Conference Agenda

Evapotranspiration (ET) is a critical component in the terrestrial water cycle, and it can quantify the crop water use in agricultural regions. Land surface temperature (LST) indicates the thermal status of the surface as a consequence of the land-atmosphere exchange of energy and water fluxes, thus serving as a pivotal lower boundary condition for retrieving ET in thermal-based evaporation models. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) has been providing high spatio-temporal TIR observations (~70 m, 1-5 days) since August 2018. Taking advantage of the ECOSTRESS observations, the European ECOSTRESS Hub (EEH) funded by the European Space Agency (ESA) focuses on the water, energy, and carbon cycles in the terrestrial ecosystems. In EEH Phase 1 (2020-2022), we produced LST and instantaneous ET data between 2018 and 2021 from models with different structures and parameterization schemes over Europe and Africa. Based on the generated LST, ET from the physically based STIC model had relatively better consistency with the measurements from the eddy covariance sites across varying aridity and diverse biomes. Taking advantage of ECOSTRESS Collection 2 data, in EEH Phase 2 (2023-2025), we target at 1) analyzing the impacts of LST estimates from different algorithms on ET retrieval over different ecosystems, 2) developing a hybrid look-up table approach for estimating daily ET at any time of day, and 3) coupling ET with GPP. Map-ready time series (>5 years) of LST, daily E, and GPP are expected to be generated with public accessibility. Overall, the EEH will provide quality-assured remote sensing products for monitoring agriculture drought and facilitate the preparation for the next generation high-resolution thermal missions, including TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA).

The Sen4Stat open source toolbox and the Agricultural Virtual Laboratory (AVL ) are funded by ESA with the objective to support the EO agriculture community.

The ESA Sen4Stat project demonstrates validated open source tools and best practices for national agricultural statistics with Sentinel data and facilitates the EO uptake by the National Statistical Offices. The Sen4Stat toolbox processes Sentinel-1 and Sentinel-2 according to the state-of-the-art and delivers 5 types of products: 10-m optical and SAR temporal syntheses, spectral indices and biophysical variables time series, 10-m crop type maps, crop growth conditions metrics and crop yield estimations. The project is working with pilot countries such as Spain, Senegal, Pakistan, etc. to address a wide diversity of cropping systems and agricultural data collection protocols and sampling frames. The Sen4Stat toolbox is available for download and the next 18 months will be dedicated to capacity building activities for the growing community.

The AVL is designed to be an online community open science tool to share results, knowledge and resources. Agriculture scientists can access and share Earth Observation (EO) data, high-level products, in-situ data, as well as open-source code (algorithms, models, tools) to carry out scientific studies and projects. Maximizing the offer of relevant data for the agriculture science community is the first objective of AVL. As Open Science project, the AVL also aims at fostering the collaboration between scientists and the sharing of data, products, results and source code (joint publications, inter-comparison exercise, benchmarking activities, etc.).

Climate change, the Ukraine war, armed conflicts across the globe, and the recent COVID 19 pandemic, demonstrate the vulnerability of global agricultural production to external shocks, with serious repercussions for world food trade, food access and ultimately food security of millions of people around the globe. Such multi-layered acute food supply shocks are expected to increasingly disrupt agricultural markets, trade, and food security, highlighting the urgent need for appropriate, timely and transparent risk management.

An enhanced capability for monitoring, forecasting and assessing shocks to agricultural regions across the globe is critical to better inform markets, humanitarian actors, and policy makers to ultimately help safeguard lives and livelihoods. Satellite data and analysis can fill serious gaps in agricultural information during food system shocks and when ground access is disrupted. For example, our ongoing work with Ukraine’s ministry of Agrarian Policy and Food, provided satellite driven, in season assessments of Ukraine’s agricultural production in the government controlled and occupied territories where ground data is not available, helping to inform policy and trade decisions. In our experience at NASA Harvest and GEOGLAM, despite a large and growing demand for such analysis, there is a notable deficit in institutional capacity to deliver the kinds of rapid, satellite-driven assessments. Many national and international organizations use remote sensing technologies for agricultural assessments, but a dedicated, on-demand agricultural analysis facility does not exist. The need for such standing capacity has been recognized by multiple governments, UN, humanitarian organizations, and policy frameworks i.e. AMIS. In response, we are building a dedicated facility that can be activated whenever events threaten agricultural production, or information transparency. The facility’s focus is on analyzing three primary types of food system shocks: armed conflict and war; extreme weather events; and on regions with high agricultural uncertainty or low data transparency. This talk will discuss the need for such a facility and provide examples of our rapid response work and its impact.

The WorldCereal project, funded by the European Space Agency (ESA), aims to provide a comprehensive understanding of global cropped areas, irrigation practices, and the distribution of major commodity crops such as cereals. WorldCereal has developed a dynamic open-source system that generates a range of products, including temporary crop extent, seasonal maize and cereal maps, seasonal irrigation maps, seasonal active cropland maps, and confidence layers. These products are based on the analysis of Sentinel-1 and Sentinel-2 imagery at 10 m spatial resolution, complemented by Landsat 8 imagery and AgERA5 meteorological information, and are updated at seasonal intervals for each agricultural system. WorldCereal has demonstrated the feasibility of global crop mapping by producing the first global, seasonally updated crop and irrigation maps for the year 2021. WorldCereal has also released a fully open, harmonized database of in-situ reference data related to land cover, crop type, and irrigation, enabling a broad community to access and contribute to this growing resource. WorldCereal is now entering a new phase, in which the system is being integrated into OpenEO and implemented as a cloud-based processing service in the new Copernicus Data Space Ecosystem. The system will also offer more flexibility and customization options to users, allowing them to generate tailored crop type products for their regions of interest. Moreover, the WorldCereal product suite will be extended with eight new crops, and the in-situ reference database will be updated and expanded. WorldCereal will also conduct a series of regional use cases and capacity building activities to demonstrate the system’s capabilities and to boost user uptake by the broad agricultural monitoring community. WorldCereal provides a vital tool for policymakers, international organizations, and researchers to better understand local to global cropping patterns and to inform decision-making related to food security and sustainable agriculture.

The EO4EU project, funded by the European Commission, aims to revolutionize access to and utilization of Earth Observation (EO) data through the development of the EO4EU Platform. This platform, hosted at www.eo4eu.eu, integrates major EO data sources such as GEOSS, INSPIRE, Copernicus, Galileo, and DestinE, offering a suite of tools and services tailored for environmental, governmental, and business forecasting and operations in various domains including agriculture.

Key features of the EO4EU Platform include Data Analytics Visualization, Knowledge Graph-based Decision Making, an XR (extended reality) System, an AI/ML Marketplace, a Generic Machine Learning Pipeline for Semantic Annotation, Fusion Engine, and serverless Function as a Service (FaaS). These tools empower users to effortlessly discover, analyze, and visualize EO data, supported by machine learning algorithms for handling large data volumes.

The platform utilizes cloud computing infrastructure and pre-exascale high-performance computing to efficiently process EO data, while prioritizing user-friendly interfaces for intuitive data exploration, even incorporating extended reality technologies.

EO4EU introduces technical and scientific innovations such as enhanced image compression rates, reduced data volumes, custom services with minimal labeled data requirements, robust representations of EO data, and optimized data fusion in HPC and GPU environments. Moreover, the project emphasizes the development of customizable visualization tools, granular analytics, and responsive web interfaces to observe trends, correlations, and cause-and-effect relationships in EO data.

The poster will provide insights into the project's progress, including the adopted architecture, initial user survey findings regarding EO data access and discovery, and upcoming milestones. Early access to the platform will be offered, alongside discussions on challenges and opportunities in EO data utilization.

The Copernicus Data Space Ecosystem has revolutionized the landscape of Earth Observation (EO) data exploration, accessibility, processing, and visualization. This platform allows for efficient satellite data processing for agricultural monitoring, e.g. to address national statistics, food security, and sustainable development goals. The Copernicus Data Space Ecosystem offers a comprehensive suite of functionalities, including access to Sentinel Hub and openEO APIs, free-of-charge up to predefined quotas, with the option for expanded computing resources on a commercial basis.

Introducing an innovative approach to disseminating satellite data, the Copernicus Data Space incorporates data streaming and on-the-fly processing, substantially reducing the time required for generating higher-level EO products crucial for timely decision-making in agriculture monitoring. A distinctive feature of CDSE is the immediate availability of data, eliminating the need for ordering and waiting periods. This facilitates seamless bulk data processing and streaming via OGC services, allowing for real-time visualization and analysis. Moreover, the adoption of optimized data formats like Cloud Optimized GeoTIFF (COG) enables partial reading, crucial for parallel computing and efficient data processing.

The Copernicus Data Space Ecosystem provides access to a comprehensive range of Copernicus satellite imagery, including Sentinel-1, Sentinel-2, Sentinel-3, and Sentinel-5p, along with services and data from other satellite missions like Landsat, SMOS, and Envisat. The Copernicus Browser further enhances support by enabling advanced data visualization, encompassing 3D rendering and timelapse animations.

For agricultural monitoring applications, CDSE provides invaluable resources free-of-charge for personal, research, or commercial use. For larger-scale processing needs, platforms like CREODIAS offer access to federated cloud environments, serverless processing of EO products, and dedicated EO services.

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