Identifying representative sites to simultaneously predict hillslope surface and subsurface mean soil water contents

Many approaches have been proposed to identify the representative sampling sites for estimating the spatial mean soil water contents. However, comparisons on these approaches have seldom been conducted to simultaneously predict the surface and subsurface mean soil water contents. In this study, five approaches were evaluated in identifying representative sites to estimate the surface and subsurface mean soil water contents on a typical hillslope in Taihu Lake Basin, China. They were temporal stability analysis (TSA), k-means clustering with environmental factors as inputs (EFs), combinations of TSA and EFs (EFs + TSA), k-means clustering with surface soil water contents as inputs (Theta), and combinations of TSA and Theta (Theta+TSA). The correlation coefficient (r) and root mean squared error (RMSE) between estimated and measured mean soil water contents were used to evaluate the accuracies during the calibration period (the first 25 dates) and validation period (the last 18 dates). Results showed the optimal number of representative sites on this hillslope was six. When >6 representative sites were selected, the TSA had the lowest accuracies for estimating both surface and subsurface mean soil water contents during validation period (mean RMSE ≥ 0.011 m3 m−3). The Theta and Theta + TSA had better accuracies in estimating surface mean soil water contents during both calibration and validation periods (mean RMSE < 0.007 m3 m−3). However, to estimate surface and subsurface mean soil water contents simultaneously, the EFs and EFs + TSA were more promising (mean RMSE < 0.011 m3 m−3 during validation period), especially the EFs which only required one-time collection of environmental factors. These findings will be beneficial for choosing proper approach to calibrate and validate the remote sensed soil water contents.