This study introduces an open-data framework that integrates satellite-derived evapotranspiration with depth-resolved soil moisture to enable precise, pixel-level irrigation scheduling. By harmonizing multi-sensor data and employing interpretable machine learning, the system generates continuous, high-resolution guidance for farmers, bridging the gap between broad remote sensing observations and field-specific needs. The framework demonstrated significant efficiency by reducing total irrigation requirements by approximately one-third compared to conventional practices, while maintaining high accuracy in predicting crop water needs. This reduction highlights the potential for substantial water conservation in semi-arid regions without compromising crop yields, offering a practical solution for sustainable resource management. This approach is vital to the open-data community as it showcases how accessible, free satellite imagery can be combined with advanced analytics to solve real-world agricultural challenges. It proves that open geospatial data, when processed through transparent and scalable methods, can directly support precision agriculture, promoting equitable access to water-saving technologies for diverse farming communities.
Source: icarda.orgPublished on 2026-08-24