As the Hass avocado crop expands exponentially in Colombia, concern about its increasing water use is on the rise. This research aimed to develop IS-SAR, a free-access web application to schedule irrigation for Valle del Cauca’s Hass growers. We calibrated the water cloud (WCM) and artificial neural network (ANN) models using field data measurements from Hass avocado orchard plots in Valle del Cauca (Colombia) and Sentinel 1 (S1) satellite imagery measurements and evaluated their performance computing the root-mean-square error (RMSE) and Pearson correlation coefficient (\(r\)). IS-SAR estimates the surface soil water content from the most recent S1 image, becomes it in water depth, and recommends to users apply irrigation according to allowable depletion limits, computed from a spatially distributed \(\theta\) at field capacity and permanent wilting point obtained from a soil database of the study area. Our results indicate that the surface soil water content was retrieved with a better performance by the ANN (RMSE = 0.05 m³ m⁻³, \(r=0.74\)), compared with the WCM (RMSE = 0.06 m³ m⁻³, \(r=0.47\)). IS-SAR simulations in validation orchard plots result in irrigation events of up to 107 L tree⁻¹ for 3.4 h. The IS-SAR web application provides near-real-time irrigation information to assist Hass avocado growers in designing better irrigation routines and improving the regional understanding of water crop consumption.

随着哥伦比亚的哈斯牛油果产量呈指数级增长，人们对其用水量不断增加的担忧也在增加。这项研究旨在开发 IS-SAR，这是一款免费访问的网络应用程序，用于为考卡山谷 (Valle del Cauca) 的哈斯种植者安排灌溉。我们使用来自 Valle del Cauca（哥伦比亚）哈斯鳄梨果园地块的现场数据测量和 Sentinel 1 (S1) 卫星图像测量来校准水云 (WCM) 和人工神经网络 (ANN) 模型，并评估其计算均值的性能-平方误差 (RMSE) 和皮尔逊相关系数 ( \(r\) )。IS-SAR 根据最新的 S1 图像估计地表土壤含水量，将其转化为水深，并建议用户根据允许的消耗限制进行灌溉，该限制是根据田间持水量和永久水量的空间分布 \(\theta\) 计算得出的从研究区域的土壤数据库获得的萎蔫点。我们的结果表明，与WCM（RMSE = 0.06 m 3 m −3 ）相比，ANN（RMSE = 0.05 m ³ m ^{−3 ，} \ ⁽ r =0.74\) ）反演地表土壤含水量具有更好的性能，\(r=0.47\) )。验证果园地块中的 IS-SAR 模拟导致高达 107 L 树^-1的灌溉事件持续 3.4 小时。IS-SAR 网络应用程序提供近乎实时的灌溉信息，帮助哈斯鳄梨种植者设计更好的灌溉程序并提高区域对水作物消耗的了解。