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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 Servicesclimate, land, permanently open call, SAR, snow and iceThis 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 Developmentpermanently open call, SARHistorically 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 (IT)Digital Platform Servicesartificial intelligence, enterprise, health, permanently open callAI 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.
Arctic Crowdsourcing This project will develop 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 and other forms of field data that support [...]C-CORE CENTRE FOR COLD OCEAN RESOURCES ENGINEERING (CA)Digital Platform Servicespermanently open call, platformsThis project will develop 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 and other forms of field data that support Arctic stakeholder needs
Automatic orthorectification service for VHR 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 S (SI)Enterprisepermanently open callThe 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.
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)Sciencecoastal zone, ocean science cluster, permanently open call, scienceThe 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)Sciencecarbon cycle, carbon science cluster, ocean science cluster, oceans, permanently open call, scienceThe 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.
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)Sciencebiosphere, carbon cycle, carbon science cluster, forestry, land, permanently open call, SAR, scienceCharacterization 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.
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 Servicespermanently open call, platforms, sustainable developmentAt 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)Enterprisepermanently open call, urbanUsually, 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)Applicationsland cover, permanently open callThe 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 capablity enable an expanded [...]PLANETEK ITALIA SRL (IT)Enterpriseblockchain, permanently open call, platforms, securityEO 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 capablity enable an expanded volume of information to be generating using distributed approaches such as cloud based storage and processing and platform based interactions, use of algorithms and proprietry datasets. This makes guaranteeing the integrity of both the data and the derived information more and more diffficult. 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) Tropical Cyclone (TC) observations over the global ocean are a key component in extreme events monitoring and in anticipating appropriate risk mitigation and emergency response at landfall. In particular, the Tropical Cyclone Programme (TCP) of [...]CLS COLLECTE LOCALISATION SATELLITES (FR)Scienceocean science cluster, oceans, permanently open call, science, Sentinel-1, SMOSTropical Cyclone (TC) observations over the global ocean are a key component in extreme events monitoring and in anticipating appropriate risk mitigation and emergency response at landfall. In particular, the Tropical Cyclone Programme (TCP) of the World Meteorological Organization (WMO), allows tropical cyclone forecasters to access various sources that provide conventional and specialized data/products, including those from Numerical Weather Predictions (NWP) and remote sensing observations, as well as forecasting tools on the development, motion, intensification and wind distribution of tropical cyclones. The operational delivery of high-resolution TC observations from SAR will significantly help the tropical cyclone forecasters of the six tropical cyclone Regional Specialized Meteorological Centres (RSMCs) and the six Tropical Cyclone Warning Centres (TCWCs) having regional responsibility to provide advisories and bulletins with up-to-date first level basic meteorological information on all tropical cyclones, hurricanes, typhoons everywhere in the world. Moreover, the unique ability of SAR systems to probe high resolution observations of the sea surface from space coupled with a strategy to maximize the acquisitions over TC will allow to start building a new database for science applications. The first demonstration of the Copernicus Sentinel-1 SAR capabilities was first triggered in 2016 under the SHOC campaigns of the ESA SEOM R&D program. The combination of high resolution and wide swath observations at C-band together with dual-polarization capability offers a unique opportunity to characterize the inner core storm structures. The late programming of S-1 acquisitions by ESA and the high quality of SHOC products has successfully illustrated the potentials of S-1 constellation mission to be part of a dedicated multi-missions hurricane observation strategy, including other sensors such as radiometers (SMOS, SMAP), and third party SAR missions (Radarsat-2, ALOS-2…). Since then, IFREMER and CLS together with ESA/ESRIN have continuously monitored hurricane with S-1 sensors on a best effort basis, while engaging an increasing number of potential end-users and stakeholders and starting several studies for science applications based on this dataset. The main objective of this proposed project is to scale up this operational service (hereafter called CYMS –CYclone Monitoring service with S-1), in view of its potential integration as part of a Copernicus Service. The service shall provide validated and fully acknowledged products, be consistent, standardized, interoperable and harmonized across international institutions and bodies supporting European policies and the international charter of risk management. The service includes not only NRT operational wind field products, but also an archive center ensuring a continual improvement cycle and full data uptake by stakeholders.
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)Scienceatmosphere, atmosphere science cluster, carbon cycle, carbon science cluster, permanently open call, science, Sentinel-5P, TROPOMIThe 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.
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)Applicationsmapping/cartography, permanently open callPoverty 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)Enterpriseartificial intelligence, land cover, permanently open call, sustainable developmentMonitoring 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.
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)Applicationsapplications, permanently open callEarth 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)Enterpriseoceans, permanently open callOne 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.
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 AND TECHNOLOGY AS (NO)Enterprisepermanently open call, platformsThe 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 Developmentpermanently open call, sustainable developmentThe 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)Enterprisepermanently open call, platforms, sustainable developmentRecent 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 Copernicus programme driven by the European Commission (EC) in cooperation with ESA, provides unprecedented volumes of free and open Earth Observation (EO) data. The Copernicus Sentinel satellites collect data in different domains for the [...]EODC EARTH OBSERVATION DATA CENTRE FOR WATER RESOURCES MONITORING (AT)Digital Platform Servicespermanently open call, platforms, Sentinel-1The Copernicus programme driven by the European Commission (EC) in cooperation with ESA, provides unprecedented volumes of free and open Earth Observation (EO) data. The Copernicus Sentinel satellites collect data in different domains for the monitoring of atmospheric, oceanic and land processes. The immense volume of data leads to difficulties in the distribution and uptake (i.e. download) of the daily generated massive amounts of satellite data (currently approx. 12 TB per day). Therefore, a paradigm shift is necessary to move the software and respective users to the data stored in a central manner at specific data centres. To address this challenge the EC established the Copernicus DIAS (Data and Information Access Service) activity. Several hubs are developed to distribute the data to the different user communities, e.g. the Sentinel data is distributed via ESA’s Collaborative Ground Segment initiative towards national stakeholders.The main ResNet objectives are to foster collaboration, interoperability and mutualization of efforts for the exploitation of EO data. This projectt shall contribute to the ResNet by the development 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)Sciencepermanently open call, science, solid earth, water cycle and hydrologyThe 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)Sciencepermanently open call, scienceThis 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.
Harmonised Landsat-8 and Sentinel-2 Analysis-Ready Products The project proposes an innovative prototype (pre-operational demonstration) 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”, [...]TELESPAZIO VEGA UK LIMITED (GB)Digital Platform Servicespermanently open call, platformsThe project proposes an innovative prototype (pre-operational demonstration) 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).
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 Developmentforestry, permanently open callA 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)Scienceionosphere and magnetosphere, permanently open call, scienceThe 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.
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)Sciencepermanently open call, SAR, scienceThe 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)Applicationsdisaster risk, permanently open call, SAR, scienceBeing 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)Sciencemarine environment, permanently open call, SAR, scienceThis 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.
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)Enterpriseatmosphere, enterprise, permanently open callThe 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)Scienceatmosphere, atmosphere science cluster, biosphere, carbon cycle, carbon science cluster, permanently open call, polar science cluster, science, Sentinel-5P, SMOSThe 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)Sciencealtimeter, climate, GRACE, ocean heat budget, ocean science cluster, oceans, permanently open call, scienceSince 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.
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 OF MARINE SCIENCES-ISMAR (IT)Scienceclimate, ocean science cluster, oceans, permanently open call, scienceThe 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)Applicationsapplications, disaster risk, permanently open callNORUT 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.
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)Enterprisemarine environment, permanently open callOne 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)Enterpriseartificial intelligence, permanently open call, platformsTraditional 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)Sciencepermanently open call, scienceLand 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.
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)Enterpriseapplications, artificial intelligence, permanently open call, Sentinel-2This 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.
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)Sciencealtimeter, permanently open call, science, water cycle and hydrologyThe 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).
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)Applicationscarbon cycle, permanently open call, SAR, Sentinel-1The 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)Applicationsapplications, coastal zone, oceans, permanently open callThe 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) Water is at the centre of economic and social development; it is vital to maintain health, grow food, and manage the environment. As over half of the world’s potable water supply is extracted from rivers, either directly or from reservoirs, [...]CNR-RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION – IRPI (IT)Scienceapplications, permanently open call, science, water cycle and hydrologyWater is at the centre of economic and social development; it is vital to maintain health, grow food, and manage the environment. As over half of the world’s potable water supply is extracted from rivers, either directly or from reservoirs, understanding the variability of the stored water on and below landmasses, i.e. the Total Runoff, is of primary importance. For this reason, the STREAM Project aims at developing an “observational” physically-based approach for deriving daily runoff estimates from satellite soil moisture (SM), precipitation (P) and terrestrial water storage anomalies (TWSA). The Project will support multiple operational and scientific applications (from flood warning systems to the understanding of water cycle). On the one hand, it will allow fine-tuning a simple modelling framework that adequately forced with satellite observations is potentially suitable for global runoff monitoring at daily time scale. On the other hand, it will allow increasing knowledge on the natural processes, human activities and on their interactions on the land. STREAM is also a feasibility study intended to answer the following research questions: To what extent satellite observations of precipitation, soil moisture and terrestrial water storage anomalies can provide reliable total runoff estimates? Is it possible to obtain a total runoff product with spatiotemporal resolutions beyond the one of GRACE and GRACE-FO measurements? Up to which spatiotemporal scales is this feasible and with which accuracy? The quality assessment of STREAM total runoff estimates will be pursued at multiple pilot basins across the world (5 large basins + multiple sub-basins) characterised by different physiographic/climatic features in order to highlight the role of climatic conditions/basin characteristics on the reliability of STREAM modelling framework and identify the optimal space/time scale that provides the best compromise between total runoff product accuracy and resolution. With respect to the state-of-the-art, the STREAM project proposes the following four innovative characteristics: The STREAM project will investigate the possibility to provide “model-independent” runoff estimates not relying on strong modelling assumptions; The STREAM runoff estimates will be derived solely from satellite observations; Differently from the available literature, the STREAM project will incorporate the fundamental role of soil moisture conditions in the runoff generation process; Beyond the use of off-the-shelf GRACE products (baseline), the STREAM project will explore finer spatial resolutions of sub-catchments by implementing tailored filters. Moreover, the STREAM project will contribute in understanding how limiting factors (e.g. freshwater availability) affect processes on the land surface and how this can adequately be represented in prediction models. The activity is led by CNR-IRPI with the participation of the Institute of Geodesy (GIS) at University of Stuttgart. The duration is of 12 months, until April 2020.  
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)Enterprisepermanently open call, platformsSatHound 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.
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 ENGINEERING CONSULTANCY (GR)Sustainable Developmentecosystems/vegetation, permanently open callGrasslands 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)Applicationsmarine environment, permanently open callThe 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)Enterpriseenergy and natural resources, permanently open callThere 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 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 LTD. (SI)Digital Platform Servicespermanently open call, platforms, scienceSentinel 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 foresees an upgrade 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
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, [...]The Royal Netherlands Meteorological Institute (KNMI) (NL)Scienceapplications, atmosphere, atmosphere science cluster, permanently open call, scienceSouth 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.
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 Servicespermanently open callSUMO4Rail 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 project aims at developing a new global tropospheric ozone datasets form TROPOMI and OMI measurements. These data will be a valuable addition to the operational TROPOMI Tropospheric ozone column product by the convective cloud differential [...]FINNISH METEOROLOGICAL INSTITUTE (FI)Scienceatmosphere, atmosphere science cluster, permanently open call, scienceThe project aims at developing a new global tropospheric ozone datasets form TROPOMI and OMI measurements. These data will be a valuable addition to the operational TROPOMI Tropospheric ozone column product by the convective cloud differential method, which will be available for tropics only. In addition, the multi-limb-instrument stratospheric ozone column dataset, compatible with nadir total ozone column measurements, which will be created as an intermediate step of the proposed development, will have its own value and can be used in climate-related ozone studies. The project has been kicked-off the 5th September. A first informal progress meeting has been on 10th December. First results have been shown and look promising.
Synergetic use of SMOS L1 Data in Sun Flare detection and analysis 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)Sciencepermanently open call, scienceThe 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 proposed atmospheric mission platform has the twofold aim of demonstrating that (1) multiple data sources (the "data triangle" namely satellite-based products, numerical model output, and ground measurements) can be simultaneously exploited [...]SISTEMA GMBH (AT)Digital Platform Servicesatmosphere science cluster, permanently open call, platforms, scienceThe proposed atmospheric mission platform has the twofold aim of demonstrating 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 (seagrasses, mud flats and saltmarshes) rank amongst the most productive ecosystems providing bird habitat and feeding areas, commercial fish nursery grounds, as well as other ecosystem services including nutrient cycling and [...]DEIMOS SPACE UK LTD (GB)Applicationscoastal zone, permanently open callIntertidal habitats (seagrasses, mud flats and saltmarshes) rank amongst the most productive ecosystems providing bird habitat and feeding areas, commercial fish nursery grounds, as well as other ecosystem services including nutrient cycling and coastal protection. They are some of the world’s most protected habitats, but this protection has failed to prevent accelerating global loss due to: over exploitation (e.g. fishing); direct damage (e.g. dredging); and numerous other stressors including algal mats, pollution and marine litter. Total Ecosystem Management (TEM) is a coherent evidence-to-advice approach supported by an integrated habitat monitoring programme. Achieving TEM for these habitats will produce significant improvements in ecosystem function, sustainable exploitation and adaptive conservation for future pressures. TEM requires extensive habitat monitoring and assessment of the threats, but currently, relevant data are: not collected; difficult to access; in unsuitable formats or not easily visualised in a holistic way. Thus, conservation and management agencies currently make habitat assessments and management decisions in an inefficient and costly way, without access to the best available evidence. Ultimately, without comprehensive and cost-effective assessment of the impacts on intertidal habitats evidence-based management cannot be implemented. This project is developing a set of EO based methodologies to address this data collection gap focussing initially on four priority issues: sediment disturbance, sewage plumes, litter accumulation and algal mats. All have significant impacts on the function and quality of intertidal habitats; reduce the potential for growth of the blue economy of coastal regions and occurring across every region of the globe where these habitats exist.
Triple-A For Exploitation Platforms The project targets 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 [...]DEIMOS SPACE S.L.U (ES)Digital Platform Servicespermanently open call, platformsThe project targets 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.
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)Applicationscarbon cycle, permanently open callIndonesia 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).
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)Scienceatmosphere, atmosphere science cluster, land, permanently open call, science, Sentinel-5PVolcanic 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.