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Demographic growth and economic development are putting unprecedented pressure on finite water resources. It is estimated that global water demand will increase by 50% by 2030 resulting in a potentially devastating water shortage 1. As 70-95% of all water withdrawals are farming-related 2, agriculture plays a key role in this dynamic. Inefficient water use in agriculture, often due to the invisibility of crop-specific water requirements, underscores the need for precise irrigation management to optimize water allocation and conservation. Ground sensors and drones can help to tackle this problem but they need to be deployed locally which does not scale. Satellites with instruments in the visible domain such as ESAs Sentinel-2 reach the necessary spatial resolution but the water needs of crops in the visible spectrum only become apparent once there has been significant damage. Essentially, once a plant is going brown, it is already too late. Thermal satellites carry the necessary information to obtain evapotranspiration estimates and observe changes in crop health long before visual signs manifest. Existing thermal missions, however, often do not bring the necessary temporal and spatial resolution for large-scale irrigation management. Recent commercial offerings from the New Space industry, such as OroraTechs upcoming Forest constellation, are beginning to turn the tide on this. Currently, we have two satellites in orbit with 9 more launches planned this year. With this, we will reach a global sub-daily revisit time for our Land Surface Temperature (LST) product which can serve as a basis for derived evapotranspiration or soil moisture data products, informing smart irrigation management At a native resolution of 200m, our LST product faces a trade-off between high temporal and spatial resolution. Exciting breakthroughs in artificial intelligence allow us to artificially enhance the resolution of our product threefold to 70m. With this, we combine the advantages of high spatial and temporal resolution for better irrigation management and crop stress detection. Our super-resolution product is evaluated based on ECOSTRESS data which comes at 70m. First validation comparisons of our super-resolved data with Ecostress look promising and we aim to explore the applicability of our enhanced data for improved irrigation management and related soil vegetation water content parameters together with the scientific community. 1 FAO, 2023. https://www.fao.org/faostories/article/en/c/1185405/#:~:text=Agriculture%20is%20both%20a%20major,water%20there%20is%20no%20exception. 2 World Economic Forum, 2023. https://www.weforum.org/impact/sustainable-water-management/
Kondmann et al. (Sat,) studied this question.