NoR sponsored projects

The objective of this project is to provide training and high-resolution NDVI and NDMI maps to facilitators of the Alito Training Center in Uganda (who manages the Alito Farm) in order to optimize farm inputs, maximize yields, and to promote sustainable agriculture practices.

As part of the understanding of the hydraulic behavior of the condo river and especially in the lower reach , as well as the protection of islands in this area, this study also aims environmental and economic aspects in the area , as it is already known, the pool Malebo is a strategic area concerning river transport (navigation), irrigation and agriculture, fishing, etc the protection of the morphological degradation of they is also one of the challenges in this work , for this purpose our study aims to master all the scientific information on the hydraulic and hydrological level in order to serve other economic activity and

environmental ,This prompts us to launch the main questions as follows: How can we develop an agent-based simulation model (ABMS) for navigation chart in the lower reach exactly in the stanely pool that includes (hydraulic, hydrological and socio-economic aspects), with this complexity, to find scenario optimal of navigation in the pool male boo , and if we can generate this approach in all the Congo river ? What are the assumptions, approaches and data needed to develop this model?

For my master thesis I am investigating the application of InSAR for glacial lakes in the Himalaya. In order to check the InSAR results I am using optical imagery – sentinel 2, which is why I would like access to the sentinel hub.

Archaeological prospection in Saruq al hadid is of significant interest to find the complete story of prehistoric settlements lived in Dubai, United arab emirates. Located at 50 km in the southeast of Dubai at the north of al Rub’al khali desert, Saruq al Hadid (SA) archaeological site is discovered since 2002. More than 15 000 artifacts have been identified after more than 20 excavations. The location of this site in the middle of desert between the dunes is mysterious because there is no available close freshwater critical for human survival and raw material sources critical for metallurgical industry. The use of remote sensing satellite high resolution radar and multispectral images enhance widely the possibilities of archaeological prospection. This project aims to prescreen potential buried archaeological sites in that desert region. This work is the first attempt made until now in evaluating the detectability of archaeological remains using satellite images data in United Arab Emirates. The outcomes are important to guide and help the excavation missions and the archaeologist for the planning of future excavation campaigns.

The objective of the project is to focus on sustainable resources in Africa, assessing deforestation in countries like Gabon, Ivory Coast, Uganda etc. This will help in understanding the potential risk of deforestation and high risk areas, so that we can take necessary measures to manage the phenomenon. Moreover, this will help us as a company to attain our sustainable goals for the future.

The workshop within the CCN 1 of the Agricultural Virtual Laboratory (AVL) aims at providing a good understanding and first hands on training to the paying agency.

The Sen4CAP project developed, validated and demonstrated an open-source toolbox (Sen4CAP system), which can process automatically Sentinel-1 SLC and Sentinel-2 L1C or L2A time series into a set of products which are relevant for the new Common Agricultural Policy. The main users of this toolbox are national Paying Agencies (and/or their sub-contractors specialized in EO), but also the private sector and researchers.

The Sen4CAP project fully relies on CREODIAS for the EO processing, and was already supported by NoR.

The Panta Rhei conference has the goal to facilitate the transfert knowledge between the Paying Agency. This opportunity is unique to express the importance of the Sen4CAP system to it’s main users. The workshop is a side event of the conference and has already 58 participants register. The workshop

will be focusing on two mains aspects : 1. communication of the main evolutions of the system up to now (and it’s futur evolution in 2022 thanks to the CCN 1 of the AVL). 2. Performing an hands-on-training with the system for the new comers (from the download of the images from the right dataset up to the generation of more advance products) and a question and answers session for the more advances users.

A previous request of sponsorship (22010a) was introduced to ingest the new dataset generated in the 2022 R&D activities.

Accurate information pertaining to the spatial distribution of various tree species in a forest stand is crucial for better monitoring and management of boreal forests. Such wall-to-wall information is lacking from field-based forest inventories. Meanwhile, remote sensing techniques based on satellite and

Unmanned Aerial Vehicle (UAV) data promises to highly reduce this information gap. The end result would be a publication and/or technical note that describes how ESA’s satellite date can be used along with UAV image data for better forest tree species mapping in boreal conditions. The benefit of userready data available from processing platforms (like F-TEP) will also be highlighted in the document.

The beneficiaries of this research project will be forest stakeholders such as forestry companies and government agencies.

The objectives of the project include the feasibility study on the use of VHR optical data for coastal erosion studies and the production of coastal erosion rates from VHR optical data for selected areas along the north Dublin coastline. The results will be shared over the GSI web mapping services for free as an example of the use of VHR to monitor coastal erosion.

The goal of the project is the measure coastal erosion/shoreline change rates along the County Wicklow coastline in Ireland. The results of the project will allow us to give an up to date, accurate, and relevant synopsis of how the soft sediment coastline of Wicklow in the east of Ireland has changed over the last

two decades and what its current state of erosion/accretion is. Using orthophoto data collected in Ireland since 2000 it has been possible to gain an understanding on shoreline change over the timeframe 2000-2012, however the quality of data has improved significantly in recent times, which is a strong opportunity to understand more recent changes to Irelands coastline.

As a results, we are trying to access VHR commercial satellite data to digitize shorelines ( e.g vegetation line) between 2012-2022 and compare the results with the orthophotos. The coastal area in question is composed of three main environments exposed bedrock, lowland beach or marshland areas, and soft sediment cliffs, with some areas classified as Special Areas of Conservation by Irelands National Parks and Wildlife Service. As climate change and rising sea levels begins to take effect in the coming decades it is important to have a good baseline understanding of the fluctuation of shorelines, especially those in low lying areas that are vulnerable to coastal erosion or habitat loss. This stretch of coastline contains several significant urban areas which represents a significant anthropogenic influence on the project, as shoreline change/coastal erosion rates can help influence informed decision making along the Irish coastline with respect to This project can feed into ongoing coastal vulnerability and coastal erosion projects occuring in Ireland and throughout Europe.

In this project, it is proposed to create a high resolution (<10m) coastal typology of the European coastline, which distinguishes land use / cover classes relevant to coastal flooding and erosion. During this sponsorship we will develop a methodology to classify the satellite imagery. Upon success we will scale this to the whole European coastline.

Previous research in a GIS Programming course requested teams of 2-3 students to develop a state-of-the-practice QGIS plugin (Anbaro#lu 2021). Consequently, students relied on Git to collaborate with each other while developing their plugins, did unit testing, provided language support and documented their plugins using Sphinx. Although, students learned valuable technical and practical skills, in order to have a critical spatial data science perspective, more theory should be integrated into teaching (Holler 2019, Kedron et al 2020). Therefore, the objectives of this experiment is to investigate how students utilise an open-source Python package, x2Polygons, to find the distance between georeferenced polygons. For this each student will digitise a number of polygons, with varying complexity – in terms of the number of edges each building possess and evaluate how different distance measures such as the Hausdorff distance; Chamfer distance, PoLiS distance (Avbelj et al 2021) and turn function distance correlate with each other. In this way, they will be able to assess the advantages and limitations of different distance measures.

The main objective of this project is to study the complementarity of spatial optical imaging, structural information from Synthetic Apertuge Radar (SAR) and environmental characterization data to model maize and sunflower seed production by aggregating these observations of different spatial and temporal resolutions. The thesis work will be based on Syngenta’s plot network in several parts of Europe and North America, where some varieties of maize and sunflower are evaluated under different environmental conditions.

The Feature Engineering of satellite observations used in the development of machine learning models will seek to estimate varietal parameters-functional extracts-which define, on the one hand, the phenology, but also the different varieties’ response to abiotic stress, and mainly to water stress. In the case of phenology, we will study the parameters that determine the development response to temperature and photoperiod, and that predict specific crop stages. The functional traits that characterize the response to abiotic stresses will make it possible to identify, from multi-environment observations, the most efficient varieties, and to predict their behavior and yield.

Among the renewable energy sources, solar and wind are rapidly becoming popular for being inexhaustible, clean, and dependable. Meanwhile, power conversion efficiency for renewable energy has improved with great technological leaps. Following these trends, solar and wind will become more affordable in years to come and considerable investments are to be expected. As solar and wind plants are characterized by their high site flexibility, the site selection procedure is a crucial factor for their efficiency and financial viability. Many aspects affect site selection, amongst them: legal, environmental, technical, and financial. Today, information gathering for site selection assessments is a manual and time-consuming process. The main objective of this project is to develop a dataset of existing solar power plants* by applying computer vision on satellite imagery.

Objective 1 (O1): Achieve 90 % accuracy for a specific data layer* using SOTA deep learning models.

Research Question 1: Can the required accuracy be achieved with publicly available 10×10 meter

image resolution, or must higher resolution imagery be used?

Research Question 2: How can the training-data creation process be made for the specific data layer to

achieve the required accuracy?

Research Question 3: How can the training of the model be made for the specific data layer to achieve

the required accuracy?

Since the project is researching the process of site selection and the utilization of data for renewable

energy projects, the idea contributes to positively influence the UN’s Sustainable Development Goal 7;

Clean energy for everyone. For the region to be able to produce enough clean energy, it is necessary to

accelerate the development of renewable energy projects.

The objectives of the project are:

1. Assessment of decadal Mass Balance and Ice Thickness of glaciers of Sikkim Himalaya (Study

Area 1200 Sq. Km)

2. Estimation of Glacier Facies using GLCM and Random Forest Classification.

3. Movement analysis of glacier in Sikkim using PSI.

Statement on availability of results.

The results shall be available online through any suitable data sharing portal on request for research purpose only. It shall also be shared with nodal agency for sub-regional level policy response. Subregional policy requires concrete evidence especially in developing Nations like India.

The objective of this project is two-fold and the requested data can be used for both tasks while testing processing capabilities on The Food Security Platform (TEP). Firstly, we will demonstrate that it’s possible to do bottom-of-atmosphere (BoA) correction on Sentinel-2 Reflectance at Bottom of Atmosphere/VISTA Algorithm (available on TEP as ‘Vista_S2-L2A) using deep neural networks. We estimate that this can improve processing speeds by x100 to x500 while keeping accuracy high. This is inspired by an existing algorithm, developed for another project, that in production as a side effect efficiently fixed incorrect Sen2Cor bottom of atmosphere correction. This is in particular interesting on, important and frequently used algorithms with long processing times like BoA processing algorithms. The results will be avg. pixel error measured against ground truth imagery. We will also present the relevant processing speeds improvements and requirements to run said algorithm (eg. GPU accelerated processing). The results will be made available on TEP as ClearSky Vision demo data, and if possible produced on TEP. It will, furthermore, be measured against data in-sample and out-of-sample, and the project will be finished off by producing a tile unavailable on the platform. This project has the potential capability of greatly reducing required resources for BoA correction on Sentinel-2 imagery by doing it in

a fraction of the time (leaving data storage as the final limitation). Not only making it a fast and efficient process but it also makes near-real time monitoring more achievable.

ClearSky Vision has already developed an algorithm for cloudless Sen2Cor imagery (using deep learning and multiple satellites for data fusion). This approach won ClearSky Vision the Copernicus Masters Bay Wa competition in 2020. It combines Sentinel-1, Sentinel-2, Sentinel-3, and Landsat 8 into daily cloudless Sentinel-2 imagery. This project will further prove, to what degree cloudless results on Vista S2-L2A will match the accuracy from prior cloudless Sen2Cor imagery tests. The objective is to determine whether this (more complex) processing method will make the cloudless process more difficult or what’s more likely, improve the consistency of the output. The results will be made available on The Food Security Platform as ClearSky Vision demo data (10 spectral bands).

Objectives of this project are: • Develop and validate methods for determining the stage and advancement of desertification via diffuse reflectance spectroscopy in the MIR aiming at obtaining prediction models for chemical and physical attributes in soils under intense degradation process. • Build a spectral library using the wavelengths observed in soil samples from the region, highlighting the distinction between the spectra observed in desertification area soil samples; • Understand the link between spectral attributes and chemical and physical attributes of the studied soils; • Prepare maps of the spatial variability of soil attributes, using the results obtained from analyzes carried out in the laboratory (measured values) and obtained by sensors (predicted values) in the study area. • Create land use and land cover maps using high-definition satellite imagery data provided by Sentinelhub.

The aim of the pilot is to develop a pan-European scale drought impact monitoring platform using the new CLMS service High Resolution Vegetation Phenology and Productivity (HR-VPP) derived from Sentinel 2 images.

This request is related to the “Drought impact monitoring platform” activity, part of Euro Data Cube project (reference 2020-12-06-DCFS-Proposal), service consumption already confirmed by ESA. The request is done on behalf of EEA in scope of ETC/DI project

CentraleSupélec – a French high school of engineering – organizes a course on Satellite Earth Observation dedicated to around 110 first year students, from the 22nd November to the end of January. This course is an introduction to optical and SAR remote sensing. It is based on the use and processing of ESA’s Sentinel images. The support of EOCARE is requested to allow the students to carry out mini-projects on 3 topics at the end of the course.

CentraleSupélec – a French high school of engineering – organizes a course on Satellite Earth Observation dedicated to around 110 first year students, from the 22nd November to the end of January.

This course is an introduction to optical and SAR remote sensing. It is based on the use and processing of ESA’s Sentinel images. The support of the RUS service is requested to deliver this course. Our objective is to enable these students to process, analyze and use these images for their personal and professional projects, mainly linked to SDGs.

The project consists of disseminating Copernicus values with data from Sentinel-2, Sentinel-1 and Sentinel-3. Spain is one of the countries in the Mediterranean basin where more forest fires occur, being one of the great problems of our forests. Our Public Educational Center trains future professionals in the extinction of forest fires. The project consists fundamentally in carrying out analyzes of forest fires that occur during the year, especially those that have an area greater than 500 hectares. The work will be published on social networks, specifically on Twitter, in the profile of @eforestal, and published on the website that I share with the students of the educational center: http://almazcara.forestry.es

Here you can see a sample of our publications this year using Sentinel-2 images for analysis of wildfires: https://e.forestry.es/GIF2021

Objectives. The main aim of the research project is to evaluate effects of agricultural expansion and practices on water quality of the Upper Lunsemfwa Catchment. Specifically, for the NOR, the research seeks to determine the extent of agricultural expansion in the Upper Lunsemfwa Catchment from the year 2015 to 2020. The envisaged output of the project is to produce detailed mapping of the area of interest in terms of extent of cultivated area, specific crops and their intensity including:

• Production of the general land cover and land use maps of the;

• Production of the spatial extent of the agricultural domain i.e. annual binary crop/non-crop maps; and

• Production of maps of crop inventories of what, where and when crops are grown.

The format of the products envisaged include DIMAP format including GeoTIFF raster images, UTM – UPS/WGS 84 Projection, XML file meta data. Statistics of the to be classified crop areas will further be generated to quantify area coverage for each identified crop. The results of the project will be available to beneficiaries via peer reviewed open access scientific publications.

This request for NoR is an extension to the initial sponsorship that was given for Project ID 60792 for the period October 2020-February 2021. The extension is being requested to enable usage of the Sen-4CAP mapper that has the potential to map grassland and permanent crops which are a significant feature of the

area of interest. In addition, the ability to generate shapefiles documenting crop types observed and their associated attributes would add value to the product considering the end result desired i.e., relate crop type and fertilizer use to water of adjacent rivers and streams in the area of interest. Monitoring of agricultural practices i.e., crop planting and harvesting/clearing would aid in determination of cropping patters. The extension is further being requested to allow for the inclusion of additional in-situ data that was collected in the period March to May 2021. The additional crop field data was necessitated by the need to capture the fragmentation and heterogeneity of crop fields managed by peasant, small scale, and emergent farmers. Farming patterns of peasant, small scale and emergent farmers are fragmented and heterogeneous owing to management practices of the said farmers. Nonetheless, it is these farmers that provide for approximately 80% of the Zambian national food basket and hence contribute largely to the food security of the nation. Understanding the peasant, small scale and emergent farmers cropland extents, crop types, cropping patterns and cycles will lead to a better understanding of the arable land use and thus help ensure sustainability of agriculture while ensuring food security. This is envisaged in the production of timely, spatially explicit, and precise information that will aid decision making

The Open Science Catalogue is one of the elements contributing to an Open Science framework and

infrastructure, with the scope to enhance the discoverability and use of products, data and knowledge

resulting from Scientific Earth Observation exploitation studies. This SAP activity aims to: enhance the Open Science Data Catalogue’s capabilities for data governance expand the catalogue with a data storage and long-term preservation component enhance interoperability and interconnectivity with the other open science and collaborative development systems enhance data indexing and discoverability in the cloud and enhance programmatic access to the catalogue.

The Open Science development team will work in an Agile way with ESA as the primary Product Owner supported by TPZ-UK. The bulk of the implementation is directly relevant to the Resource Management and is allocated to EoX and EOfarm.

Through the application of interferometry, the characterization of the phase and coherence variations, later the application of the STAMPS algorithm I intend to evaluate the surface variations in 3 high Andean salt flats of Chile, the first salt is the Coposa salt lake which maintains a close relationship with the surface hydrology and this can be distinguished in interferograms, so I seek to establish a mathematical relationship between interferometry and the intensity of precipitation events.

The second is the Salar de Atacama where anthropic activity related to lithium mining is considerable, so there is a relationship between deformation and surface and the extraction of lithium and underground water. Finally, I study the Salar de Llamara since this is a hydrogeological system very different from the previous ones, so it is possible to restrict and contrast the conclusions previously obtained.

Who will benefit from the project results: This is a research project leading to obtaining my degree in geology in Chile

-Results format: Through a PDF document validated by the university and free of charge through the university’s platform

Through the application of interferometry, the characterization of the phase and coherence variations, subsequently the application of the STAMPS alorithm, I intend to evaluate the surface variations in 3 high Andean salt flats of Chile, the first is the coposa salt flat that maintains a close relationship with surface hydrology and this can be distinguished in interferograms, so I seek to establish a mathematical relationship between interferometry and the intensity of precipitation events.

The second is the salar de atacama where anthropic activity related to lithium mining is considerable, so there is a relationship between surface deformation, lithium extraction and groundwater. Finally, I study the Llamara salt flat as this is a very different hydrogeological system from the previous ones, so it is possible to restrict and contrast the conclusions obtained previously. On the other hand, I will characterize the backscattering coefficient of the three salt flats to see how this characteristic can influence the results obtained, contrasting with LANDSAT 8 images. With this it is possible to obtain time series of deformation, phase, coherence and backscattering coefficient, which compared with the hydrological characteristics of the system speak of the dependence of these elements with the precipitation events, the change in the direction of the winds, the drainage network and in particular anthropic activity both associated with the extraction of minerals and the pumping of underground water. This research contributes to the understanding of the dynamism of the Chilean high Andean salt flats since these, due to their geological and hydrogeological characteristics, are an important source of water resources and understanding how their surface changes provides information on how the hydrogeological system changes and it is possible to interpret how the Climate change and variations in the intensity, duration and seasonality of rainfall affect them. On the other hand, due to the geographic location of the high Andean salt flats, their access is complex and dependent on many climatic conditions, so being able to obtain information remotely is essential.

The project will address a set of various use cases for the evaluation of various geological risks:

1. Ground deformation in the Canary islands;

2. Automatic identification and classification of deformation signals;

3. Ground deformation associated to Green Hydrogen injection;

4. Ground deformation for cross-border risk assessment at European level;

5. Ground deformation caused by groundwater extraction;

6. Volcanic deformation in El Salvador;

7. Geological risks in urban areas;

8. Ground deformation induced by underground mining in active mining

areas.

Our EU Interreg project operates within the region of Botnia-Atlantica in central Sweden and Finland. It supports regional businesses to develop opportunities within the “new space” economy and involves ecosystem-building support to commercialize existing space-based data. A component of our project is an end-to-end satellite mission technology demonstration process named “KvarkenSat”, having hyperspectral imaging, positioning, AIS and radio communication capabilities.

To bring the region’s forestry sector and new technological developments together, the “Kvarken Space Economy” project is organizing a “hackathon” in March 2022 called “KvarkenSat Innovation Challenge 2022 on Sustainable Forestry”. (https://ultrahack.org/kvarkensat-innovation-challenge-2022). This challenge will network the regions forestry sector actors with the latest ideas from research institutions in Finland and Sweden ( Luonnonvarakeskus (Luke) and Svenska Landsbruk Universitet (SLU), respectively). The individual, student and company start-up participants in the challenge will work with remote sensing observations and spatially dependent modeled results to address four research fronts important to the participating forest sector actors. The Forest-TEP platform will be demonstrated and offered as a platform for developing solutions during the challenge.

The four themes include supporting more timely estimations of soil moisture, preventing damages caused by spruce bark beetles, learning how to reduce damage to the forest ground caused by forest machines and developing new digital concepts for the forestry value chain.

The participating forest sector companies will have the possibility to work with the innovation challenge participants and their solutions and advance them within their companies.

The objectives of the project include: 1. Enhace the preservation, evaluation and use of olive genetic resources for improving the olive breeding and the delivery of new varieties

2. Leverage the information available through the development of a smart and user-friendly interface for the end-users 3. Predictions section: to provide information with regard to the predictable varieties’ behaviour in response to the different climate change scenarios – mapping the risked zones per each variety to avoid future economic losses.

4. We aim to determine and compare 30 olive genotypes’ behaviour in five different countries with very different environments and pedo-climatic parameters, in order to further develop breeding strategies taking into account the “environment-climate” variables. Furthermore, by knowing the Environment X Genotype interaction, we will be able to anticipate the climate change effects and select the olive progenitors for future breeding processes to obtain new resilient varieties. Specifically, we aim to: a. Determine and compare among 5 GBs the olive tree phenological parameters (blooming, fructification and maturation). b. Compare the varieties production and olive fruit and/or oil quality. c. Compare the

pests and diseases indexes – susceptibility scale in different zones and climates. d. Correlation of the aforenamed parameters with the climatic variables. e. Design breeding guidelines and strategies to face the future climate change scenarios.

5. Farmers section: i) to help determining the kind of variety to be planted in a specific geographic zone according to farmers’ necessities such as crop production, diseases risk, water availability and climate parameters; ii) to make available a free mobile app that will be able to determine the kind of disease that has affected the olive tree just by processing a mobile picture and to develop

online risk alerts for the rest of users by utilizing machine learning.

6. Breeders section: i) to provide in real time the best combination of olive progenitors for a specific breeding programme by implementing machine learning technology. ii) to provide a user-friendly but advanced mobile app that would be able to accurately identify through image processing an olive variety based on the morphological descriptors.

7. Development of a direct communication line between Germplasm Banks (GBs) and End-Users in order to ease the genetic material exchanging and speed up the collaboration between public and private sectors.

The remote sensing application in the mining industry can serve to monitor and compute a spatial-temporal model of the mining activities. It is the main inputs to an environmental impact assessment of mining sites at both local and regional level. On the other hand, the mining industry affects variety

influences on humans and wildlife habitats, and there is a need to estimate its cumulative impacts on the surrounding environment. Therefore, the information derived from multispectral and temporal remotely obtained data and imagery at the landscape level is the key data for developing designs and

decisions at local, regional, and national levels. This satellite/drone-based smart research will provide a suitable model for rural landscape changes in Mongolia and all over the world. Problem statement: Mongolia is a landlocked country and located between Russia and China; also, it has a large territory but a small population with scattered life. The consumption of Mongolia is a rural landscape, especially agriculture and pastureland for livestock. The last decade, mining and its related development projects are significantly affecting economically and environmentally to rural landscapes of Mongolia. Therefore,

the development of mining and its related infrastructure development is of great economic and social importance in Mongolia’s rural area. There are many studies on these booming effects in Mongolian development. However, there are few studies based on landscape ecology modelling for sustainable

development using geo-spatial data and modelling. In summary, there is a need to produce a more detailed study of Mongolia’s rural landscape changes in the last two decades by economic development, mining, by combining satellite imagery and integrating social data.

The key project aim of this master thesis is to apply state-of-the-art deep learning methods on satellite

imagery to detect bi-temporal changes in land usage in Germany. Achieving this research aim serves

two purposes: firstly, it showcases how state-of-the-art deep learning techniques can be used to analyse

remote sensing data for a large geospatial region (the whole of Germany) that would not be possible to

analyse in a manual fashion, and secondly, it sheds light on the key research question addressed in this

work:

Research question: How did the land cover and land usage change due to human and non-human

influences between two points in time?

Three research objectives support the research aim and the key research question: i.)

Train a deep learning model to be able to classify satellite images into separate land-use classes with a

classification accuracy that is significantly above a naïve (baseline) classifier when evaluated on holdout test data. ii.) Acquire suitable satellite image(s) from Germany at two distinct points in time that

can be meaningfully compared (i.e., where the weather and cloud conditions are sufficiently similar to

warrant comparison) and pre-process the data to be handled by the trained deep learning model. iii.)

Classify the satellite image(s) at both points in time and compare the land-use classes, both in

aggregate and individual changes.

The objectiv eof the project is to compare state-of-the-art and Hydrocoastal products in the Northern Adriatic Sea against in situ measurements.

The main objective of the project is to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. There are specific objectives for each of the Coastal Zone and Inland Water domains, and particular Technical Challenges to be addressed. However, one of the key aims is to link together and better understand the interactions processes between river discharge and coastal sea level. Key outputs are global coastal zone and river discharge data sets, and assessments of these products in terms of their scientific impact. The aim of the project is to study new approaches for processing SAR and SARin data from Sentinel-3 in coastal zone and inland water. Various approaches are evaluated in selected test zones and the most promising processing schemes are identified. The chosen approach is then implemented to generate global coastal zone and discharge datasets and the scientific impact of these products is evaluated in few case studies.

Existing methodologies for monitoring the temporal changes in forest areas have poor performances in high altitude and sloping areas such as Alp mountains or Iberian mountains. That is why, in such hilly regions, prediction of tree cover density or decision on forest existence and forest type are highly error prone, as already demonstrated by [Dostálová et al.] [Cremer et al.]. Thus, our aim in this research is to develop a robust tree cover density prediction algorithm by combining the earth observation data with topographic information and climate categories. Existing European wide forest change detection methodologies are mostly rely on classical statistical measurements and thresholding techniques, however in our research, we are aiming at using advanced machine learning techniques such as variants of UNet and Transformer architectures.

As an output of our research, we will predict more robustly which parts of European forests are subjects to degradation and how severe those changes are. Thus, our contributions will help decision makers to take preventive measures for protecting the forest areas and recovering them in the long term.

The project intends to bring information about new and current fly tipping and illegal landfills over the territories. We do that by combining very high-resolution optical images with state-of-the-art deep learning algorithms. We integrate the results of our analysis in a geographical database, and we build collaborative functions to help stakeholders coordinate their action to evacuate the waste and wipe the polluted sites. Our end users are public authorities that struggle to understand the phenomenon and the spatio-temporal patterns due to the lack of a platform centralizing all the location, date of images, approximated volume, growth over images acquisition and actions that have been undertaken on the locations.

The project implies to task AOI regarding the level of the customer (region, department), requiring between 8 and 12 images a year, depending on the feasibility, available bandwidth and cloud coverage.

The platform is available for end users without limitation of time or functionalities, and they are aware about the last date the predictions have been added to the platform. Which depends on the acquisition

process.

LAYERS is an AgTech platform currently being used by more than 3.000 users around the globe for all kinds of crops in four main products: SatTech2.0, SatPred, SoilTech and DroneTech. This platform evolved from drone-only to multi-input mainly for the operative costs and complications of the drone operations. However, drone are still being used in some “surgical crop-specific” use cases such as tree counting, weed or disease detection and monitoring.

SatTech2.0 and SatPred products use as spatial data Sentinel-2 and Sentinel-1 data accessed through SentinelHub.

The objective of “LAYERS HD Upgrade” is to explore, implement and test with real users higher resolution images in both intensive (e.g. orange trees) and extensive (e.g. sugarbeet, corn) crops.

PlanetScope HUM will be implemented in different crop types and both spatial and analytical results will be presented at least using NDVI but most probably other indexes such as NDRE. All these data will be available in LAYERS.

Rest of satellite sources will be explored in at least a fruit and an extensive field to evaluate the value that these products may have to the end-users. Imaging will be available in LAYERS.

All of these demonstrations will be offered to LAYERS users with no additional cost.

With nearly half a billion people living in the close vicinity of active volcanoes around the world, the volcanic threat nowadays represents a major subject of global societal issues focusing on both the population protection / prevention and health. The diversity of acquisition systems, as well as the availability of large quantities of data, make the use of space imagery particularly suitable to meet the technical needs raised by the dynamic monitoring of continental surfaces.

Thanks to several decades of development, optimization and exploitation, the SAR interferometry has largely demonstrated its potential for excellence and today constitutes a proven, relevant and high-performance technique. This technique reveals in particular a significant potential in many fields of application addressing natural hazards as related to population prevention

(earthquakes, volcanic eruptions, landslides, floods, etc.), agriculture / forestry (deforestation, fires, phenological evolution, structural modifications and textures of the soil and plantations, etc.), geodynamics (phenomenon of subduction, subsidence, etc.) or questions relating to town planning (modification of the urban landscape, exodus, etc.).

For that reason, the primary objective of our project focuses on the assessment of the dynamical monitoring of explosive volcanoes based on an approach combining Machine Learning methods and Differential SAR Interferometry (DInSAR) products.

The main objective of the study is to use non-parametric machine learning classifiers to map informal settlements of Old Naledi from Sentinel-2 data and Google Earth data. To achieve the main objective, the study will implement the following specific objectives: Extract buildings from European Space Imaging/Maxar WorldView and Google Earth data features using CNN and RF. Examine the significance of GLCM texture in the extraction of buildings in informal settlements. Determine the accuracy of extracting informal buildings from European Space Imaging/Maxar WorldView, and Google Earth using ground-truth data

MedEOS is a research project that aims to develop, implement and/or generalize methodologies using Earth Observation (EO) to acquire coastal water quality information about nondirectly remotely measurable parameters. It is part of the ESA Mediterranean Sea Regional Initiative within FutureEO-Segment1 ESA programmatic line (2020-2022) and aims to develop and produce high-resolution, gap-free maps of experimental EO water quality products by employing data fusion techniques to combine the high temporal resolution of S3-OLCI and high spatial resolution of S2-MSI data. Moreover, MedEOS will develop, implement and demonstrate a methodology to produce an

extensive tracking of river plumes in Mediterranean coastal waters with the use of EO products.

MedEOS is a research project that aims to develop, implement and/or generalize methodologies using Earth Observation (EO) to acquire coastal water quality information about non-directly remotely measurable parameters. It is part of the ESA Mediterranean Sea Regional Initiative within FutureEOSegment1 ESA programmatic line (2020-2022) and aims to develop and produce high-resolution, gap-free maps of experimental EO water quality products by employing data fusion techniques to combine the high temporal resolution of S3-OLCI and high spatial resolution of S2-MSI data. Moreover, MedEOS will develop, implement and demonstrate a methodology to produce an extensive tracking of river plumes in Mediterranean coastal waters with the use of EO products.

On August 7, 2020, the Mila region was hit by a moderate earthquake which caused a huge

landslide that swept away 1/4 of the city of Mila and caused the distruction of buildings and important

infrastructure. With the evolution of the space technology, this geological event can be measured with

precision, and it is possible to determine the boundaries of the affected areas while calculating the slip displacement using only two high resolution images (before and after the event). The obtained results (displacement maps) will contribute to the the understanding of the damage caused and will be used to compare radar and optic data.

Air pollution is caused by a combination of ~78% nitrogen, ~21% oxygen, ~0.9% argon and the remaining elements include carbon dioxide, methane, water vapour, hydrogen, and other trace elements emitted from factories and motor vehicles that burn fuel. The atmosphere is a delicate balance of these gaseous

elements and particles. Any imbalance, even in little extent can be inconvenient to living life forms including animals and crops.

There are different tools and techniques to study the air pollution problem. Remote sensing has been widely used for air quality studies since sum of the effects from the ground and atmosphere signal can be observed by the satellite sensors. Researchers have been using PM2.5 and PM10 to analyse the air

pollution situation using remote sensing for different countries. Our objective is to study the airpollution problem at delhi city and other metropolitan cities:

1. Modelling of PM2.5 and PM10 using Earth observation datasets

2. Fire detection model using Satellite imagery for agriculture problems

3. Development of an algorithm using an open source software interface R

4. Identification of lockdown effect during Covid-19 on atmosphere condition in Capital city of India.

We have been doing the basic analysis using ground observation datasets on cities of India. 22 of the 30 most polluted cities in the world are in India, and almost 99 percent of Indians breathe air that is above the WHO’s defined safety limits. According to the past analysis 76% of Indians live in places that do not

meet national air quality standards. In 2017 one in eight deaths in India was attributable to air pollution additionally average life expectancy of a child is reduced by at least 2.6 year.

Along Chile’s entire ~4000 km coastline, oceanic tectonic plates (the Nazca and Antarctic plates) subduct under the South American continent, repeatedly causing great to large earthquakes. Chile is thus a natural laboratory to better understand the processes related to large earthquakes. The PRECURSOR project is an initiative funded by the Chilean Ministry of Science to investigate the mechanics of slow earthquakes and their relation to precursory signals. For this purpose, we use deployed a pioneering experiment in Chile with a dense distribution of continuous 30 GNSS and 80 seismological stations (https://www.precursor.cl). Our project seeks to improve the detection of interrelated mechanisms controlling the failure of faults, to resolve spatiotemporal relationships between frequent small earthquakes and transient deformation, and provide new insights into the genesis of earthquakes.

We intend to monitor past (i.e. over recent years) changes in glacier velocity, to establish whether glacier velocity increases prior to volcanic eruptions. The results will be of interest to the scientific community, but might also help improve volcano monitoring and associated hazard prediction.

The aim of this research project is to use the MPIC-OPT-ICE tool to monitor the surface velocity of glaciers that occupy active volcanoes with a particular focus on Mount Wrangell (Alaska), Mount Veniaminof (Alaska) and Volcan Peteroa (Chile). Preliminary results are planned to be made available by June 2022.

The Junior Academy of Sciences of Ukraine is a state-funded extracurricular educational system that develops and implements methods of science education. the Junior Academy of Sciences of Ukraine received the status of Category 2 Science Education Center under the auspices of UNESCO and joined the network of Copernicus Academies. In 2012, a new section, Geographic Information Systems (GIS) and Remote Sensing of the Earth (RS), was established at the Kyiv branch of the JASU, which is supervised by the GIS and RS Laboratory. Today in Ukraine there is a full-scale war and we are fighting not only Russian terrorists but also their propaganda, which manipulates facts and distorts information, so for us, high-precision satellite images are important data for monitoring and assessing the consequences of war for my country. We are already using Copernicus data that is publicly available, but high-precision satellite imagery will help students and teachers around the world learn about the consequences of a terrible war for Ukraine. But with their use we can not identify the houses, the state of civil infrastructure. We have experience in conducting both national and international educational events for students and teachers. We will use this experience in organizing an international webinar on the consequences of the war in Ukraine. To date, Russian propaganda “says” that their terrorist-solders it does not destroy civilian infrastructure, but this is not the truth and satellite images are proof of this, and we want to show the world this truth.

The goal of the openEO platform project is to develop a cloud-based system for large-scale analysis of Earth observation data via easy-to-use programming libraries (Pyhton, JavaScript) and clients familiar to data scientists (Jupyter Notebooks, R, WebEditor). The project builds on the heritage of the H2020

project openEO and is now moving to an operational platform offering openEO as a service, embedded in a unique federated European architecture.

The development of the platform is driven by different use cases, all aiming at extending the capabilities of the platform through additional functionality linked to real-world examples. As foreseen in the project tender, part of the data access costs (in this case the requested commercial data) should be covered by additional funding.

Commercial data part:

The requested commercial data is used for a use case to determine fractional canopy cover (FCC). This parameter is important for monitoring changes in forested areas and is a key input parameter for many environmental and ecological models. In this use case, we foresee the prediction of FCC for a 900.000 km2 area in central Europe. The prediction will be based on Sentinel-1 and Sentinel-2 features such as polarization maps, individual bands or vegetation indices. The role of the Very High Resolution (VHR) data will be the delineation of forest and non-forested land cover at the pixel level in several

test sites.

The added value of the VHR data is the very high spatial resolution, which allows a detailed distinction of forested and non-forested areas in detail in many pixels at a small scale. Based on the FCC obtained for VHR pixels the FCC percentage is spatially scaled up to individual pixels of medium resolution sensors. In the use-case, the binary forest information is applied to a 20×20 m Sentinel-2 grid. Another valuable piece of information for FCC prediction over a large area is temporal resolution. Differences in phenology, forest cover density, and tree species benefit from multiple images or time series over the same site throughout the year. This strengthens the prediction of forest presence within each VHR pixel and increases the data density for the regression model and/or allows modelling of seasonal behaviour.

ORCS is an application based on Artificial Intelligence aimed to detect features like ships and airplanes over EO optical currently supporting RACE Project (https://race.esa.int/), a joint initiative between ESA and European Commission for the provisioning of several economic indicators. It has been employed a Faster RCNN architecture due to its capability to provide fast and reliable results in the object and features detection. The activity, started as internal prototype at the very beginning of the 2020 pandemic situation, it has been piloted as in-kind contribution to ESA and since September 2020 it is into operations running within the EDC platform and supporting indeed RACE project.

Earth Observation missions generate a large amount and variety of satellite data, treasure troves waiting to be fully exploited but currently underused because their data format, volume and complex geometry constitute a barrier for many users. To help remove this barrier and foster data synergy exploitation, open tools such as the Ocean Virtual Laboratory were developed with ESA support, making data discovery, access and analysis a rather easy task for science users. The aim of the OVL-NG study is to: Evolve, maintain, operate the ESA Ocean Virtual Laboratory Next Generation according to User needs.

The main technical objectives of this project are to prolong the ESA/Copernicus data visualisation and promotion activities started in OVL and S3VIEW for 24 months, to improve tools and services based on user feedback and to explore ways for improving the sustainability of these services in the long term.

Major changes are required in the core of the SEAScope application to allow it to stream data from a remote source, such as a Cloud, a DIAS or a datacenter. These developments are mandatory to facilitate the visualisation of large quantities of EO data and to make the application more attractive for users who need to explore and analyse these data without downloading full data sets. Design and implementation of user-requested features will be intertwined with the development of these core evolutions to achieve the most satisfactory outcome. The sustainability of existing and upcoming services can be improved by reducing the amount of time required to operate them and by optimising both usage and cost of the infrastructure resources. A panel of clouds and DIASes will be studied to get a clear view of the offers available to host services similar to OVL-NG. The processing system that feeds the online portals will be optimised to consume as little resources as possible, to perform more monitoring tasks and to handle minor issues autonomously so that operating the backend of OVL-NG involves less human interventions.

I am an investigative reporter with Noteworthy.ie in Ireland and I am currently carrying out an investigation into unlicensed peatland extraction in Ireland. Our investigations are published on our website and simultaneously on TheJournal.ie, the largest native Irish online news outlet with 550,000+ average daily users, aged largely between 24-55, with a 50/50 gender split. As we publish in the English language, we additionally attract a small audience from the US, the UK and beyond who have an interest in Irish and European issues. The main objective of this investigation is to outline the extent of unlicensed peat extraction across the country (historic and present) and key to the investigation is using satellite imagery to visualise/map the changes to peatlands over a series of time.

Unfortunately, the quality of publicly available satellite imagery for the land parcels in Ireland that I am examining is very poor and not up to publication standards.

In this light, I would like to ask if it would be possible to discuss our project with your team with a view to accessing the required satellite imagery to help tell this important story.

The Rapid Action on coronavirus and EO dashboard is a platform that demonstrates how the use of Earth observation data can help shed new light on societal and economic changes currently taking place owing to the coronavirus pandemic. Across all European countries and ESA Member States, the dashboard showcases examples of how different analyses over a wide range of Earth observation data coming from the Copernicus Sentinels and Third Party Missions, as well as groundbased observations and advanced numerical models via the Copernicus Services can illustrate these socio-economic and environmental changes. The dashboard not only captures the effects of the lockdown, but also shows how Europe is beginning its recovery and is relaunching a number of activities. In this framework PLES is in charge of keeping coordinate and consolidate the data indicators of the Dashboard and in this activity the Truck Detection indicator need to be updated to allow a more complete and explanantory effect of the pandemic impact on the commercial activities thought the monitoring of trucks traffic and made avaialbe on the dashboard the most recent and complete information regardig this indicator.

The research is aimed at mapping of small-scale crop farming in Kenya, with the goal of providing farmers and policy makers with information on cropland area, crop type and crop status. A prototype mobile application will be developed to allow stakeholders to quickly view information pertaining to their area of interest.

Objectives of the projects include the Soil Moisture content prediction (Machine Learning approach) using the NDVI calculated with Sentinel-2 data with the purpose of estimating wildfires risk using soil moisture content prediction.

Our team have previously used the G-POD SARvatore service to process CryoSat-2 observations over the Arctic region during summer months (May-Sep). These have been used to generate the first pan-Arctic summer sea ice freeboard data product for 2011-2020, as part of completed and ongoing ESA/NERC (UK) projects. We would like to now apply the same method to Sentinel-3A&B observations covering the Arctic sector to enable improved summer freeboard coverage and resolution. We request SARvatore for Sentinel-3A&B data processed in EarthConsole PPro for the period 01/05/2019 – 30/09/2019. The altimetry user community (and beyond) to be very interested in our new derived summer sea ice freeboard/thickness products, will benefit of the project results. The results will be available through the British Antarctic Survey Public Data Storage Facility, as for example https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01257

Croatian organizations are preparing to start mapping marine habitats for the first time using EO and acoustic data. I would like to play (test) the usage of multiple EO data together with acoustic multibeam data, side scan sonar data and in preparing optimal spatial sampling and later detecting several marine and habitats, especially spatial distribution of Posidonia oceanica. Hopefully, well mapped Posidonia will be used for better planning of future marine Natura 2000 sites in Croatian part of Adriatic as well research paper will be result of this exercise. Some other habitats of interest can be mapped using EO data due to spatial distribution in water that do not exceed 10 meters together with the coastal habitats.

For the montioring of the mass loss in Greenland Ice Sheets, several problems need to be overcome, as the one caused by the sparse crossover density. For that, a repeat-track approach should be used when using radar altimeter data for determining mass trends. In addition, the situations in the margins always show difficult because its complex terrain and altimeter data does not work well, and over ice surfaces, the altimeter waveforms are complicated by variations in the temperature in the snow pack/firn layer, indicating that proposed a new waveform retracking method is really meaningful. Traditionally, the altimetry-based estimates of the mass balance are often presented as relatively long-term averages (Shepherd et al. 2020, Yang et al., 2018, Hurkmans et al., 2014). But if we can increase the spatial and temporal resolution of the time series of the mass balance, we can see some melting and winter accumulation parts, which could be useful for the independent test of climate models. So, exploring the time-evolving mass balance of the GrIS in seasonal scale or even month scale is really meaningful to describe the relationship between the global climate change and the sea level rise.

Given the coarse hydrometric monitoring network, it is challenging to efficiently monitoring surface water dynamics and to effectively deal with droughts and floods. These extreme events are expected to increase in frequency and magnitude under climate change as well as urbanization. The advantages of multi-altimetry measurements are the global coverage and the longtime span, facilitating the research for the estimation of the river discharges with optimal space and time resolution. Moreover, sophisticated processing techniques of data acquired by the upcoming SWOT (Surface Water and Ocean Topography) allow the retrieval of ultra-high resolution water level profiles. The mission SWOT will provide critical information on the spatial variability of water surface elevation and allow a better understanding of the interactions between hydrodynamic processes.

Combining data from several altimetry missions, including SWOT, to characterize river discharge over the entire basin is essential for many important applications, such as flood forecasting, water resources management, engineering design, and reservoir operation among others. -Who will benefit from the project results: Jiaming Chen, Astronomical Physical and Mathematical Geodesy Group, Chinese Academy of Science -Results format: public papers, available code

According to the EU Ship Recycling Regulation, from 31 December 2018, large commercial seagoing vessels flying the flag of an EU Member State may be recycled only in safe and sound ship recycling facilities included in the European List of ship recycling facilities (“European List”). On this basis, the project aims to assess the potential of development for innovative applications meeting the needs and requirements of the maritime law enforcement, legal compliance and security communities for ship recycling monitoring and enforcement. Additional technical development may be tested according to the needs, requirements and priorities expressed by the end-user community in the maritime sector during a series of consultations (e.g. monitoring of ship recycling plans, of certificates issued or of requirements necessary for ship recycling facilities to be included in the European List). The planned activities aim to elaborate and validate/invalidate a first series of Sat EO based investigation and intelligence services, applications and products to monitor the respect of the EU Ship Recycling Regulation. The technical results include the construction of new EO datasets and processes as well as data fusion capabilities combining EO and nonEO data (e.g. AIS data). They will further involve tests related to the integration of the EO process into a web platform to provide easy access to the Sat EO services developed.

Scenarios to be assessed in relation to the study : Detection of EU ships heading to or located in a ship recycling facility not included in the European list of ship recycling facilities in violation of EU laws (ship recycling facility where the ship is to be recycled according to recycling plan and survey). Verification of EU ships plans to head toward and be dismantled into a ship recycling facility included in the European list of ship recycling facilities. Monitoring of listed recycling facilities to assess compliance with ship recycling plan (e.g. absence of illegal spills or other hazardous waste leaks around the recycling infrastructure)

The project deals with a pre-commercial exploration together with a startup viehfinder.com from Austria, to localize grazing cows on Alps. With the help of GNSS and LoRaWAN (Long Range Wide Area Network) we localize each cow, store the movement pattern of each cow in a cloud based spatial database. The product shall enable:

-to provide clear evidence which cow has been on which alpine region, over which time period

-analyze the grazing/movement patterns

-the farmer to find cows that are missing

In addition, the project consortium is about to explore the usage of a WebGIS portal to visualize the movement data of the cows and to plan LoRaWAN antenna positions (with the help of spatial optimization methodologies). In order to plan the antenna positions accordingly and to visualize the data properly the WebGIS benefit from a subscription of Sentinel data – in order to provide European-wide remote sensing data on the area of interest.

We intend to perform wide area Interferometric SAR (InSAR) processing based on hosted services available on the Geohazards Exploitation Platform (GEP). Our main goal is to verify the robustness of platform based solution in covering wide areas. We shall propose a methodological approach to reduce error budget included in the InSAR processing when such large processing extends are considered as well as the post-processing efforts required to combine individual results from different satellite tracks. Apart from the above mentioned research objectives, the generated dataset with country wide coverage, in our case entire Greek territory, shall be opened and disseminated to the scientific community via GEP e-collaboration tools for further utilization in geohazards related applications. Finally, such dataset may potentially support inter-verification activities of other InSAR measurements generated on comparable wide spatial scales (e.g. EGMS products).

This proposal is a component of the ESA contract GEP (Contract No.: 4000115208/15/I-NB) concerning the pre-operational demonstration activity. This ESA driven initiative consists of a wide area mapping pilot using the new service chain SNAPPING based on the ESA SNAP toolbox. The pilot it has the purpose to deliver PSI points over a large area and measure the ability of the platform to support mass production using the built-in operational metrics on the GEP. AUTh will deliver a technical report with technical feedback of the pilot and a scientific assessment of the impact and benefit of the service delivery over this high seismic risk region.

In this project, it is proposed to develop wildfire fuel map using hyperspectral imagery of PRISMA, a fundamental satellite of Italian Space Agency. For which, previously, detailed classification of vegetation types is required. In order to classify different vegetation types using various machine learning classifiers including quantum classifiers, there’s a requirement of virtual machine for processing.

-Who will benefit from the project results: Wildfire fuel map is useful for various purposes in fire risk modelling, post fire events, to develop vulnerability map etc., which would be useful to researchers, firefighters and also to stakeholders. -Results format: Result will be made available in GTiff image format and with no specific conditions. -Area of Study: Italy

The overarching goal of the WorldWater project is to empower national and regional stakeholders with advanced Earth Observation (EO) data and tools to better monitor their water resources and report on the global water agenda such as the 6th Sustainable Development Goal (SDG) on water and sanitation of the 2030 Agenda on Sustainable Development.

The WorldWater project will develop novel, robust and transferrable EO solutions for monitoring surface water dynamics both in extent and volume, which can be exploited by a large community of stakeholders involved in water management and active at all scales, from local water management to national water strategies, up to transboundary river basin management plans or largescale assessment of surface water changes.

The WorldWater project aims principally at strengthening EO capacities in countries to monitor their inland water bodies (lakes, reservoirs, rivers, and estuaries) and consequently improve their national decision-making processes on water resource management and water security.