An independent evaluation of global 1 km soil moisture products using in-situ and airborne observations

High-resolution soil moisture data are essential for applications in agriculture, hydrology, and disaster management. Four global daily SM products at 1 km resolution have recently been developed, being the Seamless Soil Moisture (SSM), Global Surface Soil Moisture (GSSM), Global Land Surface Satellite (GLASS), and a downscaled SMAP product (DSMAP). These products rely on either machine learning or empirical regression models, offering significant potential but raising concerns regarding their generalization capability and spatial fidelity. Previous evaluations of these high-resolution products have relied predominantly on point-scale comparisons using the same in-situ networks employed for model training. Consequently, this study provides an independent evaluation using 1545 global in-situ stations excluded from product development and airborne passive microwave measurements from five field campaigns across North America and Australia. Results reveal that none of the evaluated products met the target unbiased Root Mean Square Error (ubRMSE) of 0.04–0.06 m 3 /m 3 , with observed values ranging from 0.097 to 0.104 m 3 /m 3 . All products exhibited narrower dynamic ranges (0.10–0.30 m 3 /m 3 ) than those of in-situ observations (0.05–0.40 m 3 /m 3 ), particularly underestimating wet and overestimating dry extremes. GLASS ( R = 0.576) and DSMAP ( R = 0.556) generally outperformed GSSM ( R = 0.504) and SSM ( R = 0.399) in capturing temporal dynamics relative to ground measurements. Spatially, airborne-based evaluation highlighted limitations in capturing fine-scale heterogeneity, particularly for SSM (mean R = 0.19) and GSSM (mean R = 0.31), which showed a narrow dynamic range and nearly static spatial pattern with weak response to regional rainfall. In contrast, DSMAP effectively captured the temporal dynamics of airborne data (mean R = 0.57) but retained coarse resolution artifacts from its downscaling process. Expanding training datasets, enhancing the generalization capability of the machine learning methods employed, and conducting rigorous spatial evaluations are identified as critical steps to ensure the reliability of high-resolution soil moisture products for operational applications.