Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

Abstract This study investigates the feasibility of artificial neural networks ( ANN s) to retrieve root zone soil moisture ( RZSM ) at the depths of 20 cm ( SM 20) and 50 cm ( SM 50) at a continental scale, using surface information. To train the ANN s to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANN s were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANN s had high skill in retrieving SM 20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM 50. The comparison of the ANN s showed that using soil texture data improved the model performance, especially for the estimation of SM 50. It was demonstrated that the ANN s had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity ( SMOS ) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.