Conference Agenda

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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