Surface and Subsurface Soil Moisture Estimation Using Fusion of SMAP, NLDAS-2, and SOLUS100 Data with Deep Learning

Accurate knowledge of surface and subsurface soil moisture (SM) is essential for hydrologic modeling, weather forecasting, and agricultural water management. NASA’s Soil Moisture Active Passive (SMAP) satellite (level 3) provides ‘surface’ SM with 2–3 days temporal resolution, hence lacks daily and subsurface SM information. This study developed a convolutional neural network – long short-term memory (ConvLSTM) deep learning model to produce daily surface (5 cm) and subsurface (25 cm) SM products by integrating SMAP Level 3 ancillary data, North American Land Data Assimilation System Phase 2 (NLDAS-2) SM, and Soil Landscapes of the United States (SOLUS100) digital maps across the contiguous U.S. Two input scenarios were evaluated: scenario 1 used only SMAP ancillary data, and scenario 2 included both SMAP ancillary data and SOLUS100 soil maps. Model evaluation with in-situ SM data showed higher accuracy for scenario 2, indicating the importance of soil properties (texture and bulk density) in SM estimation. Coarse-textured soils showed the highest estimation accuracy, followed by medium- and fine-textured soils. The model also performed in estimating subsurface SM than surface SM for most land-cover types. Incorporating SMAP ancillary data and SOLUS100 digital soil maps into ConvLSTM improved surface and subsurface SM estimation. The results highlight the potential of deep learning for integrating multi-source multi-scale observations for improving SM estimation at large scale.