A novel framework for pixel-wise estimation of irrigation water use by integrating remote sensing and reanalysis data

Accurate and spatially explicit estimates of irrigation water use (IWU) are essential for understanding the earth system dynamics in the Anthropocene. Recent advances in remote sensing have spurred growing interest in satellite-based IWU estimation. However, large-scale IWU estimates remain limited in both accuracy and spatial resolution due to inherent deficiencies in satellite observations and methodological constraints. Here, we present a novel framework for spatially explicit IWU estimation by integrating satellite-based soil moisture and evapotranspiration (ET) products with reanalysis data. Within this framework, we developed two alternative models: one based on root zone soil moisture (RSM) and the other on surface soil moisture (SSM), both grounded in soil water balance principles. The models estimate IWU by quantifying differences in soil moisture, ET, and drainage between natural and irrigated conditions. Both the RSM- and SSM-based models perform well in predicting prefecture-level IWU during the validation period, achieving coefficients of determination (R²) between 0.72 and 0.90 and root mean square errors (RMSE) of 0.55–0.66 km³ /year, depending on the spatial scale of calibration (i.e., province, prefecture, or subregion). By combining our framework with different satellite products, we produce ensemble IWU estimates at 1 km resolution across China. The resulting dataset reveals a clear increasing trend in China’s IWU from 2001 to 2020, primarily driven by the expansion of irrigated area, while its interannual variability is largely controlled by fluctuations in IWU per unit irrigated area. This dataset shows a significant advancement in both accuracy and spatial detail over existing datasets and will be useful for irrigation-related research and agricultural water management in China.