Conference Agenda

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Plant stress, a persistent natural phenomenon, presents a significant threat to global agriculture and food security. As changing climate patterns lead to more frequent and severe drought, pest, or disease occurrences, the demand for innovative and precise approaches to evaluate and monitor plant stress in agriculture grows. In this context, a new generation of thermal remote sensing data emerges as a valuable tool. It has the potential to provide the necessary data not only to analyze and understand the impact of stress caused by different factors on crops but also to detect it promptly, allowing for timely mitigation.

constellr develops a constellation of state-of-the-art high-resolution thermal infrared (TIR) as well as visible and near-infrared (VNIR) sensors, planned for launch by the end of 2024, to monitor land surface temperature (LST). The HiVE (High-precision Versatile Ecosphere monitoring mission) constellation comprises micro-satellites in the 100 kg class, with orbits in a sun-synchronous plane at an altitude of 550 kilometers. With a remarkable 1-day temporal resolution reached starting 2026 with 5 satellites in orbit, 30 meters spatial resolution for the TIR bands, and up to 10m for the VNIR bands, HiVE is uniquely equipped to provide accurate and timely data optimized for agricultural needs.

We will present the technical specification, status of the HiVE mission, and the added value of these data for agricultural practice. Thus, constellr is currently performing different proof of concept studies to quantify the added value of thermal data for identifying plant stress. Firstly, LST time-series are analyzed to detect trends and anomalies as a proxy for crop stress in time and space. Secondly, LST data are exploited to derive actual evapotranspiration, which in turn is used to quantify the drought stress. Besides that, an outlook on further LST-based products relevant to agriculture based on LST will be shown.

In the coming years, additional hyperspectral missions, such as the Copernicus CHIME, will increment operationally the data stream already provided by the ASI PRISMA and DLR EnMap missions. This will enable new research possibilities within the “agriculture and food security” domain. In the agri-food sector, hyperspectral data, characterised by narrow bands covering the full range from VIS to SWIR, can provide a unique contribution to better i) estimate within-season crop traits and ii) quantity crop residue presence after harvesting. The retrieval of within-season crop traits allows early warning indications of potential stress, supports smart agriculture practices within a Precision Farming framework, and improves yield estimates. The identification and quantification of Non-Photosynthetic Vegetation (NPV) are fundamental to track sustainable agro-practices for soil conservation (e.g. minimum tillage) and to provide information for the carbon budget in agriculture. In this framework the ASI-PRISMASCIENZA project “PRIS4VEG” exploited a comprehensive multiyear PRISMA dataset (2020 - 2023) together with field bio-parameter measurements and ancillary farm data (e.g., crop sequence and agro-practices) acquired in Jolanda di Savoia site (North of Italy).

A hybrid approach, fine-tuned with an active learning procedure (HAL), was successfully tested on PRISMA hyperspectral data to estimate crop traits, such as leaf area index (LAI), chlorophyll and nitrogen content at both leaf (LCC, LNC) and canopy (CCC, CNC) levels. A machine learning regression algorithm (MLRA), based on enhanced hyperspectral input identified by spectroscopic modelling of diagnostic NPV cellulose-lignin specific absorption features, was used to assess the presence and cover of crop residues. The MLRA was trained using an extensive and well-documented spectral library and tested on independent ground, airborne and spaceborne (i.e., PRISMA) data. PRISMA maps provided interesting spatio-temporal patterns related to Genetics-Environment-Management interactions, demonstrating the contribution of hyperspectral data in generating spatially explicit information for the agro-monitoring sector.

The NISAR mission, a joint effort between NASA and the Indian Space Research Organization (ISRO) will be launching in the spring of 2024. Once through its commissioning and calibration/validation periods, the mission will be collecting dual-polarized L-band SAR data at a 20 m ground-projected resolution and 240 km swath, two times every 12 days over most land surfaces. In addition to these background observations the mission will be collecting S-band data inside of India, along California's west coast, and over Ecosystems cal/val sites located worldwide, many of which are derived from the Group on Earth Observation's Joint Experiment for Crop Assessment and Monitoring (JECAM).

Among the agriculture applications that NISAR will be addressing through its systematic observing strategy will be in the determination of active crop area that will be produced every quarter. Early studies using L-band SAR through the Japanese Aerospace Exploration Agency's ALOS sensor, or NASA's UAVSAR, and ESA's Sentinel-1 SARs, has show that time series of SAR data can be used for crop classification as well.

In this talk, we will discuss the status of the NISAR mission and highlight its development for agriculture applications and the larger remote sensing community.

Managing dwindling water resources responsibly and protecting crops from adverse growing conditions in the face of climate change are key challenges for sustainable and robust agriculture everywhere in the world. Thermal infrared remote sensing to measure canopy and soil temperatures in the field has a tremendous potential to directly track the water cycle in crops and soil, and directly monitor crop stress. In contrast to thermal infrared, visible and near-infrared spectral bands only detect crop stress too late, once irreversible damage is already done.

Hydrosat is launching a thermal infrared satellite constellation to provide daily, global, and high-resolution land surface temperature measurements targeting a spatial resolution of 50 m. We currently employ multi-sensor data fusion techniques and sophisticated numerical water balance models to bridge the gap until Hydrosat’s full constellation is fully operational.

IrriWatch is Hydrosat’s irrigation management decision support system, combining empirical and biophysics-based models with daily satellite data. IrriWatch allows growers to track the water demand and growth progress of their crops down to individual pixels in near-real-time, enabling cost savings and yield increases by optimizing irrigation and fertilization operations.

We present recent validation studies comparing output from the IrriWatch algorithms to ground truth data obtained from in-field sensors and drone flights, showcasing the capabilities and limitations of monitoring agricultural fields from space. For example, while we find that thermal sharpening works well under suitable conditions, it cannot replace native high-resolution data. From space imagery we observe excellent agreement between predicted and actual dry matter accumulation, even compared to high-resolution yield maps obtained from combine harvesters. IrriWatch also typically provides realistic estimates of root zone soil moisture from space, including forecasts of irrigation water demand and monitoring of total applied water. We find that water balance calculations are mostly limited when soil parameters and rainfall data are inaccurate, and we present recent developments to overcome these limitations.

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The intricate interplay between soil moisture, water retention, and carbon sequestration represents a significant challenge in comprehending and effectively managing terrestrial ecosystems. These interdependent variables play pivotal roles in shaping agricultural productivity, informing water resource management strategies, and contributing to climate change mitigation efforts. Therefore, it is crucial to accurately model and predict their dynamics and interdependencies.

This study outlines a comprehensive methodology for creating a dataset designed to explore the correlations between soil moisture, water retention, and carbon sequestration, employing satellite imagery. The primary objective is to enable the application of machine learning techniques for estimating future values based on historical trends.

The dataset construction process involves preprocessing and integration of diverse data sources, such as Earth observation data and ground-based measurements. Emphasis is placed on incorporating essential temporal and spatial features to ensure a robust analysis of soil conditions. The resulting dataset is anticipated to be instrumental to uncover patterns and correlations between soil moisture, water retention, and carbon sequestration.

This paper underscores the significance of integrating advanced technologies and multi-dimensional datasets, highlighting their potential to furnish users with a powerful tool for predicting future environmental variables. The overarching goal is to drive informed decision-making processes in the realm of sustainable land and water management. By combining cutting-edge technology with rich datasets, this approach aims to empower stakeholders in making smart and strategic decisions to address the challenges posed by changing environmental conditions.

The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On June 6 2023, the Kakhovka Dam in Southeastern Ukraine collapsed under attack, draining the 2000 square km reservoir which served as a source of water and power for 20,000 people [1]. The loss of this critical agricultural and energy infrastructure has left farmers on the left bank of the Dnieper River without irrigation in the historically arid Kherson and Zaporizhzhia oblasts [2]. In this study, we use Earth Observation to measure the change in irrigation coverage in the occupied region resulting from the loss of the dam. Given the current conflict and the lack of ground-truth data from the occupied regions, we use human-labeled validation sets derived from the greenness, thermal, and wetness characteristics of irrigated fields [3], using pre-collapse (2020-2022) imagery from PlanetScope, Landsat-8 and 9, and Sentinel-2 as the baseline. The FAO WaPOR v3 actual evapotranspiration and interception dataset is combined with precipitation in a Water Deficit Index to differentiate irrigated fields from rainfed fields. Preliminary results from an unsupervised approach show a significant reduction in 2023 of the types of fields identified as ‘irrigated’ in the pre-collapse years. Given that much of the summer crops would have been planted by June, this change in 2023 is hypothesized to be due to lack of irrigation in already-planted fields, rather than farmers changing crop types in response to irrigation loss.

References:

[1] Naddaf, M. (2023). Ukraine dam collapse: what scientists are watching. Nature, 618(7965), 440-441.

[2] Vyshnevskyi, V., Shevchuk, S., Komorin, V., Oleynik, Y., & Gleick, P. (2023). The destruction of the Kakhovka dam and its consequences. Water international, 48(5), 631-647.

[3] Deines, J. M., Kendall, A. D., Butler, J. J., & Hyndman, D. W. (2019). Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environmental Research Letters, 14(4), 044014.

Potato is the fourth staple food crop, and its planting area is constantly expanding. Accurate acquisition of potato distribution is of great significance for planting area detection, yield estimation and the planting structure adjustment. To this end, this study developed a field-parcel-based methodology for mapping Potato distribution by integrating the edge detection, image segmentation and machine learning algorithms based on multi-temporal Sentinel-2 data. The Canny edge detection results from single 10 m resolution bands of Sentinel-2 data (blue, green, red, and near-infrared bands) are aggregated by different weights can detect richer edge information and provide sufficient details for field parcel extraction. Then the watershed segmentation is used to extract the field parcel with accuracy of 85%. the random forest machine learning classifier was used by combing the spectral and index features to identify potatoes at the parcel scale and the mapping accuracy achieved 84%, which can provide technical support for accurate potato management, accurate area and yield estimation.

Timely information of pasture condition is essential for the management of cattle rotation. Farmers have to decide when and where to move cattle herds, based off the grazing activities within active pastures and the recovery rate of vacant pastures. Traditionally, this information is gathered with time consuming manual surveying techniques using Plate Meters. However, Earth Observation can observe the grass condition across a wide management area in a timely and consistent manner, helping farmers to survey reduce and acting as in invaluable, scalable management tool.

The SmartFarm system utilizes both Sentinel 1 & 2 in near-real time to estimate average pasture grass biomass (kg ha^-1). Users register fields to include in their management plan, where each field’s biomass will be comparatively displayed against other fields in a ‘Grass Wedge’ management graphic.

This system works by firstly generating biophysical variables from Sentinel 2 data, namely LAI and FAPAR, to understand the photosynthetic health of the grass. However, the timely utilization of these variables can be limited by cloud cover. Therefore the SAR backscatter response of the field, measured by Sentinel 1, is compared to the average backscatter signal of the wider landscape in a 3km radius. Using the difference between these signals reduces the impact of atmospheric noise on the Sentinel 1 signal and yields a reliable, comprehensive measure of the vegetation structure. A deep learning model was trained using these EO signals and 10,000 measurements of grass biomass gathered using Plate Meters. The nature of the data was challenging to model due to its fast, often sub-monthly cyclical patterns, but the model performed well and achieved an RMSE of 708 kg ha^-1 and a r² of 0.27. The system has been operational for over 8 months and routinely estimates pasture biomass for 523 fields across the UK.

The green area index (GAI) is a key biophysical variable for crop monitoring. The most accurate methods for its large-scale estimation rely on optical remote sensing data. However, these can be hampered by frequent cloud cover. In this context, synthetic aperture radar (SAR) offers the advantage of being able to provide dense time series that can be used to complement the sparse GAI series derived from optical data. In this study, SAR-to-optical GAI regression is performed using a transformer neural network with past and current values of SAR backscatter and interferometric coherence, as well as past values of GAI when available. Sentine-1 and -2 data acquired from 2018 to 2021 over the Hesbaye region of Belgium are used for cross-validation. The model is trained on three growing seasons and tested on the fourth for each fold. The results show that the model can successfully predict Sentinel-2-derived GAI with an average R2=0.88 and RMSE=0.74, outperforming methods relying on radiative transfer model (e.g., Water Cloud model) inversion. The method is also validated with data collected in situ in eight maize fields in Belgium (R2=0.87 and RMSE=0.75). These promising results pave the way for the generation of accurate, dense LAI time series throughout the growing season, allowing for timely crop monitoring in cloud-prone regions.

Crop insurance is a key element to increase the resilience of farmers, particularly small-scale farmers in countries exposed to the impact of climate change. Because Multi-Peril Crop Insurance based on individual loss assessment is too costly to operate for millions of small-scale farmers, Weather Index Insurance (WII) has been introduced decades ago. WII uses weather parameters, such as rainfall to determine payouts. More recently, Area-Yield Index Insurance (AYII) – a crop yield loss policy – has been introduced: it is particularly suited to the needs of small-scale farmers by providing a more comprehensive loss of yield protection for natural, climatic, and biological perils compared to WII.

Based on elements from both WII and AYII, a hybrid solution, Weather Area Index Insurance (WAII) is proposed: it is an index that on one hand significantly improves WII, and on the other hand streamlines AYII. The reasons behind this new index are:

1. WII is exclusively based on rainfall data, hence not considering when, where and how much crop area have been effectively planted and the seasonal phenological crop development. This solution creates uncertainties in payouts.
2. AYII, as applied in RIICE, is an advanced and complete insurance index solution. However, yield estimation requires a plant growth simulation model specific to each crop. Insurance companies tend to shy away from this solution because a time-consuming calibration period is needed. That is, an AYII solution should rather be understood as a long-term objective when stepping into a new geography.

WAII Rainfall and Flood indexes are obtained by combining satellite rainfall data, seasonal cultivated area and associated crop growth trends derived from high resolution remote sensing time-series, and crop-specific triggers at key phenological stages. In collaboration with stakeholders looking at low-cost insurance solutions meeting farmers' actual needs, WAII is piloted on maize, cassava and rice in Cambodia.

Independent, high-quality, and comprehensive harvest statistics are an indispensable information basis for the government, economy, and the general public. So far, regional harvest statistics in Germany have relied on labor-intensive estimates from knowledgeable experts, which are becoming increasingly difficult to recruit.

Sentinel data combined with machine learning offers a promising and objective method to not only ensure harvest statistics at the regional level but also to increase spatial granularity even further. In a pilot project, the Hessisches Statistisches Landesamt (Hessian Statistical Office) has developed an automated procedure to model crop yields for four important crops (winter wheat, winter barley, winter rapeseed, winter rye) comprehensively at the field scale. This procedure has been applied to six German states for the years 2022 and 2023 and will be scaled up nationwide in 2024 for future integration into operational statistical production.

To achieve this, two periods in May and June of each year with the least cloud-cover were identified, and Sentinel-2 L2A images were assembled into mosaics per period. Sentinel-2 data were combined with field geometry and crop type information from the Integrated Administration and Control System, in-situ yield measurements from official statistical surveys, as well as additional meteorological and soil variables to train an ensemble of various machine learning algorithms. These models were then used to model harvest yields for all fields of each respective crop, which can be aggregated to any desired units.

The cross-validation results reveal relative errors ranging from 10.6% for winter rapeseed to 19.8% for winter rye. These errors seem to be primarily influenced by the size and variability of the available training data. Hence, by incorporating data from additional states and years, we anticipate a further reduction in the prediction error associated with harvest yield.

Tillage is a fundamental agricultural practice aimed at preparing soil for planting by loosening it and integrating organic matter. The choice and frequency of tillage methods significantly influence agricultural field productivity and health. Tillage practices are categorized based on the percentage of the soil surface with crop residue cover (CRC). Conventional tillage involves extensive soil disturbance with CRC <15%, while conservation tillage aims to minimize soil disturbance with CRC >15%. Conservation tillage contributes to soil quality improvement by reducing erosion and greenhouse gas (GHG) emissions.

Sentinel-1 (S1) and Sentinel-2 (S2) play a vital role in detecting and monitoring tillage practices. S1 and S2 provide information on tillage patterns and field characteristics, aiding in the differentiation between conservation and conventional tillage practices. Vegetation indices, e.g., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Tillage Index (NDTI), derived from S2, aid in detecting CRC. S1 offers all-weather monitoring capabilities for detecting tillage by capturing changes in surface roughness and moisture content associated with tillage. Moreover, the Integration of satellite data with Artificial Intelligence (AI) algorithms enhances tillage detection accuracy and efficiency.

A challenge exists in distinguishing between crop residues and soil, especially in smallholder farming systems. However, capabilities in satellite technology, such as high spatial and spectral resolution of S2 and S1 imagery, offer solutions to these challenges.

In “AI-based tillage detection for improved agricultural and climate policies” project funded by ESA, we aim to develop AI models, i.e.,1D ResNet, for detecting conservation and conventional tillage using S1, S2, and Landsat data. The resulting models will be validated in collaboration with the Baltic Sea region’s agricultural paying agencies. Currently, we are in the process of assessing the feasibility of detecting conservation tillage and developing the conventional tillage detection model with the following achieved preliminary results: F1 score: 0.80, TNR: 0.63, TPR: 0.78.

The spatial distribution of global cropland extent serves as the foundation for various analysis in agricultural applications, including crop growth monitor and water management, natural resource utilization, environmental assessment, public health initiatives, and sustainability evaluations. With the advent of cloud computing and the better availability of high-resolution satellite imagery, numerous cropland extent products have emerged, typically offering resolutions ranging from 10 to 30 meters. However, these existing datasets exhibit significant disagreement in cropland extent delineation. In the present work, we addressed the disagreement among six existing cropland extent datasets (WorldCover, ESRI LC, GLAD, FROMGLC, Globeland30, and GLC-FCS) for the year 2020. Our analysis revealed that 51.27% of the potential global cropland areas (defined as regions classified as cropland by at least one product) were consistently labeled as cropland by all six datasets considered. We identified two primary factors contributing to the observed disagreement: (1) variations in the definition of cropland among different products and (2) misclassification during the cropland identification process based on satellite data. To further investigate the impact of misclassification, we selected three products with cropland definition similar to the "temporal crop" category in FAOSTAT (WorldCereal, WorldCover, and annually composited Dynamic World). Despite using similar cropland definition, disagreement maps still revealed that 34.76% of potential "temporal cropland" areas exhibited discrepancies among the three selected products. Verification using visually interpreted validation samples conducted in North Europe and North Asia, which are regions of large disagreement, demonstrated that WorldCereal achieved the highest classification accuracy, with an overall accuracy of 84.19%, and WorldCover exhibited the strongest correlation with statistical data from FAOSTAT (R² = 0.92). This work addresses the importance of conducting global cropland extent data following an unform applicable definition, thereby enhancing the utility of satellite-derived datasets for decision-makers and stakeholders across both public and private sectors.

The food system presents a huge challenge for the planet in terms of producing sufficient nutritious and affordable food, but also in reducing the destructive planetary impact that food is having. New technologies can make a significant contribution to this goal, especially when supported by the secure and permission-enabled sharing of data.

SPADE EU Project (HE 101060778) is acting within the research and innovation front of the agri-food and environment sectors. Technologies employed involve Unmanned Aerial Vehicles (UAVs) used for Earth Observation, also catering for Agriculture and Environmental monitoring (livestock, cropping, forestry) and aiming to support evidence-based decisions for improving food security at national to global scales.

The SPADE ecosystem involves deployment of UAV formations (single UAV, collaborating UAVs, UAV swarms) equipped with edge-computing devices (AI/ML) that detect focused risks (e.g. livestock grazing health care risks).

This work will present the first results of SPADE in livestock use cases while also focusing on how a trustworthiness framework enables connectivity among the topics of edge computing AI tools, ML modelling & data assimilation, livestock assets digital twinning and needs arising or changing due to the environment.

The aim is to establish a collaborative ecosystem for securely sharing and exchanging data amongst participating actors such as farmers, drone providers, regulators and others in order to maximise the benefits of new and aggregated data whilst minimising the risks associate with data sharing. A trustworthiness data framework will enable a collaborative governance model that considers all participant stakeholders interests as well as optimising environmental factors.

The broader vision is to integrate farm and supply chain level data sharing with regional, national, and global scale exchanges. This in turn will enable other entities such as regulators, policymakers, meteorologists and other satellite services to collectively address food challenges through benefitting from evidence-based knowledge and insights.

Over the last decade, food security has become one of the world’s greatest challenges. Reliable, timely and legitime information on food production is required to inform decision-making process. The main expectations about the Earth Observation (EO) contribution to the agriculture statistics are cost-efficiency, better granularity and timeliness improvement.

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: (i) 10-m optical and SAR temporal syntheses, (ii) spectral indices and biophysical variables time series, (iii) 10-m crop type maps, (iv) crop growth conditions metrics and (v) crop yield estimations at various aggregation levels. 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.

In Spain, a 10-m crop type map was generated (F-Scores for the main crops higher than 80%) and coupled with national statistical survey to allow reducing the confidence interval around the acreage estimates by more than 50%. An irrigation map was also produced to update the sampling frame. In Senegal and Mali, the data collection protocols were adjusted to facilitate the integration of EO data and improve the acreage and production estimates. In the Sindh province of Pakistan, a pilot activity is ongoing with an ad-hoc field campaign co-organized with the Ministry of Agriculture to estimate the irrigated wheat area.

The Sen4Stat toolbox is available for download and the next 18 months will be dedicated to capacity building activities for the growing community. FAO and World Bank are also actively contributing to the Sen4Stat uptake through the EOStat programme and pilot activities.

The conservation and creation of landscape features is recognised as a key conservation tool to halt the loss of agricultural biodiversity in European farmland.

This study introduces a new indicator to quantify landscape features in EU agricultural land, based on the LUCAS Landscape Feature survey. We developed a comprehensive methodology to measure and categorise landscape features, distinguishing Woody, Grassy, Wet, and Stony LF types. Our approach gives a robust and reproducible estimate of the indicator at the EU Member State and possibly regional levels, based on a reliable and statistically representative sample of landscape features.

The methodology combines office-based photo-interpretation with field surveys collecting 3.8 millions field points, ensuring accuracy in determining the presence and type of landscape features within agricultural contexts. Together with information on biodiversity and ecosystem services, it will play a crucial role in evaluating the performance of major policies related to biodiversity conservation in agricultural lands, aligning with the Common Agricultural Policy and the EU Biodiversity Strategy for 2030. Besides, it will play a role in the assessment of natural based solutions for mitigating climate change effects, biodiversity loss and crop production (food) security.

Our findings reveal that, in 2022, landscape features covered 5.6% of EU agricultural land. Woody features were the most prevalent, followed by Grassy, Wet, and Stony features. The percentages of landscape features varied across EU Member States, with Malta and Cyprus exhibiting higher values.

The novel indicator developed is based on a comprehensive and reproducible method for quantifying these features, providing essential insights for policy and decision-making in sustainable agriculture.

With the adopted amendment of the EU regulation 2018/841 on the inclusion of greenhouse gas (GHG) emissions and removals from land use, land use change and forestry (LULUCF), the EU member states agreed that – starting with the report in 2028 – the calculation of emission pools at national level should make use of geographically-explicit data. Earth Observation (EO) can support the implementation of the regulation by providing timely, seamless and high-resolution information for monitoring land use activities and land management practices related to GHG emissions and removals.

Frequently, EU-wide mapping initiatives that make use of data from the Copernicus program and derived products provided by the Copernicus Land Monitoring Service (CLMS) are complemented by national-level approaches that usually aim at generating more tailored datasets for specific monitoring requirements. In this context, the project KlimaFern funded by the German Federal Ministry of Food and Agriculture evaluates the potential of new EO-based national datasets on agricultural land use to enhance climate reporting in the LULUCF sector for Germany. We will present preliminary results of mapping area-wide GHG-related land use activities such as crop rotations, grassland conversion or planting of hedgerows and coppices. These products are derived on a national scale using state-of-the art machine and deep learning algorithms and multi-modal satellite image time series (e.g., Sentinel-1 and 2, Landsat, PlanetScope). All products are compared against available data at national level to assess their potential for improving climate reporting and are evaluated in terms of quality, accuracy and consistency against existing and foreseen products of the CLMS.

The presentation will summarize the preliminary project results and highlight challenges for a successful implementation of EO data for monitoring obligations. Finally, it will point out synergies and relationships of climate related land use monitoring efforts with other policy initiatives at national and EU level.

The escalating incidence of food fraud on a global scale, driven by economic incentives and increased consumer demand, poses significant challenges to food integrity and safety. Food adulteration, including the deliberate substitution or addition of inferior materials to enhance appearance and profitability, has become more prevalent, particularly with the expansion of specialized food products like organic and protected geographical indication (GI) foods. Traditional detection methods entail high costs and time demands, necessitating the pursuit of alternative approaches.

The THEROS project addresses these challenges by developing an integrated toolbox to modernize the verification process for organic and GI food products, enhancing traceability, security, and transparency in the supply chain. Leveraging technologies such as Earth Observation, photonics, IoT, DNA metabarcoding, blockchain, and artificial intelligence, the project aims to detect and prevent adulteration effectively. Key features of the THEROS toolbox include advanced analytics, machine learning algorithms, and digital interfaces to streamline verification processes. Interoperability with existing control systems ensures seamless integration, while harmonized data sharing facilitates improved decision-making among stakeholders.

The project's Earth Observation-based monitoring approach, initially developed for CAP compliance monitoring, will be expanded within THEROS, to enable large-scale verification of organic farming practices. Machine learning algorithms will customize detection algorithms to identify distinct patterns indicative of organic, under-conversion, and conventional crops, enhancing monitoring accuracy throughout the growing season. Statistical approaches will facilitate intra-field analysis, verifying practices like crop rotation and biodiversity maintenance. Additionally, will extract biophysical parameters to monitor organic crop conditions and phenological stages, providing recommendations for optimal cultivation practices. Methods to assess carbon footprint will ensure organic agriculture acts as a carbon sink.

Summarizing, THEROS project embodies a multi-actor approach to combat food fraud, offering high-quality labeled food products to consumers while improving trust, traceability, and sustainability across the EU food supply chain.

Sustainable agriculture is pivotal in achieving the 2030 United Nations Agenda, ensuring zero hunger and environmental preservation. Meeting global food demand requires producing more with less, necessitating effective agricultural monitoring. Timely crop mapping is crucial for various activities, including water management, supply chain control, and crop risk assessment. Satellite remote sensing offers a reliable means for crop mapping, particularly utilizing Sentinel mission data for its temporal frequency and multi-spectral capabilities.

However, current operational products often lack the capability for operational in-season monitoring due to limited training data and complete time series observations. To address this gap, we propose an innovative "in-season mapping approach" utilizing Sentinel-2 satellite data and dynamic crop presence probability maps generated through a Deep Neural Model. Our approach provides both long-term in-season mapping and short-term dynamic mapping, offering spatially explicit information on crop sequence, management and phenological development. Our model, a fully convolutional 3D CNN incorporating Feature Pyramid Network (FPN), simultaneously extracts spectral-spatio-temporal features for identifying crop dynamics over time.

The model produces dynamic segmentation maps at each satellite passage (short-term mapping) and aggregates them to generate annual in-season maps, capturing crop presence and sequence (long-term mapping). Our contributions include developing a novel approach for in-season mapping, providing insights into when and where crops are cultivated, and offering a technique for training and evaluating short-term segmentation maps. Evaluation on multi-site, multi-season datasets in the north of Italy demonstrates the reliability and accuracy of our model, with satisfactory overall accuracy and kappa measures for both short-term and long-term predictions. By making our dataset public, we aim to facilitate reproducibility and comparison with other models, fostering advancements in agricultural monitoring and sustainable food production.

The use of plant protection products near urban areas has raised concerns about potential pesticide exposure to nearby residents and the environment. In response, this study aims to estimate the impact of pesticide bans near sensitive areas, particularly urban regions, across the European Union. Using available EO EU data on urban settlements and crops, we estimate the agricultural areas affected by the ban and analyze the relative agricultural production loss. The study utilizes the EU Crop Map 2018, Corine Land Cover (CLC) 2018, and the Global Human Settlement layer to gain a comprehensive understanding of land distribution and characteristics in proximity to sensitive areas. Results include the percentage of agricultural land within 10 meters of urban areas, highlighting country and regional differences, and identifying crops with economic significance closer to urban areas. Furthermore, the study quantifies the hypothetical impact of the ban, explores the potential reduction in sales, and estimates the theoretical amount of pesticides used in these zones. This comprehensive EU-wide analysis aims to provide valuable insights for policymakers and stakeholders, addressing the need for evidence regarding the impact of pesticide bans in sensitive areas.

The Horn of Africa is currently grappling with its worst drought in 40 years, resulting in significantly reduced agricultural productivity and food security after five consecutive failed rainfall seasons. Nearly 1.2 million people have been internally displaced due to the impact of drought on pastoral and farming livelihoods, exacerbating hunger in 2022 [1,2]. Earth Observation (EO) plays a crucial role in gathering and analyzing information related to climate drivers.

We adopt a data-driven modern approach to understand the complex processes involved (climate-migration-conflict-food insecurity) that help evaluate and adopt mitigation policies [3]. We collect climate and socio-economical data from sources and introduce a causal inference framework for Somalia to evaluate the impact of cash-based interventions on food security. Our contributions lie in leveraging the abundance of EO data to understand the dynamic system of food insecurity, where climate and socio-economic factors interact in a complex manner, and in estimating the effectiveness of humanitarian interventions in mitigating food insecurity levels. In particular, we assess the Average Treatment Effect [4] of humanitarian aid as cash transfers, on the IPC index, considering different spatial and temporal resolutions. This work ties directly with SDG-2. Assessing the causal effects of humanitarian interventions would facilitate a quantitative estimation of policies and a way to improve them in future emergencies. Results suggest that surpassing certain thresholds of cash aid has a positive effect on the level of IPC: as the number of beneficiaries increases, the level of IPC decreases.

[1] WMO. Africa suffers disproportionately from climate change, 2023.

[2] WFP. Impacts of the Cost of Inaction on WFP Food Assistance in Eastern Africa (2021 & 2022), 2023.

[3] Fyles, H., & Madramootoo, C. Key drivers of food insecurity. In Emerging technologies for promoting food security. Woodhead Publishing. 2016.

[4] Pearl, J., Causality, Cambridge University Press, 2 edition, 2009.

Mapping and characterizing cocoa planted areas with Earth Observation data and accurately disentangling them from other land cover is not only paramount for effectively monitoring and reporting on sustainability goals related with cocoa production but also for the EU Deforestation Regulation. However, accurately representing the complexity of the cocoa planted area is a challenging task. Cocoa is grown mostly on smallholder plantations with various agricultural practices, ranging from mono-cultural plantations to agroforestry systems with cocoa shaded by other trees with varying densities and spatial distribution. Here we combine a curated dataset of cocoa plot location and very high resolution (VHR; 0.5m) multispectral satellite imagery covering ∼33% of Ivory Coast area, in a deep learning framework to map cocoa. The selected deep learning model is based on a U-net architecture with efficient-netb5 encoder. To train the model, batches of tiles of 512x512 pixels were used and two sample sizes were tested: i) 221,158 and ii) 2,069,855 (full dataset) tiles. Both samples were split into 70% training and 30% validation. An independent and randomly selected VHR image (66,244ha) served as a test set. Despite the heterogeneity of cocoa plantations, our model was able to generalize well and to differentiate between cocoa and non cocoa areas accurately at this unprecedented spatial resolution. Results show that the improvement related to the use of a larger sample was limited (F1: +2.3%) and not proportionate considering the increase in training time (22h to 153h). The best performance metrics on the test set with the first (smaller) sample size gave a F1 score of 0.92 with Precision and Recall of 0.93 and 0.91 respectively. Building on the results of this study, future work will focus on the discrimination of mono-cultural cocoa from cocoa grown areas with different shade tree densities.

Sentinel-1 data enables continuous environmental monitoring regardless of weather conditions and time of day. This capability is crucial for agriculture, where timely information is essential for decision-making. The goal of this study was to explore Sentinel-1 VH and VV backscatter coefficients (σ⁰) data in providing detailed information on maize growth stages, estimation of soil moisture content (SM), and soil clay content (SC) in an agricultural area of Casalina, Italy. Specifically, correlation coefficients (r) were calculated to quantify the strength and direction of the relationship between σ⁰VH, σ⁰VV and maize height (MH), SM and SC, respectively. Satellite images (N=8) were acquired between 09^th July and 4^th September 2023. For correlating SM and SC with σ⁰VH and σ⁰VV data only images with bare soil pixels were selected (N=2) to eliminate the backscatter influence of vegetation. In-situ MH was measured from 17^th July to 04^th September, i.e. from ~20 to ~350 cm of crop height, while in-situ SM was measured on 09^th and 16^th July and varied from ~17 to ~45% (average ~26%) on both dates. In-situ SC (considered relatively stable over time) was measured in late 2018 and averaged 27-32%. The r values between MH-σ⁰VH and MH-σ⁰VV were 0.54 and 0.18, between SM-σ⁰VH and SM-σ⁰VV 0.01 and 0.08, and between SC-σ⁰VH and SC-σ⁰VV -0.26 and 0.39, respectively. MH-σ⁰VH and MH-σ⁰VV were more correlated in the period from 16^th July to 02^nd August, i.e. from ~20 to ~150 cm (r= 0.7 and 0.65, respectively), indicating a stronger association during this period. Although the relationship between SM and SC with backscatter coefficients was weaker, Sentinel-1 data could provide valuable insights into agricultural dynamics, offering farmers timely information for informed decision-making and resource management practices.

Agriculture can make a significant contribution to the reduction of atmospheric CO2 through adapted cultivation methods and actively binding CO2 through the targeted build-up of humus in the soil. In addition, more resilient farming methods such as crop rotation and the planting of catch crops contribute significantly to more sustainable food production.

To exploit this potential of carbon farming, a solution for monitoring, reporting and verification (MRV) of these measures is required. Valuable information on vegetation and soil can be derived from high-resolution, globally available Earth Observation data from the Copernicus program. These data are therefore ideally suited to enable this solution, both for past and future periods.

Within the ESA Business Application project "EO4CarbonFarming", such an MRV tool is being developed based on Copernicus data. It can monitor the growth of catch crops and verify farming measures taken to assure CO2 uptake and humus build-up in the soil. To facilitate this, high-resolution optical and radar data (Sentinel-1, Sentinel-2) are used in combination with high-quality pre-processing methods, a radiation transfer model and newly developed algorithms specifically for the derivation of humus in the soil.

The sound determination of carbon stocks in fields is an essential component of assessing the effectiveness of carbon farming for the climate and to develop business models on this basis.

For a pilot region in Austria, carbon storage in the soil has been calculated in 2017 and 2022. From these analyses, carbon sequestration is computed and evaluated on an aggregation level. Additionally, patterns of SOC content are analysed, both for the individual timesteps and the changes between them.

This project is funded within ESA Contract Number 4000134139/21/NL/MM/mr.

The primary challenge in crop mapping and monitoring lies in the absence of field-level crop labels especially in developing world, impeding the advancement of training supervised classification models. The objective of this study is to investigate the capabilities of the C-band Sentinel-1 (S1) Synthetic Aperture Radar (SAR) for developing generalized crop type models, particularly targeting the identification and monitoring of sunflower crops in Ukraine on a national scale without requring in-season field labels. Globally, the sunflower is the fourth most essential oilseed crop with Ukraine dominating as the largest producer and exporter. This study examines the interaction between S1 signal and sunflower, aiming to identify and monitor the phenological stages of sunflower. The analysis encompasses SAR backscattering coefficients and polarizations in VH, VV, and VH/VV ratio, emphasizing disparities between ascending and descending orbits attributable to sunflower's directional behavior. Utilizing the distinctive SAR-based signature of sunflower, the study presents a generalized model for the identification and mapping of sunflower fields. The model utilizing features from S1-based descending orbits demonstrated superior performance compared to that based on ascending orbits due to sunflower's directional behavior, achieving F-score of 97%, in contrast to F-score of 90% for ascending orbits. The generalized approach to map sunflower was applied to assess the impact of the Russian full-scale invasion of Ukraine. The national sunflower planted areas and corresponding changes in 2021, 2022 and 2023 were estimated using a sample-based approach for area estimation. Sunflower area was estimated at 7.10±0.45 million hectares (Mha) in 2021 which was further reduced to 6.72±0.45 Mha in 2022 representing a 5% decrease. In 2023, sunflower acreage remained relatively stable at 6.63 Mha with no significant variation. Our findings bolstering initiatives such as the collaboration between the NASA Harvest and the Ukrainian government, aimed at providing timely information for mapping major crops.

The 'European Monitoring of Biodiversity in Agricultural Landscapes' (EMBAL) is a monitoring initiative initiated by the European Commission that gathers information on the state of biodiversity in agricultural landscapes across all 27 EU member states. Developed within the EU Pollinator Monitoring Framework, EMBAL is a standardized and sample-based in-situ survey of 500x500m landscape sections (plots).

EMBAL provides comprehensive data, including general information on land use and land cover, information about landscape elements and pollination potential at parcel level as well as specific vegetation data on a transect level in grassland and arable habitats. Both the methodology and the sampling frame are harmonized with LUCAS (Land Use and Coverage Area frame Survey).

Following a successful pilot in 2020, EMBAL was applied in all 27 EU member states in 2022 and 2023, surveying a total of 3,000 selected plots in both years. This extensive rollout served to gather harmonised baseline data on biodiversity across EU27 and provided a comprehensive field test of the EMBAL methodology across different European landscapes.

In this contribution, we offer an overview of the EMBAL 2022 and 2023 rollout, the EMBAL survey methods and parameters and provide an outlook on the results.

Carbon farming is one of agriculture's answer to climate change and includes agricultural practices able to capture and store C in soils as soil organic carbon (SOC). Unlocking the potential for Carbon farming to scale relies on the establishment of robust protocols to monitor, report, and verify (MRV) changes in SOC stocks. Different MRV protocols are available in the voluntary C market, exploiting different approaches to quantify C removal. Capturing spatial and temporal variability of SOC can be challenging since SOC values varies substantially over space and changes occur slowly through time. Some of these issues can be tackled indeed by “hybrid approaches”, i.e. by combining remote sensing (RS) and process-based models with direct field measurements to verify model predictions.

In this context, project Remote-C, funded by Italian Research Ministry, aims at developing an approach to estimate changes in SOC thanks to a spatialized version of Roth-C model fed by RS products and spectroscopic readings from proximal soil sensors. The main goal is to understand if remote and proximal sensing are added values in delivering timely and spatially accurate inputs to reduce uncertainty of soil C model. Overall project scheme is given in an attached file.

To develop and test the operating MRV tools addressed in Remote-C the consortium will exploit existing test site made available by 2 EU-funded projects (PRIMA - Farms4Climate and H2020 - ClieNFarms). These farms are pioneers in testing carbon farming solutions eligible for payment schemes. RS data will be exploited to i) map biophysical variables of crops from multispectral data; ii) characterise crop residues and iii) detect tillage operations with SAR data. These variables will be ingested by a light use efficiency model (e.g. SAFY) and outputs from crop model and RS data will be exploited by a spatial modelling toolbox based on the Roth-C model.

The project is in its early stages and the workshop could be a suitable arena to discuss the proposed approach.

There is currently a lack of high-resolution pan-European information on land use management, especially in terms of how intensively and extensively grasslands are managed. This type of information is needed for economic land use modelling and for assessing policy impacts, such as the latest reforms from the Common Agricultural Policy (CAP) and other European Union Green Deal targets. Here we present the results of a grassland management map for Europe that uses the Copernicus Corine land cover for 2018 as the basis for allocating grazing livestock (obtained from statistical sources and informed by expert knowledge on national grazing) to relevant land cover types. Using different densities of livestock (calculated from statistical sources), we use a rule-based system to map ten grassland management types to the Corine land cover map at a resolution of 100 m. These include classes such as high-, moderate- and low-density pasture systems, high-, moderate- and low-density managed grassland systems, rough grazing, silva-pastural agroforestry, and managed and unmanaged semi-natural systems. The map is currently being validated using experts as well as remotely sensing products such as the frequency of mowing events, which are currently available for individual countries such as Germany. Once the Copernicus very high-resolution layer on grassland mowing event data becomes available in the latter half of 2024, these grassland management classes will be further refined using this pan-European data set.

Australian broadacre crop production covers more than 20 million hectares across various agroecological environments, from Mediterranean-type climate and poor sandy soils in the low-rainfall zones of Western Australia to tropical climate and fertile deep soils in Queensland, for example. The main crops grown in Australia are wheat, canola, and barley. Here, we will focus on wheat with an average yield gap of > 1.7 t/ha or 50% of the water-limited yield. Changes in agricultural practices, such as a shift toward earlier dry sowing and improved control of fallow weeds, have improved water use efficiency and maintained productivity despite climate change-imposed reduction in wheat potential yields in Australia. Improvements in genetic material with better and broader adapted cultivars further contribute to improved grain production. Albeit existing opportunities for further enhancements, such as more skilful and timelier management (e.g., nitrogen), some farmers are closing the yield gap, achieving potential production in their environments. However, climate change, particularly with shifts in rainfall patterns and extreme events, continues to put pressure on Australian farmers and increases their risk exposure. Thus, evaluating the impact of farmer practice on changes in the yield gap, as well as on risk and sustainability outcomes, is yet to be fully realised. Using a combination of remote sensing data, bioeconomic modelling, and grower surveys we propose a three-component framework to systematically assess the performance and impact of technology adoption at the farm level. We discuss our developments, challenges, and opportunities to align the various dimensions of yield-gap time series and its drivers at a continental scale.

In the context of the Paris Agreement and the Green Deal targets, the agricultural sector needs to be converted from a source of more than 10% of EU's greenhouse gas emissions in to a significant sink. All agricultural practices that contribute to the conservation and sustainable increase of carbon stocks in the soil are grouped together under the term carbon farming. Among these agricultural practices, cover crops appear to be one of the most promising ways of sequestering more carbon in the soil. A cover crop is a non-commercial crop planted during the fallow period when the soil is typically bare between the cash crop harvest and the following season's planting.

Agricultural practices transformation requires an operational monitoring system to assess the impact of these practices on the carbon balance of agricultural parcels. Previous studies have shown that it is possible to detect the presence or absence of cover crops using Sentinel-1 (S1) and Sentinel-2 (S2) images. The Sen4CAP system first designed for the CAP paying agency run on calendar year while the agro-ecological activities take place on a long-term basis beyond the crop rotation. This study highlights the potential of the Sen4CAP system to assess the expected impact of consecutive cover crops along with other farming practices. In order to quantify the impact of these covers on the potential carbon storage in agricultural soils, it is necessary to obtain other characteristics about the cover, such as the duration of the cover, the periods of bare soil, the green biomass and the composition of the cover. This study presents the results obtained from 6-year time series analysis of Sentinel-1 and Sentinel-2 dataset at parcel level and assessed the spatial distribution and the evolution of these farming practices at regional level.

The EOX AgriApp is a flexible dashboard service for continuous remote sensing analysis of agricultural parcels or, more generally, any monitoring areas of interest. It visually presents satellite information such as vegetation profiles of different satellite spectral signals interactively linked to image time series down to the parcel level—even allowing for comparison between parcels.

Value-added derived information such as machine learning- or threshold-based markers (crop type, harvest events, mowing events, vegetation cover changes, …) are available as chart annotations and thematic map layers. A compliance rule engine evaluates specific parcel-level requirements and provides customised thematic map visualisations.

The service is rounded off by a high-performance full-dataset search engine with dynamic filter combinations and interactive charts allowing for detailed analysis of machine learning results and compliance rule engine outcomes.

Usage areas for the EOX AgriApp include—but are not restricted to—Common Agricultural Policy implementation, drought- and irrigation monitoring, CO2 flux visualisation and quantification, nature capital and biodiversity monitoring. In the context of the CAP Area Monitoring System the EOX AgriApp has successfully been in operative use within the Austrian and Irish Paying Agencies since the beginning of 2023, serving near real-time insights for millions of parcels.

During 2024, the Copernicus Land Monitoring Service will start releasing the products of the new High Resolution Layer Vegetation Land Cover Characteristics (HRL-VLCC) as part of the Copernicus Land Monitoring Service (CLMS). The HRL-VLCC groups the annual mapping of vegetated land cover characteristics, jointly producing for the first time the former HRL Forest and HRL Grassland together with the new HRL Crops. Within the latter, mapping of major crop types (HRL Crop Type) and crop management practices relevant for agricultural monitoring (that contain layers depicting date of emergence and harvest for main and secondary crops; bare soil duration; fallow land; and cover crops) are included, while giving continuity and enhancing the already existing layers.

With access to the pre-release of the 2017 to 2021 HRL Crop Type products, we propose an evaluation through a comparison with annual farmers’ declarations. For this purpose, we utilize publicly available GeoSpatial Application (GSA) datasets from eight countries and four years with harmonized crop types merged into a single database named CHEAP (Common Harmonized European Agricultural Parcels). Only parcels and points not used in the model's training set were included in the analysis. The validation process employs classical statistical measures to quantify user and producer accuracies, as well as f1-scores per crop, region, and year. Additionally, insights into the spatial consistency of the products are provided.

Preliminary results assessing the major crop types (including wheat, maize, rapeseed, barley, sunflower, sugar beet, and potatoes) underscore a suitable quality of the products. Independent and thorough quality assessments such as those provided here stand to benefit the uptake and impact of Copernicus Earth Observation products. This quality assessment underpins the future developments of applications using the HRL Crop Types in the domain of crop production assessments, indicator development, and land use assessments.

The high resolution (10m) imagery of the Copernicus Sentinel-2 (S2) sensor leads to a consistent, continuous, high quality and near-instantaneous Earth Observation (EO) information flow. This information can be leveraged to predict and map crop masks and crop type cultivations over a vast AOI within a growing season. In order for such maps to be suitable for crop statistics and food security assessments they need to be of high quality and supported with accuracy assessments. In this contribution we present the steps and workflow to derive crop type maps that are at the core of the Copernicus4GEOGLAM Service activated in Tanzania and Kenya under challenging circumstances with small fields and frequent mixed-cropping.

Firstly a grid based stratified systematic random sampling is applied which takes into account variability in the AOI like agro-ecological zones , elevation and landcover types including irrigation use . VHR imagery and S2 timeseries are used to digitize these samples to be visited by enumerators in the growing season. Field information like cropping pattern, crop type and crop stage is stored in digital forms and send to a database. After a quality assessment the digitized samples are used to train (75%) and validate (25%) classifiers in the IOTA2-toolbox . The classifier is run on timeseries of S2 L3A data (synthesis data of unclouded/undisturbed pixels closest to a centre-date of a month) covering the growing season. The Random Forest classifier yielded highest accuracy with up to 89% overall accuracies for crop masks and croptype maps during different seasons in both countries AOIs.

Utilizing Earth Observation (EO) and Machine Learning (ML) to automate Soil Organic Carbon (SOC) monitoring marks a significant advancement for food security and Sustainable agriculture aligning with the United Nations’ Sustainable Development Goal 2 for zero hunger. In Flanders Belgium, a comprehensive methodology is developed to explore the spatial and temporal content of an extensive collection of over 8000 sentinel 2 images on 680000 hectares of farmland. The scope of the current study is to develop valuable soil health indicators, in support of The Common Agricultural Policy (CAP). This developed methodology combines satellite products from the Copernicus services with precise soil measurements to deploy EO-based ML models for predicting SOC levels through time. The large-scale soil quality data products developed, covered all Flanders, facilitate monitoring that underpins the CAP providing detailed insights at both pixel and parcel level. This approach simplifies the creation of soil quality maps showcasing SOC values relative to average conditions and taking into consideration soil-pedoclimatic factors thus enabling targeted soil health interventions. Such detailed classifications are crucial for the effective management of soil health in Flemish croplands. Current research is focused on improving soil health EO-based evaluation using advanced technologies like sensor data analysis edge computing and Federated AI while ensuring semantic interoperability for improvement. Current efforts are trying to tackle data-sharing challenges and, the ability to integrate IoT sensors and hyperspectral satellite images.
The presented methodological framework addresses the requirements and complexities inherent in soil health and agricultural sustainability and investigates how those research priorities can be aligned with the United Nations’ Sustainable Development Goal 2.

High resolution satellite data and advanced machine learning are used to map key commodity crops, namely cacao, oil palm, rubber, coffee, pasture, and soy, with a primary goal of understanding their spatial distribution and quantifying associated carbon emissions. Such information is relevant for policies related deforestation-free supply chains (i.e. EUDR) and climate change mitigation efforts (i.e. REDD+). Our study aims to develop a robust and accurate mapping framework for major commodity crops across tropical regions, leveraging the power of machine learning algorithms. We seek to provide a comprehensive assessment of land-use changes associated with these crops and estimate their carbon emissions footprint. Utilizing high-resolution Sentinel and Planet satellite imagery and ground truth data, we employ state-of-the-art machine learning algorithms, including convolutional neural networks (CNNs) and location encoding methods. The algorithms are trained on a diverse dataset encompassing various environmental conditions and cropping systems. This allows us to achieve a nuanced understanding of the spatial and temporal patterns associated with each commodity crop. The presentation will cover the following components:

1. Cacao, Oil Palm, Rubber, Coffee, Pasture, and Soy Mapping: The machine learning models are tailored to accurately classify land cover types, emphasizing the identified commodity crops.
2. Carbon Emissions Estimation: We integrate ancillary data, including climate variables, and land-use change, to estimate carbon emissions associated with land-use changes driven by the cultivation of commodity crops.
3. Spatial and Temporal Dynamics: Our analysis explores the spatial and temporal dynamics of commodity crop expansion, providing insights into patterns of deforestation, and land-use transitions.

We anticipate that our study will yield high-resolution maps depicting the spatial distribution of commodity crops across the tropics and associated carbon emissions linked to these commodity crops. The works aligns with recent European Union(EU) regulations to curb the EU market’s impact on global deforestation and provides valuable information for monitoring land use following deforestation, crucial for environmental initiatives and carbon neutrality goals.

References:

Masolele, R. N., Marcos, D., de Sy, N., Abu, I.-O., Verbesselt, J., Reiche, J., & Herold, M. (2024). Mapping the diversity of land uses following deforestation across Africa. Scientific Reports, 14, Article 1681. https://doi.org/10.1038/s41598-024-52138-9

Masolele, R. N., De Sy, V., Marcos, D., Verbesselt, J., Gieseke, F., Mulatu, K. A., Moges, Y., Sebrala, H., Martius, C., & Herold, M. (2022). Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia. GIScience & Remote Sensing, 59(1), 1446-1472. https://doi.org/10.1080/15481603.2022.2115619

Masolele, R. N., De Sy, V., Herold, M., Marcos, D., Verbesselt, J., Gieseke, F., Mullissa, A. G., & Martius, C. (2021). Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series. Remote Sensing of Environment, 264, Article 112600. https://doi.org/10.1016/j.rse.2021.112600

The Copernicus4GEOGLAM service was established in 2021 as part of the Copernicus Global Land Service (CGLS). It aims at supporting agricultural monitoring at national/sub-national level in countries with high food insecurity. The service can be activated upon request by a country to the GEOGLAM¹ secretariat and to the EC-Joint Research Centre (JRC). The service products consist of crop type maps and crop area statistics which complement other CGLS. Products are derived over an Area Of Interest (AOI) in the country during (in-season) and at the end of the growing season. The service allows to timely detect anomalies in the crop areas and supports agricultural and food security decision making. Crop type mapping is based on Sentinel-2 imagery using a random forest classifier that is trained and validated with data from dedicated field campaigns. Stratified random sampling is applied to identify the sample areas and ensure data are statistically sound and reflect the different crop classes in the AOI.

Since 2021 the service has been activated in Uganda, Kenya and Tanzania with AOIs ranging from about 100k km² to almost 250k km². Crop maps accuracy can reach 80%, but crop specific accuracy tends to vary, with omission errors usually higher than commission errors.

The Ministries of Agriculture relies on Copernicus4GEOGLAM products to complement existing crop statistics that are often obsolete or only available for few administrative areas. Besides, knowledge on crops spatial distribution is needed to implement fertilizers programmes at national level, crop insurance mechanisms and yield forecasts.

EC-JRC supports the use of these products in the countries and distributes the in-situ data to the community working on other LULC mapping programmes, like WorldCereal and the upcoming Copernicus Global Land Cover and Tropical Forest Mapping and Monitoring. Feedback from users helped JRC improve the quality of the in-situ datasets.

The Combat Against Climate Change on Cotton Communities (C5) feasibility study created a prototype agroclimatic advisory solution. This ESA EO Science for Society activity supports farmers and the cotton industry in Bangladesh by providing information on climatic issues affecting cotton-grower health.

The health-related output products tackle the physiological stress of heat exposure, and were split into forecast and historic components based on user needs. The forecast system was designed for farmers. It downloads forecast data, calculates the Heat Index, and generates district-level summary statistics and visualisations. Tailored health advisories are prepared based on this data, and all information is disseminated fortnightly in bulletins via agricultural extension workers. The infrastructure is capable of warning about dangerously hot and cold conditions.

The historic component was designed for industry officials and used custom-generated wet bulb globe temperature (WBGT) data. The prevalence of hazardous temperature events were calculated from long-term WBGTs. A “lost labour” dataset was also derived: work-rest guidelines recommend increased rest at higher WBGTs to reduce overheating risk. Applying work rest guidelines to WBGTs calculates the amount of time outdoor workers cannot work and stay safe – a metric with economic and social implications.

C5 datasets make use of I) up-to-date cotton crop area mapping to ensure they are linked to current production areas; and II) vegetation health derived from standardise LAI anomalies to assess current crop condition. The C5 cotton crop map utilises Sentinel-1 and Sentinel-2 data. Multi-temporal surface reflectance cloud-free composites for the dry and wet seasons in Bangladesh capture phenological changes in the crop associated with changes in canopy chlorophyll content. Sentinel-1 monthly VH backscatter composites characterise changes in canopy structure. These multitemporal composites are use as features to generate a Machine Learning model using cotton farm locations as training data, and result in an annual Bangladesh cotton map.

Intensification of agriculture is causing the decline of segetal flora species, with resulting negative ecological impacts, such as increased soil erosion and food and habitat loss for animals. One way to promote the conservation of such plant species is through result-based payment schemes that reward farmers based on observed biodiversity outcomes in their fields, but cost and time required for the monitoring hampers a more widespread implementation of such schemes. Automated monitoring of segetal flora species is particularly challenging due to their small sizes and partly overlapping spectral signatures. Using the latest advances in deep learning, we investigated the potential of UAVs for segetal flora species monitoring by focusing on an arable area in a UNESCO biosphere reserve in Saxony, Germany, and evaluated the usage of different UAV sensors to disentangle the different plant species. The presentation will focus on opportunities and challenges in segetal flora species monitoring via UAV with particular emphasis on: i) species for which training data can easily be developed from RGB images, ii) sensor and flight height maximizing the classification accuracy, iii) difficult to map species, and iv) potential for result-based payment schemes for other species that were not observed in our study area, but that are of interest for the implementation of such schemes in Europe.­

Accurate and spatially explicit information on irrigation is crucial for sustainable water resource management, crop condition monitoring, and modeling. Although progress has been made in irrigation mapping using remotely sensed data, field-level irrigation mapping in temperate regions remains challenging, as most studies have focused on semiarid regions. In this study, we assessed the applicability of different time series for irrigation mapping, utilizing Sentinel-2, Sentinel-1 time series, and Landsat-based Land Surface Temperature (LST) data over northern Germany. This area is characterized by heterogeneous field sizes, crop patterns, irrigation systems, and management practices. An extensive amount of field-scale irrigation data reported by farmers was collected and used as a reference for model training and validation. The derived Vegetation Indices (VIs), Tasselled Cap components, and LST were aggregated over the growing season and specific key phenological stages. Subsequently, irrigated areas were classified using Random Forest (RF) and gradient boosting (XGBoost)-based classifiers. The overall accuracy achieved satisfactory levels (approximately 80%). The performance varied across different regions and showed the significance of the availability of observations during the growing season, with the most important variables listed as LST, optical-based VIs, and Sentinel-1 based metrics for specific crops such as maize. The synergistic use of optical, radar, and LST data significantly enhanced the classification accuracy, demonstrating the potential of integrating these data sources for improved irrigation mapping in temperate regions. In combination with process-based agro-ecosystem models, such as the simulation model for nitrogen and carbon dynamics in agro-ecosystems (MONICA), a map of irrigated/non-irrigated fields not only allows for more accurate seasonal crop yield predictions for the respective area but also opens a path towards the quantification of water use for irrigation. This integration showcases the enhanced utility of the mapping for sustainable agriculture and water resource management.

With the outbreak of conflict in Ukraine, uncertainty in the amount of commodities available to the international market has greatly increased. Ukraine itself is a major exporter of grain to the world, accounting for 12% of wheat and 16% of maize sold on the commodity market in 2019. Additional uncertainty comes from potential changes in global trade relations with Russia, who also provide significant exports to the global markets. Understanding changes in cropped area in Ukraine due to ongoing active conflict, as well as changes in overall productivity due to supply chain constraints resulting from cessation of commercial shipping and transportation can be done with EO data. Amid continuing conflict, Ukraine’s agricultural sector could produce significantly less than previous years with global consequences due to these constraints. This project focuses on using EO tools to pair very large agricultural units with nearby small farms of 10 to 100 hectares. Targeting soy, sunflower, barley, wheat and maize, we generated in-season crop classification models and production estimates based on Sentinel imagery. We used these maps to target small farms with signficant yield gaps. Identifying willing large farming institutions, we will work with input supply and financing support from companies such as Syngenta to offer favorable terms to large and surrounding small farms to facilitate accelerated yield increases. Quatitative baselines from production maps will enable appropriate targeting of these inputs and monitor the success of the program.

In this study, we demonstrate a novel remote sensing approach for early- and in-season crop type mapping across diverse geographical contexts, utilizing multi-temporal Sentinel-2 imagery. The method used the analysis of time series Normalized Difference Vegetation Index (NDVI) data, integrated with a rule-based classification system that aligns with the specific phenological stages of various crops as per their respective agricultural calendars. This approach has been effectively applied by NSL Geospatial unit of FAO, in distinct regions such as Libya, for mapping crops like dates, olives, barley, and wheat, and in Myanmar, for identifying major agricultural categories including rice, maize, pulses, oilseed, sesame, sorghum, and others. By leveraging NDVI's sensitivity to phenological changes and synchronizing it with established crop calendars, our methodology eliminates the reliance on current-year ground truth data, enabling accurate, cost-effective, and timely crop classification. This approach not only demonstrates significant potential for enhancing agricultural monitoring and management globally but also exemplifies the adaptability of NDVI-based analysis in varied agricultural settings.

In this study, we demonstrate a novel remote sensing approach for early- and in-season crop type mapping across diverse geographical contexts, utilizing multi-temporal Sentinel-2 imagery. The method used the analysis of time series Normalized Difference Vegetation Index (NDVI) data, integrated with a rule-based classification system that aligns with the specific phenological stages of various crops as per their respective agricultural calendars. This approach has been effectively applied NSL Geospatial unit of FAO, in distinct regions such as Libya, for mapping crops like dates, olives, barley, and wheat, and in Myanmar, for identifying major agricultural categories including rice, maize, pulses, oilseed, sesame, sorghum, and others. By leveraging NDVI's sensitivity to phenological changes and synchronizing it with established crop calendars, our methodology eliminates the reliance on current-year ground truth data, enabling accurate, cost-effective, and timely crop classification. This approach not only demonstrates significant potential for enhancing agricultural monitoring and management globally but also exemplifies the adaptability of NDVI-based analysis in varied agricultural settings.

West Africa is facing two major challenges of the 21^st century: climate change and population growth. Both are closely linked to food security in the region. Rising temperatures and increasingly variable precipitation threaten traditional rain-fed agriculture relying on the rainy season. Furthermore, West Africa has one of the highest population growth rates in the world, its population will increase to 1.2 billion people by 2050. To guarantee sufficient food supply and to achieve the Sustainable Development Goals (SDG), a sustainable intensification of agriculture is needed (i.e., increasing yields without additional land consumption and without adverse effects on climate change) and mitigation and adaption strategies against the negative effects of climate change are required. Sustainable intensification (SI) practices offer the opportunity to stabilize/increase yields and to operate more resource-efficiently. The implementation of SI practices are tasks of the farmers but incentives must be created to accompany the process to enable investments or bridge short term losses. Therefore, a monitoring system is needed. The monitoring of the implemented SI practices is time consuming, costly and over large areas not feasible. Remote sensing has proven to be a suitable instrument to monitor agriculture area and the management strategies in a reliable and cost-effective way. This study in the Senegal River Valley shows the potential of remote sensing to feed a monitoring system on field scale. To address the lack of field data, we trained a convolutional neural network (CNN) to delineate field boundaries from Planet data and monitor agricultural management on field level. The agricultural management like sowing and harvesting dates or irrigation and flooding events are identified using change detection in Sentinel-1 time series. Those data can be used in Manobi Africa’s agCelerant platform rewarding farmers implementation of SI practices and to link farmers with financial institutes like banks and insurance companies.

Satellites capture an extensive amount of data daily, resulting in an ever-growing collection of Earth Observation (EO) data. Despite the availability of this data, there are still challenges when it comes to extracting relevant information from long time-series data streams. The recently finished ESA’s AI4FOOD project aimed to address these challenges, particularly in data fusion and advanced time series analytics within cropland monitoring applications.

AI4FOOD focused on advanced Machine Learning (ML) techniques to develop new algorithms for the creation of continuous optical and radar data streams. Specifically, the project focused on the fusion of Sentinel-2 and Sentinel-1 data and evaluating aspects such as the predictability of time series in dynamic land environments. This collaborative effort was achieved with partners from VITO, Sinergise, and the University of Valencia. The undertaken activities led to the development of the FuseTS toolbox – an open-source toolbox supporting users in complex data fusion and time series analytics tasks.

FuseTS is created based on the close collaboration between project stakeholders and partners. Using their requirements, state-of-the-art ML algorithms for data fusion and time series analytics were integrated into the final toolbox. The resulting Python library, available on GitHub, provides a solid foundation for data fusion and time series analytics. It offers essential data fusion, gap-filling, and smoothing services, such as Whittaker and Multi-Output Gaussian Process Regression (MOGPR). Additionally, FuseTS provides functions to extract valuable insights from the data fusion pipeline by detecting peaks and valleys and extracting vegetation phenology metrics. The FuseTS library enables the seamless execution of the offered algorithms on both local xarray data structures and through openEO, a community standard for EO processing. In this presentation, we will present the main functioning of FuseTS, as well as provide examples of cropland monitoring applications, such as fusion, gap-filling and start- and end-of-season detection.

Agriculture and food security face multiple challenges, at global scale, but also in Europe. Population growth and changing dietary habits lead to increasing demands for food production, and at the same time, agricultural products are required by the bioenergy market. Furthermore, climate change and the decline natural assets ask for sustainability and adaptation measures in agriculture. For supporting knowledge-based decisions in this context, Earth observation can contribute data and information products.

In this poster contribution, we present data products and workflows on crop type and rotation mapping as well as early crop type detection for Germany. Time series of Sentinel-2 and Sentinel-1 data are used in combination with LPIS (Land Parcel Identification System) reference data and machine learning techniques to retrospectively map seventeen crop types over Germany at an annual basis. From these multi-annual datasets, we delineate maps of crop rotation including pixel-based reliability information. Further, the potential of early within-season detection of individual crops and groups of crop types is systematically tested based on the classification chain and additional Sentinel time series analyses. The results show good potential for early detection of key crops (e.g. rapeseed and maize), but with a high variability in detection accuracies between the full range of the seventeen considered crop types. The presented data products will be used within mobile and web Apps developed by our partners (DLR-DW and University of Wuerzburg) for farmers and agricultural consultants within the project Agrisens DEMMIN 4.0 (funded by the German Ministry of Food and Agriculture).

Ukraine is a breadbasket cereals and oilseeds producer and exporter. In 2021, Ukrainian farmers planted winter crops, ignoring that a full-scale invasion by Russian forces would start a few months later. Consecutive to the invasion on February 24th, 2022, the occupant took control of about one third of the country, including some of the most productive agricultural regions. Immediate concern around how much winter and summer crops would be planted arose. This work details how the NASA Harvest team for Rapid Agricultural Assessments for Policy Support (RAAPS), (a) leveraged bi-weekly three-meter spatial resolution Planet composites for building in-season crop type maps for 2022 and 2023 and (b) estimated unbiased planted areas both for free and occupied territories.

Based on clustering approaches and domain knowledge, cropland was split into winter crops and potential summer crops. Then, winter cereals and rapeseed were separated. Finally, residual cropland was either classified as summer crop or as fallow land.

Unbiased area and accuracy estimates resulted in overall accuracies of 90+-3% in free territories and 81 +- 4% in occupied territories in 2022. In 2023, overall accuracies were 88+-2% for free territories and 77+-3% for occupied territories. As of mid-July 2022, 20.5% of Ukraine's cropland was under occupation against 16.03% at the same time in 2023. A detailed assessment of changes in planted areas per crop type, between 2022 and 2023, will be presented at the workshop. This work demonstrated the importance of coupling remote sensing and domain knowledge for mapping major crops and deriving statistical information, in situations where no ground data is available.

The Democratic Republic of Congo (DRC) is the fourth poorest country globally and has a rapidly increasing population. Congolese livelihood depends on small-holder farmers production. The historically unstable Masisi and Rutshuru regions in Eastern DRC, have seen a resurgence of conflict since the end of 2021. Civilian people displacements have been observed since then. The NASA Harvest team for Rapid Agricultural Assessments for Policy Support has investigated whether people displacement leads to cropland abandonment, which might in turn lead to increased food insecurity.

Given cropland abandonment is defined by previously active cropland being left non-cultivated, yearly active cropland maps had to be produced and inter-compared. Leveraging Planet three-meter spatial resolution and bi-weekly temporal resolution composites, yearly season B (February to July) active cropland maps were generated from 2021 to 2023. For each year, training data was collected in a pseudo random manner. First, a 10*10 km grid covering the Masisi and Rutshuru regions was generated. Then, a random point was placed within each grid cell. Cropland and non-cropland samples were collected in the vicinity of each grid point, through satellite image time series annotation. Active cropland was mapped using a random forest classifier. Time series of Normalized Difference Vegetation Index, Enhanced Vegetation Index and Soil Brightness Index were used as independent variables. In order to avoid confusions between cropland abandonment and common fallow practices, active cropland proportion was computed on a 500*500 meter grid. The assumption was that, at this level, the proportion of active cropland should be stable from year to year, regardless of fallow practices.

Preliminary results seem to indicate no significant trend towards cropland abandonment in the vicinity of conflict hotspots. However, by the time of submitting this abstract, this is still a work in progress. Statistically conclusive results will be shared at the workshop.

Understanding the effects of different compost-derived organomineral fertilizers

enriched with nitrogen (N) and phosphorus (P) treatments on wheat yield and

protein content is essential for optimizing agricultural productivity while ensuring

nutrient-rich products with low environmental impact. The emergence of

commercial satellite constellations, due to their high spatial and temporal

resolution, provides new opportunities for monitoring and forecasting crop yields,

supporting informed and timely decisions to improve food security.

Our study aims to explore the use of PlanetScope imagery to retrieve wheat yield

and protein content in response to pelletized organomineral fertilizers applications.

Eleven field plots (24 m2) were treated with different P (at sowing) and N (at

tillering) fertilizations strategies, from conventional to organic, with three

replications (n=33) in the EEA Aula Dei CSIC (Zaragoza, Spain) in 2023. Field

measurements of N uptake efficiency were made during the crop cycle. At harvest,

yield and protein were measured for each plot.

Using Planet Scope, we detected treatment differences in the spectral bands and

obtained phenological metrics with error in less than 8 days. Distinct separability

was found after the N application in the NIR band and using NDVI. Plots treated with

organomineral fertilization showed higher yields (p < 0.001) and protein content.

We calibrated the Agriculture Remotely-sensed Yield Algorithm (ARYA) model to

examine its capabilities of yield predictions at high resolution, and a generalized

linear model to forecast protein content. The results showed a linear correlation of

(X, Y R2) and A, B RMSE per yield and protein content, respectively.

This study demonstrates the utility of Planet Scope for monitoring wheat yield under

different treatments and improving nutrient management strategies for sustainable

agriculture aligned with the European Green Deal objectives.

According to the Food and Agriculture Organization (FAO), agriculture accounts for 70 percent of global freshwater withdrawals. As agricultural water management is key for sustaining food security, accurate soil moisture monitoring is crucial for mitigating the impacts of drought on crop production.

The satellite Sentinel-1 with the active microwave remote sensing Synthetic Aperture Radar (SAR) imaging has emerged as an effective tool to estimate surface soil moisture. This EO technology shows great potential for high temporal and spatial resolution soil moisture monitoring across agricultural landscapes. Therefore, the is an amazing opportunity for mapping the spatial and temporal soil moisture dynamics.

However, for data calibration and validation, ground-truth measurement of soil moisture is required ensuring the accuracy and reliability of remote sensing-derived soil moisture estimates.

There are different techniques for in-situ soil moisture estimation, but they are mostly at the point scale, which can make it challenging to use them for remote sensing calibration and validation.

Recently, the Cosmic Ray Neutron Sensor (CRNS) which is an in situ nuclear technology shows the capability to estimate field-scale soil moisture in large areas of up to 20 ha and has demonstrated its ability to support agricultural water management. The need for in-situ data, such as that provided by CRNS lies in its ability to offer ground-truth measurements of soil moisture at landscape level.

The integration of in-situ CRNS soil moisture and EO data facilitates not only the validation and calibration of remote sensing imagery but also provides real-time soil moisture information valuable for implementing climate-smart irrigation strategies, contributing to a sustainable food production initiative.

This approach represents a significant advancement in soil moisture monitoring by combining remote sensing with nuclear technology.

Advances in field-based plant phenotyping, ranging from low-cost handheld devices to extensive satellite imagery, are opening new avenues for understanding and optimising plant responses to environmental factors. The scientific challenge is to scale-up such observational capabilities from in-orbit systems. The present study aims to characterise the within-field spatial and temporal variability of environmental conditions affecting crop growth. After pre-processing the Sentinel-2 data with the open source system Sen4Stat, the Green Area Index (GAI) was retrieved by inversion of a radiative transfer model, i.e. a locally tuned BV-Net algorithm. For each year of Sentinel-2 acquisition (2017-2023) and each parcel of the study area (Wallonia), the GAI time series was used to (1) infer the growing season of the main crop and (2) produce a spatial indicator of vegetation growth heterogeneity based on this in-season time series. The resulting maps effectively capture the homogeneity or heterogeneity observed in the GAI profiles. The evaluation of the seasonal maps included an analysis of agronomic factors such as crop rotation, agrometeorological data, soil maps and slope, which helped to interpret the sources of heterogeneity within fields. Despite significant variation in the seasonal maps between years, discernible patterns emerged, highlighting similarities in conditions or crops between years. An important finding is that certain fields, identified as spatially homogeneous on the basis of soil characteristics, exhibit heterogeneity in vegetation growth. Conversely, fields that appear to have strong spatial heterogeneity based on the soil map may either have a fair degree of homogeneity in vegetation growth, or a pattern of heterogeneity that differs from the soil map. The versatility of the method extends its applicability to different agricultural settings and to any type of crop. The resulting maps could guide dynamic agricultural practices towards greater sustainability, including irrigation, fertilisation and spraying management, and could also provide opportunities for new soil sampling designs and targeted in-field phenotyping.

Monitoring and preserving biodiversity in mountain agroecosystems are critically important because these systems provide several services such as carbon sequestration, food and wood supply, as well as offering habitat diversity for different species. For example, extensively managed alpine pastures in South Tyrol, Italy, host rare and endemic plant species and represent one of the few examples of traditional management in the area. Remote sensing techniques can provide useful information on biodiversity over large areas. In this context, we aim to explore the promising potential of the hyperspectral sensor on the Italian Space Agency's PRISMA mission, providing data in 239 spectral bands.

In this study, we exploit PRISMA images to estimate species diversity in the grasslands of the Sciliar Natural Park in South Tyrol (Italy). Specifically, we verify the existence of a direct relationship between remotely sensed reflectance values and species diversity derived from field data by using the Spectral Variation Hypothesis. According to this hypothesis, areas showing pronounced spectral variation in an image are often indicative of high environmental heterogeneity, thus serving as a powerful indicator to estimate species diversity. A field data collection campaign was carried out during summer 2023 to quantify species diversity indices, e.g., the Shannon Diversity Index and the Pielou´s Evenness Index. From two PRISMA images, acquired during the summer and autumn of 2023, spectral diversity indices like the Rao's Q Index were calculated. To verify the relationship between in situ and remotely sensed data, we compare the satellite-based spectral variation with the ground-based species diversity by regression analysis. Results provide interesting insights into the strengths of PRISMA hyperspectral data, such as spectral resolution, and their limitations, such as low spatial resolution and availability of images.

Companies are increasingly under pressure to address their environmental and social impacts, including in their supply chain. Many stakeholders, including customers, investors and regulators are demanding greater transparency and accountability in regards to environmental performance. In this context, monitoring the environmental impacts of a company’s supply chain is essential for ensuring compliance, reducing risk and enhancing sustainability. Given the urgent need for companies to take action to evaluate, plan and mitigate environmental impacts, LandGriffon fills an essential gap enabling companies to act in environments of limited information.

LandGriffon, developed under a Horizon Europe project, is inspired by the need to move beyond life-cycle assessment approaches and provide robust spatially explicit information on agricultural supply chain impacts. It addresses the challenge of a lack of traceability by providing a framework for companies to spatialize agricultural supply chain knowledge and evaluate impacts across a range of indicators, including, land footprint, greenhouse gas emissions, land conversion, water use and pollution and biodiversity, as accurately as possible.

In this presentation, we will introduce the Landgriffon methodology (https://landgriffon.com/) and demonstrate its application to company supply chain data to align with forthcoming regulations and commitments, including EU deforestation regulation, Science Based Targets Network and the Taskforce for Nature Related Financial Disclosures. However, given the scale and complexity of agricultural supply chains, there are considerable uncertainties and limitations associated with data and methods. We will also highlight these gaps and aim to stimulate a community of practice to improve our capabilities in a coordinated way, because collaboration and openness will be critical to achieving real improvements in the sustainability of agricultural supply chains. We hope that LandGriffon can be a tool around which this could be achieved and so help drive more positive futures for society and nature.

Consistent Rice Information for Sustainable Policy (CRISP) is a 2-years ESA funded project that aims to address Indicator SDG 2.4.1, which measures the proportion of agricultural land area under productive and sustainable agriculture. This initiative, in collaboration with FAO and a selected group of Early Adopters (EA), endeavors to contribute in the achievement of sustainable food production systems and resilient agricultural practices by 2030.

CRISP focuses on scaling up advanced and cost-effective Earth Observation (EO) solutions to provide crucial information on seasonal rice planted area, growing conditions, yield forecast, and production at harvest. The project adopts a user-oriented approach, recognizing the importance of active users' involvement in introducing and understanding the proposed solutions.

At this stage, needs have already been collected and translated into the most valuable products for EAs. Similarly, most relevant Test Sites were identified and the focus is in addressing the best practices in EO technology. CRISP leverages existing operational rice area-yield services, such as RIICE (Remote Sensing based Information and Insurance for Crops in emerging Economies) to serve as a foundation for the solution development that once operational, will offer a comprehensive suite of tools designed to facilitate the generation of products on a global scale.

This approach of active involvement not only educates Early Adopters on the capabilities and limitations of the proposed solutions but also ensures to meet realistic and feasible requirements, leading to successful service endorsement. The CRISP Project leverages EO Platform as a Service cloud native technologies provided by the CGI Insula platform to address large processing in a cost effective manner.

CRISP's methodology involves a thorough review of EO best practices, experimental algorithm evaluation, and the use of multi-mission EO systems, including Sentinel-1, Sentinel-2, PlanetScope, and forthcoming NISAR, to ensure the provision of a robust and scalable EO solution.

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<p>Dragonfly is an innovative farming application that offers farmers a comprehensive set of tools to manage their farms effectively, starting from seeding to harvesting. Through the processing and analysis of satellite imagery, Dragonfly provides farmers with insights and information that allow them to optimize their use of nitrogen fertilizer in plots. With Dragonfly, farmers can reduce costs, increase yields, and promote sustainability by making the best use of their resources.</p>

The world is changing due to global warming and the need for accurate worldwide temperature monitoring has never been more important.

LST (Land Surface Temperature) plays a decisive role not only when it comes to an optimized irrigation management within the agricultural industry, as the world faces increasing water scarcity and the need for food security. Implementing sustainable water management and monitoring plant stress and health is also crucial within the forest sector as the demand for wood and paper continues to grow. Not only are both agriculture and forestry challenged by water scarcity, but the risk of wildfires is rising with global warming, posing a fundamental threat to both. By measuring LST and analysing further processed data such as ET (Evapotranspiration), more insight can be gained with regard to employing predictive analytics for irrigation scheduling, yield estimation, frost and heat event prediction.

To provide the necessary information and therefore be able to act and react, OroraTech has already 2 working sensors in orbit, delivering data reliably (FOREST-I and II). It is equipped with a single Mid-Wave Infrared (MWIR) and two Long-Wave Infrared (LWIR) sensors that scan our planet every day. To increase the revisit rate and overcome the difficulties of cloud cover, OroraTech's ambitious schedule foresees 8 satellites operating in orbit by 2025 (12 hour revisit) and up to 100 sensors by 2027 (30 minute revisit) in lower earth orbit.

From 2025, OroraTech's constellation will be able to monitor the diurnal cycle of temperature, providing valuable data on day and night temperature fluctuations that are crucial for assessing plant stress. The satellite's on-board processing capabilities, which allow for real-time data analysis, will enable even faster responses to detected anomalies.

With a swath of 410 km, large areas can be covered at the same time. This and a GSD of 200m (super resolution 70m) will provide the capability to detect timely critical land surface temperature changes, complementing larger missions with data that fill the gap between Trishna's or LSTMs overpasses.

The FAO has developed the WAPOR database, a publicly accessible, near real time database containing evaporation, transpiration, interception and net primary production maps at different scales, using data gathered by a range of satellites including, among others, Sentinel-2 and meteorological data from ERA5. This database is the backbone of the WaPOR project that, now in its second phase, works with ten partner countries to build their capacity in the use of WaPOR data for its different applications, and to generate solutions to local challenges linked to water and land productivity as well as water management.

WaPOR Accounter bridges the gap between this database and end-users, by enabling them to quickly and easily convert its data into actionable information at two different scales.

At field-scale, users can select agricultural fields and instantly see WaPOR data linked to the selected fields. The app helps users to identify crop seasons for the selected fields and aggregates data accordingly, allowing comparisons of water consumption, biomass production and water productivity between neighbouring farms and/or between seasons. Additionally, users are able to compare a fields water consumption to its optimal consumption, helping them identify water-stressed or waterlogged fields.

At the basin-scale, the app lets users see a basins water-balance and related statistics and indicators, such as the basins exploitable water, utilized flows and water consumption per landuse class.

ARIES is exploring the potential of spaceborne hyperspectral data (PRISMA and EnMap) to address water management and food security in Africa. Within the project prototype EO products are being created and important recommendations for the design of future Copernicus missions (CHIME) are being made. The product design process takes place in close collaboration with several African Partners from Southern, Western and Eastern Africa in order to ensure usefulness and applicability as well as knowledge and capacity transfer.

Getting accurate and timely information on plant water content has been identified as one of the main challenges across the continent, especially for farmers. Plant Water, Leaf Area Index and Canopy Water have thus been chosen as key information products to be produced within the project. We are deriving these, using the agricultural applications available within the free and open source QGIS plugin EnMap Toolbox, which is also implemented on the FS-TEP platform, thus allowing access to the chosen approaches for anyone at any time even after the project lifetime.

The parameters are retrieved from EnMap and PRISMA data collected from March 2023 onwards over six different test sites located in Senegal, Mali, Niger and Zambia, using a dual approach that relies on radiative transfer modelling (PROSAIL) for leaf area retrieval and on canopy water retrieval based on a water absorption feature between 930 and 1060 nm of the electromagnetic spectrum (Wocher et al. 2018). Combining the two products, plant water content can be calculated.

During the workshop we would like to present our approach and results from our test site in Zambia, where knowledge about plant water is essential for decision making with regards to irrigation and harvest.

The project runs from September 2022 until September 2024 and is funded by ESA (ESA Contract No: 4000139191/22/I-DT).

Accurate early-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenges.
While current deep learning-based methods show promise in crop type classification from single- and multi-modal time series, most existing methods rely on a single modality, such as satellite optical remote sensing data or crop rotation patterns. We propose a novel approach to fuse multimodal information into a model for improved accuracy and robustness across multiple crop seasons and countries.
The approach relies on three modalities used: remote sensing time series from Sentinel-2 and Landsat 8 observations, parcel crop rotation and local crop distribution.
To evaluate our approach, we release a new annotated dataset of 7.4 million agricultural parcels in France (FR) and the Netherlands (NL). We associate each parcel with time-series of surface reflectance (Red and NIR) and biophysical variables (LAI, FAPAR). Additionally, we propose a new approach to automatically aggregate crop types into a hierarchical class structure for meaningful model evaluation and a novel data-augmentation technique for early-season classification.
Performance of the multimodal approach was assessed at different aggregation levels in the semantic domain, yielding to various ranges of the number of classes spanning from 151 to 8 crop types or groups. It resulted in accuracy ranging from 91% to 95% for the NL dataset and from 85% to 89% for the FR dataset.
Pre-training on a dataset improves transferability between countries, allowing for cross- domain and label prediction, and robustness of the performances in a few-shot setting from FR to NL, i.e., when the domain changes as per with significantly new labels.
Our proposed approach outperforms comparable methods by enabling deep learning methods to use the often overlooked spatio-temporal context of parcels, resulting in increased precision and generalization capacity.

Timely crop type mapping is crucial to inform actionable interventions for food security, serving as a key input for estimating crop area and deriving information relevant for yield forecasting. While traditional approaches rely on in-season Ground-Truth (GT) data collection, recent methods utilize deep-learning models solely based on data from previous years. This can contribute to reducing costs related to data collection. To perform well, such models require a substantial amount of data for training. In Europe, a source of data is the annual GeoSpatial Application (GSA) datasets, i.e., the farmers declarations supporting the aid application under the Common Agricultural Policy, including the agricultural parcels boundaries with cultivated crop types. When made public, these datasets become valuable sources of extensive GT data for mapping crop types but lack harmonization across national datasets. Leveraging this GT data source, we have established the CHEAP (Common Harmonized European Agricultural Parcels) database. It consists of a multi-annual parcel dataset with harmonized crop types across years and countries, spanning from 2008 to 2022, covering 13 countries (more than 22M parcels). Here, we directly integrate the CHEAP dataset with multi-annual Sentinel-2 time series to evaluate the capacity for in-season crop type prediction at parcel level. Using a stratified random sampling, we obtained raw and smoothed multi-annual time series (TS) of Sentinel-2 (S2) spectral data for 5 million parcels per year. This Satellite Image Time Series, which stands out for its distinctive spatial and temporal coverage, was employed to assess the capability of a recently published multi-modal deep-learning model (Barriere, Claverie et al. 2024, RSE) to predict crop type at parcel level during the early stages of the season for ten countries and four seasons (2019-2022). This unique and vast evaluation highlights the potential of the algorithms to perform over a large variety of cropping systems.

Timely information on nutrient concentrations (micro-nutrients, macro-nutrients, protein) in staple crops over large areas 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. Conventional methods for measuring the grain nutrient levels typically consist of collecting grains at harvest and performing wet chemical analysis, near-infrared spectroscopy, or hyperspectral imaging of the crop grains in the laboratory. These methods are time-consuming and cost-prohibitive and, consequently, unsuitable for consistent quantification of nutrients across large spatial extents. In addition, as the nutrients are only measured after harvest, this approach precludes effective intervention with fertilizers while the crop is still growing.

To overcome the scale and cost limitations of laboratory analysis, the EO4Nutri team is investigating the potential of various Earth Observation data including hyperspectral, multispectral, and thermal data to estimate wheat grain protein content, as well as Calcium (Ca), Iron (Fe), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Potassium (K), Selenium (Se), Sulphur (S), and Zinc (Zn) in wheat, maize and rice grains and soil. The team collected soil, plant, and grain samples together with field spectroscopy data and satellite EnMAP and PRISMA satellite images in Jolanda di Savoia and Munich-North-Isar (MNI) test sites and assessed the lifecycle of nutrients of wheat, maize, and rice from the soil to crop canopy to crop grains with state-of-the-art ML analytical techniques. Preliminary results show the potential of hyperspectral data to predict the nutrients and protein in the final agriculture production.

The Russian forces invaded Ukraine on 24th February 2022 leading to widespread disruption of Ukraine's agricultural system. Ukraine is a major exporter of crops , the invasion therefore poses a significant risk to global food security. Total production is one of the critical indicators in this regard which in turn is directly proportional to the total harvested area. The majority of remote sensing based harvest detection studies require a complete satellite phenological time series and use the assumption that senescence leads to harvest. Both these conditions cannot be applied in this case as all planted fields need not be harvested.

Given these constraints and challenges, we developed a method to monitor crop harvest near-real time using high resolution Planet satellite imagery. Our method includes training a model to cluster change patterns on historic window based spatio-temporally sampled data and then identify harvest patterns in the current season. The sampling approach ensures we capture a complete representation of change patterns that exist. Clusters are assigned as ‘harvested’ or ‘non-harvested’ by visually inspecting imagery at a higher temporal resolution, using which harvest can be seen as a change event. Our method works in the absence of training labels.

In free Ukraine we found 94% of planted winter crops to be harvested and in occupied Ukraine it was 88% as of 19th September 2022. Strong visual patterns of non-harvested crops were observed along the occupation borders.We visually interpreted satellite imagery at a higher temporal frequency to generate statistically significant validation data for model accuracy calculation. We obtained an overall accuracy of 85% with an f1-score of 90% for the harvested class and 73% for the non-harvested class. Our assessments and analysis were directed to different organizations and agencies dealing with the Ukraine crisis and led to several key insights and derived interpretations.

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83% of greenhouse gas emissions from cereal and oilseed crops originate from fertiliser when both fertiliser-induced field emissions and emissions from the manufacture of synthetic fertiliser are included [HGCA/AHDB 2012]. Nitrogen Use Efficiency (NUE) is the nitrogen in the crop at harvest as a percentage of the nitrogen supply including both Soil nitrogen supply and applied fertiliser. UK farmers achieve, on average, around 60% nitrogen fertiliser use efficiency, but research suggests 80% efficiency could be attainable. Excess applied nitrogen is lost through leaching into water courses, and as Nitrous oxide emissions, which impacts the environment and farmers’ costs.

NUE-Profits (funded by Innovate-UK/DEFRA) is a farmer-led collaboration with industry specialists and academic experts to combine measurement, monitoring, modelling, and forecasting of crop and soil to enable farmers to improve NUE without compromising yield or grain protein thus improving farmer profit margins and reducing crop environmental impact.

Earth observation data compliments in-field measurements, reducing the number and frequency of measurements a farmer needs to make to monitor crop growth and plant nitrogen uptake and to observe spatial and temporal variation. Leaf Area Index (LAI) can accurately characterise the plant phenology, particularly the timing and rate of crop growth, which provides information about plant nitrogen uptake. We generate gap-filled LAI time-series from Sentinel 2 observations with cloud and cloud shadow removed using a cloud mask we optimised for monitoring cereal crops. We also assimilate LAI, alongside in-situ measurements and other datasets (for example soil moisture), into a physiological crop growth model to localise the model to the field and sub-field level. This allows us to model and forecast crop yield and development based on historical and forecast weather data. The data assimilation framework is adaptable to incorporating additional data sets when they are available and, importantly, allows quantification of uncertainty.

Climate change, environmental degradation, and the rising demand for sustainable food systems urgently demand a transformation of our agricultural practices and supply chains. From ensuring food safety and reducing waste to promoting fair trade, the complexity of our food systems underscores the need for greater transparency and traceability in how we source our food. Earth Observation (EO) has proven a powerful tool for understanding land use, deforestation, and agriculture's environmental impacts. However, emerging technologies like blockchain have the potential to disrupt the sector further, promising unprecedented visibility and accountability. This could empower consumers, farmers, and regulators alike to make informed choices that support sustainable farming practices and facilitate compliance with regulations like the EU Deforestation Regulation (EUDR).

The RUDEO project, a 1-year ESA project carried out by GMV and GMV-SES, proposes use cases to carry out a critical examination of the transformative potential of blockchain within agriculture. The project plans to evaluate existing solutions and propose new workflows that will assess the actual implications of using blockchain technology to streamline certification processes, ensure fair compensation for farmers who adopt sustainable methods, support carbon markets linked to cropland carbon stocks, create transparent platforms connecting producers and consumers, and trace the journey of commodities for ethical sourcing.

The RUDEO project's investigations will shed light on whether blockchain, alongside established EO technologies, can truly revolutionize agricultural practices and even go beyond aligning with the goals of the European Green Deal's Farm to Fork Strategy. Success in this area could offer solutions to the urgent challenges facing agriculture under pressure, facilitating more robust Monitoring, Reporting, and Verification (MRV) systems. In turn, this would support initiatives like the CAP Eco-Schemes by streamlining certification processes and incentivizing innovative agricultural practices with low environmental impact. Additionally, this integrated approach has the potential to reduce GHG emissions, combat biodiversity loss, and promote deforestation-free products. If achievable, the combination of EO data with blockchain could offer significant added value by driving collaboration, fostering trust, and accelerating the adoption of sustainable practices that benefit both farmers and the planet.

The Common Agricultural Policy implementers are on their roadmap to switch to a full Area Monitoring System (AMS). AMS is not just to integrate IT and EO technological novelties into the Integrated Administration and Control System (IACS), it is a real paradigm shift towards a performance-based CAP. The full-scale and time-wise continuous cloud processing has opened a wide range of possibilities to analyse land management and land cover systems with comprehensive territorial and seasonal coverage. The administration must be capable of addressing via the direct payments the challenges of climate mitigation, soil protection, biodiversity and different agro-ecosystem services. Beyond the parcel-level validation duties of CAP Paying Agencies for planning and monitoring the payment schemes, AMS requires further multi-annual EO data analysing capabilities. Extended data sharing and data interoperability are also key conditions of new AI-based modelling techniques.

The proposed Agri-Dashboard implements Data as a Service and visualisation by widget-based apps. It enables Paying Agencies to get fast access to reliable information on agricultural parcel level required for customised monitoring, expert judgements and policy reporting obligations.

We highlight solutions for understanding large scale outputs of Machine Learning products, detections taken along the threshold-driven marker logic, analysing class-based decision models or integrating Geotagged Photos. Agri-Dashboard supports data slicing and data analysis using misclassification matrices, class-level accuracy measures and statistics or analysing the quality of training and test data. These data insight methods are fundamental for AMS Quality Assurance.

The multi-annual EO-data of AMS seriously has the potential to become a solid knowledge-base of a real performance-oriented payment system opening higher flexibility and spatial sensitivity to design eligibility conditions, impact indicators in zonal logic, meaning that AMS products could form the base of strategic planning.

Agricultural landscape features (LF) are small fragments of non-productive (not directly used for agricultural production) and typically, but not only, natural or semi-natural vegetation (hedges, tree, grass margin, stone walls, ponds, ditches etc). They are located in the agricultural landscape and can provide ecosystem services and support for biodiversity. In the latest LUCAS - a harmonized land cover and land use data collection exercise that extends over the whole EU territory - Landscape Features module done in 2022, the survey is comprised of 3.8 million sampling points, distributed over 93,000 one-hectare quadrats and the methodology combines office-based photo interpretation with field surveys . In a first phase, all points for the field survey were photo-interpreted in the office. Surveyors classified points as non-LF or one of four types of LF (woody, grassy, wet, and stony) based on areal EO ortho-photo of ~ 20 cm resolution. This was followed by field observation to confirm/correct the previous step. Based on the outcomes of the LUCAS LF module, we assess the influence of the density and type of landscape features on the biodiversity of pollinators and farmland birds. We also consider information on agricultural intensity and compare the results with previous landscape surveys within LUCAS. We assess how this information can be used to evaluate the performance of the Common Agricultural Policy in halting and reversing the loss of farmland biodiversity for a more sustainable agriculture.

Feeding a growing world population sustainably is crucial, but sustainable practices have varied, ever-changing impacts. This is because agriculture is a complex system, influenced by various local factors such as soil composition, soil health, land use, and climate variability. It is important to understand the heterogeneity of effects of practices at the field-level to help farmers personalise the decisions.

Analysing complex interactions and estimating effects boils down to answering causal queries. In recent years, observation-based causal inference approaches have emerged as a mature field. This research introduces a causal inference framework utilising Double Machine Learning to estimate Conditional Average Treatment Effects. The primary objective is to uncover the nuanced and spatially diverse impacts of crop rotation (farming practices of interest) on Soil Organic Carbon (SOC) levels in Lithuania's heterogeneous regions.

The study uses the farmers' declarations of the crops they cultivate (LPIS) from 2018-2022 to derive a binary crop rotation treatment, determined by cultivating at least 4 different crops in 5 years. We control for climate variables from ERA5 (e.g. surface net short-wave radiation, soil temperature), and soil properties (e.g. clay) to help us identify and estimate the impact of crop rotation on SOC content.

Beyond estimating local effects, the study explores the diversity of outcomes across space and time. This deeper analysis helps identify the key drivers influencing these variations, such as soil composition, climate, or even specific crop rotation combinations. The results indicate that crop rotation appears to be more effective in the western croplands of the country while exhibiting a negative impact on SOC in the central-northern regions.

Estimating the effects of practices at the field level allows farmers to make informed choices tailored to their specific conditions. This local-specific approach contrasts with traditional one-size-fits-all recommendations, empowering farmers to optimise their practices for both environmental and economic sustainability.

Monitoring Land Use Land Cover Change (LULCC) due to oil palm plantations in near-real time is instrumental for regulating palm oil imports to EU in the context of EU Deforestation-free Regulation (EUDR).

Earth Observation satellite missions providing free global imagery with high revisit frequencies, are critical for monitoring tropical rainforests and their conversion to oil palm plantations. SAR imagery (Descals et al. 2021) and LandTrendr algorithms (Du et al. 2022) have been used in previous studies to identify closed-canopy oil palm plantations and planting years. However, these approaches have limitations in accurately mapping young and sparse plantations.

This study aims to fill these gaps by detecting plantations in a timely manner. Thanks to the Proba-V full mission Collection 2 spanning 2014 to 2020, we developed a sensor-agnostic method for monitoring deforestation and plantation rotation. RED, NIR and SWIR wavelength allows computing the NDWVI, a new index for discriminating vegetation from land cleared for plantations. Normalisation reduces atmospheric and seasonal variability, while statistical boundaries identify pixels that deviate from the normal distribution of forest values. The algorithm detects LULCC with a bi-weekly timestep at global scale.

The annual maps depict the dynamics typical for oil palm plantations, from land preparation to mature plantations, including dates of clearing and planting. Validation involves 1016 points randomly selected using a stratified sampling. Preliminary results in Southeast Asia show F1-score and OA of 81% for plantations detection. 70% of detections were accurate for the exact year, surpassing significantly previous studies.

This method can support the EUDR implementation for imports and provides valuable insights into yield estimation. This work demonstrates the suitability of 100m spatial resolution and 5-day temporal frequency for global mapping, particularly in cloudy regions and for perennial crops. Release of this oil palm plantation map series is scheduled for May 2024, with publication.

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Climate change is a challenge for all sectors, but especially for agriculture. Rising temperatures in Africa negatively affect agricultural production. In addition, the demand for food is increasing, making it necessary to develop variables that describe agriculture, such as yield forecasts or phenology development stages of the different varieties cultivated. Remote sensing provides spatial and temporal continuous information that allow us to accurately assess the evolution of a particular field. In this study, we use Sentinel-2 optical data to analyse rice and wheat seasons between 2016-2022 in the Gharbia governorate (Egypt) where ground data were collected by the University of Tanta. We applied a yield model for each crop type trained in Spain and study their transferability to this region. In the case of wheat, we also test a second model that aggregates SAR data from Sentinel-1, thus evaluating the fusion of both products. We also studied the integration of the accumulated Growing Degree Days (GDDaccum), since the seasons do not start on the same dates and depending on the temperature accumulation the crops phenology evolves differently. Using the GDDaccum, the models show a better performance that when considering the timeseries evolution against the dates. The optical models provide a RMSE of about 1 T/ha for rice and 0,9 T/ha for wheat. The optical + SAR model manages to reduce the wheat model error to 0,7 T/ha. We also developed a crop type mask to evaluate the yield models performance at governorate level. Preliminary results show a RMSE of 2 T/ha for rice and wheat.

Climate change is threatening food security. To ensure food security, we do not only have to safeguard agricultural production but also optimally distribute the yields between regions. For that, decision-makers need reliable crop yield forecasts so that they can plan which regions are likely to experience crop yield losses and which regions will produce a surplus. Earth observation and machine learning are key tools to calculate such forecasts. Especially Sentinel-1 and 2 data has been used a lot as it provides regular high-resolution information about the state of crops and soil moisture. However, crop yield forecasts based on machine learning are strongly limited by the availability of field-level crop yield data, which farmers often do not like to share publicly. In this study, we evaluated if a model trained with data from a certain region can be applied elsewhere, to use training data more efficiently. Our field-level crop yield forecasts were trained using crop yield data from a farm (846 fields) in the Czech Republic for winter wheat. It was based on Sentinel-1 and 2 data and the machine learning model Extreme Gradient Boosting. The model was then tested for various farms with increasing geographical distance. The baseline was a forecast for fields of the same farm, that were not used for training. Next, the model was applied to another farm in Czechia, one in Ukraine and one in the Netherlands. The model transferability worked well for the other farm in Czechia (R²=0.64 between the forecast 1 month before harvest and observed yield). However, the model performed poorly further away than that (R²<0.13). This was related to very different weather conditions. Adding meteorological predictors or applying the model to more similar areas may help in the future to improve the transferability of the forecasts.

In this work we develop a model to forecast wheat yield at field level from Sentinel-1 (S1) and Sentinel-2 (S2) data. To do this, we calibrate the model with precise yield data acquired between 2018 and 2022 in different fields located in an agricultural region (mostly cereals) in the province of Valladolid and we validate it with data acquired across different provinces of across Spain. To minimize the temporal variability that each season and region may present, we normalized the signal from the two satellites based on the plant temperature accumulation (accumulation of Growing Degree Days, GDD_acum) based on the ERA5 air temperature product. Additionally, we test different Start-Of-Season inputs to start the GDD accumulation: a) the average planting date, b) the WorldCereal crop calendars, or c) Land Surface Phenology (LSP) metrics based on MODIS daily data. Finally, we calibrate the yield model based on a two-parameter linear regression for each GDD_acumrange considering three possible combinations: (1) using only optical parameters of S2, (2) using only microwave parameters from S1, or (3) using one parameter from S2 and one parameter from S1. The calibration results show that with S2 the yield can be estimated with errors of 800 kg/ha around 1400 ºC, S1 provides errors of 1000 kg/ha around 1700 ºC and the integration of S1 and S2 provides the lowest errors of 700 kg/ha around 1400 ºC. We tested the transferability of the model across the major wheat producing Autonomous Communities in Spain (Castilla y Leon, Castilla La Mancha, Aragon, Andalucia and Cataluña). The results show consistent results with the calibration data with minimum seasonal errors ranging from the lowest of 700 kg/ha in Andalucia to the largest 1200 kg/ha in Aragon.

The escalating frequency and intensity of climate shocks underscore the critical necessity of timely and precise data on agricultural conditions to inform responses. This data is often absent in countries where rainfed, low-input and smallholder farming systems are ubiquitous, conditions that heighten vulnerabilities to climate risks. Earth observation (EO) data can fill these gaps and support in-season agricultural monitoring, providing frequent and transparent information to guide government interventions. In smallholder agricultural systems however, there is a need to validate EO data, and build confidence in new data sources. FAO and NASA Harvest collaborated to bridge data and analytical gaps in Malawi and Namibia, binding EO applications, digital tools and field-level assessments to foster an integrated approach to agricultural monitoring. The collaboration focused on three areas: 1) mobile-based tools for the collection in situ data; 2) cropland mapping of small-holder agricultural systems; and 3) yield forecasts based on earth observation data. The creation of a suite of mobile-based survey tools to collect ground truth data was a critical element of this work, with the primary intention to provide actionable information to governments, but with secondary benefits of building an extensive validation and training data set for yield forecast and cropland mapping models. The tools built off existing developments from ArcGIS Survey123, adding new components that sped-up and lowered costs of collecting field level data, including field boundaries and yield estimates, whilst importantly developing features to provide information back to the farmer; a critical aspect to foster trust with farmers. This integrated approach provided government entities with multiple sources of corroborative evidence (field level data, early yield estimates and interpretable EO analysis) to facilitate a more informed and timely response.

Since ground-based statistical methods of yield estimation are currently not available in the Ukraine due to the war, VISTA has provided the Ukrainian Ministry of Agriculture with high resolution information on expected production volumes for winter wheat, rapeseed, winter and spring barley, grain maize and sunflower, i.e. the YPSILON service, in 2022 and 2023. The crop types are classified using Sentinel-2 data. The temporal development of the leaf area is calculated for hundreds of thousands of fields and assimilated into the PROMET crop growth model. The large number of analyzed fields enables an aggregation of yield forecasts in t/ha on different administrative levels. Through combining area under cultivation and simulated yield from PROMET, forecasts can be made for the expected production volume. Coherence information from Sentinel-1 data is used for harvest detection on field level, to determine the share of the harvested area of the total cultivated area. The results for 2023 will be shown, illustrating the resilience of the Ukrainian farmers in terms of food production as well as the consequences of the front line for agricultural production.

In the current workflow as employed for these analyses, forecasts of yield and production are possible up to 6 weeks in advance. Within the Horizon Europe Project STELAR, VISTA aims at making these forecasts possible even earlier, up to 10 weeks in advance. For this, sophisticated tools for data imputation and data fusion are being developed and integrated in STELAR’s Knowledge Lake Management System. A first outlook towards these advances will be given.

The work for Ukraine presented here has been supported by co-funding from ESA’s Network of Resources under contract numbers 3a06VS and 3717ds, as well as from BayWa AG. STELAR is funded by the European Commission’s Horizon Europe program under grant agreement number 101070122.

Precise within-field estimation of cropland productivity is crucial for informed agricultural decision-making, particularly in enabling the optimization of management practices and the pre-harvest anticipation of crop yields. The current availability of high spatiotemporal resolution of optical satellite data offers the opportunity for continuous monitoring and assessment of croplands, allowing for enhanced agricultural productivity and resource utilization. This study presents a workflow for predicting within-field grain yields focusing on winter cereal crops in Switzerland (wheat, barley and triticale). NDVI and the recently introduced kernel NDVI time series were derived from Sentinel-2 data as descriptive indicators of crop status and evolution. To ensure temporal continuity, Gaussian Process Regression (GPR) was used as a curve-fitting function to reconstruct cloud-free time series throughout the growing season. The performance of various machine learning methods (GPR, Kernel Ridge Regression, and Random Forest) to forecast yield at any point in time during the season was compared. The integration of Growing Degree Days (GDD) information as the temporal spacing reference of the time series considerably improved the accuracy and consistency of in-season yield forecasting. Using data from the same year, results indicate that grain cereal yield can be reliably predicted approximately 2-2.5 months before harvest, with an RMSE of up to 0.71 t/ha and a relative RMSE of 7.60%. Although the forecasting accuracy decreases when predicting for unseen years, the results remain satisfactory (RMSE = 0.97 t/ha, relative RMSE = 11.47%). These findings showcase the potential of the proposed workflow for in-field yield monitoring and targeted interventions, potentially reducing yield losses, optimizing farmers' management planning and enhancing food availability.

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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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The agricultural sector is the biggest and least efficient water user, accounting for around 80% of total water use in Northern Africa, which is already strongly impacted by climate change with prolonged drought periods, imposing limitation to irrigation water availability. The objective of this study was to develop a procedure for the monitoring and forecasting of anthropogenic irrigation water use for the irrigation districts of Doukkala in Morocco from 2013 to 2022.

The analysis is based on the FEST-EWB model, that computes continuously in time both soil moisture (SM) and evapotranspiration based on the energy-water balances. The model has been calibrated and validated over non-irrigated areas, against land surface temperature (LST) from downscaled Sentinel3 data at 30m and modeled evapotranspiration from MOD16, GLEAM and FAOWapor. The model has been run using as input the past meteorological forcings (ECMWF ERA5-Land) and vegetation data from Sentinel2. Then, the actual irrigation volumes have been estimated through the calibrated model implementing three different irrigation strategy: the FAO approach based on SM crop stress thresholds (Allen et al., 1998), the separate and joint assimilation of satellite LST and SM (1km SMAP-Sentinel1) to update the modeled fluxes and estimate the irrigation volumes. Overall, the results suggested that the yearly total irrigation volumes modeled with the FAO approach are quite in agreement with the observed water allocations; and similar outcomes are obtained when the joint assimilation of satellite LST and SM. Finally, the calibrated model was used to implement a seasonal forecasted optimized irrigation strategy, by implementing the FEST-EWB model with seasonal meteorological forecast (ECMWF data) and the FAO irrigation strategy to provide forecasted crop water demand and optimized irrigation water needs.

This research was developed within the ESA AFRI-SMART project EO-Africa multi-scale smart agricultural water management, in the framework of EO AFRICA activities as Natioanl Incubators.

The anticipated increase in agricultural water usage, driven by a warming climate and rising population, underscores the need for operational large-scale monitoring tools in agricultural water governance. In this context, irrigation mapping plays a crucial role in sustainable management of agricultural resources.

Remote sensing technology offers a cost-effective alternative to traditional census methods for monitoring agricultural water usage. It provides spatially exhaustive near-real-time data, enabling timely interventions and adaptive management strategies.

In the context of the ESA Sentinel for Agricultural Statistics (Sen4Stat) project, a pixel-based map of irrigated areas was generated in Spain for 2022, using Sentinel-2 image time series, as well as high-quality in-situ datasets provided by the Spanish National Statistical Office and farmers' declarations.

Specific statistical and temporal metrics were designed to highlight phenological differences between irrigated and rainfed parcels at specific dates and throughout the growing season. To account for varying phenological response to irrigation across crop types, we employed a pixel-based categorical gradient boosting model. This model classified each pixel with known crop types (information from the farmers' declarations) as rainfed or irrigated. The overall accuracy of the obtained map is 91%; this good performance is also observed for individual crop types across a range of agricultural environments, spanning from olive groves in Andalucia to intensive cereal fields like barley in Castilla y Leon, and citrus orchards lining the Mediterranean coastline.

In the Sen4Stat context, this map will be used to update the statistical survey sampling frame, but it can also be very useful to support the transition towards more sustainable agriculture. In the future, it is planned to test the potential of the developed method in other places of the world.

Irrigation is the heaviest human alteration on the natural water cycle. Satellite technology represents a unique tool for disclosing irrigation dynamics (i.e., extent, timing, and quantification) that are generally poorly documented despite their primary importance in water resources management. In this contribution, we retrace main findings obtained in the fields of irrigation mapping and quantification through remote sensing data under the ESA (European Space Agency) funded Irrigation+ and 4DMED-Hydrology projects. The achieved results consist in the development of an irrigation mapping method called TSIMAP (Temporal-Stability-derived Irrigation MAPping) and of a framework for estimating irrigation water use, i.e., the SM-based (Soil Moisture-based) inversion approach. The latter led to the development of the first satellite-derived, high-resolution irrigation water use data sets, produced over four Mediterranean basins (the Po basin in Italy, the Ebro basin in Spain, the Medjerda basin in Tunisia, and the Herault basin in France) and over the Murray-Darling basin in Australia. In a new precursor project funded by ESA, i.e., the CCI-AWU (Climate Change Initiative – Anthropogenic Water Use), the SM-based inversion approach will be implemented, together with other satellite-based-approaches, to produce long-term irrigation water use estimates to benefit climate-related studies.

Groundwater plays a pivotal role for drinking water supply, agriculture and ecosystems in general. In arid areas such as the Saq-Ram aquifer at the Arabian Peninsula, groundwater levels (GWL) are strongly declining. In these regions, the observed decline in GWL can be attributed to intensive agriculture characterized by excessive groundwater usage for irrigation purposes. In the face of climate change, agricultural irrigation demands are increasing even in dry sub-humid regions in central Europe, where they previously where they previously held minor importance. As part of the KIMoDIs project, the aim of this study is to assess the effect of agriculture on the GWL using deep learning methods, focusing specifically on the federal state of Brandenburg, which is one of Germany's driest regions.

We develop an inverse approach: The predicted GWL is decomposed into partial scale-respective signals using the discrete wavelet transform. Gradient-based feature attribution methods, such as expected gradients, are being applied on these. We infer an agricultural map of the study area using optical satellite data, such as Sentinel-2, as an input and the EuroCrops dataset as a reference. That map serves as an input to a global GWL model combined with meteorological and static parameters. After model training, the computational graph of the model is expanded by the decomposed partial signals of the predicted GWL. Based on that expansion, the gradients and, hence, the agricultural attribution to the GWL is analyzed with respect to multiple scales which shall ease algorithmic sensitivity. That attribution is to be compared to crop type specific irrigation requirements. Our approach will help both groundwater management and farmers to decide on which crops, and potentially associated irrigation practices, may lead to groundwater depletion under certain climatic conditions.

Over the past 6 years Morocco has been heavily affected by droughts, with declining rainfall resulting in limited water available in reservoirs. The challenge is to optimise the water allocation between agriculture, urban areas and tourism.

This presentation will demonstrate how we use satellite data to monitor agricultural water use from basin to field level in the Tensift Basin of Morocco. Complex geospatial data of FAO’s open-access WaPOR database together with eLEAF’s high resolution data is translated into credible, tangible information that can be directly used for reporting, decision making and planning.

eLEAF and RESING co-developed the Water Consumption Dashboard (WCD) with (1) the water board (ABHT) mandated for integrated water resources management and (2) the irrigation office (ORMVAH) managing irrigation schemes and training farmers in optimal water use. The information provided is intended to be easy-to-digest and aimed at professionals without prior GIS experience. The dashboard provides users with actual information every ten days.

Furthermore for the Haouz plain we assessed the conversion to drip irrigation in 2018. Long-term WaPOR analyses indicate a slightly decreasing trend in water consumption (ETa) of -4,2%, and increasing trends agricultural productivity (NPP +3,5%) and water productivity (WP +6,7%). The analysis does also reveal a reduction in cultivated areas. However, because of the recent high interannual fluctuations of precipitation and the stop of surface water allocation in 2021 from reservoirs and the limited series of data available after conversion, does not allow definitive results on the impact of modernization (yet).

The cooperation with ABHT and ORMVAH recently entered a two years demonstration phase to internally adapt the WCD to identify illegal groundwater withdrawals, monitoring of water consumption, crop area and development and irrigation efficiency. The developed tools contribute to more transparent, equitable and sustainable water use.

African drylands are particularly important for research and innovation because they support 75% of the continent’s agriculture; are highly vulnerable to the impacts of climate change; have high rates of food and economic insecurity that trigger humanitarian crises; are biodiversity hotspots; and play an important role in regulating the Earth's climate system via carbon storage and rainfall triggered by evapotranspiration (i.e., “rainbow water”). Researchers and advisory services increasingly leverage advanced remote sensing technology such as the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and evapotranspiration (ET)-derived metrics like the evaporative stress index (ESI) to monitor agricultural droughts in African drylands. However, there is an opportunity to enhance the reliability of these products with the emergence of a new generation of hyperspectral remote sensing missions: Hyperspectral Precursor and Application Mission (PRISMA) and the Environmental Mapping and Analysis Program (ENMAP). In this study, we employ eddy covariance flux towers in Kapiti Ranch of Machakos County, Kenya to evaluate the performance of ECOSTRESS ET and ESI products driven by PRISMA and ENMAP like hyperspectral vegetation indices resampled from fluorescence box (FloX) ground spectra. The enhanced products are demonstrated with actual PRISMA and ENMAP imagery. Our findings reveal the superiority of hyperspectral narrowbands over thermal infrared, particularly in distinguishing transpiration and soil evaporation dynamics. Such information will feed into a potentials and limitations analysis for the upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM) missions. CHIME and LSTM afford the opportunity for delivering the highest quality ET and ESI estimates consistently across Africa at frequent intervals.

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Food security remains a critical global concern, exacerbated by climate change-induced disruptions in weather patterns, leading to more frequent and severe extreme weather events. Addressing these challenges demands innovative solutions in the agricultural sector. Remote Sensing (RS) offers a promising avenue for monitoring and mitigating agricultural stressors, yet, integrating the increasing quantity and quality of Earth Observation (EO) data holistically is a challenge. The EO4CerealStress project aims to overcome this obstacle by developing a new framework for processing these multisource data and monitoring multiple stressors in cereal-based agricultural systems. Leveraging optical sensors, along with in-situ measurements, the project focuses on detecting the impact of various stressors such as salinity, nutrient deficiency, water stress, lodging, etc. on crop productivity. This synergistic approach enables continuous monitoring and a comprehensive understanding of the complex interactions between different stress factors and their impact on crop yield. So far, intensive data collection has been conducted at three pilot sites in Austria, Italy, and Spain. The further analysis integrates data from new EO missions such as PRISMA, ENMAP, and Sentinel-2 measurements with the ground and airborne sensors like Headwall and Analytical Spectral Devices (ASDs) and utilizes vegetation radiative transfer models and machine learning algorithms to identify crop stress indicators, contributing to the Agricultural Science Precursor Experimental Dataset. The project will also explore the operational use of developed products and algorithms at new locations across Europe and Canada, ensuring that its outcomes translate into tangible solutions for agricultural challenges. Through its comprehensive approach and practical implementation, EO4CerealStress aims to lay the foundation for building resilience within agricultural systems.

Drought denotes a prolonged water supply deficit impacting various realms such as the atmosphere, soil, streamflow, groundwater, and economic activities. It has posed substantial challenges to Africa's food security and water resource inequality. Therefore, urgent efforts are needed to monitor agricultural drought in sub-Saharan Africa. Diverse drought indices have been developed, reliant on meteorological variables and remote sensing (RS) data. However, the efficacy of meteorological drought indices on a regional scale is hindered by the limited distribution of in-situ sites, and the indices derived from modeled data have not been evaluated in Africa. RS-based drought indices typically normalize indirect indicators derived from vegetation and land surface temperature (LST) anomalies; however, they face limitations in temporal sampling frequency and cloud cover. Additionally, current gap-free soil moisture (SM) products still grapple with coarse spatial resolutions, rendering them unsuitable for local irrigation management.

In this study, in-situ SM measurements from the International Soil Moisture Network (ISMN) in Africa serve as the ground truth for agricultural drought. The SMAP SM product, the ESA Soil Water Index (SWI), the Keetch-Byram Drought Index (KBDI), the Shortwave Infrared Transformed Reflectance (STR), the hybrid Scaled Drought Condition Index (SDCI), the Normalized Difference Water Index (NDWI), and the ECOSTRESS Evaporative Stress Index (ESI) are included for sensitivity analysis to soil moisture for different climate and land cover types. Results reveal SMAP's greater performance, followed by SWI. STR correlates with SM but includes scattered values. ECOSTRESS ESI effectively captures the spatial nuances of local drought stress at the seasonal scale; however, it is limited by the sampling frequency, impeding the variability analysis at intra-monthly scales. No single index universally excels, underscoring the need for refinement. Advocating for a high-resolution RS data-driven drought index, this study provides insights for future mission applications, offering a roadmap for enhanced drought monitoring in Africa.

Drought significantly impacts agricultural systems, affecting crop yields, food security, and socio-economic stability. Earth Observation (EO) data enhances drought monitoring, providing insights into crop conditions in near-real time. Yet, current monitoring primarily identifies drought hazards, not their impacts or risks. Understanding these requires context-specific information on management and cropping systems. Our study, conducted in Kenya, co-developed solutions with stakeholders to create EO-based products assessing drought risk and impacts, using Sentinel-2 and Sentinel-3 data and evaluating crop condition, evapotranspiration, and farming systems (irrigated/rainfed, mono/mixed cropping). The Sentinel-2 time series and vegetation indices were used to assess agricultural impacts by tracking crop changes and classifying drought-affected areas with a random forest method. National-scale maps for irrigated/rainfed areas were produced using random forest and harmonics, and Sentinel-2 and PlanetScope data fusion was tested to map mixed cropping systems using Convolutional Neural Networks. Crop yield data and biophysical predictors (SPI, NDVI, NDII, LST, albedo) informed a drought hazard model. Calibration of MODIS and Sentinel-3 data extended the time-series analysis for LST, NDVI, and NDII. The project linked drought hazard and impact data with information on farming systems, incorporating socio-economic and environmental data for a comprehensive risk assessment. Furthermore, Sentinel-2 and -3 data were used to derive daily 20-m evapotranspiration estimates using machine learning and energy balance models. The crop condition accuracy ranged from 75-90%, and farming systems classification accuracy was 97.87%. A static drought vulnerability map, combined with hazard/exposure data, visualized monthly drought risk at a 1 km resolution. The developed products showed high agreement with existing datasets, confirming their reliability in drought risk and impact assessment.

Drought Early Warning Systems (DEWS) are crucial components of a proactive, risk-based management approach. However, these systems often fall short in providing accurate and detailed impact-based seasonal forecasts at more localized spatial scales, such as small and moderate-sized basins or agricultural districts.

The InfoSequia-4CAST service, a project supported by the ESA-InCubed program, aims to deliver seasonal forecasts of risk of impact on crop yield and water supply. This is achieved by merging EO-based and climate indices and machine learning. Two decision tree-based ensemble forecasting methods, resting on the Fast and Frugal Trees and XGBoost techniques, have been tested in Mozambique and Spain. The design and evaluation of this tool and its outcomes have been collaboratively supported by local stakeholders.

Forecast models are trained and calibrated using a large dataset of enhanced drought predictors generated across several timescales, and with data collected from multiple sources and satellite sensors. For the crop yield pilot case tested in Mozambique, anomalies of the End-Of-Season Water Requirement Satisfaction Index (EOS-WRSI) is used as a proxy of crop performance. Water level observations in reservoirs serve as the basis for forecasting water supply scenarios in the Spanish case. InfoSequia-4CAST generates probabilities of failure predictions up to six months in advance, metrics of forecast performance and recommendations for early action. Outcomes are provided monthly to stakeholders to support the decision-making process. The system has been tested in a real-world operational context during the 2023-2024 season.

InfoSequia-4CAST has shown promising performance, meeting the key requirements previously set by local stakeholders. Further technical developments may improve InfoSequia-4CAST, including a) the employment of drought precursors able to better detect compound extremes and flash events, b) the combination of statistical and dynamic forecasting methods, and c) the retrieval of alternative and more accurate predictands when ground-based impact observations are lacking or unreliable.

The destructive insect known as Cotonet de les Valls (Delotococcus aberiae) in the province of Castellón (Spain) is causing significant economical losses in the Spanish agricultural sector, particularly in citrus fruits. The European Copernicus program has enabled the creation of numerous agricultural surveillance instruments by utilizing remote sensing technology. In this context, the objective of this research is to comprehend how light reflectivity changes based on the level of tree infection by examining temporal data from satellite. Sen2Like processor is used to retrieve the images. Furthermore, given the morphology of the studied crop (trees), shadows can introduce noise in terms of the spectral response. This is why the Bidirectional Reflectance Distribution Function (BRDF) is used to minimize the angular effects [1]. This investigation was carried out in the vicinity of Vall d'Uixó (Castellón, Spain) by analyzing around 25 hectares of land spread across various orange tree fields affected by cotonet, each with varying degrees of infestation, which were categorized as either healthy or diseased during the 2020-2021 season. Initially, we explored the connection between the cotonet infestation level and various optical bands (such as RED, NIR, SWIR, derived from Sen2Like), along with the Normalized Difference Vegetation Index (NDVI). To mitigate seasonal variations and concentrate on trend analysis, monthly linear regressions were applied to each group of fields and spectral range. The findings indicate that remote sensing data can be instrumental in the timely, objective, and cost-efficient management of the cotonet pest. It has been observed that it is feasible to distinguish between affected and healthy fields throughout the year using specific spectral ranges, with SWIR demonstrating particular efficacy, enabling differentiation throughout the latter half of the year. This study contributes to the advancement of novel surveillance tools for effective and sustainable measures against agricultural threats.

Advances in the quality and accessibility of Earth Observation (EO) information have led to rapid advances in data driven decision support, especially in pest risk. Historically, applications associated with pest management have focussed on the monitoring and detection of pest incursions, however in many cases early intervention is required before detectable damage has occurred. Where preventative action is needed, strong linkages with agricultural extension systems are required to understand how information can better inform preparedness and decision making. Here we describe the development of a suite of projects that use optical, radar and weather EO data products combined with ecological modelling methods to provide information to farmers and decision makers on when to intervene and where risks will be highest on a broader spatial scale. We describe how actors and decision makers are involved in the design process to ensure maximum impact of information. Firstly, we describe the Pest Risk Information SErvice (PRISE) which uses ERA5 weather data to produce advisories for smallholder farmers on when to intervene against pests commonly found in mixed maize growing systems in Africa. Secondly, we describe how spatial pest risk estimations can be improved by using Sentinel 2 datasets to improve the mapping of when and where irrigation occurs. Thirdly, we describe a framework for application of EO data to biosecurity decision making. A layered approach, overlapping temporal and spatial crop maps with environmental suitability modelling, can identify areas of high risk of the wheat blast pathogen. This information can guide where to use hyperspectral detection methods for emerging outbreaks in inaccessible areas.

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Abstract: Agricultural productivity is influenced by various natural factors, making its assessment critical for ensuring food security. In this study, we introduce a robust empirical index, the Land Productivity Index (LPI), designed to evaluate the relative productivity of agricultural areas globally. The LPI integrates key environmental parameters including climate, soil attributes, and water availability, providing a comprehensive assessment of agricultural potential at a spatial resolution of 250 meters.

Priority 1: Monitoring stressors and changing growing conditions The LPI methodology enables the monitoring of stressors and changing growing conditions by incorporating parameters such as mean monthly temperature, evapotranspiration, soil pH, organic carbon content, and water holding capacity. Through this holistic approach, we identify areas prone to environmental stressors, facilitating targeted interventions for sustainable agriculture.

Priority 2: Supporting increased resilience and risk management To support increased resilience and risk management, we extend the LPI methodology by integrating the ECOCROP database of the Food and Agriculture Organization (FAO). By customizing the LPI for key crops, we assess crop suitability across different spatial and temporal scales. This enables us to provide valuable insights into irrigation advice, crop monitoring, and risk mitigation strategies, enhancing agricultural resilience in the face of evolving environmental challenges.

Priority 3: Analyzing historical trends and variations Furthermore, our study analyzes historical trends and variations in land productivity by assessing historical average conditions and deviations from these averages over time. By identifying spatial shifts and temporal trends, we gain a deeper understanding of agricultural dynamics, empowering policymakers and stakeholders to make informed decisions for sustainable agricultural development.

Conclusion: Our integrated approach combining Earth observations with crop suitability analysis offers a comprehensive framework for assessing agricultural productivity and resilience under changing environmental conditions. By addressing the priorities of monitoring stressors, supporting resilience, and analyzing historical trends, our methodology contributes to enhancing agricultural sustainability and food security on a global scale.

This abstract introduces a comprehensive framework aimed at enhancing climate adaptation strategies for maize cultivation, focusing on the evolution of the Plan-T platform developed as proof-of-concept for the Food and Agriculture Organization of the United Nations (FAO) in the framework of the ESA project EO4YEMEN.

The methodology foresees three main steps: crop seasonality assessment, crop yield estimation and identification of the optimal planting date.

The assessment of crop seasonality integrates diverse data sources, including meteorological, agronomic, and satellite data. Advanced techniques such as the integration of ECOSTRESS data (Evapotranspiration) and climate stressor analysis contribute to more accurate variety suitability mapping.

The enhancement of crop productivity estimation involves refining the AQUACROP agronomic model, incorporating new input data, and calibrating model parameters. This process includes refining varietal parametrization, modeling yield response, and validating outputs with field data.

Variety selection and optimal planting date assessment are based on detailed analysis of physiologic responses to stressors and soil water balance, utilizing climate and soil moisture data as well as short-term high resolution weather forecast from ECMWF.

The Plan-T service is offered through a dedicated web application that can be exploited on both desktop and portable devices. It allows selecting the site of analysis down to a single field, or manually providing coordinates. Once done, it automatically shows the soil chemical parameters and ranks the maize variety based on climate suitability and productivity. It finally allows assessing the optimal planting date for each variety.

Overall, this framework facilitates informed decision-making for climate adaptation in maize cultivation, providing valuable insights into variety suitability, planting dates, and potential crop productivity. The integration of advanced methodologies and tools within the Plan-T platform enhances its usability and effectiveness in supporting agricultural practices amidst changing climatic conditions.

Recent studies on the impact of climate change on agricultural systems show significant changes that will occur in the global food production chain. New climatic conditions are expected to have a severe impact on the productivity of main crop types such as wheat, maize, soybean, and corn [1]. This will alter the global map of agriculture in the 21st century, facilitating crop migration and changes in agricultural practices. This process is ongoing and can already be observed in historical cropland maps [2].

To study this process, we employed climate velocity principles, allowing us to provide spatial-temporal characteristics of climate change and generate flow fields for climate parameters [3]. This technique was applied to a collection of 20 years of MODIS-based winter wheat maps to uncover spatial-temporal trends in winter wheat production changes and estimate the directions of winter wheat expansion and migration. This data on "Winter Wheat Velocity," together with Climate Velocity maps built based on ERA-5 Cumulative Biological-Active Temperature for winter crops, allowed us to link changes in wheat production with changes in climate and estimate the percentage of wheat migration and expansion caused not only by agricultural intensification but also by environmental changes in Europe.

References:

[1] Jägermeyr, Jonas, et al. "Climate impacts on global agriculture emerge earlier in new generation of climate and crop models." Nature Food 2.11 (2021): 873-885.

[2] Sloat, Lindsey L., et al. "Climate adaptation by crop migration." Nature communications 11.1 (2020): 1243.

[3] Loarie, Scott R., et al. "The velocity of climate change." Nature 462.7276 (2009): 1052-1055.

Rising temperatures, unpredictable precipitation patterns and extreme climate events are disrupting crop production globally. This is a major challenge for rice production, the key staple food for over half of the world’s population and a primary calorie source for millions of vulnerable people. The TAPAS platform combines remote sensing data and AI to monitor both environmental stressors and cultivation conditions of crops, with potential to positively impact on crop production by enabling decision-making for more resilient rice cropping systems. We identify, quantify and track crop production anomalies using a combination of supervised and unsupervised algorithms. We combine archival and real time Earth Observation (EO) data augmented by machine learning and deep learning techniques to monitor rice production quality, yielding up-to-date analyses specific to climate-impacted hotspots. Applying classification algorithms to Landsat imagery using periodic spectral information allows us to monitor changes or patterns in annual land use mapping that are specific to rice cropping cycles. Historical MODIS data (2000-2014) is used to train Long Short Term Memory Network models (LSTM) for predicting such cycles from 2015 onwards. This establishes a ‘climate baseline’ for previous decades which allows us to determine crop cycle performance relative to this baseline by examining differences in predicted and observed NDVI. Our method allows a remote-sensing only approach to measuring climate change adaptation of crops, validating the use of deep learning and EO in extracting key insights into climate adaptation strategies. The role of AI in EO to model climate effects and interacting geo-factors has implications for food production sustainability and decision making in reaching Sustainable Development Goal 2 targets. Overall, our approach has the potential to support evidence-based scaling of climate smart agriculture practices leading to more resilient agrifood systems, while providing critical crop productivity specific intelligence that can help mitigate food insecurity.

Rice is the staple food for half the world's population. Around 90% of the world's rice is produced in Asia, particularly in the main river deltas. However, due to their geographic location and low altitude, Asian deltas are classified among the regions most vulnerable to the impacts of climate change. Additionally, impacts are exacerbated by the activities of rapidly growing populations.

Within the Space Climate Observatory program, the objective of the VietSCO project is to study such impacts on the Mekong Delta, one of the most important rice deltas in the world. Sentinel-1 data was used to determine rice area, crop calendar, number of crops per year, etc. and to assess the changes that have occurred over the past decade. ALOS-PALSAR data was used to detect the flooding status of rice fields, which determines methane emissions.

It is seen that the habitat suitable for rice has undergone drastic changes. Options have been identified to reduce and adapt to the risks of increasing salinity intrusion, drought and flooding, and land submergence. Among mitigation practices, we highlight practices that mitigate methane emissions from rice. For the Global Methane Pledge, rapidly reducing methane emissions from rice fields is considered the most effective strategy to reduce global warming. Work is currently underway to evaluate the use of ALOS PALSAR, calibrated by automatic water level devices, to locate continuously flooded rice fields, where incentive actions must be taken to reduce methane emissions through proper drainage practices.

The work carried out aimed to arouse the interest of local and national decision-makers in the use of Earth observation to monitor the evolution of rice production under the impacts of climate change and anthropogenic pressures. To this end, a platform has been created to allow decision-makers to visualize current and projected impacts according to scenarios. Finally, we will discuss the potential for application of the approach developed to other large deltas in Asia.

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Smart-farming applications have seen a steep rise in the incorporation of EO data, mainly due to the availability of Sentinel 1 and 2, and to some extent the increased availability of affordable high-resolution satellite data. This increased uptake also results in ever increasing demands by the users in the level of detail as well as thematic accuracy of the products. While some of these demands can be met with high-resolution satellites, other demands and use cases remain too challenging. To fill this monitoring gap, Unmanned aerial vehicles (UAVs) or drones have become a popular and useful tool to identify and map (a)biotic stress at (sub)cm resolution. However, an important limitation remains the poor scalability due to the high costs and the need for manual, specialized, interventions. Here, a solution is proposed to circumvent these limitations, bringing UAV-based crop monitoring a step closer as a complementary companion to the satellite-based monitoring tools. As a first step, methodologies are presented with small, cheap drone systems that do not require elaborate training or licenses to operate. Secondly, a drone sampling methodology is proposed to optimize the cost-efficiency of data collection and processing, based on a direct georeferencing workflow to project the individual images on a regional Digital Surface Model. This allows for low or even negative image overlap which will be at least 70% more efficient compared to a traditional Structure-From-Motion workflow. Image analysis is performed on the individual images to identify plants or plant features like weeds, disease spots or flowers. To scale-up the adaptation of the drone sampling technology, the workflow has been integrated into MAPEO, VITO’s drone processing platform, culminating in the generation of consistently accurate and reliable data products. The overall workflow will be demonstrated with several use cases like agricultural insurances and invasive weed detection.

Spatially explicit information on land cover (LC) is commonly derived using remote sensing. The lack of training data remains a major challenge to produce accurate LC products. Although in-situ surveys are generally regarded as reliable information sources, there tend to be inconsistencies between in-situ LC data and the information derived from satellites due to the different view points.

Here, we develop a computer vision methodology to extract LC information from photos from the Land Use-Land Cover Area Frame Survey (LUCAS). A representative sample of 1120 photos covering eight major LC types across the European Union was selected. We then applied semantic segmentation to these photos using a neural network (Deeplabv3+) trained with the ADE20k dataset. For each photo, we extracted the original LC identified by the LUCAS surveyor, the segmented objects, and the pixel count for each ADE20k class. Using the latter as input features, we then trained a Random Forest model to classify the LC of the photo. Examining the relationship between the objects/features extracted by Deeplabv3+ and the LC labels provided by the LUCAS surveyors demonstrated how the LC classes can be decomposed into multiple objects, highlighting the complexity of LC classification from photographs. The results of the classification show a mean F1 Score of 89%. In a stage, we analyzed the semantic gap between this subset of LUCAS 2018 in-situ LC data and three high-resolution thematic LC products derived from satellite data, namely, Google’s Dynamic World, ESA’s World Cover, and Esri’s LC maps. Following an experimental analysis, we explore the feasibility of using geo-referenced street-level imagery to bridge the gap between information provided by field surveys and satellite data.

The spatial extent of croplands globally is a crucial input to global and regional agricultural monitoring systems. Although many new remotely sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely sensed cropland products: the European Space Agency’s WorldCereal and the cropland layer from the University of Maryland (GLAD croplands). Since the fallow class is included in the GLAD croplands map and we consider this relevant for food security applications we use this layer as in inital base map. We then aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics of the individual layers, but more importantly, it represents a better spatially explicit cropland layer for early warning and food security assessment purposes.A version of the map has been used as part of JRC's early warning systems ASAP.

Ground truth reference data availability has long been a significant limiting factor for remote sensing data-driven model training. A vastly underutilized source of data comes from administrative data collected by mainly public service bodies. A demonstrated successful utilization is the EuroCrops project where we created training labels for crop type classification from farmers’ self-declarations on the crop types they cultivate. However, a key challenge of the project was the lack of domain-specific expert knowledge and incompatibilities between data released by different governments.

Recently, large language models (LLMs) have demonstrated remarkable abilities in information extraction and synthesis. We therefore propose training an LLM-based domain-specific foundation model to automate the data extraction and harmonization process from administrative data to ready-to-use training data.

This presentation will discuss a toy example of training a small language-based transformer model using the administrative data collected during EuroCrops with agriculture-based text data such as Wikipedia articles.

Preliminary experimentation with open-source not field-specific LLMs shows that they do offer some interesting domain-specific insights when decoding acronyms. However, they do not outperform traditional machine translation methods yet, such as Google translate, and at times output extremely believable but misleading results in the niche field of agriculture. Such bogus generations highlight the need for a grounded generation mechanism in the usage of LLMs.

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