Detecting Irrigation From Spectral Differences Between Satellite and Modeled Soil Moisture Across the Contiguous United States

Irrigation alters the terrestrial water cycle, yet its spatial distribution and temporal variability remain poorly constrained because existing data sets often rely on indirect proxies or inventories rather than observations tied to land-surface water-balance dynamics. Here, we introduce a wavelet-based method to detect irrigation from spectral differences between modeled and satellite-observed soil-moisture time series, implemented using Noah–MP simulations and Soil Moisture and Ocean Salinity (SMOS) observations. The method, applied across the contiguous United States, exploits differences in seasonal to sub-seasonal soil-moisture variability between irrigation-off model simulations and satellite observations, particularly in the 6–18-month band, enabling irrigation detection. The resulting soil-moisture-based irrigation classification complements high-resolution optical irrigation data sets, while reflecting the coarse-scale information contained in SMOS soil moisture. When incorporated into Noah–MP simulations, it improves the distribution of simulated irrigation water use. This framework supports scalable irrigation detection from satellite soil moisture and improved irrigation representation in Earth system models.