A 1 km Surface Soil Moisture Product Downscaled from SMAP Using MODIS Shortwave-Infrared Reflectance for the Tibetan Plateau Validated Across Five In Situ Networks

Surface soil moisture (SSM) regulates land–atmosphere water and energy exchanges over the Tibetan Plateau (TP); yet, passive microwave SSM products, such as those from the Soil Moisture Active Passive (SMAP) mission, are limited to coarse spatial resolutions of tens of kilometers. Although various downscaling approaches have been developed using radar backscatter, thermal infrared observations, and optical–thermal indicators, the value of shortwave infrared (SWIR) reflectance—whose moisture-dependent absorption characteristics offer fine-scale soil moisture information—remains insufficiently assessed across contrasting environments. To address this gap, we developed a 1 km SSM product for 2017–2020 by downscaling 36 km MCCA-SMAP estimates over the TP, combining the normalized shortwave difference soil moisture index (NSDSI) derived from MODIS reflectance with a first-order Taylor expansion and daily sensitivity calibration. The product was evaluated against ground observations from five monitoring networks spanning diverse climatic and land surface conditions using spatial, temporal, regional-scale, and spatiotemporal comparison frameworks, and was benchmarked against three published 1 km downscaled products based on radar backscatter, diurnal temperature amplitude, and soil evaporation efficiency, respectively. Compared with the original MCCA-SMAP SSM, the spatial regression slopes of the NSDSI-based downscaled product moved closer to unity at all five networks, with spatial correlations increasing at four networks particularly at SMN-WDL (from −0.04 to 0.59) and Shiquanhe (from 0.12 to 0.44), although the near-unity slopes at Shiquanhe were sensitive to unusually high estimates at SQ11. The NSDSI-based downscaled product showed lower mean station-level temporal correlations across the five networks, whereas regional-scale temporal correlations remained nearly unchanged. Relative to the existing 1 km products, the NSDSI-based product had spatial slopes closer to unity at all networks except Maqu, although correlation and error rankings varied among networks. At Maqu, where the vegetation cover is relatively high, the NSDSI-based downscaled SSM had spatial correlations below 0.2 and exhibited lower correlations and a higher ubRMSD than the radar- and thermal inertia-based downscaled products. The resulting downscaled SSM maps preserved the broad west-to-east moisture gradient and showed finer spatial detail. These multi-network results identify improved spatial pattern representation as the principal benefit of the evaluated SWIR-assisted downscaling approach, while demonstrating that gains in spatial agreement do not necessarily improve temporal performance or reduce ubRMSD.