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EO AFRICA – Water Resources Management (WRM)
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The project plans to estimate crop water stress and evapotranspiration, exploiting ECOSTRESS and PRISMA data by experimental EO analysis techniques. Expected outcome: an open source innovative model will be developed to assess actual crop [...] |
Planetek Italia (IT) |
Applications |
agriculture science cluster, crop, EO Africa, Explorer, water resources |
The project plans to estimate crop water stress and evapotranspiration, exploiting ECOSTRESS and PRISMA data by experimental EO analysis techniques. Expected outcome: an open source innovative model will be developed to assess actual crop evapotranspiration (ETa) using EO-derived crop coefficient (Kc) and crop water stress index (CWSI). The solution will be integrated into a web platform as a Decision Support System (DSS) to improve irrigation water management. Demonstration test site: large cultivated area (13.800 ha) located in northern Egypt.
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EO AFRICA Food Security and Safety in Africa – AFRI4Cast
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Afri4Cast project will develop a modeling platform making full exploitation of satellite remote sensing of PRISMA and ECOSTRESS sensors for climate change impact analyses on agriculture and making it available to African stakeholders for shaping [...] |
AGROAPPS PC (GR) |
Applications |
africa, agriculture, agriculture science cluster, crops and yields, EO Africa, Explorer, Food Security, hyperspectral |
Afri4Cast project will develop a modeling platform making full exploitation of satellite remote sensing of PRISMA and ECOSTRESS sensors for climate change impact analyses on agriculture and making it available to African stakeholders for shaping future agricultural policies in the African Continent.
Afri4Cast will provide national-, regional-, parcel-, pixel-specific in season production estimates, mycotoxin formation risk and disease outbreak probability. Apart from the in-season yield forecast production line, AFRI4CAst will execute seasonal and long-term model simulations for multiannual yield predictions and mycotoxigenic fungi contamination risk under various climate scenarios at a coarse spatial scale.
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EO4Cerealstress – Theme 3: Crop response to multiple stressors
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Despite advances in agricultural production, approximately 800 million people around the globe still face severe food insecurity. Biotic and abiotic agricultural stressors reduce and limit productivity (e.g., yield reduction) and ecosystem [...] |
UNIVERSITY OF SOUTHAMPTON (GB) |
Science |
agriculture, agriculture science cluster, crops and yields, Diseases and Pests, Ecosystems |
Despite advances in agricultural production, approximately 800 million people around the globe still face severe food insecurity. Biotic and abiotic agricultural stressors reduce and limit productivity (e.g., yield reduction) and ecosystem services (e.g., loss of carbon sequestration). These devastating impacts are increased by climate change, particularly by frequent and stronger extreme weather events.
EO4Cerealstress will evaluate the synergistic use of multi-source Earth Observation data, particularly hyperspectral data, in-situ crop physiological parameters, soil, climate, and other ancillary data- taking advantage of their complementarity – to understand the effects of multiple stressors and their cumulative effects on crops. New and planned European satellite missions are providing data at high spatial, spectral and temporal resolutions, which offer the opportunity not only to understand and monitor the impacts of single crop stressors but also multiple crop stressors. The project aims to develop products that can be used to monitor these stressors and provide a scientific roadmap for the future development of EO products and techniques for monitoring multiple crop stressors.
EO4Cerealstress will engage the user community and scientists in order to develop a scientific roadmap that will provide recommendations to the European space agency and the European Commission on priority scientific issues that need to be addressed to further the understanding and monitoring of the impacts of multiple stressors on crops.
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EO4NUTRI: Earth Observation for estimating and predicting crop nutrients
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Background: Timely and large-area information on nutrient concentrations in staple crops is lacking which limits our understanding of how nutrients vary across various geographic areas. In the absence of this information, we cannot efficiently [...] |
UNIVERSITY OF TWENTE (NL) |
Science |
agriculture, agriculture science cluster, crop, science |
Background: Timely and large-area information on nutrient concentrations in staple crops is lacking which limits our understanding of how nutrients vary across various geographic areas. In the absence of this information, we cannot efficiently guide research activities dedicated to alleviating potential nutrient deficiencies through genetic biofortification or agronomic biofortification by applying fertilizers.
Overall goal: EO4Nutri will develop innovative scientific solutions that bring together the capabilities of various Earth Observation (EO) data to estimate and predict the nutrient content of the soil, crop canopy, and harvested crops for several global staple grains.
Target crops: maize, rice, sorghum, teff and wheat.
Target nutrients: Calcium (Ca), Iron (Fe), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Potassium (K), Selenium (Se), Sulphur (S), and Zinc (Zn)
The project will focus on two scientific cases: (1) advancing our understanding of the lifecycle of nutrients from the soil to crop canopy and further to crop grains with innovative analytical techniques and EO data, and (2) deepening our understanding of Nitrogen uptake from soil to crop canopy to crop grains and its relationship to grain protein content using Radiative Transfer Models (RTMs) and machine learning methods. The EO4Nutri team will focus on transferring the developed products and datasets into actionable information that can enhance management and decision support systems dedicated to crop nutrient monitoring. Generated scientific results will be integrated into operational activities and a Digital Twin Earth.
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European Ecostress Hub
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The European Ecostress Hub is focusing on the development and implementation of the European Ecostress Hub (EEH) in support of the Copernicus High Priority Candidate Land Surface Temperature Monitoring mission (LSTM). ECOSTRESS data acquired [...] |
LUXEMBOURG INSTITUTE OF SCIENCE AND TECHNOLOGY (LU) |
Applications |
agriculture science cluster, applications, land surface, platforms |
The European Ecostress Hub is focusing on the development and implementation of the European Ecostress Hub (EEH) in support of the Copernicus High Priority Candidate Land Surface Temperature Monitoring mission (LSTM). ECOSTRESS data acquired over Europe and Africa together with user interfaces and application programming interfaces, e.g. scene and area selection, selection of different retrieval methods, different parametrisation and auxiliary information, etc.) shall be made available on a suitable cloud environment.
In addition, the hub shall provide a dedicated interface for ingesting campaign data (e.g HyTES campaign data). The study also comprises a detailed analysis of the retrieval performances under consideration of different scene settings (different cover types, different stages in the growing cycle, different climate zones (tropical, dry, mild mid-latitude, cold mid-latitude) over a full growing cycle.
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WorldCereal – Global crop monitoring at field scale
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The overarching goal of the WorldCereal project is to develop an open source EO solution for monitoring of global crop area extent, which can be exploited by a wide community of stakeholders involved in the agricultural sector and active over a [...] |
VLAAMSE INSTELLING VOOR TECHNOLOGISCH ONDERZOEK VITO (BE) |
Applications |
agriculture, agriculture science cluster, applications |
The overarching goal of the WorldCereal project is to develop an open source EO solution for monitoring of global crop area extent, which can be exploited by a wide community of stakeholders involved in the agricultural sector and active over a range of scales – from national agricultural reporting, regional crop productivity management, up to global assessment of cultivated crop area extent. Delivering maps of crop area extent in a timely manner and tracking its seasonal changes over time will be emphasized to monitor the dynamics of the global agricultural productive area.
The WorldCereal project has the following principal objectives:
to demonstrate the feasibility of global crop mapping at field scale based on open high resolution EO data such as Sentinel-1, Sentinel-2 and Landsat-8;
to develop innovative and efficient open source EO algorithms and tools making full use of cloud computing capabilities for mapping the global extent of annual cropland and two of the major staple crops wheat and maize on a seasonal basis;
to build a collaborative approach to exchange with the agricultural community relevant in-situ data sets and disseminate the global crop mapping results in a transparent manner
to showcase the utility of the WorldCereal products by conducting use case studies related to the GEOGLAM initiative and SDG reporting.
As the global crop monitoring at field scale is a true global challenge we are happy to count on an impressive international user group supporting WorldCereal: FAO, AMIS, GEOGLAM, AAFC, AFSIS, BAGE, CIMMYT, CIMMYT-GLTEN, DSSI, DG-JRC, GEOSYS, GODAN, ICARDA, ICRISAT, IFPRI, INTA, JECAM, N2AFRICA, NASAHarvest, ONESOIL, PlantVillage, RADI, ROTHAMSTED RESEARCH, WFP. The user group remains open to new stakeholders interested in contributing to the goals of WorldCereal which aims to be a community effort.
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YIPEEO: Yield Prediction and Estimation from Earth Observation
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Crop yield forecasting is a vital tool to support stakeholders and decision-makers in preparing for potential yield deficiencies. Most crop yield forecasts so far have been implemented on a regional to national scale. Field-scale forecasts can [...] |
TECHNISCHE UNIVERSITAT WIEN (TU WIEN) (AT) |
Science |
agriculture science cluster, crops and yields, Sentinel-1, Sentinel-2, Sentinel-3 |
Crop yield forecasting is a vital tool to support stakeholders and decision-makers in preparing for potential yield deficiencies. Most crop yield forecasts so far have been implemented on a regional to national scale. Field-scale forecasts can add vital information for farmers and insurers but still have much potential to improve. Especially the increasing availability of high-resolution climate data from sources such as Copernicus Sentinel-1, Sentinel-2, Sentinel-3 data, and Proba-V can significantly improve such forecasts. The goal of the YIPEEO project is to improve field-scale crop yield forecasts by using these datasets in combination with novel machine-learning techniques or crop growth models. For this purpose, we are working with various field datasets distributed over Europe (Ukraine, Finland, Netherlands, Denmark, Italy, and several in central Europe). In addition, we will explore the impact of droughts on crop yields, assess the impact of the war on Ukraine’s crop production, and develop an irrigation timing and fertilizer advisory tool
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