Root-zone soil moisture estimation based on remote sensing data and deep learning.

Soil moisture in the root zone is the most important factor in eco-hydrological processes. Even though soil moisture can be obtained by remote sensing, limited to the top few centimeters (<5 cm). Researchers have attempted to estimate root-zone soil moisture using multiple regression, data assimilation, and data-driven methods. However, correlations between root-zone soil moisture and its related variables, including surface soil moisture, always appear nonlinear, which is difficult to extract and express using typical statistical methods. The artificial intelligence (AI) method, which is advantageous for nonlinear relationship analysis and extraction is applied for root-zone soil moisture estimation, but by only considering its separate temporal or spatial correlations. The convolutional long short-term memory (ConvLSTM) model, known to capture spatiotemporal patterns of large-scale sequential datasets with the advantage of dealing with spatiotemporal sequence-forecasting problem, was used in this study to estimate root-zone soil moisture based on remote sensing-based variables. Owing to limitation of regional soil moisture observation data, the physical model Hydrus-1D was used to generate large and spatiotemporal vertical soil moisture dataset for the ConvLSTM model training and verification. Then, normalized difference vegetation index (NDVI) etc. remote sensing-based factors were selected as predictive variables. Results of the ConvLSTM model showed that the fitting coefficients (R) of the root-zone soil moisture estimation significantly increased compared to those achieved by Global Land Data Assimilation System products, especially for deep layers. For example, R increased from 0.02 to 0.60 at depth of 40 cm. This study suggests that a combination of the physical model and AI is a flexible tool capable of predicting spatiotemporally continuous root-zone soil moisture with good accuracy on a large scale.