Using a Remote Sensing Driven Model to Analyze Effect of Land Use on Soil Moisture in the Weihe River Basin, China

In-depth study of the soil moisture mechanisms and understanding of the soil moisture transport law has an important practical significance for regional water resources management and the challenge of the water resources scarcity. Using traditional methods of soil moisture monitoring, deep soil layers can be monitored, but continuous monitoring of soil moisture at the regional level cannot be achieved. Although remote sensing simulation models can meet regional scale needs, these models are confined to the surface soil layer, and research on deep soil moisture inversion is still lacking. This paper focuses on these two issues, and investigates a remote sensing-driven soil moisture monitoring model for the Weihe River Basin. Considering water resource management needs in the Weihe River Basin, we improved the structure of the soil moisture balance model and optimized model parameters to build the remote sensing driven soil moisture balance model (RS-SWBM). Based on soil moisture modeling, the effect of vegetation on soil moisture in the Weihe River Basin was analyzed. The RS-SWBM developed for the Weihe River Basin was validated with observational data and Global Land Data Assimilation System (GLDAS) soil moisture data products. Based on the correlation analysis, correlation coefficients were all above 0.80, reflecting the effectiveness of the model. The effects of different vegetation types on soil moisture dynamics and consumption efficiency were analyzed. The results indicated that different vegetation types experienced different seasonal variations, vertical patterns, and consumption efficiencies, with strong correlations existing between these parameters and land use as well as precipitation.