Prediction of deep soil water content (0–5 m) with in-situ and remote sensing data

Accurate and timely evaluation of soil water content (SWC) in water-limited arid and semiarid regions will provide an essential reference for scientifically based water management strategies and vegetation restoration practices. Theoretical and empirical models based on remote sensing data have been successfully applied to predict large-scale SWC. However, most existing models are limited to the surface soil layer, while the prediction of SWC in the deep soil layer (1–5 m) has been overlooked because of the shortage of long-term and large-scale filed-observed data. In this study, a south-north transect (ca. 860 km) was selected across the Chinese Loess Plateau (CLP). Based on both long-term remote sensing and in-situ data (2013–2016), pedo-transfer functions (PTFs) for the SWC in the 0–5 m profile were developed using multiple regression, random forest, and artificial neural network. The results showed that evapotranspiration, potential evapotranspiration, soil surface temperature, total shortwave broadband albedo, and normalized difference vegetation index were important remote sensing parameters, whereas soil texture and bulk density were important in-situ parameters for SWC PTFs development. Among the three PTF development methods, machine learning (i.e. random forest and artificial neural network) obtained a higher accuracy than multiple regression. For different combinations of input parameters, the introduction of in-situ factors significantly improved the accuracy of PTFs compared with PTFs based on remote sensing data only. The artificial neural network developed a PTF with only five input variables that predicted SWC with reasonable accuracy (root mean square error = 0.039, R2 = 0.697, mean absolute percentage = 24.8) and is thus useful for many applications on the Loess Plateau of China. In the future, more attention should be given to the role of in-situ parameters when developing PTFs for deep SWC prediction.