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Chile has been in a mega-drought since 2010, leading to critical water scarcity in the central zone. Research projects that to 2040 climate change, in conjunction with the loose use of water for irrigation, will cause severe water stress to the whole country. Agriculture amounts to 72% of the water consumption in the zone, and in cities like El Tabo, 18% of the population consumes water provided in cistern trucks. Therefore, reliable registering of water use is essential for sustainable water management. However, in-field manual measurements of water use can be tedious, time, and labor- intensive and may exhibit significant spatial variability, and be inefficient for surveying large areas. Currently, private initiatives lead the efforts in water use optimization, consisting mainly of expensive in-site sensors and drones’ imagery, and the public sector is currently designing new policies, regulations, and management. Still, there is no record of the current status and their variation through time of the farms and paddocks at a country level. This paper aims to develop and test an automatic paddock’s boundaries segmentation/recognition system using hyperspectral images from Sentinel 2. The developed system includes new image enhancements, bands selection, and training a model using the images and handmade polygons. Having the boundaries, farmers could use them to understand the impact the climate change is causing on their crops and hopefully enable them to optimize data-driven irrigation scheduling and other optimization tools to preserve the water and keep its use at the minimum possible. The method was tested with sentinel-2-l2a data with 10-meter ground resolution in four spectral bands: Band 02 (visible blue), Band 03 (visible green), Band 04 (visible red), Band 08 (near-infrared). Multiple metrics are presented for results. It can play a vital role in supporting improved Chile’s water accounting—both independently and in combination with in-situ monitoring.
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