Artificial neural network-based estimation of root zone soil moisture in the western Liaohe river basin

Soil moisture is the core of water conversion and circulation that connects the atmosphere, surface, soil, and subsurface. As a basic climate variable of the global climate observing system, soil moisture plays a vital role in regional-scale water and energy exchange. The estimation of root zone soil moisture (RZSM) and the analysis of its spatio-temporal variations are of great significance for crop yield assessment, flood and drought prediction, and soil and water conservation. Based on the artificial neural network (ANN), this study estimated the daily RZSM in the Western Liaohe River basin during 2019—2020 with remote sensing image-based surface soil moisture, cumulative precipitation, cumulative daily maximum and minimum temperatures, relative humidity, sunshine duration, cloud coverage, wind speed, soil attributes, normalized difference vegetation index, and actual evapotranspiration as explanatory variables, the in-situ measured RZSM as the target variable, and the 2013—2018 data used for model training. The estimated results show that the average RMSE and average R between the RZSM estimated based on ANN and the in-situ measured RZSM were 0.056 7 m³/m³ and 0.611 7, respectively. Therefore, the ANN can effectively estimate the RZSM in the Western Liaohe River basin. In addition, this study shows that the variation in the soil moisture is closely related to precipitation.