattern Recognition, Machine Learning and Artificial Intelligence, Progressive Systems, that delivers solutions to simplify Earth Observation data exploitation and brings significant expertise and experience to the consortium based on years of collaboration with ESA and on-site presence at ESRIN, and REMEDIA Italia, that has relevant experience in designing and realising printed, web, multimedia and technology enhanced scientific communication projects, systems and tools developed inside the Earth Observation Department of ESA (ESRIN). Aim of the project is to develop an innovative, scalable and accurate process to produce West Nile Disease (WND) risk maps, using EO data and specific AI algorithms. Vector-borne diseases (VBDs) are an important threat with an increasing impact on public health due to wider geographic range of occurrence and higher incidences. West Nile virus (WNV) is one of the most spread zoonotic VBD in Italy and Europe. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation (EO) data and the continuous development of innovative Artificial Intelligence (AI) methods can be of great help to automatically identify patterns in big datasets and to make highly accurate predictions. Our project aims to develop an innovative, scalable and accurate process to produce West Nile Disease (WND) risk maps, using EO data and specific AI algorithms. Using historical ground truth data of WND cases and EO data derived from different sources (e.g. Sentinel-2, Sentinel-3, PROBA-V, etc.), a learning architecture, based on Convolutional Neural Network (CNN) and Graph Theory, will be applied on ground truth WND cases and satellite images and tested. This process will produce AI based risk maps that will be then compared with classical statistical methods to evaluate the degree of improvement in forecasting the disease occurrence and spread. Knowledge acquired with this project can be potentially used to define intervention priorities within national diseases surveillance plans. Moreover, the definition and development of algorithms working on available and frequent satellite images could be applied in early warning systems not developed so far, and could be integrated into the Information Systems of the Italian Ministry of Health and made available to other interested stakeholders. This work will therefore lay the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of the disease. This will be achieved in three key phases: Phase 1: Definition of requirements Information regarding EO data to be used, criteria to select ground truth data, temporal interval to be analysed and different Deep Neural Network models will be evaluated and defined. Selection criteria and preparation of remotely sensed products will then be investigated, considering data from multiple sources, various sensors, spectral bands, spatial resolutions and revisit times. WND and EO data will be selected to guarantee a correct spatial and temporal representation of the last ten-years epidemics. Phase 2: Data retrieval and processing WND cases will be extracted from the official repository of the Italian Ministry of Health (National Information System of Animal Disease Notification – SIMAN), integrated with laboratory data coming from the national veterinary laboratories, validated and selected, in space and time, according to the requirements defined in phase 1. WND ground truth outbreaks will be split in different datasets that will be used to train and test the DNN model, then fine-tune the model and hence make predictions and evaluate the overall accuracy. Selected EO data will be collected from different sources and stored in a centralised system where they will be organised and pre-processed according to the requirements defined in phase 1. Classical statistical models for WND spread (suitability analysis, logistic regression, etc.) will be developed to be compared with AI model performance. Phase 3: Train, fine-tuning and validation of the AI model AI models/algorithms for the analysis and prediction of WND “behaviour” will be developed and parameters estimated. Graph-based DNN models will be explored for merging geo-referenced local sites information with satellite images, the latter being processed through Convolutional Neural Networks (pre-trained or trained from scratch). Temporal deep models (e.g. RNN – Recurrent Neural Networks, LSTM – Long-short term memory) will then be employed for an effective forecasting of the behaviour based on EO data. The accuracy of the chosen model will then be evaluated together with the need to include additional data or to change the train model hyper-parameters. We will hence produce the final model that will be compared with the classical statistical models developed in phase 2. Dissemination of information and project results will last for the entire duration of the project and will be made available to stakeholders, relevant institutions, organisations and individuals through workshop and congress presentations, publications in peer reviewed journals, websites. |
| AI FOR ANIMAL CENSUS AND HABITAT MONITORING | Development of AI based methods to classify herds of different animals based on the spatial distribution patterns adopted within different herd species. AI based pattern recognition methods based on distribution patterns of animals within a herd [...] | EOLAS Insight Ltd (GB) | Enterprise | AI4EO, generic platform service, permanently open call | Development of AI based methods to classify herds of different animals based on the spatial distribution patterns adopted within different herd species. AI based pattern recognition methods based on distribution patterns of animals within a herd detected in EO imagery. |
| AI4Sen2Cor | Supporting the monitoring of the Earth’s condition by observing its changes and variability is the main target of the S2 mission.In the spirit of the S2 goals, AI4Sen2Cor is designed to extend the capability of the Sen2Cor_3 processor by [...] | TELESPAZIO GERMANY GMBH (DE) | Enterprise | permanently open call, Sentinel-2 | Supporting the monitoring of the Earth’s condition by observing its changes and variability is the main target of the S2 mission. In the spirit of the S2 goals, AI4Sen2Cor is designed to extend the capability of the Sen2Cor_3 processor by applying a synergic approach that combines systematic AI-based algorithms with the existing and available Sen2Cor quantities and qualities. The AI4Sen2Cor study’s first goal is adding Geospatial Detection capability to Sen2Cor_3 with a systematic production of AI-enhanced and spectral-based single-sensing-time (Static) Augmented Scene Classification (S-ASCL). The second main goals is to produce a tool to analyse a series of S-ASCL belonging to different observations (Sensing Times) of a given tile to produce a Temporal-ASCL (T-ASCL), where temporal variations can be analysed and associated statistics produced. Conference proceedings: Francesco C. Pignatale, Davinder P. Singh, Satish Madhogaria, Bodo Werner, Patrick Griffiths “AI4SEN2COR: A SEN2COR ENHANCEMENT FOR GEOSPATIAL DETECTION” Proc. of the 2023 conference on Big Data from Space (BiDS’23) |
| AKROSS: Altimetric Ku-Band Radar Observations Simulated with SMRT | Accurate estimates of sea ice thickness are essential for numerical weather prediction, ice extent forecasts for navigability and to demonstrate the impacts of climate change on sea ice. The main source of uncertainty in sea ice thickness [...] | CORES SCIENCE AND ENGINEERING LIMIT (GB) | Science | altimeter, CryoSat, permanently open call, polar science cluster, science, snow and ice | Accurate estimates of sea ice thickness are essential for numerical weather prediction, ice extent forecasts for navigability and to demonstrate the impacts of climate change on sea ice. The main source of uncertainty in sea ice thickness measurements from radar altimetry is due to snow. Scattering of the radar signal as it travels through snow changes the return received by the altimeter. AKROSS will determine how snow properties affect the radar return and therefore the accuracy of sea ice thickness estimates. AKROSS has three main objectives: Collection of a suite of field observations of the properties of snow on sea ice suitable for evaluation of electromagnetic models across a range of different satellites, with a focus on radar altimetry. Evaluation and consolidation of the Snow Microwave Radiative Transfer Model in altimeter mode. Investigate origin of signal returns through analysis of the dependence of the altimeter waveform to snowpack structure. The field campaign will take place in Eureka, Canada, timed to coincide with CryoSat2 and ICESat2 satellite overpasses. Snow measurements will include specific surface area, density, layer boundary roughness and casted samples for x-ray tomography imaging. AKROSS will complement and co-ordinate with other activities including studies for the Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) candidate mission. |
| AnREO: Retrieval of Total Ozone using OLCI-S-3 over Antarctica | The main part of the project is to develop a total ozone product for Ocean and Land Colour Instrument (OLCI) on board Sentinel 3 A,B. The product will be derived using the Sentinel-3A, B OLCI Level 1 Full Resolution data. The cloud mask, snow [...] | VITROCISET BELGIUM SPRL (BE) | Science | atmosphere science cluster, atmospheric chemistry, OLCI, permanently open call, science, Sentinel-3 | The main part of the project is to develop a total ozone product for Ocean and Land Colour Instrument (OLCI) on board Sentinel 3 A,B. The product will be derived using the Sentinel-3A, B OLCI Level 1 Full Resolution data. The cloud mask, snow mask, and atmospheric correction procedures will be also developed. OLCI measurements make it possible to understand the intra-pixel variability of the total ozone and observe rapid changes on the total ozone with a high spatial detail. The accuracy of the retrievals will be assessed using ground and collocated satellite (e.g., OMI) measurements of total ozone. |
| Arctic Crowdsourcing | The Arctic Crowdsourcing project has been successfully completed. The objective was to create an enhanced Earth Observations (EO) services for Arctic applications planned for C-CORE’s Coresight Platform to include community/crowd sourced very [...] | C-CORE (CA) | Digital Platform Services | permanently open call, platforms | The Arctic Crowdsourcing project has been successfully completed. The objective was to create an enhanced Earth Observations (EO) services for Arctic applications planned for C-CORE’s Coresight Platform to include community/crowd sourced very high-resolution drone data, ESA Sentinel mission data and other forms of field data that support Arctic stakeholder needs. The Arctic Crowdsourcing project included: 1) Engagement of Arctic communities to develop skills around drone operations, as well as GIS, and EO satellite knowledge. The community engagement also investigated remote sensing based services for that could directly benefit communities. 2) The development Arctic Crowdsourcing Service for collecting community-sourced knowledge, targeting community sourced Drone Data, and geotagged video and image data. 3) The prototype development of enhanced EO based services and incorporate other community sourced data or new products created via the Polar TEP. The developed products were on display and ready for live demos at ESA’s Living Planet Symposium May 2019 in the C-CORE booth, and available publically to all, after the symposium. The project involved direct engagement with community members via several face-to-face meetings with communities, supporting the establishment of training programs and the hiring of local commercial drone operators to collect test scenario data. Initial community engagement highlighted two obstacles to support crowdsourcing of drone imagery which were the lack of in region drone operation skills, and lack of high bandwidth connectivity to transfer the high number of large bandwidth files created by drones and their higher resolution sensors. While this project has completed, the opportunity of developing Arctic crowdsourced drone data will continue to be developed as numbers of drone operators in the Arctic increase, and further engagement and feedback are received from Arctic communities. |
| Assesscarbon | The Assesscarbon project (Feb 2020 – Feb 2021) developed and demonstrated at a pre-operational level an approach for large area forest biomass and carbon modelling, combining ground reference data, Sentinel-2 imagery and primary production [...] | VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD (FI) | Applications | applications, Biomass, carbon cycle, forestry, permanently open call, Sentinel-2 | The Assesscarbon project (Feb 2020 – Feb 2021) developed and demonstrated at a pre-operational level an approach for large area forest biomass and carbon modelling, combining ground reference data, Sentinel-2 imagery and primary production modelling. The overall goal of the project was to develop a foundation for a novel approach to derive large area biomass and carbon pool and flux estimates and forecasting in a scalable fashion on an online platform. The project was coordinated by VTT Technical Research Centre of Finland and funded by ESA under the EO Science for Society Permanently Open Call funding mechanism. The main input data for the project were Copernicus Sentinel-2 satellite data, forest plot measurements and climatic datasets. The Sentinel-2 mosaics was created by Terramonitor (Satellio Oy) using their novel image mosaicking approach. This image composite was used together with field sample plots provided by the Finnish Forest Centre to create forest variable estimation models. Finally, dynamic forest primary production variables were modelled using the forest structure variables and climatic data. The forest structural variable models were based on the Probability software package developed by VTT. It contains three different parts, which together form a comprehensive package of classification/estimation tools combining field data with satellite imagery. The primary production modelling is based on the PREBAS models developed by the University of Helsinki. The models were further developed utilizing multi-temporal observations. The practical processing of the primary production estimates for the area of interest was carried out by Simosol Oy. The demonstration was conducted on the Forestry TEP. Forest structural variable and primary production information were produced for a test area covering the entire Finland and the Russian boreal forests until the Ural mountains. All components of the project were implemented in a manner that enables scalable execution of the models in Forestry TEP environment. The chosen approach utilized the Sentinel-2 tiling structure as the building blocks. All software components were redeveloped to enable processing of a given number of Sentinel-2 tiles in a coordinated manner, in order to produce consistent results over large interest areas. |
| Assessing unpaved road condition from optical satellite imagery using machine learning in th e Global South | Rural roads in Africa provide mobility to the poorest in society, enabling them to access economic opportunities and essential services such as health and education. A study on rural access estimated that over a billion people living in rural [...] | TRL LIMITED (GB) | Applications | africa, climate, permanently open call, sustainable development | Rural roads in Africa provide mobility to the poorest in society, enabling them to access economic opportunities and essential services such as health and education. A study on rural access estimated that over a billion people living in rural areas do not have reasonable access to an all-season road (World Bank, 2016). Latest data (World Bank Data Catalogue, 2019) indicates that 59% of Africans live in rural areas, with the difference in livelihoods between rural and urban being most pronounced (OECD, 2019). At least 80% of goods and 90% of passengers are transported by road in Africa, and 53% of these roads are unpaved (African Development Bank, 2014). Rural roads can be a great enabler of economic and social transformation and are key to a number of Sustainable Development Goals (SDGs), especially SDG 9.1. (TRL, 2019). Climate change is affecting the resilience of rural roads to resist more frequent and extreme weather events. Low- and Middle-Income Countries (LMICs) are struggling to keep pace with the revised requirements for resilience and in many cases do not have the basic information on their road networks to allow them to make essential decisions. The aim of this research is to develop an Earth Observation (EO) based system to rapidly assess the condition of unpaved road networks in LMICs and provide an overview of accessibility for improving road asset management. This minimises the time-consuming and logistically difficult process of gathering road condition information locally, whilst enabling efficient interventions where and when needed, hence optimising the use of resources through more efficient maintenance planning and prioritisation. To be attractive to LMIC road authorities the system must be cost-effective. Previous research focused on using very high-resolution satellite imagery to identify road condition on a Good/Fair/Poor/Bad basis by using Machine Learning classifiers and Convolutional Neural Networks. To minimise imagery costs this research is exploring the possibility of using lower resolution imagery to replicate the results, which would save up to 70% of the imagery costs. The project foresees a ground truthing process with local road managers partners, and will be first developed in two trial countries, Malawi and Madagascar, which are two of the countries with the least developed roads. A cost-benefit analysis will be also carried out, ensuring to determine the most appropriate level of accuracy against cost, to the level of accuracy required by road asset managers in LMICs. The project will produce an open GIS plug-in compatible with Road Asset Management Systems (RAMS). Two local key partners support the activity, specifically the Roads Authority in Malawi, and the NGO Lalana in Madagascar whose mission is to promote sustainable development process focusing on road infrastructure and transportation. |
| Assessment of wave energy resource in the European and Mediterranean coastal zones using high resolution altimetry products – WAPOSAL | The project’s primary objective is to evaluate the potential of wave renewable energy sources in coastal zones of Europe, Mediterranean and archipelagos where the energy can be efficiently harnessed. To achieve this objective, the project is [...] | INSTITUTO SUPERIOR TECNICO (PT) | Science | altimeter, coastal processes, coastal zone, CryoSat, Mediterranean, permanently open call, renewable energy, science, Sentinel-3 | The project’s primary objective is to evaluate the potential of wave renewable energy sources in coastal zones of Europe, Mediterranean and archipelagos where the energy can be efficiently harnessed. To achieve this objective, the project is processing the whole CryoSat, Sentinel-3A, and Sentinel-3B missions data over specific coastal zones and using the advanced SAMOSA+ retracker for the retrieval of improved geophysical quantities. The proposal will deliver a state-of-art database of along-track wave power density estimates and maps of seasonal and average wave power density, its variability and trend maps in the coastal zones. The innovative aspect of the proposal capitalizes on the application of the high spatial resolution and improved quality near the coast of the along-track wave energy density estimates, to determine the locations with the optimal conditions for harvesting wave energy with a high resolution. This 15-month activity, kicked-off in July 2024, will be led by IST-ID- CENTEC (PT). Background and Justification: In the context of the present energy crisis, harvesting energy from waves constitutes a possibility to relieve the energy crisis and accelerate the transition from fossil fuels to a climate-neutral Europe in 2050. Satellite altimetry missions have brought a new perspective and paved the way for renewable energy assessment from space. High-resolution SAR altimetry products, from the ESA CryoSat-2, Sentinel-3 and Sentinel-6 Michael Freilich missions, processed with coastal zone algorithms such as SAMOSA+ offer a new opportunity to improve coastal wave energy assessments. References: Ponce de León, S.; Restano, M.; Benveniste, Assessing the wave power density in the Atlantic French façade from high-resolution CryoSat-2 SAR altimetry data, Energy, Volume 302, 2024, 131712, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2024.131712 Ponce de León S., J.H. Bettencourt, J.V. Ringwood, J. Benveniste. Assessment of combined wind and wave energy in European coastal waters using satellite altimetry. Applied Ocean Research, Volume 152, 2024, 104184, ISSN 0141-1187 https://doi.org/10.1016/j.apor.2024.104184 Ponce de León, S.; Restano, M.; Benveniste, J. Assessment of Wave Power Density Using Sea State Climate Change Initiative Database in the French Façade, J. Mar. Sci. Eng. 2023, 11, 1970 https://doi.org/10.3390/jmse11101970 |
| Automatic Looting Classification From Earth Observation Activity – ALCEO | Illegal excavation of archaeological sites aimed at collecting historical material culture (‘looting’) to introduce it in the illicit market of antiquities is a pressing problem on a global scale. Under favourable circumstances, looting can be [...] | ISTITUTO ITALIANO DI TECNOLOGIA (IT) | Enterprise | AI4EO, artificial intelligence, permanently open call | Illegal excavation of archaeological sites aimed at collecting historical material culture (‘looting’) to introduce it in the illicit market of antiquities is a pressing problem on a global scale. Under favourable circumstances, looting can be exposed on Earth Observation (EO) data by detecting changes that have occurred between two or more consecutive EO images of a time-series (fig 1.). The main goal of ALCEO (Automatic Looting Classification from Earth Observation) project is to develop Artificial Intelligence (AI) methods for the automatic identification and classification of Cultural Heritage looted sites on EO multi-temporal series. ALCEO aims to set a benchmark in the use of remote sensing for the identification of looting activities as: i) it will develop a novel and efficient semi-supervised change detection technique for identifying looting activities relying only on small number of labelled data ii) it will produce the first large EO training dataset of looted sites incorporating information provided by cultural heritage and Law Enforcement Agencies’ (LEA) experts; iii) it will develop new image restoration techniques to enhance the quality of EO images and make them more appropriate for looting detection tasks. The proposed methodology for detection and tracking of looting activities will have a major impact on the protection of endangered cultural heritage sites by strengthening the ability of Law Enforcement Agencies to promptly react to ongoing illicit activities or acquire criminal conduct patterns to be used for behavioural profiling and further investigations. |
| BathySent – An Innovative Method to Retrieve Global Coastal Bathymetry from Sentinel-2 | The BathySent project aims at the development of an automated method for mapping coastal bathymetry (water depths) on the basis of Copernicus Sentinel-2 mission. The interest of using Sentinel-2 data lies on the capacity to cover large areas [...] | BUREAU DE RECHERCHES GEOLOGIQUES ET MINIERES (BRGM) (FR) | Science | coastal zone, ocean science cluster, permanently open call, science | The BathySent project aims at the development of an automated method for mapping coastal bathymetry (water depths) on the basis of Copernicus Sentinel-2 mission. The interest of using Sentinel-2 data lies on the capacity to cover large areas (National and European scale targeted), while benefiting from the high repeat cycle (5 days) of the mission. The systematic acquisition plan of Sentinel-2 is of major interest for studying and monitoring coastal morphodynamics. The proposed methodology avoids limitation of exiting techniques in terms of dependency on water turbidity and requirement for calibration. The main objective of the project is to propose a method for deriving coastal bathymetry on wide areas (National/European scale) based on Sentinel-2 data and assess its performances. Today knowledge of near-shore bathymetry is essential for multiple applications such as for the study of submarine morphodynamics. These data are vital for planning sustainable coastal development, coastal risks assessments (including tsunamis) and conservation of submarines ecosystems. Moreover, they represent a crucial input for near-shore navigation and submarine resources exploration. The reasons why space-borne remote-sensing techniques must play an essential role in retrieving near-shore bathymetry are threefold. First, space-borne imagery makes it possible to access remote areas with wide spatial coverage at high spatial resolution. Second, because space-borne imagery is acquired on a regular basis, a historical data archive is accessible for most sensors, which enables scientists to access information from the past. Third, the cost of the data is relatively affordable compared to airborne or ground missions. In the BathySent project, we propose to extract bathymetry from a single Sentinel-2 dataset, exploiting the time lag that exists between two bands on the focal plane of the Sentinel 2 sensor. To tackle the issue of estimating bathymetry using two Sentinel 2 images acquired quasi simultaneously, we plan to develop a method based on cross-correlation and wavelet analysis that exploits the spatial and temporal characteristics of the Sentinel 2 dataset to jointly extract both ocean swell celerity (c) and wavelengths (λ). Our team has already started to develop this method based on the French Space Agency’s (CNES) SPOT 5 dataset (Système Probatoire pour l’Observation de La Terre) with promising results (Pourpardin et al., 2015). We called it the CWB method, which stands for Correlation, Wavelets and Bathymetry. Our method combines the direct measurement of c presented in (de Michele et al., 2012) with an original wavelet-based adaptive λ estimate (that we published in Poupardin et al., 2014) to retrieve a spatially dense cloud of (λ, c) couples that are then used to estimate water depth (h) via the dispersion relation presented in equation (1). The method preferably applies to the zone between the coast and an area of depth less than or equal to half the wavelength of the waves (typically up to a hundred meters deep), with the exception of the wave breaking zone. Bibliography Poupardin, A., D. Idier M. de Michele D. Raucoules “Water depth inversion from a single SPOT-5 dataset” IEEE Trans. Geosci. Remote Sens. vol. 54 no. 4 pp. 2329-2342 Apr. 2016. de Michele M., Leprince S., Thiébot J., Raucoules D., Binet R., 2012, “Direct Measurement of Ocean Waves Velocity Field from a Single SPOT-5 Dataset”, Remote Sensing of Environment, vol 119, pp 266–271. |
| BICEP – Biological Pump and Carbon Exchange Processes | The ocean carbon cycle is a vital part of the global carbon cycle. It has been estimated that around a quarter of anthropogenically-produced emissions of CO2, caused from the burning of fossil fuels and land use change, have been absorbed by the [...] | Plymouth Marine Laboratory (GB) | Science | carbon cycle, carbon science cluster, ocean science cluster, oceans, permanently open call, science | The ocean carbon cycle is a vital part of the global carbon cycle. It has been estimated that around a quarter of anthropogenically-produced emissions of CO2, caused from the burning of fossil fuels and land use change, have been absorbed by the ocean. On the other hand, significant advances have been made recently to expand and enhance the quality of a wide range of Remote Sensing based products capturing different aspects of the ocean carbon cycle. Building on recommendations made in a series of recent meetings and reports, on ESA lead initiatives and projects and on other relevant international programmes, the objective of the BICEP project is to bring these developments together into an holistic exercise to further advance our capacity to better characterise from a synergetic use of space data, in-situ measurements and model outputs, the different components of the ocean biological carbon pump, its pools and fluxes, its variability in space and time and the understanding of its processes and interactions with the earth system. To achieve this goal, the BICEP project will first synthesise the current state of knowledge in the field and produce a consolidated set of scientific requirements that define the products to be generated, as well as how these products will be evaluated and used to produce an enhanced BICEP dataset. Major emphasis will be placed on developing unified products to ensure that the carbon budgets made are in balance. Uncertainties in the derived products will also be quantified. A large in situ dataset of ocean carbon pools and fluxes will be created, to be used to evaluate and select the algorithms, with a focus on five key test sites, representative of the range of conditions in the global ocean. Using these selected algorithms, a 20-year time series of data will be generated, built through application of the selected algorithms to the ESA OC-CCI time series, a merged, bias-corrected ocean-colour data record explicitly designed for long-term analysis. The dataset will be used as input to a novel, satellite-based characterisation of the ocean biological carbon pump, quantifying the pools and fluxes, how they vary in time and space, and how they compare with ocean model estimates. The satellite-based Ocean Biological Cabon Pump analysis will then be placed in the context of carbon cycling in other domains of the Earth System, through engagement with Earth System modellers and climate scientists. Finally, a workshop will be organized, to be used as a vehicle to engage the international community in a discussion on how the BICEP work could be pushed forward, and integrated with results from other components of the ocean carbon cycle (e.g. CO2 air-flux and ocean acidification) not covered in the project, and how the representation of satellite-based ocean carbon work could be further improved in the context of large international Earth System analysis, such as the Global Carbon Project and assessments made within the International Panel of Climate Change (IPCC). The proposed work will be delivered by a consortium of twelve international Institutes, led by the Plymouth Marine Laboratory (PML, Plymouth, UK) and composed of top-level scientists, with collective expertise on Remote Sensing, statistical modelling, ocean carbon cycling, theoretical ecology and Earth System science. |
| BiomAP | The BiomAP project aims to Integrate active and passive microwave data towards a novel global record of aboveground biomass maps. This comprises an end-to-end assessment of active and passive microwave observations at coarse spatial resolution [...] | GAMMA REMOTE SENSING AG (CH) | Science | Biomass, carbon cycle, forestry, permanently open call, SMOS | The BiomAP project aims to Integrate active and passive microwave data towards a novel global record of aboveground biomass maps. This comprises an end-to-end assessment of active and passive microwave observations at coarse spatial resolution at the longest wavelengths available in space to generate global AGB estimates. This work will eventually provide a 5-years baseline, from 2015 to 2020, relevant to carbon-related studies. Global and repeated microwave observations will come from ESA (SMOS), Eumetsat (ASCAT), JAXA (AMSR2) and NASA (SMAP) missions and will be used in combination with NASA LiDAR observations (ICESat GLAS, GEDI and ICESat-2). The overarching objective of this study is to enhance the accuracy of global AGB estimates compared to existing data products and reported statistics by integrating the satellite observations currently most sensitive to the biomass stored in aboveground vegetation |
| BIOMASCAT: Assessing vegetation carbon dynamics from multi-decadal spaceborne observations | Characterization of forest biogeochemical cycles is of paramount importance in Earth system science to understand contemporaneous dynamics and for expanding global land models in order to predict future trends of vegetation and climate. Thanks [...] | GAMMA REMOTE SENSING AG (CH) | Science | biosphere, carbon cycle, carbon science cluster, forestry, land, permanently open call, SAR, science | Characterization of forest biogeochemical cycles is of paramount importance in Earth system science to understand contemporaneous dynamics and for expanding global land models in order to predict future trends of vegetation and climate. Thanks to the increasing amount of spaceborne observations of land and ocean surfaces, data-driven models are revealing intriguing trends and mechanisms and model evaluation exercises are reaching global insights into temporal dynamics, which would not be achievable otherwise. The global characterization and the accurate knowledge of terrestrial carbon pools have been acknowledged as a fundamental variable for driving research in the terrestrial component of Earth system models. Traditionally, carbon pools are best estimated from measurements of forest inventories. However, these estimates are sparse in time and sometimes only locally relevant. There is therefore a strong requirement for data collection approaches that expand these spatial-temporal representativeness limits. However to date, despite the long term records of observations from space, only one dataset of biomass extended over multiple years so far – a 10 year passive microwave data. This project is developing a more comprenensive approach to the inforamtion gap by combining SAR and scatterometer data collected since the early 1990s to estiamte biomass properties. As the spatial resolution of both sensors is consistent with the range of length scales typcially used within ecosystem models it is expected that this development will provide a unique contribution to improving ecosystem modelling and assessment. |
| CITYSATAIR | More than half of the world’s population is living in cities. According to the WHO air quality database 80% of people living in urban areas that monitor air pollution are exposed to air quality levels that exceed WHO limits. Narrowing down to [...] | KNMI (NL) | Applications | air quality, atmosphere science cluster, atmospheric chemistry, atmospheric indicators, health, permanently open call, public health, science | More than half of the world’s population is living in cities. According to the WHO air quality database 80% of people living in urban areas that monitor air pollution are exposed to air quality levels that exceed WHO limits. Narrowing down to cities in low and middle income countries with more than 100 000 inhabitants, this number increases to 98%. To resolve urban air pollution problems a clear understanding of the local situation is essential. Low-income cities, which are most impacted by unhealthy air, usually have less resources available for a good reference network. It is here where a combination of low-cost sensors and satellite data can make a difference. So far, only very few studies aim at joining heterogeneous data sources of urban air quality, and to our knowledge no previous work has provided practical solutions which can be implemented in cities everywhere. We therefore propose to develop and demonstrate a methodology that is capable of exploiting the various available data sources, to combine them in a mathematically objective and scientifically meaningful manner, and to provide value-added maps of urban air quality at high spatial resolution. |
| CLIMATE DATA RECORD OF STRATOSPHERIC AEROSOLS (CREST) | Stratospheric aerosols impact the radiative forcing and thus the energy balance of the Earth’s atmosphere, therefore information about their distribution and variability is of high importance for climate related studies. The main [...] | FINNISH METEOROLOGICAL INSTITUTE (FI) | Science | Aerosols, atmosphere, atmosphere science cluster, atmospheric chemistry, atmospheric indicators, climate, permanently open call | Stratospheric aerosols impact the radiative forcing and thus the energy balance of the Earth’s atmosphere, therefore information about their distribution and variability is of high importance for climate related studies. The main scientific objective of the project CREST is creating a new merged long-term time series of the vertically resolved aerosol extinction coefficients using data records from six limb and occultation satellite instruments: SAGE II, OSIRIS, GOMOS, SCIAMACHY and OMPS-LP instruments for the years from 1984 to present. The merged aerosol extinction coefficient is computed as the median of the adjusted data from the individual instruments. The merging of aerosol profiles is performed by transformation the aerosol datasets from individual satellite instruments to the same wavelength, i.e., 750 nm, and their de-biasing and homogenization by adjusting the seasonal cycles. The merged time series of vertically resolved monthly mean aerosol extinction coefficients at 750 nm is provided in 10° latitudinal bins from 90°S to 90°N, in the altitude range from 8.5 km to 39.5 km. The time series of the stratospheric aerosol optical depth (SAOD) is created by integration of aerosol extinction profiles from the tropopause to 39.5 km; it is also provided as monthly mean data in 10° latitudinal bins. The created aerosol dataset is in open access at: https://fmi.b2share.csc.fi/records/8bfa485de30840eba42d1d407f4ce19c |
| COMMUNITY EARTH OBSERVATION INTELLIGENCE SERVICE: PROTOTYPING FOR SCALE | At present NGOs/CSOs have limited expertise in accessing and utilizing EO data. This project is working with NGOs adressinghuman rights concerns and will develop methodologies for integrating in-situ (citizen data collection), drone and EO data [...] | OMANOS ANALYTICS (GB) | Digital Platform Services | permanently open call, platforms, sustainable development | At present NGOs/CSOs have limited expertise in accessing and utilizing EO data. This project is working with NGOs adressinghuman rights concerns and will develop methodologies for integrating in-situ (citizen data collection), drone and EO data to enhance the collection of information and evidence on activities affecting human rights in developing countries |
| CONSTRACK – Remote construction site monitoring | Usually, construction projects are structured through different phases: analysis, planning, design, construction, closing and post monitoring.
The project execution phase (Phase 2 – Construction) is usually the longest phase in the project [...] |
STARLAB BARCELONA SL (ES) | Enterprise | permanently open call, urban | Usually, construction projects are structured through different phases: analysis, planning, design, construction, closing and post monitoring. The project execution phase (Phase 2 – Construction) is usually the longest phase in the project life cycle and it typically consumes the most energy and the most resources. Global construction companies cannot be physically present all along the execution phase to control the implementation of the construction on-site. Then, they are used to control advancement only from local contact reporting that may differ from the exact reality of the project status, and usually have high expenses in travelling around the different project sites to get frequent updates. So, monitoring this phase is crucial to prevent from financial, timing and quality risks. Construction companies are then actively looking after monitoring remotely those construction sites to limit their presence on site and frequently get an unbiased vision of the project status. The difficulty in applying automated techniques based on EO data to this market is the high degree of variability of features and processes to be detected and monitored. This project addresses this issue by concentrating on automated detection of anomalies and involving the construction companies to translate the anomalies into actual engineering information. The project is operating as a series of test cases to determine the viability of an eventual commercial market. |
| CROWDVAL: Using Crowdsourcing and Innovative Approaches to Evaluate and Validate ESA’s Land Cover Products | The CrowdVal project had five main objectives:
Develop new innovative sampling schemes that allow a stratification and bias removal via road networks and that take other constraints into account for in-situ data collection; Enhance [...] |
INTERNATIONAL INSTITUTE FOR APPLIED (AT) | Applications | land cover, permanently open call | The CrowdVal project had five main objectives: Develop new innovative sampling schemes that allow a stratification and bias removal via road networks and that take other constraints into account for in-situ data collection; Enhance LACO-Wiki and LACO-Wiki Mobile with the new sampling strategies, functionality for opportunistic map evaluation on the ground, and the addition of auxiliary data sets including Flickr geo-tagged pictures and time series of NDVI; Create an open source version of LACO-Wiki Mobile; Demonstrate the enhanced tools through crowdsourcing data collection campaigns (online and in-situ) to validate the first land cover map of Africa at a 20m spatial resolution; and Investigate the possibility of developing a business model around an open source version of LACO-Wiki Mobile with a payment model around access to enhanced features, e.g. additional data sources, commercial satellite imagery, increased sample size, etc. |
| CTEO – CryptoTradeable EO | EO derived information are increasingly being used as the basis for a range of sensitive decisions linked to commercial operations, public safety and environmental security. At the same time, developments in ICT capability enable an expanded [...] | Planetek Italia (IT) | Enterprise | blockchain, permanently open call, platforms, security | EO derived information are increasingly being used as the basis for a range of sensitive decisions linked to commercial operations, public safety and environmental security. At the same time, developments in ICT capability enable an expanded volume of information to be generated using distributed approaches such as cloud based storage and processing and platform based interactions, use of algorithms and proprietary datasets. This makes guaranteeing the integrity of both the data and the derived information more and more difficult. This project is testing various Blockchain based approaches to support the different verification elements needed to guarantee the integrity of the data and the analysis. In particular, this project is investigating and testing approaches for dividing, encrypting and distributing large datasets (typical EO imagery) to a group of peers (e.g. in the ground segment and on-board) for enabling tradeable distributed processing, encrypting and distributing metadata in the peer-to-peer network, with guarantee of correct association to the related datasets, signing and uniquely identifying smart contracts (this may be also full-fledged algorithms) based on their input requirements and output products so that the P2P network can guarantee processing traceability and security and implementing a runtime environment suitable for running EO smart contracts, which is able to perform processing with specific execution time constraints, storage constraints, device usage constraints, network usage constraints, metrics constraints applied to output quality. |
| CYMS (Scaling-up Cyclone Monitoring Service with Sentinel-1) | CYMS is an ESA-funded project aiming at scaling up an operational service for Tropical Cyclone (TC) monitoring, in view of its potential integration as part of a Copernicus Service. The main scientific and technical objectives are to:Develop a [...] | CLS COLLECTE LOCALISATION SATELLITES (FR) | Science | ocean science cluster, oceans, permanently open call, science, Sentinel-1, SMOS | CYMS is an ESA-funded project aiming at scaling up an operational service for Tropical Cyclone (TC) monitoring, in view of its potential integration as part of a Copernicus Service. The main scientific and technical objectives are to: Develop a sustainable acquisition strategy dedicated to TC ; Consolidate S-1 end-to-end processing chains for ocean surface wind field with dedicated and up-to-date algorithms for extreme events ; Build an archive center with homogeneous and consistent l2 products, for the TC product validation purpose and scientific applications ; Build a single integrated portal easing dissemination and outreach activities. |
| DACES – Detection of Anthropogenic CO2 Emissions Sources | The project aims at developing a new methodology for detecting anthropogenic carbon dioxide emission sources. CO2 data from OCO-2 and NO2, SO2 and CO data from Sentinel-5P are collocated. The plan is to analyze these data in synergy to better [...] | FINNISH METEOROLOGICAL INSTITUTE (FI) | Science | atmosphere, atmosphere science cluster, carbon cycle, carbon science cluster, permanently open call, science, Sentinel-5P, TROPOMI | The project aims at developing a new methodology for detecting anthropogenic carbon dioxide emission sources. CO2 data from OCO-2 and NO2, SO2 and CO data from Sentinel-5P are collocated. The plan is to analyze these data in synergy to better detect anthropogenic CO2 sources and plumes. In detail OCO-2 XCO2 data is deseasonalized and detrended, and further correlated/clustered to the spatial distribution of other species such as NO2, SO2, CO. Further a direct detection of emission plumes is done for anthropogenic sources using NO2, SO2 and CO datasets, and collocating the plumes with XCO2 data. The corresponding CO2 enhancements and ratios between different species at local level is then calculated. The project has been kicked-off the 5th October. |
| DeepSent – Deep Learning-Based Multiple-Image Super-Resolution for Sentinel-2 Data | The aim of the activity is to apply super-resolution reconstruction multispectral Sentinel-2 images, using multiple images of the same region, captured at different points in time (MISR, multiple-image super-resolution). This is achieved by [...] | KP Labs Sp. z o.o. (PL) | Enterprise | AI4EO, permanently open call, Sentinel-2 | The aim of the activity is to apply super-resolution reconstruction multispectral Sentinel-2 images, using multiple images of the same region, captured at different points in time (MISR, multiple-image super-resolution). This is achieved by adapting recent deep neural networks that were recently proposed for dealing with MISR, to the particularities of Sentinel-2 data. In particular the project focusses on three aspects: adapting the existing networks to process multispectral images, proposing techniques for preparing the training data, and selecting and pre-processing the input low-resolution data. The existing networks are applied to super-resolve the Sentinel-2 images in a band-wise manner (each band treated independently), followed by exploiting the correlation among the multiple bands. The output contains a panchromatic image, as well as an RGB/multispectral image of higher resolution than the one presented at the input. Read about the project achievements in the following publications: 1. M. Kawulok, J. Nalepa, P. Benecki, D. Kostrzewa (2020): Deep learning for super-resolution reconstruction of Sentinel-2 images, Phi-Week 2020. 2. M. Kawulok, T. Tarasiewicz, J. Nalepa, D. Tyrna, D Kostrzewa (2021): Deep learning for multiple-image super-resolution of Sentinel-2 data, in Proc. IEEE IGARSS 2021, pp. 3885–3888. 3. J. Nalepa, K. Hrynczenko, and M. Kawulok (2021): Multiple-image super-resolution using deep learning and statistical features, in Proc. Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). Springer, 2021, pp. 261–271. |
| DYNAMOS – Dynamic Mosaic Service | DYNAMOS is being implemented as a cloud-based, dynamic mosaicking service, initially focussing on Sentinel-2 data. The service will provide users the ability to request the creation of large area mosaics according to their requirements, [...] | SPACEMETRIC AB (SE) | Digital Platform Services | generic platform service, permanently open call, platforms, Sentinel-2 | DYNAMOS is being implemented as a cloud-based, dynamic mosaicking service, initially focussing on Sentinel-2 data. The service will provide users the ability to request the creation of large area mosaics according to their requirements, primarily in terms of area and time frames, image selection and prioritisation considerations. DYNAMOS is building on the concept of dynamic mosaic creation. Here “mosaic recipes” capture the required data details and processing steps for the on-demand creation of the mosaic. This also allows actual processing operations to only occur for areas directly demanded e.g. for visualisation or storing only the virtual recipe rather than a large dataset. The DYNAMOS activity is driven by a set of use cases in the agriculture and forestry application areas. The service is currently being designed for and deployed in AWS. |
| E. Ccoli Alert Data Service (EADS) | The aim of this activity is to investigate the viability of developing an Escherichia coli (E. coli) Alert Data Service for environmental agencies and local authorities. This will be carried out via the development of an analysis method for the [...] | TECHWORKS MARINE LTD (IE) | Enterprise | coastal zone, generic platform service, natural hazards and disaster risk, permanently open call | The aim of this activity is to investigate the viability of developing an Escherichia coli (E. coli) Alert Data Service for environmental agencies and local authorities. This will be carried out via the development of an analysis method for the fusion of in-situ (lab and sensor measurements) and satellite data (optical and radar), validated by stakeholders with an interest in investing in a long term commercial service. The information from the service will be available on TechWorks Marine’s CoastEye platform, which allows access to a wide range of geospatial data. The expected impact of this service would be to provide local authorities, environmental agencies and government departments with improved information on the likelihood of a contamination event occurring, allowing for an informed decision on whether or not to restrict access to a given coastal area. The benefit of this would be to reduce the risk of illnesses associated with the presence of E. coli in coastal waters, as the areas could be closed as a precautionary measure before the E. coli reaches these areas. |
| Earth Observation for Poverty – EO4Poverty | Poverty is one of the chronic problems of the XXI century and, despite the recent decrease of global economic inequalities between and within countries, in 2016 about 800 million people still lived in extreme poverty condition, with many of them [...] | MindEarth (CH) | Applications | mapping/cartography, permanently open call | Poverty is one of the chronic problems of the XXI century and, despite the recent decrease of global economic inequalities between and within countries, in 2016 about 800 million people still lived in extreme poverty condition, with many of them located in sub-Saharan Africa and Southern Asia. In this context, poverty alleviation programmes generally rely on data about local economic livelihood for identifying places with highest need for aid. Nevertheless, this information traditionally comes from patchy and logistically challenging household surveys which normally happen to be extremely expensive. As a result, policymakers and public sector stakeholders lack key data necessary for targeting anti-poverty programs or properly measuring their effectiveness. Given the challenges of scaling up traditional data collection efforts, in the past few years alternative strategies have been proposed for assessing the degree of poverty based on satellite data. The main objective of EO4Poverty is to implement a novel system based on advanced machine- and deep-learning techniques for generating national spatial poverty maps by jointly exploiting EO-based products (in particular derived from Copernicus Sentinel data) and non-EO based products (e.g., roads and transportation networks, social media) coupled with in-situ reference information gathered from publicly available household surveys. The project aims to improve existing approaches and to provide an easily transferable service for creating maps of actual support to the end-users. |
| EARTHSIGNATURE_AI | Monitoring of cropland has been critical for several national and international programmes (e.g., Sustainable Development Goals – #2 Zero Hunger, European Common Agriculture Policy). Furthermore, early identification of crops is becoming more [...] | CS SYSTEMES D’INFORMATION (FR) | Enterprise | artificial intelligence, land cover, permanently open call, sustainable development | Monitoring of cropland has been critical for several national and international programmes (e.g., Sustainable Development Goals – #2 Zero Hunger, European Common Agriculture Policy). Furthermore, early identification of crops is becoming more stringent in the context of climate change that can influence severely crop yields in some parts of the world. Given the size of te areas to be addressed and the volume of demand, EO based crop monitoirng must increasingly utiliuze AI based approaches. However, cropland classification is a challenging topic because of the constantly changing radiometric signature of crops due to seasons and weather and climatic conditions. This requires the development of a system capable of taking seasonal and weather and climatic variations into account. WIthin the framework of AI based approaches, in order to be economically sustainable, processing costs must also be reasonable. This project is addressing the entire processing and analysis chain for usiing ML analysis of EO data for crop classificaiton. This includes the identifiaction of which available land cover dataset(s) can provide the best levels of crop information and quality to perform an efficient and conclusive study while meeting specific user needs related to crop monitoring, testing different neural network (NN) configurations, including different input datasets and different approaches to represent data time-series as NN input which are then compared with a baseline classical approach and finally testing different Cloud computing configurations, including the use of the GPU. Beyond the calculation time assessment, this objective will inform on the trade-off between calculation time and platform configuration costs. |
| Ease QC – Development of a Service to detect anomalies in Earth Observation data using AI (Artificial Intelligence) models | The EASEQC project aimed at expanding the use of AI/ML for quality control of EO products. The traditional approach to quality control, usually involving deterministic models together with considerable manual intervention, is no longer feasible [...] | TELESPAZIO VEGA UK LIMITED (GB) | Digital Platform Services | artificial intelligence, generic platform service, permanently open call, platforms | The EASEQC project aimed at expanding the use of AI/ML for quality control of EO products. The traditional approach to quality control, usually involving deterministic models together with considerable manual intervention, is no longer feasible given increasing data volumes of EO data archives. ML/AI has potential to make the process of quality control more efficient. EASEQC focused on the development of semi-supervised ML models for detection anomalies in EO products. This entailed that models can be trained with limited training data and that a model is capable of identifying generally anomalous data products i.e. different anomalies can be detected by the same model. The service has been implemented in a cloud environment and is accessible via an API. Overall, the outcome of the project has seen significant steps made towards the establishment of an operational Ease QC service. Further work is still required to improve the ML models, but the infrastructure successfully developed by the project both with respect to the development of the ML models, and their deployment / operation alongside the data (be that on the cloud or otherwise) is an extremely significant development with respect to the long term objectives of the Ease QC team. |
| EO for a Resilient Society: Intertidal Topography Mapping in the temporal domain (SAR-TWL), towards operationalisation of a global monitoring tool. | Intertidal zones form an interface between land and sea. They are important features of the coastal landscape providing a multitude of ecosystem services and forming a critical habitat for a wide range of species. Satellite Earth Observation [...] | National Oceanography Centre (NOC) (GB) | Regional Initiatives | Atlantic, Ecosystems, oceans, permanently open call, regional initiatives, SAR, Sentinel-1 | Intertidal zones form an interface between land and sea. They are important features of the coastal landscape providing a multitude of ecosystem services and forming a critical habitat for a wide range of species. Satellite Earth Observation (EO) unlocks new capabilities for monitoring intertidal zones, which are under significant pressure from multiple sources including coastal development, impacts from upstream land use and changes in sea level. The unique capabilities of EO for intertidal mapping have been demonstrated by research groups such as Murray et al. who, using optical data from the Landsat archive, have shown a declining trend in the global extent of intertidal flats. To meet the higher spatial and temporal monitoring needs of regional and local authorities the UK National Oceanography Centre (NOC) have developed a new approach based on S1 SAR (Synthetic Aperture Radar), making use of temporal waterline methods (TWL). This time domain, or per pixel approach, reduces the manual interpolation steps inherent in optical methods and unlocks a new and unique way of observing intertidal dynamics. The work builds on nearly two decades of research into environmental monitoring with X-Band Marine Radar , complementing the synoptic and temporal frequencies that in-situ radar monitoring provides .Following two years of development and demonstration carried out in partnership with the Channel Coastal Observatory, the Environment Agency, Wales Coastal Monitoring Centre and local authority programmes, this new project will enable further development of the methods and the processing software. The objective is to enable more widespread access to this innovative method, with inherent potential for long term monitoring of intertidal dynamics at local to national scales. Previous development work was carried out as part of the Atlantic Region Initiative, under the Blue Economy, Marine Spatial Planning and Innovation Clusters project. https://eo4society.esa.int/wp-content/uploads/2022/11/MorecambeBay_TWL_POLPRED_MSL_filt.mp4 Video shows intertidal elevations for Morecambe Bay, from January 2017 to August 2022. At 310km2 Morecambe Bay is the largest intertidal area in the UK. ————————————————————————————– Murray N. J., Phinn S. R., DeWitt M., Ferrari R., Johnston R., Lyons M. B., Clinton N., Thau D. & Fuller R. A. “The global distribution and trajectory of tidal flats” Nature. 565:222-225. (2019). See Bell, Bird & Plater “A temporal waterline approach to mapping intertidal areas using X-band marine radar” Coastal Engineering (2016), & Bird, Bell & Plater “Application of marine radar to monitoring seasonal and event-based changes in intertidal morphology” Geomorphology https://marlan-tech.co.uk/ |
| EO Mammals | Earth Observation (EO) data has been extensively used over the years to assist on the management of marine mammal populations either by establishing protected areas where stakeholders’ activity are reduced, or by minimizing the impact of [...] | THE OCEANIC PLATFORM OF THE CANARY ISLANDS (ES) | Applications | applications, permanently open call | Earth Observation (EO) data has been extensively used over the years to assist on the management of marine mammal populations either by establishing protected areas where stakeholders’ activity are reduced, or by minimizing the impact of anthropogenic threats. It is considered a basic and essential tool for the conservation of species, both by researchers and governments. Some examples include weekly predictions of fin whale (Balaenop-tera physalus) distribution that represent a valuable conservation tool in marine protected areas to prevent collisions with ships. Remotely sensed environmental parameters have the potential to identify biological hotspots for cetaceans and to therefore establish areas of marine conservation priority. Satellite measurements of ocean have proved an effective tool to map the environmental variables and processes occurring. It is the main tool for measuring ocean productivity (ocean colour) and its response to climate change/variability. Other variables also related with the presence and movements of cetaceans can be measured from space, e.g. sea surface temperature, sea surface height, etc. This project aims to identify biological hotspots for cetaceans and help the management of marine protected areas, using Earth Observation and other collaborative network’s data. |
| EO tracking of marine debris in the Mediterranean Sea from public satellites | One of the most significant unknown factors in marine debris is the flux of plastic from land based sources into the marine environment. This project is testing techniques to combine EO and UAV data to detect different types and volumes of [...] | ARGANS LIMITED (GB) | Enterprise | oceans, permanently open call | One of the most significant unknown factors in marine debris is the flux of plastic from land based sources into the marine environment. This project is testing techniques to combine EO and UAV data to detect different types and volumes of plastic in order to establish a methodology to characterize this flux in hotspot areas which are the main sources of plastic. |
| EO4WR – EARTH OBSERVATION FOR WATER RESOURCE EXTRACTION IN INDONESIA | The project is exploiting new AI techniques using EO data to support water extraction. The service provide generated a precise terrain motion map using Sentinel-1 data with InSAR and this was combined with global and local geosptial data such as [...] | Planetek Italia (IT) | Enterprise | permanently open call, Sentinel-1, water resources | The project is exploiting new AI techniques using EO data to support water extraction. The service provide generated a precise terrain motion map using Sentinel-1 data with InSAR and this was combined with global and local geosptial data such as a database concerning water extraction activities including a map of wells in the area of Jakarta. Seismic risk assessment, subsidence and water resources management in indonesia Support to Water and Food Security Planning and Investments in Indonesia |
| EOSAT 4 SUSTAINABLE AMAZON | EOSAT 4 Sustainable Amazon demonstrates near real time monitoring of forest disturbances in the Colombian Amazon to support the country in reaching its sustainable development goals. | SARVISION BV (NL) | Applications | applications, forestry, permanently open call, sustainable development | EOSAT 4 Sustainable Amazon demonstrates near real time monitoring of forest disturbances in the Colombian Amazon to support the country in reaching its sustainable development goals. |
| ESADEMICS | It has become abundantly clear during the COVID-19 pandemic that environmental factors can be important in the emergence, spread, health impact, social political response, and socioeconomic recovery plan from Sars-Cov-2/COVID-19. Having tools [...] | Science [&] Technology Netherlands (NL) | Enterprise | air quality, covid19, health, permanently open call, public health, water quality | It has become abundantly clear during the COVID-19 pandemic that environmental factors can be important in the emergence, spread, health impact, social political response, and socioeconomic recovery plan from Sars-Cov-2/COVID-19. Having tools available to study the impact of the environment for this pandemic and future pandemics is important to increase preparedness and curtail future societal and economic costs. However, despite the fact that many useful data sources are existing, it remains a tremendous challenge for scientists to combine all these data for their research. The various satellite missions of the European Space Agency (ESA) have led to a plethora of data assets. These assets are publicly available and are used in many scientific projects. The relation between well-being and changes in our habitat require data about our living environment. These data can be reliably and effectively collected using earth observation instruments such as satellites. These satellite data are available from data stores that have been developed by ESA or their operational organisations. For many of the data stores a historical archive is available as well. Even though the data from the various missions are reliable and timely stored in these data stores, their usability for research on the relation between our well-being and our living environment is somehow limited. This is caused by a number of factors. First of all data is not easily accessible to epidemiologists, since they lack specific knowledge on data stores, file formatting standards, etc. In addition, epidemiological research often requires derived data rather than the data stored. And finally, the data stores from ESA and other providers have not been designed with the idea that data can be combined; linking atmospheric data to land cover maps is not a simple query. ESADEMICS aims at making a number of air quality, water quality and mismanaged waste data sources available that are relevant for epidemiological use cases. The focus of this project is to combine a set of relevant data sources from (amongst others) ESA and develop methods to link these different data sources related to air quality, water quality, and mismanaged waste on geolocation and time period. These methods hide the complexity from the epidemiologists to deal with different spatial and time scales of the different data sources. The resulting data set can be retrieved and combined with other data sources (population health data) in their statistical environment. |
| EW-EXPLORE: SENTINEL-1 EW-MODE ARCHIVE EXPLOITATION FOR POLAR RESEARCH | EW-Explore is a pilot project to investigate interferometric (InSAR) applications of Sentinel-1 (S1) Extra Wide mode (EW) beyond the ocean- and sea ice applications where this mode was designed for. | NORCE Norwegian Research Centre AS (NO) | Science | applications, permanently open call, polar flagship, Sentinel-1 | EW-Explore is a pilot project to investigate interferometric (InSAR) applications of Sentinel-1 (S1) Extra Wide mode (EW) beyond the ocean- and sea ice applications where this mode was designed for. |
| eXperimental jOint inveRsioN | The Earth crust represents less than 1% of the volume of our planet but is exceptionally important as it preserves the signs of the geological events that shaped the Earth. This thin layer is the place where the natural resources we need can be [...] | GEOMATICS RESEARCH AND DEVELOPMENT (IT) | Science | gravity and gravitational fields, ionosphere and magnetosphere, Mediterranean, permanently open call, solid earth | The Earth crust represents less than 1% of the volume of our planet but is exceptionally important as it preserves the signs of the geological events that shaped the Earth. This thin layer is the place where the natural resources we need can be accessed (e.g. minerals, critical raw materials, geothermal energy, fresh water, hydrocarbons). For these reasons, a thorough understanding of its structure is crucial for both scientific and industrial future activities. In recent years, thanks to the increasing availability of seismic/seismological data and to satellite missions, the Earth crust has been thoroughly investigated and modelled at global and continental scales. However, despite this progress, the crust remains poorly understood in many regions as global models are often too coarse to provide detailed information about the regional and local dynamics. Potential field methods, which exploit gravity and magnetic data, are a powerful tool to recover information on the Earth’s crust structure. A wide variety of gravity and magnetic data in fact have been nowadays collected at near surface altitudes in most regions of the world. These measurements, if properly combined with global satellite data can be used to refine at regional/local scale the modelling of crustal structures, depicting the boundaries between geological units and stratification of the crust. To fully exploit these satellite-derived and terrestrial data ad-hoc physics integrated approaches, to reconcile all the measurements, are required. A promising solution to this issue is represented by the joint processing of both gravity and magnetic fields observations, possibly incorporating the available geological knowledge and constrains coming from seismic acquisitions. In the XORN project an innovative, fully integrated approach will be developed to perform a complete 3D joint inversion of gravity and magnetic fields data, constrained by seismic and geological a-priori information. The developed algorithm will be used within the project to recover a 3D regional model of the Earth crust in the Mediterranean Area in terms of density and magnetic susceptibility distribution and in terms of depths of the main geological horizons. |
| Flexible ONBoard Data Analysis | The amount of data coming from imaging sensors increases steadily and a modern imaging sensor creates frames of several megapixels at a high frame acquisition rate. These imaging sensors with their large data output are mounted on spaceborne [...] | Science [&] Technology Norway (NO) | Enterprise | permanently open call, platforms | The amount of data coming from imaging sensors increases steadily and a modern imaging sensor creates frames of several megapixels at a high frame acquisition rate. These imaging sensors with their large data output are mounted on spaceborne platforms, but the downlink capability of these spaceborne platforms, especially for small platforms, has not been increasing at the same rate as the data generation of the imaging sensors. This has resulted in a ‘big data problem’ on board these spaceborne platforms. An industry trend towards smaller satellites – with smaller antennas, less power and worse pointing accuracy- leads to an expectation that the downlink capability will remain well below the data generation capability for such imaging satellites. In order to use more acquisitions and have a high ‘usability’ of the satellite, the on-board processing of payload data is a solution. In this project, S&T will determine and test the technology platform that is best suited for onboard intelligent processing of imaging payload data. This will include testing techniques such as development of low volume data products instead of raw image files for downlink, verifying using concrete algorithms and implementation choices how performant such processing can be, exploring the implications of moving certain parts of the processing functionality to FPGA and conducting tests using HyperSpectral imagery on a cubesat. |
| ForEarth | The objective of the ForEarth project is to provide a mobile-oriented environmental alert service dedicated to public institutions, scientists and citizens to keep a close watch on their surrounding environment based on freely-available [...] | GEOMATYS (FR) | Sustainable Development | permanently open call, sustainable development | The objective of the ForEarth project is to provide a mobile-oriented environmental alert service dedicated to public institutions, scientists and citizens to keep a close watch on their surrounding environment based on freely-available satellite Earth Observation data. A microservices infrastructure, customised for hosting EO data will be developed and deployed. The infrastructure will be accessed by an EO-specific social networking smartphone app, SnapPlanet, which empowers users of any skill level to trigger web processing of selected EO products and view or download the results. The service will address questions about local environmental variables, through simple and robust remote sensing techniques: change detection over forest, surface water in reservoir dams, irrigated surface area detection. The targeted audience are non-experts: local businesses or simply curious citizens, NGOs, consulting or insurance companies that would not be capable to get this information from elsewhere and in a near real time. More advanced users could use the enquiries collected from users as a feedback to learn what environmental issues are common in the place where the users are querying the app. |
| GAME.EO | Recent years have brought tremendous advancements in the area of automated information extraction from Earth Observation (EO) imagery, but problems still remain since even state-of-the-art algorithms based on imagery alone do not provide a [...] | GISAT S.R.O. (CZ) | AI4EO | permanently open call, platforms, sustainable development | Recent years have brought tremendous advancements in the area of automated information extraction from Earth Observation (EO) imagery, but problems still remain since even state-of-the-art algorithms based on imagery alone do not provide a satisfactory solution. In these situations, it is possible to exploit the crowdsourcing of human intelligence, which is a recent promising area for EO. This is of particular interest with respect to providing information on devleoping countries to International Finance Institutions such as the World Bank.In this project an integrated (hybrid) crowdsourced and EO data-based information extraction framework is being developed. Mobile-based tools for supporting crowdsourcing campaigns and gaming approaches will be developed, and then used to mobilize and train volunteers to provide data via dedicated EO-based workflows to extract the required information in a more timely and accurate manner, with lower costs than would be incurred using professional datacollection services. The approach will be demonstrated using specific service cases for EO-based monitoring of Informal Settlements/Slum Areas (SDG11), with the aim to enhance current machine-learning algorithms for the identification, delineation and further characterization of these areas. The developed framework and tools will be tested in cooperation with World Bank users and stakeholders (GWASP/GSURRP) in an ongoing internal project1 for Dhaka, Bangladesh, to demonstrate the potential and the added value of the synergies of crowdsourcing- and EO-based information to support the World Bank’s research and operational activities. |
| GammaCloud: Feasiblity of using S1 Terrain Flattened Gamma_0 backscatter across EO platforms | The project prototyped a prototype of a processing workflow for improved Sentinel-1 backscatter data, providing a temporal stack of analysis ready data (ARD) that can be integrated into a data cube system allowing to access the data in a spatial [...] | EODC EARTH OBSERVATION DATA CENTRE FOR WATER RESOURCES MONITORING (AT) | Digital Platform Services | permanently open call, platforms, Sentinel-1 | The project prototyped a prototype of a processing workflow for improved Sentinel-1 backscatter data, providing a temporal stack of analysis ready data (ARD) that can be integrated into a data cube system allowing to access the data in a spatial and temporal domain. |
| GOCE gravity gradients for time-variable applications (GOCE4TV-APPs) | The gravity gradients of the highly successful ESA Earth Explorer mission GOCE (Gravity field and steady-state ocean circulation explorer), which have been reprocessed by applying enhanced calibration strategies in the frame of the ESA project [...] | TECHNICAL UNIVERSITY OF MUNICH (DE) | Science | permanently open call, science, solid earth, water cycle and hydrology | The gravity gradients of the highly successful ESA Earth Explorer mission GOCE (Gravity field and steady-state ocean circulation explorer), which have been reprocessed by applying enhanced calibration strategies in the frame of the ESA project GOCE High-level Processing Facility (HPF), have reached a very high quality level, especially in the long-wavelength spectral range where the main time-variable gravity field signals occur. In the frame of this project, it shall be investigated if time-variable gravity field signals can be extracted from these newly processed gravity gradient data. Due to their direct relation to mass and mass change, temporal variations reflect mass transport processes in the Earth system, which by themselves are subtle indicators of climate change. Therefore, the GOCE gradients shall be analysed for and used in dedicated time-variable geophysical applications, such as the detection of earthquakes reflecting pre-, co-, and post-seismic mass movement, land hydrology reflecting changes in the total water storage of large hydrological catchments, and ice mass balance trends such as ice mass melting in Greenland and Antarctica. For this purpose, existing processing strategies based on spherical harmonic modelling of the gravity field, as well as promising contemporary processing and parameterization strategies, among them a so-called Mascon approach, shall be applied. The latter is routinely used in temporal gravity modelling based on data of the inter-satellite ranging mission GRACE (Gravity Recovery And Climate Experiment), but has never been applied to GOCE data yet, and will be developed and adapted for GOCE data assimilation and data exploitation. The main outcome will be gradiometry-only regional and global data sets of identified gravity changes, giving information about the amplitude, seasonal/periodic and drift behavior of the changes. The results will be validated against GRACE temporal gravity models. Especially, it will be evaluated if by means of GOCE gradients the spatial resolution of recoverable time-variable gravity signals can be increased. The data sets shall be provided in common and easy to use data formats in order to be used within relevant related fields of applications. |
| Gravitational Seismology | This project analyses the extent to which tectonic processes at plate boundaries give rise to changes that can be detected as variations in gravitational acceleration. The requirements for sensitivity are now being fed into preparatory studies [...] | UNIVERSITA DEGLI STUDI DI MILANO (IT) | Science | permanently open call, science | This project analyses the extent to which tectonic processes at plate boundaries give rise to changes that can be detected as variations in gravitational acceleration. The requirements for sensitivity are now being fed into preparatory studies for future gravity measurement missions. |
| Grazing detection from Copernicus data for agricultural subsidy checks | The project will develop methodology for grazing detection based on Sentinel 1 and 2 data. address grazing intensity, set out benchmarks for detection units (LU/ha) and test the methodology with selected paying agencies in Europe (Czech, [...] | KAPPAZETA LTD (EE) | Enterprise | agriculture, permanently open call, Sentinel-1, Sentinel-2 | The project will develop methodology for grazing detection based on Sentinel 1 and 2 data. address grazing intensity, set out benchmarks for detection units (LU/ha) and test the methodology with selected paying agencies in Europe (Czech, Spanish, Estonian, Swedish). |
| Harmonised Landsat-8 and Sentinel-2 Analysis-Ready Products | The project developed a prototype service providing analysis-ready harmonized Landsat-8 and Sentinel-2 data/products to the user for easy exploitation. The service will be embedded as a SNAP “plug in”, aiming to be available in the Copernicus [...] | TELESPAZIO VEGA UK LIMITED (GB) | Digital Platform Services | permanently open call, platforms | The project developed a prototype service providing analysis-ready harmonized Landsat-8 and Sentinel-2 data/products to the user for easy exploitation. The service will be embedded as a SNAP “plug in”, aiming to be available in the Copernicus Data and Information Access System (DIAS). |
| Harmonizing and advancing retrieval approaches for present and future polarimetric space-borne atmospheric missions (HARPOL) | Atmospheric aerosol particles strongly influence climate by scattering and absorbing light (direct forcing) and by changing cloud properties (indirect forcing). The corresponding radiative forcing represents one of the most uncertain radiative [...] | Netherlands Institute for Space Research (NWO-I) (NL) | Science | Aerosols, Altitude, atmosphere, atmosphere science cluster, permanently open call | Atmospheric aerosol particles strongly influence climate by scattering and absorbing light (direct forcing) and by changing cloud properties (indirect forcing). The corresponding radiative forcing represents one of the most uncertain radiative forcing terms as reported by the Intergovernmental Panel on Climate Change (IPCC). To improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, optical properties like Aerosol Optical Thickness (AOT) and Single Scattering Albedo (SSA), as well as the aerosol height profile are of crucial importance. It has been demonstrated by studies on synthetics measurements, airborne measurements, and space-borne measurements that Multi-Angle Polarimetric (MAP) measurements are needed to provide information about detailed aerosol properties like size distribution, refractive index, SSA, in addition to the AOT. The only MAP instrument that has provided a multi-year data set (2005-2013) in the past has been the French POLDER-3 instrument on the PARASOL mission. Now space agencies realize the large potential of MAP instrumentation, in the 2020s several of such instruments will be launched, e.g. 3MI on METOP-SG (ESA-2023), SPEXone and HARP-2 on PACE (NASA-2023), and a MAP on the CO2-Monitoring mission (ESA-2025) and A-CCP (NASA-2028). To cope with the increased information content on aerosols of MAP instrumentation and to assess the climatic effect of aerosols, new tools for retrieval need to be (further) developed. So far, this development has lagged behind the instrument development, which is the reason for the under-exploitation of the existing POLDER-3/PARASOL data sets. Currently, there are two algorithms that have demonstrated capability at a global scale to exploit the rich information content of MAP measurements: the Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm, developed at the Laboratory of Atmospheric Optics (LOA) of the University of Lille and the GRASP-sas company, and the Remote Sensing of Trace gases and Aerosol Properties (RemoTAP) algorithm developed at SRON – Netherlands Institute for Space Research. Both algorithms show good performance against ground based AERONET measurements and already important scientific advancement has been made using the corresponding data products. Nevertheless, when looking at global maps, significant differences are apparent between the two algorithms. In order to improve retrieval products from PARASOL and the upcoming missions containing MAP instrumentation (3MI/METOP-SG, SPEXone/PACE, HARP2/PACE, MAP/CO2M) it is essential to understand the reasons for the differences between the GRASP and RemoTAP algorithms. Therefore, in this project we propose to perform an extensive and systematic comparison between the two algorithms. We expect this will lead to optimized algorithm choices for both algorithms leading to better aerosol products and error characterization. The project will results in improved global data sets of aerosol properties from both algorithms. |
| High information content ozone profile algorithm for ground-based passive remote sensing instruments (OPA) | LuftBlick Earth Observation Technologies (LuftBlick) and the Royal Belgian Institute for Space Aeronomy (BIRA) propose to develop a novel algorithm to derive ozone profiles from measurements of ground-based passive remote sensing instruments. [...] | LUFTBLICK OG (AT) | Science | Altitude, atmosphere, atmosphere science cluster, atmospheric chemistry, permanently open call | LuftBlick Earth Observation Technologies (LuftBlick) and the Royal Belgian Institute for Space Aeronomy (BIRA) propose to develop a novel algorithm to derive ozone profiles from measurements of ground-based passive remote sensing instruments. These are the highlights of our proposed activity: The novel algorithm distinguishes itself in several ways from existing approaches: It uses MAX-DOAS sky observations combined with direct sun measurements; It relies on absolute slant columns instead of relative ones; It combines results from UV and VIS spectral regions (Huggins and Chappuis ozone bands respectively) to make use of their different path lengths; It adds the retrieved effective ozone temperature to the input; It analyses entire days as a whole instead of single measurement sequences; It includes temperature profiles from re-analysis to be used in combination with the retrieve effective ozone temperatures. Once validated and made operational, the novel algorithm can be applied to new and existing datasets such as from the Pandonia Global Network (PGN). By this it would be an extremely valuable contribution to our knowledge of tropospheric ozone with direct impact to air quality, tropospheric chemistry and satellite validation. Having a working operational technique to derive TropO3 information from ground-based passive remote sensing measurements would increase our knowledge about TropO3 substantially at hardly any additional cost. Pandoras (or other MAX-DOAS instruments) are distributed in existing networks, e.g. the Pandona Global Network (PGN) with >100 locations around the world and are most often already performing the types of measurements which we plan to use for the algorithm we propose to develop. Hence a working ozone profile algorithm can be applied to these observations as well as on additional worldwide data sets which extend several years into the past. |
| High-resolution methane mapping with hyper and multispectral data (HiResCH4) | The detection and repair of methane leaks from fossil fuel production activities have been identified as a key climate change mitigation strategy. In the last years, a number of optical satellite missions with a spatial resolution of 30-m or [...] | UNIVERSITAT POLITÈCNICA DE VALÈNCIA (ES) | Science | atmosphere, atmosphere science cluster, permanently open call, science, Sentinel-2 | The detection and repair of methane leaks from fossil fuel production activities have been identified as a key climate change mitigation strategy. In the last years, a number of optical satellite missions with a spatial resolution of 30-m or better have shown potential for the detection of strong methane plumes emitted by point sources, which is key to guide emission reduction efforts. Those high resolution missions include two types of optical imagers, namely hyperspectral (e.g. PRISMA) and multispectral (e.g. Sentinel-2). The number of studies using either of those classes of spaceborne instruments to map methane point emissions is rapidly increasing. The overarching objective of this project is to assess the potential and limitations of spaceborne hyperspectral and multispectral missions for high-resolution methane mapping. Critical tasks to achieve this goal are the implementation of a realistic end-to-end simulator, the development of advanced methane retrieval methods, and the evaluation of methane emissions at different sites using real data from those missions. Methane emissions from fossil fuel extraction and transport Methane (CH4) emissions from fossil fuel production activities have been found to account for 35% (range 30%–42%) of total global anthropogenic emissions. Emissions mostly originate from oil and gas production infrastructure, such as wells, gathering stations, compressor stations, storage tanks, pipelines, processing plants, and flares, and also coal mines can be strong methane emitters. These industrial methane emissions typically happen as so-called “point emissions”, namely plumes emitted from small surface elements and containing a relatively large amount of gas. The detection and elimination of unintended methane emissions from fossil fuel production activities have been identified as a key means to reduce the concentration of greenhouse gases in the atmosphere. Detecting methane point emissions from space The Sentinel-5P/TROPOMI mission, launched in 2017, is leading a revolution in this field, but its 7-km pixel size does not generally allow for sampling of individual point sources. Fortunately, very recent scientific developments are showing that high-resolution (30 m or better) methane retrievals are possible using land-oriented satellite missions with optical imagers sampling the 2300-nm methane absorption. On the one hand, hyperspectral missions have a relatively high sensitivity to methane due to their dense spectral sampling of the strong methane absorption at 2300 nm, but only provide sporadic acquisitions over pre-selected sites. On the other hand, multispectral missions offer a continuous global coverage within some days, but with a lower sensitivity to methane than hyperspectral missions. Better understanding the potential and limitations of these new data sets, and the synergies between them and with TROPOMI, are key for future satellite-based methane emission mitigation efforts. |
| HR-AlbedoMap: Generation of high-resolution spectral and broadband surface albedo products based on Sentinel-2 MSI measurements | The project aims at improving a current surface albedo generation system by adapting and integrating a deep learning system for cloud detection, an advanced atmospheric correction model which considers the surface BRDF effects, and a new [...] | UCL CONSULTANTS LTD (GB) | Science | carbon science cluster, permanently open call, science, Sentinel-2, Surface Radiative Properties | The project aims at improving a current surface albedo generation system by adapting and integrating a deep learning system for cloud detection, an advanced atmospheric correction model which considers the surface BRDF effects, and a new technology allowing to retrieve high-resolution albedo from high-resolution reflectance by combining with downscaled MODIS BRDF climatology |
| HyNutri: Sensing “Hidden Hunger” with Sentinel-2 and Hyperspectral | The project will perform an experimental campaign to investigate the potential of Sentinel-2 and PRISMA data to estimate and predict the concentration of different nutrients (K, P, Ca, Fe, Mg, Zn, N and S) in the final agricultural production. | UNIVERSITY OF TWENTE (NL) | Science | agriculture, permanently open call, science, Sentinel-2 | The project will perform an experimental campaign to investigate the potential of Sentinel-2 and PRISMA data to estimate and predict the concentration of different nutrients (K, P, Ca, Fe, Mg, Zn, N and S) in the final agricultural production. |
| HyperSpectral pre-operational data injection into Copernicus | The aim of this project is to develop and validate novel EO-based hyperspectral data generated by an unprecedented miniaturized hyperspectral sensor (HyperScout-2) employed in the ESA EO directorate small sat mission PhiSat-1. The processed data [...] | cosine Remote Sensing BV (NL) | AI4EO | AI4EO, hyperspectral, permanently open call | The aim of this project is to develop and validate novel EO-based hyperspectral data generated by an unprecedented miniaturized hyperspectral sensor (HyperScout-2) employed in the ESA EO directorate small sat mission PhiSat-1. The processed data output of this project will advance existing EO capabilities. The processing and the distribution of the data to the community will enable original advancements in open science practices. It will boost up leading-edge short term studies advancing key areas of Earth system science and maximizing the scientific impact of European EO assets. The data processed in this project will serve as primary applications the ones foreseen in FSSCAT and PhiSat-1 missions. Following the distribution, many other applications will be possible by users, e.g. flooding, fire prediction, detection and monitoring, Urban Heat Islands, vegetation and crop status, water quality, change detection, soil moisture. The overall goal of this project is to pre-process HyperScout 2 data from level 0 to level 1C, thus making them available for different non-space applications. The main objectives derived from this goal are listed below: OBJ-01: Define the L-1C product scheme. The L-1C product will be a georeferenced hyperspectral cube of TOA reflectances along with associated meta-data such as processing related information, mapping specifications, viewing angles, etc. Here, which specific metadata to be included will be decided along with the file format (e.g. HDF5) and organization of the final product. OBJ-02: Select and fine tune the most suitable processing chain. A processing chain will be selected among the ones currently employed at cosine for different projects, and will be fine tuned to the specific needs of the project. The data processing chain begins with a L-0 product and outputs a L-1C product which match the specifications defined from OBJ-01. OBJ-03: Develop methods to validate the quality of the product. A validation environment will be developed which quantifies the quality of the hyperspectral cubes geometrically, radiometrically and spectrally. The accuracy of the meta-data will also be assessed in the environment. OBJ-04: Process the selected L0 VNIR dataset and assess the quality of the final product. Using the environment developed for OBJ-04, the quality of the L-1C product will be assessed. OBJ-05: collect lessons learned and identify improvements for the data processing algorithms. |
| IMAGE COMPRESSION FOR REMOTE SENSING USING VECTOR-QUANTIZED AUTOENCODERS (CORSA) | Development of novel AI based clustering methods to support enhanced compression performance (time and information loss).
The use of AI based methods to support the optimization of the compression and decompression process as well as enabling [...] |
VLAAMSE INSTELLING VOOR TECHNOLOGISCH ONDERZOEK VITO (BE) | Enterprise | permanently open call | Development of novel AI based clustering methods to support enhanced compression performance (time and information loss). The use of AI based methods to support the optimization of the compression and decompression process as well as enabling optimized feature extraction based on the lower volume compressed datasets. |
| Impact of COVID-19 on harvest of row crop | The project aims at determining changes in agricultural management patterns, particularly in crop harvest dates, as accurately as possible using Sentinel-1 radar data, and assessing whether linked to Covid-19. | VISTA GEOWISSENSCHAFTLICHE FERNERKUNDUNG GMBH (DE) | Science | agriculture, covid19, permanently open call, science, Sentinel-1 | The project aims at determining changes in agricultural management patterns, particularly in crop harvest dates, as accurately as possible using Sentinel-1 radar data, and assessing whether linked to Covid-19. |
| Innovator supporting developing countries in cloud based forest monitoring for REDD+ | A range of EO based prototype capabilities have been developed and tested in relation to the implementation of REDD+.
With the launch of Sentinel-1A/B and Sentinel-2A/B, a new era of frequent coverage of the earth surface by high resolution [...] |
GAF AG (DE) | Sustainable Development | forestry, permanently open call | A range of EO based prototype capabilities have been developed and tested in relation to the implementation of REDD+. With the launch of Sentinel-1A/B and Sentinel-2A/B, a new era of frequent coverage of the earth surface by high resolution (HR) satellite imagery was initiated. Together with the Landsat and other satellite missions, it is now possible to build up dense time series with sufficient spectral and geometrical resolution which allow new analysis methods for improved forest and land cover mapping. The application of dense time series of Sentinel and the HR data provides the possibility to overcome mapping inaccuracies caused by seasonal changes of forest cover (leaf fall in dry season), to compensate data gaps caused by cloud coverage, to improve the analysis of human induced changes and to make an early detection of deforestation and forest degradation events possible. However, the data volumes of dense time series data stacks from Sentinel and other satellite systems are, compared with traditional processing methods (mono- and bi-temporal analysis), tremendously increasing and therefore require a sophisticated IT infrastructure to compute wall-to-wall land cover maps. It has been proven more efficient for the European Service Providers to make use of cloud processing options instead of purchasing, maintaining and constantly upgrading existing IT infrastructure. On the other side, the handling of huge data volumes and the application of complex processing algorithms pose an enormous infrastructure and capacity challenge for developing countries. Thus, technology transfer and capacity building are major pillars of development cooperation programmes but however, the status of having up-to-date hardware and software is almost always lacking behind the requirements of a fast developing technology. Therefore, working on cloud-based processing chains will be an opportunity for improved technology transfer and capacity building to developing countries. The overall goal of the current project is to enable Stakeholders and Users from developing countries to create sophisticated applications for forest monitoring and assessment within an innovative cloud-based Front Office which unifies the Big Data functionalities of the C-DIAS back storage with already verified processing algorithms for tropical dry forest mapping. In particular the project outcomes are expected to be provision to Users from developing countries of improved access and processing methods for cloud based forest monitoring, based on Sentinel-2 data, a web based Graphical User Interface (GUI) to select, pre-process and classify Sentinel-2 data and capacity building activities related to testing, validation and training on the developed system. |
| INTENS – Characterization of IoNospheric TurbulENce level by Swarm constellation | The purpose of the project is to investigate the turbulent nature of geomagnetic field and plasma parameters (electron density and temperature) in the ionosphere as recorded by the Swarm constellation during a period of 4 years (from 1 April [...] | ISTITUTO NAZIONALE DI GEOFISICA E VULCANOLOGIA (IT) | Science | ionosphere and magnetosphere, permanently open call, science | The purpose of the project is to investigate the turbulent nature of geomagnetic field and plasma parameters (electron density and temperature) in the ionosphere as recorded by the Swarm constellation during a period of 4 years (from 1 April 2014 to 31 March 2018). Specifically, fluctuations of these quantities, as well as their scaling features, will be thoroughly investigated during different geomagnetic disturbance conditions to shed light on the role played by the magnetohydrodynamic turbulence in creating multi-scale plasma structures and inhomogeneties in the ionospheric environment at different latitudes. Focused analyses of the parameters recorded by the Swarm constellation are expected to provide a reliable characterisation of the nature and level of the ionospheric turbulence on a local scale, which can be displayed either along a single satellite orbit or through maps over the region of interest. The same parameters can be used also to study space-climatological variations of scaling features of the geomagnetic field and ionospheric plasma according to different interplanetary magnetic field orientations. Swarm measurements will give the opportunity to get a precise characterization of the different ionospheric turbulence regimes of the medium crossed by satellites on scales from hundreds of kilometres to a few kilometres, when considering low resolution data, and from tens of kilometres to a few meters, when considering data at the highest resolution. Ground-based observations from the SuperDARN network at high latitudes and the ENIGMA array at low-middle latitudes will complement Swarm data. The obtained results will be interpreted in the light of previously theoretical, numerical and observational published works. The analysis performed at high latitudes in both hemispheres will allow, for instance, a thorough investigation of the North-South asymmetries, while the analysis at mid and low latitudes will improve our understanding about the impact of magnetospheric ring current variations on the ionospheric plasma at Swarm altitudes. The investigation proposed in the framework of the project is an example of the excellent capability of Swarm data to provide new insights on the ionosphere-magnetosphere coupling. |
| Introducing physics to artificial intelligence methods to improve satellite monitoring of the water cycle | The project aims to develop, train, and apply a hybrid neural network model to optimise EO data for a coherent, balanced water cycle at the global scale resulting in a new pixel-resolution datasets for the four water cycle components: [...] | ESTELLUS SAS (FR) | Science | AI4EO, hydrology science cluster, permanently open call, water cycle and hydrology | The project aims to develop, train, and apply a hybrid neural network model to optimise EO data for a coherent, balanced water cycle at the global scale resulting in a new pixel-resolution datasets for the four water cycle components: precipitation, evapotranspiration, change in water storage, and runoff (or river discharge). These data will cover the entire globe on quarter-degree grid cells and on a monthly time scale. |
| Machine Learning Methods for SAR-derived Time Series Trend Change Detection (MATTCH) | The MATTCH project - Machine Learning methods for SAR-derived Time Series Trend Change Detection - aims to apply Machine Learning techniques to InSAR (Interferometric Synthetic Aperture Radar) derived surface deformation measurements, with the [...] | TRE ALTAMIRA s.r.l. (IT) | Science | permanently open call, SAR, science | The MATTCH project – Machine Learning methods for SAR-derived Time Series Trend Change Detection – aims to apply Machine Learning techniques to InSAR (Interferometric Synthetic Aperture Radar) derived surface deformation measurements, with the goal of identifying, among the huge number of measurement points (MP) identified by advanced InSAR algorithms, the ones exhibiting displacement time series characterized by a change in trend or, more generally, an “anomalous behavior”. This data screening step is extremely important to support the End Users Community in the exploitation of frequently updated (every few days) and highly populated (millions of MPs) information layers resulting from advanced InSAR analyses over large areas.MATTCH aims to identify whether and how a Machine Learning approach can be applied successfully to the “data screening and data mining” step (with a particular emphasis on the detection of changes in trends), relying on the experience in SAR data processing of TRE ALTAMIRA and the extensive knowledge of POLIMI (Politecnico di Milano – Dipartimento di Elettronica e Informazione e Bioingegneria) about Machine Learning algorithms and their applications.To capture the temporal dependencies in the long displacement time series, the main Deep Learning architectures proposed for the analysis are Long Short-term Memory (LSTM) and Gate Recurrent Unit (GRU).The main objectives of the project are:Making SAR-derived surface deformation products more user-friendly and effective, supporting the analysis and the exploitation of InSAR-derived data, through the generation of a reliable layer of information driving the attention of the final users on a set “hotspots deserving special attention”;Enhancing the SqueeSARTM processing chain, via the implementation of a Machine Learning approach for time series trend detection, which is expected to improve the reliability and reduce the computational cost with respect to the statistical procedure currently in use;Increasing the knowledge about Machine Learning techniques applied to Earth Observation Big Data in both TRE ALTAMIRA and POLIMI groups, strengthening an effective cooperation between industry and academia in this relatively novel research field;Increasing the knowledge of Graphic Process Units (GPU) and cloud-based services to perform high throughput data processing and flexible scale-up;Improving the exploitation of ESA Sentinel-1 data, by creating innovative solutions, spurring new services to end-users and hopefully increasing the Earth Observation market |
| Mapping and characterization of unstable slopes with Sentinel-1 multigeometry InSAR | Being a mountainous country, with long fjords and steep valley sides, Norway is particularly susceptible to large rock avalanches. In the last 100 years, over 170 people have been killed by tsunamis in fjords caused by large rock avalanches. In [...] | NORTHERN RESEARCH INSTITUTE (NORUT) (NO) | Applications | disaster risk, permanently open call, SAR, science | Being a mountainous country, with long fjords and steep valley sides, Norway is particularly susceptible to large rock avalanches. In the last 100 years, over 170 people have been killed by tsunamis in fjords caused by large rock avalanches. In each case, the rock avalanche was preceded by many years of slow movement, with acceleration prior to slope failure. With several thousand kilometres of inhabited coastline and valleys, it is a challenge to identify similar hazards in an efficient manner. Once we suspect an area to be sliding, it may take several years of measurements to confirm it, and an extensive ground instrumentation to characterize the type of motion. The Geological Survey of Norway (NGU) is responsible for hazard and risk classification of large rock slope instabilities in Norway. They also assist the Norwegian Water Resources and Energy Directorate (NVE) with long term monitoring of high risk instabilities. A very important factor in determining hazard is the determination of rates of movements. This is predominantly done using InSAR, although GNSS and in situ instrumentation (crack meters, tilt meters, borehole instrumentation, total stations etc.) are also applied at site level. In Norway, there has been a significant interest from the public stakeholders (NGU and NVE) to use InSAR, mainly for mapping of landslides. NGU launched a development project in 2016, with Norut a prime contractor, to set up a national InSAR-based deformation mapping service, based upon satellite data from Sentinel-1. The first national deformation map, produced by using Sentinel-1 Persistent Scatterer Interferometry (PSI), was publicly released in November 2018. The system, when in operational phase, will provide updated displacement maps at a national scale, and with an open data policy. It is however well know that when the true displacement direction differs from the satellite line-of-sight (LoS), the sensitivity decreases and interpretation of InSAR deformation measurements may become challenging. Relating InSAR displacement maps to ongoing surface displacement processes can be difficult. A knowledge of the LoS direction for the applied satellite geometry as well as factors controlling the direction of displacement (gradient and aspect of the terrain, orientation of controlling geological structures) is required to understand how much of the true three-dimensional (3D) displacement can be observed. Combining InSAR data from ascending and descending satellite orbits can increase sensitivity for displacement by providing information about the displacement, decomposed into the East-West and Vertical vector surface. The resulting products will contain information about both the magnitude and the direction of surface displacement. Combination is possible in areas covered by at least two spatially and temporally overlapping InSAR datasets, from ascending and descending orbit geometries. By combining InSAR information determined from different lines of sight, the understanding of the type of movement taking place is improved. For example, surface parallel, mostly vertical, toppling etc. In this project, we will develop higher-order products based on combination of different InSAR datasets in order to ease the interpretation of site-specific deformation processes. The aim of our project is to define and develop geologically meaningful InSAR products to provide meaningful information about slope processes, which could extend the use of Sentinel-1 InSAR in landslide risk management in Norway. |
| MARINE LITTER SIGNATURES IN SYNTHETIC APERTURE RADAR IMAGES (MIREIA) | This project complements on-going activities and other activities started under this call for proposals by focussing on optimising the techniques for the detection of marine litter in SAR data. This complements the use of optical data and [...] | ISARDSAT S.L. (ES) | Science | marine environment, permanently open call, SAR, science | This project complements on-going activities and other activities started under this call for proposals by focussing on optimising the techniques for the detection of marine litter in SAR data. This complements the use of optical data and modelling in order to progressively build up an integrated picture as to how marine litter (and marine plastics in particular) are entering the marine environment, how they are transporeted, how they break down and how they are impacting different ecosystems. Results: “A first approach to the automatic detection of marine litter in SAR images using artificial intelligence”, Salvatore Savastano, Ivan Cester, Marti Perpinya, Laia Romero, Proceedings of IGARSS 2021, Brussels |
| Methane Emission Detection from Satellite Measurements | The project, Methane Emission Detection from Satellite Measurements, is being developed by the U.K. National Physical Laboratory (NPL), experienced in emission detection and rate measurement using in-situ measurement technologies, in particular [...] | NATIONAL PHYSICAL LABORATORY (NPL) (GB) | Enterprise | atmosphere, enterprise, permanently open call | The project, Methane Emission Detection from Satellite Measurements, is being developed by the U.K. National Physical Laboratory (NPL), experienced in emission detection and rate measurement using in-situ measurement technologies, in particular with application to land fill sites and the oil and gas industry, and GHGSat Inc., a company operating a state-of-the-art satellite system to detect atmospheric methane. Scope of the activity is a demonstration exercise to determine current and emerging capabilities to detect surface methane emissions from small and facility scale areas using satellites (e.g. leaks from high pressure gas infrastructures and unlicensed land fill sites), which would have a considerable impact for both gas pipeline operators and national EPAs. The aim is to characterize the level of performance, in particular, to what extent leaks can be detected with reference to operator’s requirements. This includes integration of the latest GHGSAT satellite (i.e. GHGSat-C1), which is expected to provide an order of magnitude improvement in methane detection (400 tons per year in the relative absence of wind to 1,000 tons per year in moderate winds) over the previous GHGSat satellite (i.e. GHGSat-D), operational for more than two years. The project envisages a co-design exercise with end-user communities (e.g gas pipeline operators, infrastructures technical services providers, etc…), stakeholders (UK Environment Agency) and partners (U.K. National Grid, which is the owner and operator of the UK National Transmission System comprising approximately 7660 kilometres of high pressure pipeline and 618 above-ground installations).The project will provide key outputs to underpin and stimulate the development of commercial services for the determination of methane mass emissions, in particular from the gas industry. This will be achieved through three key phases Phase 1 – Measurement requirement definition – a key outcome will be the definition of a comprehensive measurement service and data product requirement specification. This will be achieved through discussions and interaction with industry bodies and gas supply companies. A key point is that there is likely to be no single measurement requirement and a range of capabilities will be needed. The project will therefore assess the range of needs from industry, including for example the quantification of methane emissions from sites/facilities and the identification of leaks from distributed infrastructure. By identifying the key needs and drivers, a range of potential data services can be identified and this will enable satellite providers and data providers to tailor current and future services to meet the needs of industry. Phase 2 – Satellite capability validation and calibration – satellite methane column measurements are validated against ground stations such as those in TCONN. However, this does not provide the necessary calibration and validation data to support methane mass emission quantification or specific leak detection data products and services. To support the development and ongoing operation of these services a suitable calibration infrastructure is necessary. This project will develop such an approach utilising existing methane sources. As a demonstration of the feasibility of this approach a landfill site will be used, as these sites emit methane on a continuous basis. Such ground calibration sites would then be available in subsequent commercial data services as routine mass emission rate calibration sites. This project will therefore develop a key element of mass emission data product services, enabling the commercial deployment of such services. Phase 3 – Operational review of GHGSat and other satellites – Satellite capabilities will be reviewed for their applicability specifically to pipeline monitoring. Subject to this review and the results of Phase 1, the project partners aim for a demonstration of GHGSat-C1 capabilities and methane mass emission data products for applicability to monitoring pipeline facilities such as compressors and terminals. |
| MethEO – Methane emissions in the Northern Hemisphere by applying both data from Earth Observing (EO) satellites and global atmospheric methane inversion model estimates | The project will investigate Northern Hemisphere methane (CH4) sources and their connection to the soil freezing and thawing at high latitudes. We will innovatively combine methods for monitoring of CH4 (methane) emissions in the Northern [...] | FINNISH METEOROLOGICAL INSTITUTE (FI) | Science | atmosphere, atmosphere science cluster, biosphere, carbon cycle, carbon science cluster, permafrost challenge, permanently open call, polar science cluster, science, Sentinel-5P, SMOS | The project will investigate Northern Hemisphere methane (CH4) sources and their connection to the soil freezing and thawing at high latitudes. We will innovatively combine methods for monitoring of CH4 (methane) emissions in the Northern Hemisphere by applying both data from Earth Observing (EO) satellites and global atmospheric methane inversion model estimates. The EO data consists of global soil F/T estimates obtained from the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission (from the SMOS+ Frozen soil project) as well as retrievals of atmospheric methane obtained from the Greenhouse Gases Observing Satellite (GOSAT) and the newly launched Sentinel 5 Precursor TROPOMI (S5P-TROPOMI) observations. The project has been kicked-off the 5th September. A first informal progress meeting has been on 20th December. First results have been shown and look promising. |
| MOHeaCAN: Monitoring Ocean Heat Content and Earth Energy ImbalANce from Space | Since the industrial era, anthropogenic emissions of Greenhouse gases (GHG) in the atmosphere have lowered the total amount of infrared energy radiated by the Earth towards space. Now the Earth is emitting less energy towards space than it [...] | MAGELLIUM (FR) | Science | altimeter, climate, GRACE, ocean health flagship, ocean heat budget, ocean science cluster, oceans, permanently open call, science | Since the industrial era, anthropogenic emissions of Greenhouse gases (GHG) in the atmosphere have lowered the total amount of infrared energy radiated by the Earth towards space. Now the Earth is emitting less energy towards space than it receives radiative energy from the sun. As a consequence there is an Earth Energy Imbalance (EEI) at the top of the atmosphere. Because of this EEI, the climate system stores energy, essentially in the form of heat. This excess of energy perturbs the global water-energy cycle and generates the so-called “climate changes”. The excess of energy warms the ocean, leading to sea level rise and sea ice melt. It melts land ice, leading to sea level rise. It makes land surface temperature rise, changing the hydrological cycle and generating droughts and floods. It is essential to estimate and analyse the EEI if we want to understand the Earth’s changing climate. Measuring the EEI is challenging because it is a globally integrated variable whose variations are small (smaller than 1 W.m-2) compared to the amount of energy entering and leaving the climate system (~340 W.m-2). Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. An accuracy of <0.1 W.m-2 at decadal time scales is desirable if we want to monitor future changes in EEI associated with anthropogenic forcing, which shall be a noncontroversial science based information used by the GHG mitigation policies. To date, the most accurate approach to estimate EEI consists of making the inventory of the energy stored in different climate system reservoirs (atmosphere, land, cryosphere and ocean) and estimating their changes with time. At large scale, variations in internal and latent heat energy dominate largely over the variations in other forms of energy (potential energy and kinetic energy). The ocean concentrates the vast majority of the excess of energy (~93%) associated with EEI. For this reason the global Ocean Heat Content (OHC) places a strong constraint on the EEI estimate. Thus it is crucial to characterise the uncertainty in EEI and OHC to strengthen the robustness of this estimation. Four methods exist to estimate the OHC: The direct measurement of in situ temperature based on temperature/Salinity profiles (e.g., Argo floats). The estimate from ocean reanalyses that assimilate observations from both satellite and in situ instruments. The measurement of the net ocean surface heat fluxes from space. The measurement of the thermal expansion of the ocean from space based on differences between the total sea-level content derived from altimetry measurements and the mass content derived from GRACE data (noted “Altimetry-GRACE”). To date, the best results are given by the first method mainly based on Argo network. However, one of the limitations of the method is the poor sampling of the deep ocean (>2000 m depth) and marginal seas as well as the ocean below sea ice. Re-analysis provides a more complete estimation but large biases in the polar oceans and spurious drifts in the deep ocean due to the too-short spin up simulations and inaccurate initial conditions of the reanalysis, mask a significant part of the OHC signal related to EEI. The method based on estimation of ocean net heat fluxes from space is not appropriate for OHC calculation due to a too strong uncertainty (±15 W.m-2) for the science objective on EEI. The last option based on the “Altimetry-GRACE” approach is promising because it provides consistent spatial and temporal sampling of the ocean, it samples nearly the entire global oceans, except for polar regions, and it provides estimates of the OHC over the ocean’s entire depth. To date the uncertainty in OHC from this method is ±0.47 W.m-2, which is greater than what is needed (<0.3 W.m-2) to pin down the global mean value of EEI. This activity focuses on the “Altimetry-GRACE” approach to estimate the EEI. The objectives are twofold: To improve global OHC estimation from space and its associated uncertainty by developing novel algorithms; To assess our estimation by performing comparison against independent estimates based on Argo and on the Clouds and the Earth’s Radiant energy System (CERES) measurements at the top of the atmosphere. This innovative study will be performed in coordination with initiatives focused on climate change studies and EEI as the Global Water and Energy Exchanges project (GEWEX) and the Climate and Ocean Variability, Predictability and Change project (CLIVAR) of WCRP. “Scientific Highlights” The MOHeaCAN product contains monthly time series (between August 2002 and June 2017) of several variables, the main ones being the regional OHC (3°x3° spatial resolution grids), the global OHC and the EEI indicator. Uncertainties are provided for variables at global scale, by propagating errors from sea level measurements (altimetry) and ocean mass content (gravimetry). In order to calculate OHC at regional and global scales, a new estimate of the expansion efficiency of heat at global and regional scales has been performed based on the global ARGO network. A scientific validation of the MOHeaCAN product has also been carried out performing thorough comparisons against independent estimates based on ARGO data and on the Clouds and the Earth’s Radiant energy System (CERES) measurements at the top of the atmosphere. The mean EEI derived from MOHeaCAN product is 0.84 W.m-2 over the whole period within an uncertainty of ±0.12 W.m-2 (68% confidence level – 0.20 W.m-2 at the 90% CL). This figure is in agreement (within error bars at the 90% CL) with other EEI indicators based on ARGO data (e.g. OHC-OMI from CMEMS) although the best estimate is slightly higher. Differences from annual to inter-annual scales have also been observed with ARGO and CERES data. Investigations have been conducted to improve our understanding of the benefits and limitations of each data set to measure EEI at different time scales. The MOHeaCAN product from “altimetry-gravimetry” is now available, documented and can be downloaded at https://doi.org/10.24400/527896/a01-2020.003. Users will be mainly interested in ocean heat content time series at regional (grids) and global scales, and Earth energy imbalance time series. Feedback from interested users on this product are welcome. |
| Monitoring, Measurement, Reporting and Verification System for Cocoa sector in the Dominican Republic | The MRV4C project, developing a Monitoring, Measurement, Reporting and Verification (MRV) System for cocoa agroforestry in the Dominican Republic (DR) was a 1-year activity funded by the European Space Agency through the Open Call funding [...] | GMV NSL LTD (GB) | Enterprise | agriculture, AI4EO, artificial intelligence, Biomass, climate, forestry, permanently open call, SAR, Sentinel-1, Sentinel-2, sustainable development | The MRV4C project, developing a Monitoring, Measurement, Reporting and Verification (MRV) System for cocoa agroforestry in the Dominican Republic (DR) was a 1-year activity funded by the European Space Agency through the Open Call funding opportunity, addressing activity line 6, “EO for Sustainable Development”, of the ESA EO SCIENCE FOR SOCIETY programme. The aim of the MRV4C was two-fold: use of EO and development of an interactive tool for supporting sustainable management and decision-making in a supply chain that is key for the DR economy – that of cocoa; and contribute to the sustainability and strengthening of the national REDD+ MRV system, funded by the Bio-Carbon Fund of the World Bank (FCPF). With the support of the World Bank, GMV NSL engaged the DR Ministry of Environment and Natural Resources, The Cocoa Department at the Ministry of Agriculture, the National Cocoa Commission (CONACADO) and the private sector, represented by the country’s main confederation of cocoa producers (DR Cocoa Foundation), besides several NGOs and international observers. Using EO and AI, GMV NSL demonstrated the incredible potential of Sentinel data to benefit cocoa agroforestry and the DR economy by identifying the best areas to grow this crop, mapping current cocoa farms extension and determining above-ground biomass. Furthermore, GMV built a system and an interactive tool within a web-based platform that enables, for instance, the planning of suitable land for growing cocoa and the verification of a zero-deforestation cocoa supply chain. The MRV4C project, leveraging on the data provided through the Copernicus Programme, demonstrated the role of EO to back up EU policies that aim to boost sustainable cocoa production by contributing to enhancing the economic, social, and environmental sustainability of cocoa in several countries (including the Dominican Republic, one of the largest supplier of cocoa beans to the EU). As well as contributing to the DR country objectives, the project provided tangible evidence of how the Sentinel satellites can contribute to the United Nations Sustainable Development Goals (SDGs), in particular, Goals 1, 2, 8, 13 and 15: “No-Poverty”, “Zero Hunger”, “Decent Work and Economic growth”,”Climate Action”, and “Life on Land”. |
| NEW PLANT BREEDERS USING EO (NEWBIE) | Plant breeding is the science of changing the traits of plants, in order to produce desired characteristics. Plant breeding has been practiced by farmers since the dawn of agriculture, as they selected plants for larger seeds, tastier fruits, [...] | AGROAPPS PC (GR) | Enterprise | agriculture, permanently open call | Plant breeding is the science of changing the traits of plants, in order to produce desired characteristics. Plant breeding has been practiced by farmers since the dawn of agriculture, as they selected plants for larger seeds, tastier fruits, and other valuable traits. In other words, the goals of plant breeding are to produce crop varieties that boost unique and superior traits for a variety of agricultural applications, a process which may last up to 12 years. NEWBIE is an Assistance Tool for crop selection that aims to improve the selection efficiency of the breeding programs, accelerating genetic gain and reducing the costs. More specifically, NEWBIE Assistance Tool will be based on the combination of Proximal Phenotyping and Remote Sensing Phenotyping. Proximal phenotyping will be used in the early breeding stages, under multiple location trials that include small size plots and many seed varieties. Remote Sensing Phenotyping will be combined with proximal phenotyping at later breeding stages, established at larger plot sizes. Both types of phenotyping combined with climate intelligence and coupled with machine learning and artificial intelligence techniques, will allow for a comparison of multi-temporal feature datasets through the Assistance Tool for crop selection. The result of the process will be the identification of optimal performing candidates that can be used for further breeding, in less time and with less resources than conventional breeding requires, while also building the know-how on the varieties that better suit specific areas of interest. |
| NOvel cOmputational methoDs for reLiablE SAteLlite-based Air quality Data (NOODLESALAD) | NOODLESALAD aims to develop computational methods for improving the satellite-based air quality estimates. More specific, it will concentrate on improving the air quality key indicator PM2.5, which is the dry mass concentration of fine [...] | FINNISH METEOROLOGICAL INSTITUTE (FI) | Science | Altitude, atmosphere, atmosphere science cluster, permanently open call, Sentinel-3 | NOODLESALAD aims to develop computational methods for improving the satellite-based air quality estimates. More specific, it will concentrate on improving the air quality key indicator PM2.5, which is the dry mass concentration of fine particulate matter with an aerodynamic diameter of less than 2.5 micrometers (micrograms per cubic meter of air). This activity will be developing a novel artificial intelligence approach for retrieving PM2.5 from earth observation data. The innovative strategy will be based on machine learning post-process correction that we recently developed. The novel approach will utilize an innovative fusion of Sentinel-3 satellite data, simulation model information, ground-based observations, traditional satellite retrieval techniques, and machine learning to produce satellite-based PM2.5. In this development work, data from the year 2019 will be used and select Central Europe as region of interest. The project will produce and validate PM2.5 estimates with a high spatial resolution of 300 meters for the Sentinel-3 satellites overpasses. In addition, this prototype approach will be used to create high temporal resolution air quality datasets for 5-10 European cities. Finally, the PM2.5 datasets produced will be publicly shared together with an open-source code package for Sentinel-3 PM2.5 retrieval. |
| Ocean CIRculation from ocean COLour observations (CIRCOL) | The monitoring of the oceanic surface currents is a major scientific and socio-economic challenge. Ocean currents represent one of the fundamental elements that modulate natural and anthropogenic processes at several different space and time [...] | CNR – INSTITUTE FOR ELECTROMAGNETIC SENSING OF THE ENVIRONMENT (IREA) (IT) | Science | climate, ocean science cluster, oceans, permanently open call, science | The monitoring of the oceanic surface currents is a major scientific and socio-economic challenge. Ocean currents represent one of the fundamental elements that modulate natural and anthropogenic processes at several different space and time scales, from global climate change to local dispersal of tracers and pollutants, with relevant impacts on marine ecosystem services and maritime activities (e.g. optimization of the ship routes, maritime safety, coastal protection). An appropriate monitoring of the oceanic currents must rely on high frequency and high resolution observations of the global ocean, which are achieved using satellite measurements. At present, no satellite sensor is able to provide a direct measurement of the ocean currents – The indirect and synoptic retrieval of the large-scale geostrophic component of the sea-surface motion is given by satellite altimetry at a spatial (~100km) and temporal (~one week) resolution which is not sufficient for many applications, even more in semi-enclosed basins as the Mediterranean Sea where the most energetic variable signals are found at relatively small scales. In this context, the objective of the CIRCOL (Ocean Circulation from Ocean Colour Observations) project is to improve the retrieval of altimeter-derived currents in the Mediterranean Basin combining the largescale, altimeter-derived geostrophic currents with the high-resolution dynamical information contained in sequences of satellite-derived surface Chlorophyll (Chl) observations. The project will be implemented in two phases. During Phase 1, an Observing System Simulation Experiment (OSSE) based on CMEMS (Copernicus Marine Environment Monitoring Service) physical and biogeochemical models will be implemented to investigate the potentialities of the proposed approach for the improvement of the altimeter derived currents. During Phase 2, the optimal Chl-based reconstruction of the sea-surface currents will be implemented using the satellite-derived multi-sensor, L4 (gap-free) altimeter and sea-surface Chl for the Mediterranean Sea distributed by CMEMS. The resulting products will be validated against in-situ velocity measurements (drifting buoys, HF radar). |
| Operational Snow Avalanche Detection Using Sentinel-1 | NORUT has developed an automatic avalanche detection method within a pre-operational processing chain that uses Sentinel-1 data to detect avalanches. This system is being tested in Northern Norway and is used operationally during winter [...] | NORTHERN RESEARCH INSTITUTE (NORUT) (NO) | Applications | applications, disaster risk, permanently open call | NORUT has developed an automatic avalanche detection method within a pre-operational processing chain that uses Sentinel-1 data to detect avalanches. This system is being tested in Northern Norway and is used operationally during winter 2017-2018 with the Norwegian Avalanche Warning Service. The goal of this project is to develop our avalanche detection processing chain to operational status anywhere on Earth, where Sentinel-1 data is available. This will be done by setting up the processing chain for five selected avalanche forecasting regions worldwide including Switzerland, North America and Northern Afghanistan with the aim to transfer the methodology to users with in mind the challenge of delivering consistent avalanche activity monitoring data. in space and time. |
| OrthoVHR: Automatic Orthorectification Service For Very High-Resolution Optical Satellite Data | The main objective of this project is to develop a prototype automatic orthorectification service for optical satellite images that will be ready for deployment into the cloud.
The project will specify, implement and test a service for [...] |
ZRC SAZU – Research Centre of the Slovenian Academy of Sciences and Arts (SI) | Enterprise | permanently open call | The main objective of this project is to develop a prototype automatic orthorectification service for optical satellite images that will be ready for deployment into the cloud. The project will specify, implement and test a service for automatic orthorectification of optical high-resolution (HR) and very high-resolution (VHR) satellite images based on a prototype system (STORM) developed previously. The service will interface with existing elements in the European EO platform services ecosystem (eg thematic exploitation platforms, DIAS, etc) and will meet the demands of the remote sensing community and other public or private sector users interested in reliable, high accuracy and fast orthorectification of data for various applications. The primary objective is for the service to be easy to use, in particular by non-experts and this will be reflected in the primary user interface although other interface options will also be put in place. |
| PASS-SWIO | PASS-SWIO, a project funded by ESA (via the Permanent Open Call), aims to establish a sea level monitoring system for Madagascar based on the installation and deployment of a low-cost relocatable tide gauge (Portagauge). Portagauge uses GNSS [...] | National Oceanography Centre (NOC) (GB) | Science | coastal zone, permanently open call, science, tides | PASS-SWIO, a project funded by ESA (via the Permanent Open Call), aims to establish a sea level monitoring system for Madagascar based on the installation and deployment of a low-cost relocatable tide gauge (Portagauge). Portagauge uses GNSS interferometric reflectometry (GNSS-IR) technology alongside a conventional radar. By combining these measurements with the analysis of satellite altimeter sea level data we will provide validation and wider scale knowledge of sea-level variability. Madagascar has very limited tidal prediction, primarily based on model data. It has no national sea level monitoring capability. There is currently only one functioning tide gauge station. A previous tide gauge, in the cyclone-prone north of the island, was destroyed several years ago. The project partners will work with the national Madagascar Meteorological Agency (DGM – Direction Générale de la Météorologie). DGM will take responsibility for the local maintenance and operation of the Portagauge. They will also receive training to carry out the data processing and analysis of tide gauge and satellite altimeter data. Discussions will be held with key stakeholders to review the project and agree a long-term Road Map for the sustainable implementation of a national sea-level monitoring system for Madagascar. This will serve as model for other island states and coastal countries in the South West Indian Ocean (SWIO) region and beyond. If you would like to access any of the data sets produced, please contact the Project Manager via the Project website. The Kick Off meeting was held on 5 May 2022. The activity has a duration of one year. |
| PHAB-IV: PHAse-Based sentinel-1 Ice Velocity | The project aims to develop the technical basis for an advanced Sentinel-1 Ice Velocity (IV) product for ice sheets and ice caps with improved spatial resolution and accuracy, based on Sentinel-1 interferometric phase measurements. | Technical University of Denmark (DK) | Science | Glaciers and Ice Sheets, permanently open call, polar science cluster, science, Sentinel-1 | The project aims to develop the technical basis for an advanced Sentinel-1 Ice Velocity (IV) product for ice sheets and ice caps with improved spatial resolution and accuracy, based on Sentinel-1 interferometric phase measurements. |
| Pioneer new EO applications: tipping and cueing for maritime surveillance service | One of the advantages of constellations of EO satellites is the capability to observe areas or features of interest more often than a conventional satellite would allow.
However this usually requires rapid tasking of follow-on satellites [...] |
DEIMOS IMAGING S.L.U. (ES) | Enterprise | marine environment, permanently open call | One of the advantages of constellations of EO satellites is the capability to observe areas or features of interest more often than a conventional satellite would allow. However this usually requires rapid tasking of follow-on satellites based on information collected from a lead satellite. This “Tipping and cueing” is critical to ensuring an effective monitoring response but there are a range of issues to be addressed if it is to be successfully implemented. Considerations as to what information is available compared to what is ideally required, what constitutes an optimized follow-up observation in different circumstances and how fast cueing is required are all dependent on the feature or target being observed. This contract is testing a range of scenarios and assessing the effectiveness and utility of different tipping and cueing approaches. |
| POINTOUT (Automatic Target Detection in Planet Imagery) | Traditional empirical and analytical Earth Observation (EO) algorithms retrieving physical parameters are getting to a fundamental change where learning algorithms without any prior background will be able to set themselves through the ingestion [...] | STARLAB BARCELONA SL (ES) | Enterprise | artificial intelligence, permanently open call, platforms | Traditional empirical and analytical Earth Observation (EO) algorithms retrieving physical parameters are getting to a fundamental change where learning algorithms without any prior background will be able to set themselves through the ingestion of Inputs/Outputs training datasets. Nowadays, Deep Learning (DL) networks among many other Machine Learning (ML) techniques are accurate enough, and computation technology is available to run such models. One of the key issue of such approach is the availability of massive or, large enough, reference datasets to train the models. As the models learn from the available data within the training datasets, if the size of such dataset is relatively small, the models learn very specific features that do not allow o generalize to any input data due to the lack of representativeness of the training dataset. This project addresses this issue in the context of a specific ML application, ie target/feature detection. The main goals are (1) to develop a PLATFORM to build and share collaborative training datasets for combined EO/ML communities, and (2) to implement a generic ML algorithm to detect targets in EO scenes for expert and/or non-expert users online |
| Pre-Operational Sentinel-3 snow and ice products (SICE) | Land ice mass loss is the largest source of global sea level rise. Since 1992, two thirds of sea level contribution from land ice comes from the Arctic. Roughly half of Greenland ice sheet mass loss is from increased surface melting. The [...] | GEOLOGICAL SURVEY OF DENMARK AND GREENLAND (DK) | Science | permanently open call, polar science cluster, science | Land ice mass loss is the largest source of global sea level rise. Since 1992, two thirds of sea level contribution from land ice comes from the Arctic. Roughly half of Greenland ice sheet mass loss is from increased surface melting. The fraction from surface melting is even higher for smaller Arctic ice masses. The dominant energy source for melt is absorbed sunlight controlled by surface albedo. Bare ice and snow impurities, including biological effects present strong melt amplifiers through surface albedo. NASA MODIS sensors provide a climate data record (CDR) of snow extent and ice albedo since 2000 with the hosting Terra and Aqua missions now several years beyond design lifetime. The NOAA VIIRS sensor bridges the need for a satellite-derived albedo. However, Copernicus Sentinel-3 also fulfils the WMO essential climate variable mandate and for decades to come with the following additional advantages over VIIRS and MODIS: 1. The Sentinel-3 OLCI instrument offers higher (300 m) finest spatial resolution (SR). The finest SR for MODIS is 500 m. For VIIRS, the finest SR is 750m. 2. Sentinel-3 OLCI and SLSTR instruments offer more spectral coverage than MODIS or VIIRS, with the OLCI channel 21 being of particular value being located in the part of the spectrum most sensitive to snow grain size. Neither MODIS nor VIIRS measure in this spectral channel. 3. The algorithms proposed here are a full physics based retrievals vs often used empirical techniques. 4. The recently completed Scientific Exploitation of Operational Missions (SEOM) Sentinel-3 for Science (S34Sci) Land Study 1: Snow (S3 Snow) albedo algorithm outperforms NASA MODIS MOD10A1 product for dry clean snow. Main objectives / end goals of the study are: 1. deliver an automated open source processing chain using Sentinel-3 OLCI and SLSTR sensors to determine a dry/wet snow and clean/polluted bare ice spectral and broadband optical albedo 1 km daily product for land ice (glaciers, ice caps, ice sheet). 2. determine an optimal cloud clearing process for cryospheric application leveraging cloud ID insight from SEOM Sentinel-3 for Science, Land Study 1: Snow 3. test the above for application to sea ice (as opposed to land ice). 4. implement terrain correction for slopes under 4 degrees typical of more than 90% of land ice. Justification: terrain slope and azimuth has a strong impact on snow and ice anisotropic reflectance in optical wavelengths. Above 4 degrees remains in development elsewhere, and does not comprise a significant portion of the ice sheet. 5. validate the algorithms using field data. 6. deliver daily 15 March – 30 September 1km pan-Arctic glacierized region albedo products for years 2017 and 2018 via the PROMICE.org web portal. 7. demonstrate a pre-operational near-realtime (under 6 hours latency) capability for Sentinel-3A and Sentinel-3B for delivering spectral and broadband albedo. |
| Privacy Preserving Federated Machine Learning in EO Science | Big Data and artificial intelligence (AI) pave the way for new pathways in the improvement of healthcare. But they also hide risks for the security of sensitive clinical data stored in critical healthcare ICT infrastructure. The EU-funded [...] | GMV SOLUCIONES GLOBALES INTERNET SA (ES) | AI4EO, Digital Platform Services | AI4EO, generic platform service, permanently open call, platforms | Big Data and artificial intelligence (AI) pave the way for new pathways in the improvement of healthcare. But they also hide risks for the security of sensitive clinical data stored in critical healthcare ICT infrastructure. The EU-funded FeatureCloud project proposes a transformative security-by-design concept aiming to reduce the possibility of cyber crime and allow safe cross-border collaborative data mining efforts. The concept will be applied to a software toolkit employing the worldwide first privacy-by-architecture method. Central features of this method are no sharing of sensitive data via any communication channels and no data storage in one central point. FeatureCloud will integrate federated machine learning with blockchain technology to safely apply next-generation AI technology in medical innovations. The digital revolution, in particular big data and artificial intelligence (AI), offer new opportunities to transform healthcare. However, it also harbors risks to the safety of sensitive clinical data stored in critical healthcare ICT infrastructure. In particular data exchange over the internet is perceived insurmountable posing a roadblock hampering big data based medical innovations. FeatureCloud’s transformative security-by-design concept will minimize the cyber-crime potential and enable first secure cross-border collaborative data mining endeavors. FeatureCloud will be implemented into a software toolkit for substantially reducing cyber risks to healthcare infrastructure by employing the world-wide first privacy-by-architecture approach, which has two key characteristics: (1) no sensitive data is communicated through any communication channels, and (2) data is not stored in one central point of attack. Federated machine learning (for privacy-preserving data mining) integrated with blockchain technology (for immutability and management of patient rights) will safely apply next-generation AI technology for medical purposes. Importantly, patients will be given effective means of revoking previously given consent at any time. Our ground-breaking new cloud-AI infrastructure only exchanges learned model representations which are anonymous by default. Collectively, our highly interdisciplinary consortium from IT to medicine covers all aspects of the value chain: assessment of cyber risks, legal considerations and international policies, development of federated AI technology coupled to blockchaining, app store and user interface design, implementation as certifiable prognostic medical devices, evaluation and translation into clinical practice, commercial exploitation, as well as dissemination and patient trust maximization. FeatureCloud’s goals are bold, necessary, achievable, and paving the way for a socially agreeable big data era of the Medicine 4.0 age. |
| QuantEO: a new Intelligent Automation (IA) service for Sentinel-2 Data | This project developed a service for automated clustering of Sentinel-2 pixels which allows its users to focus on Earth surface changes rather than on remote sensing problems, and hence to develop their own downstream applications.
Available [...] |
PIXSTART (FR) | AI4EO | AI4EO, applications, artificial intelligence, permanently open call, Sentinel-2 | This project developed a service for automated clustering of Sentinel-2 pixels which allows its users to focus on Earth surface changes rather than on remote sensing problems, and hence to develop their own downstream applications. Available via a standard interface, it produces on demand and in near real time classified Sentinel-2 images at 10 meter resolution, in a new 2D space which preserves all properties of Sentinel-2 information (spectral complexity and topology, maximum spatial resolution), and which is consistent over time and space (from a Sentinel 2-tile to another). This simplified new space is allowing pixel labelling or grouping by a posteriori classes identification, change detection by distance computation, interpolation, and may be used as a pre-processing step for all kinds of machine learning algorithms. |
| R4OpenEO – Integrating R Analytics with openEO Platforms | This project will develop, test and demonstrate the use of the R data science language within openEO platform.
This involves the continuation of development of the openEO R client, integration of openEO software components in R integrated [...] |
EURAC RESEARCH – ACCADEMIA EUROPEA (IT) | Digital Platform Services | permanently open call, platforms | This project will develop, test and demonstrate the use of the R data science language within openEO platform. This involves the continuation of development of the openEO R client, integration of openEO software components in R integrated development environments (Rstudio, Project Jupyter), as well as R user-defined functions that directly operate on data cubes and their interaction with the openEO back-end drivers. Three selected use cases, from the context of the ESA Regional Initiatives, will demonstrate the usability of the developed components and foster collaboration with the EO4Alps regional initiative and the openEO Platform project. |
| RESTORE-IT: GLOBAL SATELLITE-BASED IMPACT MONITORING TOOL FOR RESTORATION INITIATIVES | The Restore-IT project is a collaboration between academia, NGOs, and commercial satellite providers and is supported by the European Space Agency (ESA). Within the project, VanderSat , in collaboration with the University of Leicester – inspect [...] | VANDERSAT B.V. (NL) | Applications | permanently open call | The Restore-IT project is a collaboration between academia, NGOs, and commercial satellite providers and is supported by the European Space Agency (ESA). Within the project, VanderSat , in collaboration with the University of Leicester – inspect how factors such as surface temperature, soil moisture, and land cover can be combined to measure the impact of landscape restoration more accurately from space. To understand best what is needed for effective satellite monitoring, two case studies have been selected to test the new methodology (in Kenya and India) With this new way of impact monitoring, the progress of landscape restoration projects on a larger scale can be measured. |
| RIDESAT – RIver flow monitoring and Discharge Estimation by integrating multiple SATellite data | The RIDESAT Project (RIver flow monitoring and Discharge Estimation by integrating multiple SATellite data) aims at developing a new methodology for the joint exploitation of three sensors (altimeter, optical and thermal) for river flow [...] | CNR-RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION – IRPI (IT) | Science | altimeter, permanently open call, science, water cycle and hydrology | The RIDESAT Project (RIver flow monitoring and Discharge Estimation by integrating multiple SATellite data) aims at developing a new methodology for the joint exploitation of three sensors (altimeter, optical and thermal) for river flow monitoring and discharge estimation. Even if with a number of limitations, satellite radar altimetry over surface inland water has demonstrated its potential in the estimation of water levels useful for hydrological applications. Optical sensors, thanks to their frequent revisit time (nearly daily) and large spatial coverage, are recently used to support the evaluation of the river discharge variations. Despite the moderate spatial resolution of the optical data (about 250 – 300 m), their complementary with radar altimeter data enables to benefit of the different characteristics of the satellite sensors. In particular, the combination of radar (i.e. altimeters), optical and thermal instruments (i.e., multispectral sensors), allows for a continuous monitoring of the inland water and, hence, of the river discharge. The RIDESAT Project aims at: better understanding the use of optical and thermal sensors through the study of the physical meaning behind the process and their field of applicability; and developing a procedure of merging the three different satellite data through a physically based method that uses hydraulic variables obtained by satellites (e.g. water height, slope, width, flow velocity). The goal is to provide, for the first time, an accurate satellite-based river discharge product for small to large rivers. In order to test the ability of the different sensors to retrieve the river discharge at global scale, 10 pilot sites are selected all over the world, based on the availability of in situ measurements of hydraulic and morphological variables: water level, cross section, width, surface and bottom slope, flow velocity and discharge. The selection of pilot sites is also based on the climatic area (Tropical, Arid, Temperate, Cold), flow regime (glacial, nival, pluvial and tropical pluvial) and the size of the basin (large, medium and small). The developed procedure and the results obtained in the RIDESAT project will have an impact both on the scientific and the operational communities. In this respect, the estimation of the river discharge has a large interest in the hydrology community and an efficient and productive procedure can prove an advancement for the understanding and the knowledge of the hydrological processes. Users and stakeholders potentially interested include space agencies, government agencies, basin authorities, civil protection authorities and, more in general, all the organisations interested in the sustainable management of water for people and societies. This 12 month activity will be led by CNR-IRPI (IT) with the participation of DTU (DK). The Project is now closed but the research activity is still ongoingunder the STREAMRIDE Project to explore the possibility of improving the RIDESAT algorithm andcomplementing it with a different satellite approach for riverdischarge estimation developed in the STREAM Project. |
| Road-DL: Development of a Road Pavement Condition Classifier Utilising Deep Learning Techniques Applied to SAR Data | The objective of ROAD-DL service is to provide road condition assessment based on remote sensing data. The service aims to provide up to date classification of specific features related with road condition that could trigger inspection or [...] | TELESPAZIO VEGA UK LIMITED (GB) | Enterprise | infrastructure, permanently open call, SAR, Sentinel-1 | The objective of ROAD-DL service is to provide road condition assessment based on remote sensing data. The service aims to provide up to date classification of specific features related with road condition that could trigger inspection or maintenance actions. Specifically, the service ingest Synthetic Aperture Radar (SAR) Sentinel-1 satellite images, provided by ESA through the Copernicus initiative. Telespazio UK built and tested an approach driven by Deep Learning (DL) to provide road condition assessment to National Highways (NH). The project used multi-temporal SAR Sentinel-1 satellite data and NH collected road condition parameters to train a neural network to identify damaged and degraded roads in the UK strategic road network. The DL classifiers were trained and validated with independent in-situ laser scanner measurement data, which is routinely collected by NH. The solution is easily scalable and shall act as a continuous monitoring service providing routine insights between ground-based surveys. |
| SAR4Wildfire | The objective of tge SAR4Wildfire project is to develop a novel and automatic method, using Sentinel-1 SAR time series and a deep learning framework, for near real-time wildfire progression monitoring and burn severity mapping in preselected [...] | KUNGLIGA TEKNISKA HÖGSKOLAN (SE) | Applications | carbon cycle, permanently open call, SAR, Sentinel-1 | The objective of tge SAR4Wildfire project is to develop a novel and automatic method, using Sentinel-1 SAR time series and a deep learning framework, for near real-time wildfire progression monitoring and burn severity mapping in preselected wildfire sites in Sweden and British Columbia, Canada. Whenever available, Sentinel-2 MSI data will be incorporated in the framework for active fire detection and burn severity mapping. Once validated the method will be applied to several 2017 and 2018 wildfire sites around the world such as California (e.g., Camp Fire and/or Mendocino Complex Fire), Russia (e.g., Siberia Fire) and Africa to explore its global applicability. |
| Sargassum monitoring service | The project objective is to develop and implement an innovative automated service based on Earth Observation (EO) data to monitor floating Sargassum algae in the Caribbean area, estimate their drift and eventual landings on the coasts, and [...] | CLS COLLECTE LOCALISATION SATELLITES (FR) | Applications | applications, coastal zone, oceans, permanently open call | The project objective is to develop and implement an innovative automated service based on Earth Observation (EO) data to monitor floating Sargassum algae in the Caribbean area, estimate their drift and eventual landings on the coasts, and provide dedicated bulletins to the end-users. |
| SaTellite-based Run-off Evaluation And Mapping (STREAM) | The STREAM Project (SaTellite based Runoff Evaluation And Mapping), led by CNR-IRPI with the participation of the Institute of Geodesy (GIS) at University of Stuttgart, aimed at developing innovative methods able to maximize the recovery of [...] | CNR – INSTITUTE FOR ELECTROMAGNETIC SENSING OF THE ENVIRONMENT (IREA) (IT) | Science | applications, permanently open call, science, water cycle and hydrology | The STREAM Project (SaTellite based Runoff Evaluation And Mapping), led by CNR-IRPI with the participation of the Institute of Geodesy (GIS) at University of Stuttgart, aimed at developing innovative methods able to maximize the recovery of information on runoff contained in current satellite observations of climatic and environmental variables (i.e., precipitation, soil moisture, terrestrial water storage anomalies). In situ observations of river discharge, used for the quantification of total runoff, typically offer little information on its spatial distribution within a watershed. Moreover, river discharge observation networks suffer from many limitations such as low station density and often incomplete temporal coverage, substantial delay in data access and large decline in monitoring capacity. Paradoxically, this issue is exacerbated in poor non-industrialized nations where the knowledge of the terrestrial water dynamics is even more important. On the other hand, land surface and hydrological models are very highly data demanding, based upon complex modelling systems and might suffer from an incorrect representation of the pre-storm condition, which is paramount for a proper runoff estimation In this context, the STREAM project aimed at: Investigate the possibility to use satellite data for the hydrological cycle modeling; and developing a conceptual hydrological model, STREAM, directly ingesting satellite observation of soil moisture (SM), precipitation (P) and terrestrial water storage anomalies (TWSA). The goal of the project was to estimate runoff and river discharge time series for large basins in the world at high spatial and temporal resolution. During the 12 months of project activity, a quality assessment of STREAM river discharge and runoff estimates was carried out over five basins (Mississippi, Amazon, Danube, Niger and Murray-Darling). In these areas, the model was able to accurately simulate continuous daily river discharge and total runoff time series for the period 2003-2016. Only for specific case studies, such as for basins with high human impact or for highly vegetated areas, unsatisfactory model performances were found. To address this issue, the project activity has been extended of 1 year through a CCN (STREAMRIDE) to explore the possibility both to improve the STREAM model and to complement the model with a different satellite approach for river discharge estimation (RIDESAT) |
| SatHound: Multi-object detection solution based on artificial intelligence for non-expert EO users | SatHound is a solution to allow non-technical users configuring and executing multi-object detection processes over satellite images, based on deep learning technologies providing the following features:
Two modes of operation in a clean [...] |
THALES ALENIA SPACE ESPANA (ES) | Enterprise | permanently open call, platforms | SatHound is a solution to allow non-technical users configuring and executing multi-object detection processes over satellite images, based on deep learning technologies providing the following features: Two modes of operation in a clean and powerful user interface with Web-GIS capabilities: The training mode is used for teaching the system how to recognize new objects or to improve previous trainings with new examples. No more training datasets to be shared! This is a user-driven training mode, allowing to define unlimited detection targets. The hound mode is used for searching target objects on different map areas using already acquired knowledge. Batch training lets the user to continue using the hound mode while a SatHound improves it cognitive functions in background. Training status can be consulted at any time. Models are trained incrementally providing fast trainings and the possibility to roll-back to previous knowledge status in case of a wrong training. REST API for integrating with other systems. This lets external systems to scan geographic areas using the hound mode as you would do using the user interface. On-premise, Hybrid or Cloud deployment capabilities. The scalable architecture lets you scale-out the whole application for high availability or heavy load scenarios A local catalogue for hosting the satellite data products used in the searches. |
| Semi-supervised SENtinel-2 TREE Species Detection (SENTREE) | The objective of the SENTREE (Semi-supervised SENtinel-2 TREE Species Detection) project is to detect tree species in Norwegian production forests by developing deep learning models which will combine high resolution aerial imagery with [...] | Science [&] Technology Norway (NO) | Applications | applications, forestry, permanently open call, Sentinel-2 | The objective of the SENTREE (Semi-supervised SENtinel-2 TREE Species Detection) project is to detect tree species in Norwegian production forests by developing deep learning models which will combine high resolution aerial imagery with Sentinel-2 data. A major challenge in utilizing deep learning for tree species detection is the limited amount of training labels and their quality. The SENTREE project will address these issues with semi-supervised learning, noise tolerant training schemes and with automatic label noise detection. The results will be evaluated on a large area covering multiple municipalities in Norway. Allskog SA is participating in this project as a pilot customer, providing ground truth data, aerial imagery, domain knowledge, and support on validation activities. The project is primed by Science [&] Technology AS and funded by ESA under the EO Science for Society Permanently Open Call funding mechanism |
| SEN3GCP – Sentinel for 3D Ground Control Point | SEN3GCP is conceived as a service that will provide GCPs (Ground Control Points) and precise co-registration of EO products as an automated service accessible via the web and API. The service will be based on a global database of GCPs that is [...] | Planetek Italia (IT) | Digital Platform Services | generic platform service, permanently open call, platforms, SAR | SEN3GCP is conceived as a service that will provide GCPs (Ground Control Points) and precise co-registration of EO products as an automated service accessible via the web and API. The service will be based on a global database of GCPs that is derived from SAR imagery (3D coordinates including height information) and for which corresponding image chips of multispectral data are derived and maintained. The implemented service will provide GCPs via API and other interfaces. The service will also provide a mechanism for precise co-registration of EO data enabling for example the precise co-registration of multi-spectral and SAR data. |
| SenCYF: Sentinel-2-based estimation and forecasting of winter wheat crop yield at field scale, with national coverage | The SenCYF project proposes an innovative crop yield forecasting model based on Sentinel-2 data, validated with a France-wide in situ yield data set. It aims at addressing two core scientific questions: What are the performances of a nation-wide [...] | UNIVERSITY OF CATHOLIQUE DE LOUVAIN (BE) | Science | agriculture, permanently open call, science, Sentinel-2 | The SenCYF project proposes an innovative crop yield forecasting model based on Sentinel-2 data, validated with a France-wide in situ yield data set. It aims at addressing two core scientific questions: What are the performances of a nation-wide S2-based winter wheat yield estimation model at farm level? What are the performances of a nation-wide S2-based winter wheat yield-forecasting model at farm level one month before harvest? |
| SenSPa – Sentinels for Sustainable Pasture Management | Grasslands are a major part of the global ecosystem, covering 37 % of the earth's terrestrial area.
For a variety of reasons, mostly related to overgrazing and the resulting problems of soil erosion and weed encroachment, many of the world's [...] |
kartECO – Environmental and Energy (GR) | Sustainable Development | ecosystems/vegetation, permanently open call | Grasslands are a major part of the global ecosystem, covering 37 % of the earth’s terrestrial area. For a variety of reasons, mostly related to overgrazing and the resulting problems of soil erosion and weed encroachment, many of the world’s natural grasslands are in poor condition and showing signs of degradation. There is general agreement that effective management of grasslands would make a significant contribution to global food security and mitigating greenhouse gas emissions. However putting in place effective monitoring systems supporting management policies is complex. Considering only the use of grasslands for pasture, governmental authorities, policy makers, land managers and livestock farmers have to take decisions about sustainable pasture management according to the rangeland productivity and status. However, collecting field data regarding the current condition of vegetation (plant cover, forage production) is time and labour intensive. This project is developing prototype capabilities to prove systematic information at a range of scales (local, national, regional) to support estimation of the primary grassland status indicators characteristics such as sward height, biomass, quality, phenological stage, productivity level, species composition. Sentinel 2 measurement of the reflectance at visible and infrared wavelengths can enable discrimination of different grassland status at national and local scales, relying on the efficient coupling of remote sensing data with in-situ data for the development of efficient predictive models. Especially reflectance at the red edge part of the spectrum, where there is a rapid increase in reflectance from the red to NIR reflectance, has a strong correlation with the grass chlorophyll content of the canopy and the leaves. Inclusion of measurements made in a red-edge channel are expected to be a reliable indicator for grassland status, relating to foliar chlorophyll content, vegetation stress, plant chlorophyll concentration, and leaf area index. Additionally, the project will investigate the use of time series of imagery acquired through the growing season to provide maximum information on yields and management. The prototype capabilities are being developed, demonstrated and validated for grassland areas in Azerbaijan. |
| Sentinel coastal charting worldwide | The advent, with Sentinel-2, of a satellite constellation offering High Resolution (HR), high revisit time and free images has raised encouraging hopes amongst Coastal States and the Maritime community of Users who, in 2018, still suffer from [...] | ARGANS LIMITED (GB) | Applications | marine environment, permanently open call | The advent, with Sentinel-2, of a satellite constellation offering High Resolution (HR), high revisit time and free images has raised encouraging hopes amongst Coastal States and the Maritime community of Users who, in 2018, still suffer from the persistence of inaccurate nautical charts. These expectations however did not materialise because the Government agencies in charge of producing official charts still consider they do not have a reliable tool to take advantage of satellite imagery. Thirty years after it was first introduced in its national chart series with immense caveat by the French Hydrographic Office (Shom), Satellite Derived Bathymetry (SDB) is still the object of a global rejection by the International Hydrographic Organisation (IHO)’s Member States and professional users primarily concerned by Safety of navigation and the catastrophic consequences of ship groundings. Many of the contributing factors to this are well known. To address the concerns from the perspective of the hydrographic community this project is providing a structured assessment of the use of EO as a strictly hydrographic tool (ie not just an estimation of water depth). This will require a clarification of the relationship between rigorous radiation transfer equations and practical charting methods, completing radiative transfer theory to address priority gaps from the perspective of the national Hydrographic offices and adapting satellite derived bathymetry methodologies to the stringent requirements of Safety of Navigation and IHO Cartographic standards. |
| Sentinel data for the detection of naturally occurring hydrogen emanations (sen4H2) | There is considerable interest in the use of Hydrogen as an alternative to hydrocarbon energy sources.
However naturally occurring sources of hydrogen are not common and historically there has been significant controversy as to mechanisms [...] |
TERRADUE SRL (IT) | Enterprise | energy and natural resources, permanently open call | There is considerable interest in the use of Hydrogen as an alternative to hydrocarbon energy sources. However naturally occurring sources of hydrogen are not common and historically there has been significant controversy as to mechanisms that may be related to the generation of hydrogen. It appears that natural emanations of hydrogen have been detected in several places (e.g. Russia, USA, Brazil and Mali) with several ongoing activities to drill and recover it. As with water and oil, hydrogen comes out of the ground in various places. Even if the origin of these emanations is not yet very well understood, possible geophysical and environmental changes associated with their formation and continuous emissions were observed. These empirical observations have associated elliptic terrain depressions and vegetation changes on areas where significant hydrogen leakages are detected. Several studies are currently being conducted by the IFP Energies nouvelles (IFPEN) in partnership with ENGIE to better constrain hydrogen generation and migration in the subsurface. This project will integrate EO derived information in support of these studies to determine the potential utility of EO based approaches for the detection of areas where hydrogen may be found. The team will integrate data from Sentinels 1, 2 and 3 with available in-situ data, prototype and potentially validate novel EO-based analysis linked with in-situ data to assess the feasibility of automatically detecting areas of possible hydrogen emissions and thus enabling the development of new EO-based services in this emerging market. |
| SENTINEL FOR WHEAT RUST DISEASE (SEN4RUST) | Ethiopia is the largest wheat producer in sub–Saharan Africa, but also a hot spot for wheat rust diseases. Based on wheat rust surveillance on the ground, the Ethiopian Wheat Rust Early Warning and Advisory System (EWAS) was established by a [...] | UNIVERSITY OF CATHOLIQUE DE LOUVAIN (BE) | Applications | africa, applications, permanently open call | Ethiopia is the largest wheat producer in sub–Saharan Africa, but also a hot spot for wheat rust diseases. Based on wheat rust surveillance on the ground, the Ethiopian Wheat Rust Early Warning and Advisory System (EWAS) was established by a consortium of national and international partners including Cambridge University, UK Met Office, and CIMMYT. This project will contribute to integrate information derived from satellite Earth Observation to enhance the advanced meteorologically driven spore dispersal and epidemiological models to forecast in-season disease risk. |
| Sentinel Hub for Network of Resources | Sentinel Hub services are operational services running on several platforms (AWS EU-Frankfurt, AWS US-West, Creodias, Onda and Mundi web services), providing seamless access to various satellite missions over web service API. They are used by [...] | Sinergise Solutions d.o.o. (SI) | Digital Platform Services | permanently open call, platforms, science | Sentinel Hub services are operational services running on several platforms (AWS EU-Frankfurt, AWS US-West, Creodias, Onda and Mundi web services), providing seamless access to various satellite missions over web service API. They are used by thousands of users (free and payable) all over the world and two million requests are processed on average every single day. Two freely accessible web applications are operated within Sentinel Hub suite – Sentinel Playground, easy-to-use Google Maps-like web client and EO Browser providing a more advanced access to various data-sets supported by Sentinel Hub services. Various advanced features are available as well – export to GeoTiff, statistical analysis, time-lapse generation, custom scripting, etc. This project has performed an upgrade to Sentinel Hub services to make them ready for integration in Network of Resources, including: • User management (authentication and integration with EDUGAIN to make the access available to tens of thousands of Open Science Cloud users without additional registration) • Integration of Sentinel Hub services on the back-end level (to increase system performance, availability, and efficiently exploit separate deployments) • Security • Data fusion to make it possible to combine data from different missions in the same custom script, also adding further attributes (sun angle, quality, projections, etc..) • Upgrade of Python libraries and Web clients to support all above-mentioned new features |
| SENTINEL-1 FOR OBSERVING FORESTS IN THE TROPICS (SOFT) | The world’s forests have undergone substantial changes in the last decades. Deforestation and forest degradation in particular, contribute greatly to these changes. In certain regions and countries, the changes have been more rapid, which is the [...] | GLOBEO (FR) | Applications | applications, forestry, permanently open call, Sentinel-1 | The world’s forests have undergone substantial changes in the last decades. Deforestation and forest degradation in particular, contribute greatly to these changes. In certain regions and countries, the changes have been more rapid, which is the case in the Greater Mekong sub-region recognized as deforestation hotspot. Effective tools are thus urgently needed to survey Illegal logging operations which cause widespread concern in the region. Several research and government organizations have developed systems that provide regular updates to the public, principally based on satellite data. However, most monitoring approaches rely predominantly on optical remote sensing. Nevertheless, a major limitation for optical-based near real time applications is the presence of haze in the dry season (caused by fire) and, more importantly, of clouds persistent in the tropics during the wet season. Cloud cover free SAR images have great potential in tropical areas, but have rarely been used for forest loss monitoring compared to optical imagery. Yet, the dense time series of the Sentinel-1 constellation offer a unique opportunity to systematically monitor forests at the global scale. In addition, it has been recently demonstrated that forest losses can be monitored using Sentinel-1 dense time series based on reliable indicators that bypass environmental effects on SAR signals. In this context, the primary science objective of the SOFT project is to provide near real time forest loss maps over Vietnam, Cambodia and Laos using Sentinel-1 data to the users of public sectors to support their efforts to control logging and log trade. SAR-based Algorithms of forest loss detection were first adapted and tested over eleven test sites in the frame of the proof-of-concept (PoC) development. The forest loss detection method from Bouvet et al. (2018) was considered as the best potential candidate algorithms for the reasons detailed in the Final Report. Regarding the Sentinel-1 data processing, we used the pre-processing chain developed at CESBIO and CNES as an operational tool for Sentinel-1 GRD data processing. The chain is based on open source libraries and can be used freely. We selected an adapted forest definitions, selected the test sites and reference data for the PoC, which covered various landscapes and terrain slopes. We also selected relevant ancillary data such as a forest mask, the quality of which has a big impact on the final forest loss detection results. Using these dataset, we deeply analyzed the Sentinel-1 backscatter signal over forest loss and intact forest areas of Vietnam, Cambodia and Laos, which was needed to adapt the forest loss detection method. The quality of maps resulting from the PoC was analysed and assessed qualitatively and quantitatively. The results of the PoC were extended to the whole Vietnam, Laos and Cambodia for the years 2018 to 2020. We optimized, installed and ran the scripts (in Python) onto the high performance computing (HPC) cluster of the CNES. Then, the processing of the whole study area has been achieved. We mosaicked the resulting maps, checked their quality and manually corrected outliers. This led to the final map which is the main outcome of the SOFT project. The map provides clear hints of the spatial and temporal distribution of forest losses. For example, the difference between high forest losses currently happening in Northern Laos versus low forest losses in Northern Vietnam is clearly seen, although the whole Northern mountainous region is covered by similar forest types. We also compared the forest loss surface areas obtained from our method with the results from GFW and GLAD. Although we do not consider the maps of GFW and GLAD as a benchmark and although the use of Sentinel-1 is basically much more relevant in term of timely detection of forest losses, we quantitatively compared the statistics per year and country and qualitatively compared both maps. The results from this study and from GFW are remarkably similar, the largest difference (23%) being found for Laos in 2019. This result highlights the fact that our detection system can be used as an alert system (fast detection from sentinel-1 data) and as an annual detection system similar to GFW, used for example to compute national statistics. The final map was thoroughly validated following the recommandations from Olofsson (2014 and 2020). We chose as sampling design a stratification with stratas defined by the map classes, mainly to improve the precision of the accuracy and area estimates. We specified a target standard error for overall accuracy of 0.01 and supposed that user’s accuracies of the change class is 0.70 for forest disturbances and 0.90 for intact forest. The resulting sample size was therefore n=803 in total, which we have rounded up to 1 000 samples. We then assessed the allocation of the sample to strata so that the sample size allocation results in precise estimates of accuracy and area. We followed Olofsson’s recommendations and allocated a sample size of 100 for the forest disturbance stratum, and then allocated the remainder of the samples to the intact forest classes, i.e. 200 in the buffer areas around detected disturbances, and 700 in intact forest outside of these buffers. We used when possible freely accessible very high spatial resolution imagery online through Google Earth™, which presents low cost interpretation options. When Google Earth images were not available at the relevant dates, we instead accessed Planet’s very high-resolution analysis-ready mosaics as reference data. We then calculated the resulting confusion matrix presented in terms of the sample counts and the confusion matrix populated by estimated proportions of area, used to report accuracy results. The estimated user’s accuracy ( 95% confidence interval) is 0.95 for forest disturbances and 0.99 for intact forest (including buffer areas around disturbance) and the estimated producer’s accuracy is 0.90 for forest disturbances and 0.99 for intact forest. Finally, a quality assessment was performed by comparing the final map to existing optical-based products. The estimated area of 2018 and 2019 deforestation according to the reference data was 23 437 +/- 2 140 km2. |
| SOLFEO – Spaceborne Observations over Latin America For Emission Optimization applications | South America hosts the Amazon rain forest, the largest source of natural hydrocarbons (HC) emitted into the atmosphere. However, the forest undergoes continuous pressure due to increasing needs for pasture and agricultural land. Next to this, [...] | KNMI (NL) | Science | applications, atmosphere, atmosphere science cluster, permanently open call, science | South America hosts the Amazon rain forest, the largest source of natural hydrocarbons (HC) emitted into the atmosphere. However, the forest undergoes continuous pressure due to increasing needs for pasture and agricultural land. Next to this, large urban centers of South America face acute air quality problems. In this tense situation, it is important to closely monitor both the natural emissions released by the rainforest (hydrocarbons) and the rapidly changing anthropogenic emissions from agricultural activities (NH3 and NOx) and fossil fuel burning (NOx). By using satellite observations combined with a state-of-the-art model representation of the relevant processes, we develop advanced inversion algorithms for the estimation of emissions of ammonia(NH3), NOx and hydrocarbons, providing both qualitative and quantitative biogenic and anthropogenic emissions. SOLFEO takes advantage of the fine spatial resolution of OMI (AURA), IASI (METOP) and TROPOMI (Sentinel 5p) data to improve emission estimates over a largely understudied region. |
| SPATIAL – Soybean Price forecAsting based on saTellite-derIved services and Artificial intelligence | The main objective of SPATIAL is to provide a proof-of-concept (PoC) prototype for forecasting soybean futures contracts price moves using Artificial Intelligence models based on financial & macroeconomic features and Earth Observation [...] | HYPERTECH S.A. (GR) | Digital Platform Services | agriculture, AI4EO, applications, permanently open call | The main objective of SPATIAL is to provide a proof-of-concept (PoC) prototype for forecasting soybean futures contracts price moves using Artificial Intelligence models based on financial & macroeconomic features and Earth Observation products. SPATIAL is realizing two distinct Machine Learning (ML) models, one for soybean crop yield forecasting and one for prediction of soybeans futures contracts price moves, to demonstrate the feasibility of the method, the benefits of integrating Copernicus EO products and to showcase the potential of such approach. Predictability of soybeans futures contract price moves is particularly important to agricultural organizations, food companies or even to traders. The SPATIAL solution builds upon a) the expertise of the prime contractor, HYPERTECH S.A. (www.hypertech.gr), in financial assets price forecasting through machine learning and predictive analytics models for financial asset prices prediction based on traditional and alternative data sources along with its multi-year expertise on financial markets dynamics and deep knowledge on the key factors affecting commodities prices b) the expertise on the development and deployment of Space-based applications of NOA (www.noa.gr) for estimating soybeans crop yields and production. |
| STREAM-NEXT | This project is a proposal extension of the ESA STREAM project (SaTellite based Runoff Evaluation And Mapping, Contract Number 4000126745/19/I-NB) and it is addressed to investigate the possibility to extend at global scale the estimation of [...] | CNR-RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION – IRPI (IT) | Science | applications, permanently open call, science, water cycle and hydrology | This project is a proposal extension of the ESA STREAM project (SaTellite based Runoff Evaluation And Mapping, Contract Number 4000126745/19/I-NB) and it is addressed to investigate the possibility to extend at global scale the estimation of runoff and river discharge by using satellite observations. In particular the project will explore the feasibility to: provide long-term independent global-scale gridded runoff and river discharge estimates from solely satellite observations (i.e., satellite precipitation, soil moisture, water level and and Terrestrial Water Storage Anomalies) without the need for exploiting ground-based observations. These estimates will be compared against land surface model runoff estimates to establish the added value of satellite data above all over highly anthropized areas were modelling the processes could be a limiting factor; understand how much the spatial and temporal resolution of satellite data and specifically the spatial resolution of the gravimetry data affect the model results. This aspect would be important for assessing the benefit of the future gravimetry “NGGM-MAGIC” mission; analyze standardized runoff anomalies to evaluate the impact of climate change on runoff and river discharge trend and to reconstruct past flood or drought events relevant for water resources management. The activity is led by CNR-IRPI with the participation of the Institute of Geodesy (GIS) at University of Stuttgart and the Technical University of Denmark (DTU). The duration activity is of 24 months, until November 2025. |
| Streamride | This project is a proposal extension of the ESA STREAM (SaTellite based Runoff Evaluation And Mapping, Contract Number 4000126745/19/I-NB, ) project and it is addressed to investigate the possibility to improve river discharge estimates by [...] | CNR-RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION – IRPI (IT) | Science | applications, permanently open call, science, water cycle and hydrology | This project is a proposal extension of the ESA STREAM (SaTellite based Runoff Evaluation And Mapping, Contract Number 4000126745/19/I-NB, ) project and it is addressed to investigate the possibility to improve river discharge estimates by merging STREAM approach with the one developed within the ESA RIDESAT (River flow monitoring and discharge estimation by integrating multiple SATellite data, Contract Number 4000125543/18/I-NB, ) project. In particular the project will explore the feasibility to: refine the satellite-based approaches developed into STREAM and RIDESAT projects. New modules and formulations will be added to the original approaches to include elements which allow to overcome the limitations highlighted within the two projects. integrate the two approaches to enhance the river discharge estimation. For the specific case studies, a merging configuration will be selected to optimally integrate the river discharge estimates obtained by STREAM and RIDESAT. The impact of the integration will be established through the comparison with in situ observations and the evaluation of the river discharge accuracy. The activity is led by CNR-IRPI with the participation of the Institute of Geodesy (GIS) at University of Stuttgart and the Technical University of Denmark (DTU). The duration activity is of 12 months, until February 2022. |
| SUMO4RAIL | SUMO4Rail aims at a concise evaluation of the German ground motion service (Boden-Bewegungs Dienst – BBD) deformation products with dedicated consideration of the requirements of the Eisenbahn-Bundesamt (German Federal Railway Authority, Germany [...] | GAF AG (DE) | Digital Platform Services | permanently open call | SUMO4Rail aims at a concise evaluation of the German ground motion service (Boden-Bewegungs Dienst – BBD) deformation products with dedicated consideration of the requirements of the Eisenbahn-Bundesamt (German Federal Railway Authority, Germany – EBA) including the valorization of these deformation products for the EBAs monitoring and decision support system. This include: Performing post-processing of BBD base information to “harmonized”/comparable deformation maps. Specification and prototyping of robust procedures and processes, designed to be highly automatable. Validation of processes. User validation of generated information and technical results. Specification and conceptualization of any additionally identified value-added or support services or VHR-monitoring. |
| SUNLIT – Synergy of Using Nadir and Limb Instruments for Tropospheric ozone monitoring | The SUNLIT project aimed at developing new global tropospheric ozone datasets using combination of total ozone column from OMI and TROPOMI with stratospheric ozone column dataset from several available limb-viewing instruments (MLS, OSIRIS, [...] | FINNISH METEOROLOGICAL INSTITUTE (FI) | Science | atmosphere, atmosphere science cluster, permanently open call, science | The SUNLIT project aimed at developing new global tropospheric ozone datasets using combination of total ozone column from OMI and TROPOMI with stratospheric ozone column dataset from several available limb-viewing instruments (MLS, OSIRIS, MIPAS, SCIAMACHY, OMPS-LP, GOMOS). The novelty of the SUNLIT approach is using measurements from several satellite instruments in limb-viewing geometry for deriving the stratospheric ozone column dataset. Several methodological developments have been made within the project. The main datasets developed in the SUNLIT project are: Monthly 1°x1° global tropospheric ozone column dataset using OMI and limb instruments Monthly 1°x1° global tropospheric ozone column dataset using TROPOMI and limb instruments Daily 1°x1° interpolated stratospheric ozone column from limb instruments. The data are in open access at Sodankylä National Satellite Data centre https://nsdc.fmi.fi/data/data_sunlit.php Other datasets, which are created as an intermediate step of creating the tropospheric ozone column data, have their own value. These datasets are daily gridded with 1°x1° horizonal resolution and include (i) homogenized and interpolated dataset of ozone profiles from limb instruments, (ii) stratospheric ozone column from limb instruments, and (iii) clear-sky and total ozone columns from nadir instruments. |
| SURFCLASS | AI for satellite interferometry (InSAR) to simplify data exploitation and interpretation by adding a classification layer to large inSAR point cloud databases.
Over the years, InSAR has become a common approach to map and monitor ground [...] |
TRE ALTAMIRA s.r.l. (IT) | Enterprise | AI4EO, permanently open call, SAR | AI for satellite interferometry (InSAR) to simplify data exploitation and interpretation by adding a classification layer to large inSAR point cloud databases. Over the years, InSAR has become a common approach to map and monitor ground displacement at different scales, from local to regional and national. At large scales, InSAR provides such a volume of data, which is dramatically demanding for final users to interpret. Additional layers that can fasten and support data interpretation are crucial to properly tackling users’ operations. AI can be employed to integrate InSAR data with other modalities to automatically predict new relations and extract ready-to-use information. The project goal is to support the analysis of large databases of InSAR displacement measurements by identifying and classifying spatial patterns corresponding to driving phenomena (e.g. landslide, subsidence, local instabilities), using Machine Learning (ML) methodologies. SURFCLASS is built upon the results reached by MATTCH (Machine Learning Methods for SAR-derived Time Series Trend Change Detection), which has already confirmed the suitability of ML approaches to perform change trend detection in InSAR time series. SURFCLASS addresses the design of a more powerful DL model, which can exploit diverse geographical layers (SAR, DEM, Land cover, Sentinel-2 images) and the spatiotemporal correlations among measurement points, searching for similarities and obtaining a “classification” of the points with respect to driving deformation phenomena. TRE Altamira is a leading company providing InSAR services globally, with extensive experience in processing satellite radar (SAR) data. Polimi-DEIB contributes to the project with its significant expertise in Artificial Intelligence and Machine Learning methodologies. |
| SwellStats – Unfolding the Sea State Bias: Isolating a physical mechanism causing swell dependence of SAR altimeters | The Unfolding the Sea State Bias: Isolating a physical mechanism causing swell dependence of SAR altimeters (SwellStats) project is a project funded by ESA aiming at improving the reliability of the estimates of the sea surface’s geophysical [...] | ISARDSAT S.L. (ES) | Science | altimeter, oceans, permanently open call, science | The Unfolding the Sea State Bias: Isolating a physical mechanism causing swell dependence of SAR altimeters (SwellStats) project is a project funded by ESA aiming at improving the reliability of the estimates of the sea surface’s geophysical parameters made by SAR altimetry processing, increasing its value to assess informed climate-related decisions. Estimates of the geophysical parameters of sea surface can be obtained from satellite-based altimetric measurements by interpreting the way in which the surface shapes the reflected pulses of the radar. To do so, the state-of-the-art model used in conventional altimeters considers three significant parameters: the mean surface height, the standard deviation of the sea surface and the backscatter cross-section of the surface. However, with SAR altimetry, the picture is not so clear. When there is a distinct swell in addition to the local wind waves, these three parameters are not sufficient to adequately determine the backscattered waveform. In these cases, using the state-of-the-art model to interpret the returned pulse will give biased estimations of the geophysical parameters. This bias depends on swell, which is variable over the oceans. SwellStats will develop a specific physical mechanism that causes swell dependence of the backscattered waveform, and a method to test this hypothesised mechanism. This method will be a practical means of determining the swell sea directly from SAR altimetric data and avoiding the swell induced bias. The project kicked-off in June 2023 and will last for one year. |
| Synergetic Retrieval from GROund based and SATellite measurements for surface characterization and validation (GROSAT) | Reflectance of the Earth surface is one of the natural major components affecting climate. Surface interaction with incoming solar radiation and the atmosphere has a substantial impact on the Earth’s energy budget. Moreover, the accurate [...] | GRASP-SAS (FR) | Science | Aerosols, Altitude, atmosphere, atmosphere science cluster, permanently open call, science, Sentinel-2, Sentinel-3, Sentinel-5P | Reflectance of the Earth surface is one of the natural major components affecting climate. Surface interaction with incoming solar radiation and the atmosphere has a substantial impact on the Earth’s energy budget. Moreover, the accurate description of the surface reflection is crucial for different atmospheric studies including aerosol and trace gases characterization. One of the grand science challenges in remote sensing and climate studies is the accurate separation of surface and atmosphere contributions to the satellite signal. This separation is a crucial requirement of any algorithm for the accurate retrieval of atmosphere and surface properties from remote sensing measurements (Dubovik et al., 2011, 2021; Hasekamp et al., 2011). Despite the evident need for the universal and robust reference dataset for surface reflectance, BRDF (Bidirectional Distribution Function) and BPDF (Bidirectional Polarization Distribution Function) retrieval validation, it still does not exist. In this project it is proposed to perform a simultaneous synergistic retrieval of aerosol and surface properties using combined ground-based (for example, AERONET) and satellite measurements for obtaining the surface reflectance product with enhanced accuracy (Figure 1). In such approach the main information about aerosol comes from AERONET direct sun and diffuse sky-radiance measurements, whereas the information about surface reflection properties originates from satellite observations. The synergetic AERONET + satellite retrieval approach has already been prototyped within GRASP algorithm in the frame of ESA S5P+Innovative AOD/BRDF (Litvinov et al., 2020; https://eo4society.esa.int/projects/sentinel-5pinnovation). Figur1.. Schematic representation of the GROSAT approach based on synergetic retrieval from satellite and AERONET measurements. Further adjustment of GRASP algorithm to the synergistic retrieval from the combined ground-based (AERONET) and satellite measurements provides new possibilities for aerosol and surface characterization. This GRASP synergetic approach promises to become a rather robust and universal tool that can be applied to any space-borne instruments independently of spatial resolution or information content: for any spectral bands, radiance only or polarimetric measurements, single or multiple view instruments. |
| Synergetic retrieval from multi-mission space-borne measurements for enhancement of aerosol characterization (SYREMIS) |
Atmospheric aerosol is one of the main drivers of climate changes. Importance of accurate global aerosol characterization for climate studies and air pollution monitoring is a well recognized problem (e.g., see IPCC AR5 by Boucher et al.2013). [...] |
GRASP-SAS (FR) | Science | Aerosols, air quality, Altitude, atmosphere, atmosphere science cluster, permanently open call, science, Sentinel-2, Sentinel-3, Sentinel-5P | Atmospheric aerosol is one of the main drivers of climate changes. Importance of accurate global aerosol characterization for climate studies and air pollution monitoring is a well recognized problem (e.g., see IPCC AR5 by Boucher et al.2013). In addition to the traditional spectral Aerosol Optical Depth (AOD) such characterization should also include such extended aerosol information asaerosol size and type. The global information about aerosol can be obtained from space-borne measurements only. Therefore, climate studies are becoming more and more relying on high quality aerosol characterization from space. At present time there are a number of different satellites on Earth orbit dedicated to aerosol studies. However, due to limited information content, the main aerosol products of the most of satellite missions is AOD while the accuracy of aerosol size and type retrieval from space-borne remote sensing still requires essential improvement. The problem of accurate extended aerosol characterization from satellite measurements is strongly affected by the complexity of reliable separation of atmosphere and surface signals. In addition to this, the information content of the measurements should be enough for aerosol characterization itself. Since the end of the POLDER/PARASOL mission in 2013, no single currently operating satellite satisfies completely the requirements for extended aerosol characterisation. At the same time, different satellites dedicated to atmospheric studies may overpass the same area on Earth surface during the same day but at different times or different relative positions. As a result, being properly collocated, such combined measurements can provide multi-angular,multi-temporal measurements in extended spectral range. More independent satellite measurements with different complementary capabilities are combined,the richer the information content of combined measurements becomes. Thetreatment of these data seems to be beyond the capacity of most of the existent traditional algorithms since the processing of multi-instrument observations is not commonly used. In contrast, such retrieval algorithms of the new generation like GRASP (Generalized Retrieval of Atmosphere and Surface Properties) were specifically designed for synergetic processing of diverse observations and can be highly useful for multi-instrument data processing (Dubovik et al. 2011,2021). The GRASP multi-pixel retrieval concept has already been successfully applied to the observations of different single space-borne instruments: polar-orbiting like POLDER/PARASOL, MERIS, AATSR/ENVISAT, OLCI/Sentinel-3, TROPOMI/S-5p and geostationary, for example, Himawari, satellites. Moreover, the synergetic approaches were successfully approved on the synergy of MERIS and AATSR measurements (ESA CAWA-2 project) as well as on the synergy of the ground-based and satellite (AERONET+OLCI, AERONET+ TROPOMI/Sentinel-5p etc retrieval) measurements (ESA GROSAT project (Litvinov et al., 2021), https://www.graspsas.com/projects/grosat/). In the SYREMIS project we develop the prototyped synergetic retrieval with GRASP algorithm of combined measurements from diverse satellite instruments to bring the accuracy and scope of space-borne aerosol characterization to a new level required for climate studies and air-quality monitoring. In particular, these developments are expected to enhance the accuracy of traditional spectral AOD retrieval and allow the characterization of such aerosol properties as particle size, absorption, and chemical composition. Moreover, the proposed synergetic retrieval is expected to increase essentially the spatial and temporal coverage of the available aerosol product, which is absolutely required to identify aerosol sources and monitor aerosol transport. In this regard, the enhanced synergetic aerosol product is projected to have a significant impact on regional and global climate models (for example, CAMS and MERRA-2 global models). It is also expected to achieve the monitoring of natural or anthropogenic aerosol emissions which is crucial for air quality monitoring. The synergetic retrieval in SYREMIS project is planned to be tested on the currently operating polar-orbiting (TROPOMI/Sentinel-5p, OLCI/Sentinel-3, SLSTR/Sentinel-3) and geostationary (Himawari) satellites. Moreover, the constellation of these multi-mission satellites is expected to be extended in future by the new generation of satellites like Sentinel-5, 3MI/EPS-SG, Sentinel-4, etc. The input for the synergetic retrieval may be diverse measurements from different satellites. Themain attention in this project will be played on the operating polar orbiting and geostationary satellites to enhance current state of aerosol characterization and to test the developments on the actual aerosol events. In particular,the multi-mission constellation in this project includes measurements from such polar-orbiting satellites like OLCI/Sentinel-3 A and B, TROPOMI/Sentinel-5p as well as the geostationary Himawari. On one hand such a constellation will extend the spectral range of the measurements. On another hand it will provide unprecedented spatial and temporal coverage which is crucial for global climate studies and air-quality monitoring. Moreover, the synergetic retrieval tested on this constellation can be easily adapted for future instruments like 3MI, Sentinel-5, Sentinel-4 etc. The brief description of the selected satellites for the prototyped synergetic retrievalis summarized in Table 1. Satellites Description OLCI/Sentinel-3A and OLCI/Sentinel-3B – Polar-orbiting, global coverage – One observation per grid point (4 by 4 pixels) – Moderate spatial resolution – Radiance measurements in VIS and NIR spectral range TROPOMI/Sentinel-5p – Polar-orbiting, global coverage – Hyperspectral measurements in UV, VIS, NIR, SWIR spectral range Himawari – Geostationary. Coverage area: Asia – Every 10 min daily measurements – Radiance measurements in VIS, NIR and SWIR spectral range Table 1.Multi-mission constellation for prototyped synergetic retrieval INNOVATION ASPECTS The synergetic multi-mission retrieval developed in SYREMIS is expected to enhance essentially the characterization of such aerosol produced from space-borne measurements as spectral AOD, SSA, and aerosol size characteristics etc. The proposed synergetic retrievals are expected both to improve accuracy of the retrievals and increase spatial and temporal coverage of the aerosol dataset. As a result, the enhanced synergetic aerosol product is expected to be of particularly high value for global climate studies and aerosol data assimilation in global aerosol models such as CAMS and MERRA-2. RELATED PUBLICATIONS 1. Climate Modelling UserGroup (CMUG), User Requirement Document, version 0.6,2015, 2. Dubovik O. et al.,“Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations“ 2011 : Atmospheric Measurement Techniques, 3. O. Dubovik, D, Fuertes, P. Litvinov at al. “A Comprehensive Description of Multi-Term LSM for Applying Multiple a PrioriConstraints in Problems of Atmospheric Remote Sensing: GRASP Algorithm,Concept, and Applications” Front. Remote Sens., 19 October 2021 4. Litvinov P., O. Dubovik, Ch. Cheng, B. Torres,I. Dubovik et al. “Combined Retrieval from Ground Based and Space-borneMeasurements: New Possibilities for Surface Validation and Beyond.” AGU, 1-17December, 2020. |
| Synergetic use of SMOS L1 Data in Sun Flare detection and analysis (SMOS-FLARES) | The aim of the project is to develop a systematic retrieval of Sun Brightness Temperature in L-Band as measured by the SMOS Mission and analyse its correlation with measurements of solar flares currently used in Space Weather, as GOES X-ray [...] | DEIMOS SPACE s.r.l. (RO) | Science | permanently open call, science | The aim of the project is to develop a systematic retrieval of Sun Brightness Temperature in L-Band as measured by the SMOS Mission and analyse its correlation with measurements of solar flares currently used in Space Weather, as GOES X-ray flux. The analysis will also focus on dedicated re-processing activities on selected dates with new Sun retrieval algorithms recently developed for the SMOS Mission, to assess the suitability of these new techniques and explore further evolutions. The full set of available SMOS Mission Data will be used to perform a systematic timing analysis of Sun L-band brightness temperature measurements with soft X-ray flux at different channels from GOES during flare events. Complementary data sources, as the Stokes polarization parameters from SMOS/MIRAS, hard X-ray flux from HESSI or Extreme UV emission from Proba-2/Lyra will also be analysed for additional correlation insight. The analysis of the results will allow us to relate the relative timing of solar flares observed at different wavelengths to physical processes in flares. X-class flares are the initial objective, but M flare may be included in the sample. The project will also issue recommendations (based on the findings of the analysis part) for the operational use of SMOS data as an asset to SWE monitoring and the possibility of continuing missions in this regard. The Sun Brightness Temperature in L-Band data to be extracted in the scope of this project will be deployed in an online data portal which aims at providing the SWE community with a source of usable data for further analysis than the one performed in this contract. The service will be based on an online portal that will contain SMOS Sun Brightness temperature data for the complete mission, additional reprocessing campaigns and ad-hoc processing data can also be accessed by the users of the service. The data shall be also accessible through dedicated web services, in order to be exposed to other existing systems. The end users of the service would be able to access the Sun BT data by login in into the web portal and selecting the respective dates and processor version. The service can be continuously improved in operation by providing feedback loops, where the quality of its data is compared to other Ground based sources. |
| Technology and atmospheric mission platform – OPerations (TOP) | The atmospheric mission platform has demonstrated that (1) multiple data sources (the "data triangle" namely satellite-based products, numerical model output, and ground measurements) can be simultaneously exploited by users (mainly scientists), [...] | SISTEMA GMBH (AT) | Digital Platform Services | atmosphere science cluster, permanently open call, platforms, science | The atmospheric mission platform has demonstrated that (1) multiple data sources (the “data triangle” namely satellite-based products, numerical model output, and ground measurements) can be simultaneously exploited by users (mainly scientists), and (2) a fully Virtual Research Environment that allows avoiding the download of all data locally, and retrieving only the processing results is the optimal solution. |
| TEMITH – Total Ecosystem Management of the InterTidal Habitat | Intertidal habitats are highly productive areas; they provide bird habitat and feeding areas, commercial fish nursery grounds as well as the ecosystem services of nutrient cycling and coastal protection. Globally, these habitats are in decline [...] | DEIMOS SPACE UK LTD (GB) | Applications | coastal zone, permanently open call | Intertidal habitats are highly productive areas; they provide bird habitat and feeding areas, commercial fish nursery grounds as well as the ecosystem services of nutrient cycling and coastal protection. Globally, these habitats are in decline due to overexploitation, direct damage and numerous other stressors. Achieving total ecosystem management (TEM) to support conservation and sustainable exploitation of the intertidal ecosystem requires extensive habitat monitoring and assessment of pressures. However, budgets for conservation and management are often limited and relevant data may not be collected or may be difficult to access or visualise in a holistic way. The Total Ecosystem Management of the InterTidal Habitat (TEMITH) project aimed to design and prototype a solution to monitor pressures in the intertidal habitat in the Solent region, on the south coast of England, using EO data in addition to existing sources of information. From a proposed four pressures (algal mats, litter, sediment disturbance, wastewater plumes), two became the primary focus for model development as the project progressed. Six U-Net convolutional neural network models were trained to achieve detections of three key sediment disturbance activities (bait digging, shellfish dredging, and boating) from drone and aerial imagery and from high resolution satellite imagery. Three ResU-Net models were developed for detection of algal mats, which can indicate nutrient enrichment, as well as seagrass and saltmarsh (intertidal vegetation of conservation importance), from high resolution satellite imagery. One Random Forest model was developed for their detection from Sentinel-2 imagery. TEMITH was an inter-disciplinary collaboration between Deimos Space UK and the University of Portsmouth, combining expertise in EO and Deep Learning for feature extraction and ecology, respectively. With a statutory duty to protect and conserve intertidal habitats, Natural England and the Southern Inshore Fisheries and Conservation Authority were key partners associated with the project and provided valuable input on data needs and user requirements. Feedback from these and other prospective end users in an Evaluation Workshop highlighted the relevance of the TEMITH outputs and the potential to achieve a more holistic overview (spatial/temporal) of the detected intertidal activities and features with further development of the TEMITH services. |
| Towards the retrieval of lake ice thickness from satellite altimetry missions (LIAM) | Lakes that form a seasonal ice cover are a major component of the terrestrial landscape. They cover approximately 2% of the Earth’s land surface, with the majority of them located in the Northern Hemisphere. The presence (or absence) of ice [...] | H2O GEOMATICS INC. (CA) | Science | altimeter, permanently open call, science, snow and ice, Surface Radiative Properties | Lakes that form a seasonal ice cover are a major component of the terrestrial landscape. They cover approximately 2% of the Earth’s land surface, with the majority of them located in the Northern Hemisphere. The presence (or absence) of ice cover on lakes during the winter months affects both regional climate and weather events, such as lake-effect snowfall. Monitoring of lake ice is critical to our skill at forecasting high-latitude weather, climate, and river runoff as well as for ship navigation and transportation on winter ice roads. Lake ice cover (extent) and ice thickness have been identified as two ECVs by GCOS (2016). However, ground-based measurements of lake ice thickness are sparse in both space and time, and the number of sites where such measurements are made has dramatically decreased over the last two to three decades in many northern countries. In light of this and in support of GCOS, there is an urgent need to develop ice thickness products from satellite observations. Altimetry missions could play an important role in this respect, allowing for systematic measurements of ice thickness for many lakes of the globe. The goal of this 12-month study is to pave the way for the eventual retrieval of lake ice thickness from satellite altimetry missions, supported by a thermodynamics lake ice model (CLIMo; Duguay et al., 2003) and a microwave radiative transfer snow model (SMRT; Picard et al., 2018). SMRT has recently been revised to include lake ice. The study will investigate the sensitivity of backscatter (σ0) and brightness temperature (TB) data collected by satellite altimetry missions to lake ice and on-ice snow properties. To meet this goal, the study will be divided into four main tasks: 1) review of the state-of-the-art in lake ice thickness retrieval as well as an analysis of requirements; 2) forward simulations of σ0 and TB using the latest active/passive version of the SMRT model; 3) comparison of SMRT model simulations with measurements from altimetry missions for a selection of North American and European lakes; and 4) formulation of conclusions and recommendations for future work (a roadmap), including the provision of various options for the development of retrieval framework. The framework could be applied, at a later stage (beyond the scope of this short study), to retrieve lake ice thickness from past, current and, eventually future altimetry missions such as Sentinel-6 and CRISTAL. |
| Triple-A For Exploitation Platforms | The project has provided a pre-operational demonstration of a Triple-A system (Authentication, Authorization and Accounting) for Exploitation Platforms using modern standards such as Open ID Connect (OIDC) and User Managed Access (UMA) based on [...] | DEIMOS SPACE S.L.U (ES) | Digital Platform Services | permanently open call, platforms | The project has provided a pre-operational demonstration of a Triple-A system (Authentication, Authorization and Accounting) for Exploitation Platforms using modern standards such as Open ID Connect (OIDC) and User Managed Access (UMA) based on open source technologies.The proposed solution addresses significant gaps on current Authentication, Authorization and Accounting services made available to science users and application developers on exploitation platforms. The projeect results are operationally offered as support services to the “Network of Resources” to integrate platfrom services with federeated identity management. |
| Tropical Peat View | Indonesia counts more than 16 million ha of peatland, and a substantial part of these have been converted into plantations (in particular oil palm and acacia) or have been degraded. Both plantations and degraded peatlands are generally drained, [...] | SARVISION BV (NL) | Applications | carbon cycle, permanently open call | Indonesia counts more than 16 million ha of peatland, and a substantial part of these have been converted into plantations (in particular oil palm and acacia) or have been degraded. Both plantations and degraded peatlands are generally drained, primarily through networks of canals. Canal/road mapping on peat. Forest: green, non-forest: white; canal gaps: yellow The Indonesian government is committed to improve peatland management and has established the Peat Restoration Agency (BRG) to coordinate its efforts. This commitment includes the rehabilitation of 3 million ha of degraded peatland, blocking 10,000 km of canals and construction of 10,000 dams in the next few years. SarVision and Wageningen University have developed under the Tropical Peat View project a peat monitoring system to address this large environmental challenge. The system is based on radar imagery and is called the Tropical Peat View monitoring system (TPV). Testing and development were done for the provinces Central Kalimantan and Riau (Sumatra) in Indonesia with the goal to provide support to peat conservation and restoration in Indonesia. TPV provides information to the Indonesian Space Agency LAPAN, BRG, Ministry of Environment and Forestry (KLHK) and other national and international users and stakeholders. Information is provided on deforestation, forest degradation, development of drainage canals, changes in hydrology, fire and fire damage, through innovative use and integration of multiple Earth observation data sources from the European Copernicus Programme (Sentinel-1 C-band radar, Sentinel-2 optical imagery) and other missions (PALSAR L-band radar, Landsat optical, MODIS thermal imagery). |
| Understand and mitigate impacts of 3D clouds on UV-VIS NO2 trace gas retrievals by AI exploration of synthetic and real data (MIT3D) | Operational retrievals of trace gas column amounts assume (near) cloud free conditions. However, the large pixel size of the satellite instruments (for example the TROPOspheric Monitoring Instrument on Sentinel 5P, TROPOMI-S5P, is 5.5 km by 3.5 [...] | NILU – NORWEGIAN INSTITUTE FOR AIR RESEARCH (NO) | Science | atmosphere, atmosphere science cluster, permanently open call, science, Sentinel-5P, SUOMI-NPP, TROPOMI | Operational retrievals of trace gas column amounts assume (near) cloud free conditions. However, the large pixel size of the satellite instruments (for example the TROPOspheric Monitoring Instrument on Sentinel 5P, TROPOMI-S5P, is 5.5 km by 3.5 km at nadir) imply that pixels may be contaminated by sub-pixel sized cloud(s). Furthermore, clouds in neighbour pixels may lead to in-scattering of radiation or cloud shadow effects, both which are three-dimensional (3D) radiative transfer effects that may both decrease (cloud shadow) and increase (in-scattering) the retrieved trace gas amount. The goal of the ESA MIT3D project is to understand and mitigate impacts of 3D clouds on UV-VIS NO 2 trace gas retrievals by AI (artifical intelligence) exploration of synthetic and real data. The main objectives of the activity are to: Use AI to find parameters that affect NO2 retrievals using a unique synthetic TROPOMI-S5P data set based on 3D Monte Carlo simulations which includes realistic clouds from large eddy scale simulations. Identify associations between TROPOMI-S5P NO2 and Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) products using maximal information-based nonparametrix exploration statistics. Improve standard 1D NO2 cloud correction. The MIT3D activity thus aims to reduce errors due to the impact of 3D clouds. The achievement of the main objectives will be demonstrated by analysis of cloud affected synthetic and real TROPOMI-S5P data and the quantitative comparison of the MIT3D improved NO2 cloud correction and the standard NO2 cloud correction. |
| UNITE: Resolving Scientific Challenges of cloUdy sky evaporatioN and LST in the dIurnal cycle with geosTationary and polar orbiting satellitEs | The UNITE project objectives are:
to develop a protocol for estimating all-sky evapotranspiration and LST for the future thermal missions, exploring the high temporal resolution geostationary satellites, advancing the state of the art [...] |
POLITECNICO DI MILANO (IT) | Applications | land surface, Modelling and forecasting, permanently open call, water cycle and hydrology | The UNITE project objectives are: to develop a protocol for estimating all-sky evapotranspiration and LST for the future thermal missions, exploring the high temporal resolution geostationary satellites, advancing the state of the art in ET modeling in water-limited ecosystems The planned activities are: Eddy covariance stations selection consolidation Download of eddy covariance data, consistency control and data formatting for models input / validation Satellite data analyses for the selected EC stations to be used for the modeling activities Project KO the 1st of June. |
| Using deep learning with CryoSat radar altimetry to adjust elevations and map SURFace penetration (CryoSURF) | Using CryoSat-2 interferometric synthetic aperture radar (SARIn) altimetry together with NASA’s operation IceBridge and IceSat-2 Lidar data in a multi-layer neural network (NN) in order to enhance CryoSat-2 SARIn swath measurements. Further [...] | UNIVERSITY OF EDINBURGH (GB) | Science | altimeter, CryoSat, permanently open call, polar science cluster, science, snow and ice | Using CryoSat-2 interferometric synthetic aperture radar (SARIn) altimetry together with NASA’s operation IceBridge and IceSat-2 Lidar data in a multi-layer neural network (NN) in order to enhance CryoSat-2 SARIn swath measurements. Further investigation will be carried out into the use of these corrections to derive surface condition state and change. The European Space Agency (ESA), Earthwave and The University of Edinburgh (UoE) have made significant progress with the completeness and accuracy of CryoSat-2 SAR-In Swath elevation models. However the scientific perfectionists in all of us strive for the next level using the latest technological tool set. Ice-sheets are a current contributor to sea-level rise and the fresh water they bring into the oceans can impact global oceanic circulation with global consequences (Vaughan et al., 2013). It is thus very important to monitor ice-sheet elevation and elevation change. Spaceborne Radar and Lidar sensors have revolutionised our ability to monitor the mass imbalance of the cryosphere globally and its contribution to sea level change (Shepherd et al., 2012). Lidar (NASA, 2018) is often used in local airborne campaigns to obtain precise elevation measurements however these campaigns have limited spatial and temporal coverage and are impacted by by weather. Radar performs in all weather conditions but suffers from uncertainties due to time-variable penetration into snow and firn (McMillan et al., 2016; Nilsson et al., 2015). This project uses CryoSat-2 radar altimetry elevation and NASA’s operation IceBridge local airborne Lidar together in a multi layer neural network (NN) to create a timeseries of maps of penetration of CryoSat-2 Swath radar altimetry into the snow and firn. The map will be across large regions of the Greenland margins where CryoSat-2 is in SARIn mode going back to CryoSat-2’s launch in 2010. In addition, IceSat-2 data will be added to the framework for the period post its launch in 2018 enabling maps across a wider range of months beyond the operating window of Operation IceBridge. As the penetration of the Ku microwave signal into the snow and firn relates to the condition of the surface, the maps generated during this project have the potential to be used to inform about surface conditions and change in surface conditions. Additionaly, the maps of penetration can be used to explore the impact of change in surface condition on Lidar and microwave signals and its impact on the use of radar and laser altimetry for the study of ice sheet mass balance and processes. |
| Vine irrigation from earth observation data – WineEO | Optimizing water resources is a real issue in some geographical area. Temperature have increased by 0,85°C on average between 1880 and 2012 and couldreach 4.8°C by 2100 compared to the period from 1986 to 2005, according to the last IPCC report. [...] | TERRANIS SAS (FR) | Enterprise | agriculture, climate, permanently open call, platforms, Sentinel-2, water resources | Optimizing water resources is a real issue in some geographical area. Temperature have increased by 0,85°C on average between 1880 and 2012 and couldreach 4.8°C by 2100 compared to the period from 1986 to 2005, according to the last IPCC report. As a result, agriculture and viticulture are facing an increasing water scarcity at the same time as a growing demand. This growing pressure leads to the necessity to optimize available water resources without losing neither yield production nor quality. Vine irrigation has been used for a very long time in the so-called “new world” vineyards (Australia, Argentina, United States (California) and Chile) and is widely practiced there. Its adoption in Mediterranean regions is much more recent and is one of the first adaptations of wine growers to the consequences of climate change (Ojeda and Saurin, 2014). Irrigation and water stress management of grapevines is essential in arid and semi-arid areas with limited water supplies to maintain both the quality and quantity of the harvest. This has led the scientific community and companies to develop new technologies for irrigation control, allowing to rationalize the inputs according to the needs of the crop. WineEO project is a step in this direction with the objective of developing an operational irrigation scheduling service for winegrowers. This service, named Wago, is based on a water balance model (named Sa’irr) mixing three data sources: satellite imagery with optical images coming from the Copernicus program (Sentinel-2), in situ data and meteorological data. It provides farmers with irrigation recommendations (when, where and how much water amount apply over the vineyard to optimize water inputs). Two main challenges are identified in this project: Adapting the existing water balance model developed by the Cesbio to vine specificities. Indeed, in contrast to annual crops, vines are characterized by a cover sparsity and a large variability of geometry (rows, inter-rows, vegetation height). Sat’irr model has been mostly developed for one dimensional crops such as wheat and maize. To adapt the model to the vine, it is mandatory to take into account the geometry of the crop. Sentinel-2 optical images are used to determine the growth stage of vineyard. Nonetheless, the spatial resolution of Sentinel-2 bands is one of the limitations for their use in precision viticulture due to the intra-variability of the plots. Advances in Deep Learning in the field of Computer Vision allows enhancing the spatial resolution of these images by using single image super-resolution (SISR) techniques. In the WineEO project, a deep learning SISR was developed to recover a super-resolved Sentinel-2 image at 2.5m in the visible and near-infrared part of the spectrum from its low resolution counterpart. Developing a user friendly platform to allow winegrowers to access Wago products. Wago is a decision-making tool developed to help farmers manage their irrigations by providing irrigation recommandations. The tool is based on Sat’irr model and optical images and calculates the water balance on a daily-basis. The project is led by TerraNIS, which is in charge of the industrialization and commercialization of the service. The Cesbio, a French laboratory, will adapt the Sat’irr water balance model embedded in Wago. Finally, four end-users are targeted in four different countries – Portugal, Italy, Spain and Chile – to test the application in different agronomic conditions (soil, climate, agricultural practices, etc.). |
| VINESAT- SATELLITE – UAV DATA FUSION FOR EARLY ANOMALY DETECTION IN ROW – CROPS | Satellite-UAV Data Fusion for Early Anomaly Detection in Row Crops - Application to Vineyards and Olive Groves" (VINESAT). The project develops Data Fusion of Sentinel-2 and UAV data to obtain high-frequency, high-resolution imagery in both time [...] | SPINWORKS (PT) | Enterprise | crops and yields, permanently open call | Satellite-UAV Data Fusion for Early Anomaly Detection in Row Crops – Application to Vineyards and Olive Groves” (VINESAT). The project develops Data Fusion of Sentinel-2 and UAV data to obtain high-frequency, high-resolution imagery in both time and space. |
| Volcanic monItoring using SenTinel sensors by an integrated Approach (VISTA) | Volcanic monItoring using SenTinel sensors by an integrated Approach (VISTA) project is aimed at developing a novel ensemble of algorithms to completely characterized the effects of volcanic emissions on land and atmosphere. Volcanic activity is [...] | GEO-K SRL (IT) | Science | atmosphere, atmosphere science cluster, land, permanently open call, science, Sentinel-5P | Volcanic monItoring using SenTinel sensors by an integrated Approach (VISTA) project is aimed at developing a novel ensemble of algorithms to completely characterized the effects of volcanic emissions on land and atmosphere. Volcanic activity is observed worldwide with a variety of remote sensing instruments, each one with advantages and drawbacks. Because a single remote sensing instrument able to furnish a comprehensive description of a given phenomenon doesn’t exist, a multi-sensor approach is required. In particular, the aim of this study is the definition of a new generation of integrated methods which aim at exploiting the information of the COPERNICUS Sentinels data (from Visible-VIS to Thermal Infrared-TIR) by means of already consolidated retrieval algorithms and novel ML procedures. The increasing availability of Sentinel’s data allows an innovative perspective to achieve the objective of a complete monitoring of the eruptions effects by a unique satellite mission. Currently the possibilities offered by the COPERNICUS Sentinel missions are only partially explored to provide new consistent and statistically reliable information about volcanic cloud quantification and dispersion in the atmosphere and ash deposits on the ground. Such information is crucial for aviation safety and civil protection purposes therefore new tools to exploit satellite observations are required. The project will develop specific methodologies integrating inverse modeling techniques (based on radiative transfer models) with dedicated machine learning (ML) approaches to formulate a set of novel integrated methods. The expected outcomes of the project are improvements in satellite volcanic ash/ice/water vapour particles/SO2 cloud detection and retrievals (altitude, extension, mass, concentration, aerosol optical depth and effective radius), the development of a specific ML based algorithm to map the presence of ash deposits over land and the generation of new satellite-based prototypal services to mitigate the effect of volcanic eruption on health, environment, aviation and to better understand volcanic processes. |