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 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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