Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping

Soil moisture is one of the core hydrological and climate variables that crucially influences water energy budgets. The spatial resolution available soil products generally coarser than 25 km, which limits their hydro-meteorological eco-hydrological applications management resources at watershed agricultural scales. A feasible solution to overcome these limitations downscale coarse with support higher-resolution information. Although many auxiliary have been used for this purpose, few studies analyzed applicability effectiveness in arid regions. To end, we comprehensively evaluated four commonly variables, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), TVDI (Temperature Dryness SEE (Soil Evaporative Efficiency), against ground-based observations during vegetation growing season Heihe River Basin, China. Performance metrics indicated most sensitive (R2 ≥ 0.67) because it controlled by evaporation limited moisture. similarity patterns also showed best captures changes, STD (standard deviation) HD (Hausdorff Distance) less 0.058 when compared PLMR (Polarimetric L-band Multi-beam Radiometer) products. In addition, was mapped RF (Random Forests) using both single 11 types multiple variable combinations. found be scaling mapping accuracy 0.035 cm3/cm3. Among combination LST, NDVI, enhance 0.034