Surface-water irrigation underpins global food production, yet its management is increasingly strained by climate variability. Shifts in temperature and precipitation patterns create uncertainty for farmers and water managers, often causing overwatering or underwatering, which reduces crop yields and limits available water resources. Current irrigation advisory systems exhibit key limitations: most are not open-source, operate at coarse spatio-temporal scales, lack scalability, and offer limited integration of local inputs such as in situ sensors or station-based weather data. Advances in satellite remote sensing, open-access datasets, and cloud computing offer opportunities to overcome these gaps. Here we introduce sDRIPS (satellite Data Rendered Irrigation using Penman-Monteith and SEBAL), a modular, open-source Python package that generates weekly, locally tailored irrigation advisories at the farm scale (30 m). sDRIPS integrates satellite observations, global weather model products, and ground-based sensor measurements when available. Its flexible architecture accommodates diverse agricultural contexts, enabling estimation of evapotranspiration, incorporation of nowcast and forecasted precipitation, and computation of net irrigation demand at field and regional scales. To lower adoption barriers, sDRIPS includes tutorials and documentation. By translating state-of-the-art Earth observation and model data into an accessible, customisable decision-support system, sDRIPS enables optimised surface-water irrigation planning under growing climate uncertainty and water scarcity.
Khan et al. (Thu,) studied this question.