Comparing Irrigation Identification Methods in Colorado: Limitations of Downscaled SMAP Soil Moisture

With limited freshwater resources and growing water demands, it is imperative to identify and monitor water needs. Agricultural irrigation is the world’s largest water user, comprising 45–90% of freshwater withdrawals. Identifying and tracking changes in irrigated land is necessary for sustainable water management and forecasting agricultural water needs and patterns; however, the low resolution of available remote sensing observations hinders field-scale analysis. Downscaling soil moisture observations has been offered as a solution to this problem. Our study compares the performance of five irrigation identification methods using a newly developed downscaled deep soil moisture extrapolation method used to estimate Soil Moisture Active Passive (SMAP) soil moisture (SM) at a spatial resolution of 400 m for 5 cm, 20 cm, and 50 cm depths. Using this data along with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation and Landsat Normalized Difference Vegetation Index (NDVI), we evaluate these methods with respect to crop type, irrigation type, and observation depth on agricultural fields in Colorado using the irrigation maps provided by the Colorado Decision Support System from 2015 through 2024. With no crop/irrigation type–observation depth combination exceeding an F1 score of 0.149 or MCC value of 0.163, we find that none of the methods can accurately identify irrigated land regardless of crop type, irrigation type, and observation depth. Because these methods succeeded in earlier small-area studies, and because the classified maps resolved into large, spatially uniform blocks, we interpret this as a limitation specific to field-scale detection over large, heterogeneous regions rather than a defect in the dataset. These findings highlight a limitation of this downscaled SM dataset and raise the question of whether other downscaled soil moisture products share this limitation.