DOI: 10.5281/zenodo.8363749
title: DroughtScope dataset for AI4EO IMAGIN-e Challenge

description: Climate change poses new challenges to the food industry, which is required to optimise production in terms of quantity and quality of products against reduced water and land availability due to increased periods of drought and urbanisation. The availability of accurate and real-time information about ET is expected to improve decision-making concerning the optimisation of agricultural practice by properly allocating the resources needed. In this regard, the idea of using real-time ET estimates has a significant societal value, as it contributes to <strong>sustainable agriculture</strong>, <strong>food security</strong>, <strong>water conservation</strong>, and <strong>climate change adaptation</strong>. The envisioned business model includes data-driven agricultural consulting services, a subscription-based platform and partnerships with agritech companies.<br> Evapotranspiration (ET) data helps irrigation planning and is a powerful tool for managing land and water resources. Water scarcity makes knowledge of crop water consumption essential for water budgeting of connected ecosystems like agriculture, industry and cities. In a context in which severe droughts induce farmers to change crop typology to face reduced water availability, even in areas historically characterised by water abundance, offline processing of satellite data can result in delayed or sub-optimal decision-making. Timely information about the status of the cultivations can dramatically reduce water consumption, leading to cost savings, increased quality and quantity of food production and profitability. In this context, exploiting pre-trained models is an enabling factor for onboard processing, which is not feasible for direct retrieval of ET from measured brightness temperature via computationally expensive radiative transfer models.<br> This project aims to develop a decision support system helping public and private agrifood decision-makers optimise water resource management. Our solution provides <strong>early warnings</strong> about <strong>crop water stress</strong> based on the estimate of the evaporative stress index (<strong>ESI</strong>), which is one of the most important indicators of stress conditions identified in the literature.<br> <strong>Main innovations</strong> Original deep learning architecture to infer information typically contained in the thermal domain from the near-infrared one Real-time onboard estimates thanks to lightweight processing Multi-task architecture for ESI estimation and LULC mapping <strong>Benefits for users/stakeholders</strong> Optimization of water resources management to tackle increasing water scarcity Possibility to implement local actions based on daily warning maps Optimization of food production Reduction of costs and resources employed for field monitoring <strong>Benefits for the scientific community</strong> Availability of new data with better resolution of the ones today available provided by the ECOSTRESS mission Daily LULC maps to tackle with high-dynamic phenomena (river mobility, sedimentation, deforestation) <strong>Data</strong><br> This research exploits L1C data from the <strong>IMAGIN-e hyperspectral</strong> (HS) sensor, which features 50 spectral bands within the range [450,950] nm with a spatial resolution of 45 metres. The primary data sources are integrated with auxiliary data useful for classifying agricultural lands and estimating Evapotranspiration (ET).<br> To this end, we use the <strong>ESA WorldCover 2021</strong> dataset (https://worldcover2021.esa.int/) as the ground truth for Crop Classification; the original dataset at 10m spatial resolution is resampled at 45 meters with a max-voting strategy. Finally, a binary map is obtained in the code, considering only the Cropland values as 1 and 0 otherwise.<br> For ET training, we have considered <strong>ECOSTRESS</strong> data as a reference. ECOSTRESS provides ET data based on the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) method, with a spatial resolution of approximately 70 metres, which will be rescaled to 45 metres for consistency with input HS data.<br> The patches (14) included in the dataset are a stack of all these data. So we have 54 channels in this order: 1:50: hyperspectral bands 51: ESA WorldCover 2021 at 45m 52: Evaporative Stress Index (ESI) 53: Quality Control Band 54: Evapotranspiration In the data folder are also available original ESI and LULC data.