Estimation of Root Zone Soil Moisture in Semi-Arid Area Based on Swi

To deepen the understanding of the spatiotemporal variability of root zone soil moisture (RZSM) in semi-arid regions, this study focuses on the Xiliaohe Basin in northern China. By integrating in situ measurements and remote sensing-derived surface soil moisture (SSM) data, we developed an RZSM estimation model for the basin through a hybrid framework combining exponential filtering and the random forest algorithm. The optimal characteristic time parameter T was calibrated using the soil water index (SWI) method, allowing reconstruction and spatio-temporal analysis of RZSM from 2013 to 2020. Key findings include: The SWI effectively captured temporal trends in RZSM, with a significant correlation coefficient (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}=\mathbf{0. 4 7 3 9}, \mathbf{p}&lt;\mathbf{0. 0 5}$</tex>) and low error (RMSE <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=0.072 ~\mathrm{m}^{3} / \mathrm{m}^{3}$</tex>) against ground observations at the station scale. The SWI also demonstrated strong consistency with the GLDAS2-1 product (RMSE <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=0.066 ~\mathrm{m}^{3} / \mathrm{m}^{3}$</tex>). The RZSM exhibited distinct spatiotemporal patterns: interannual variability showed an initial decline followed by an upward trend, seasonal dynamics were characterized by higher values in summer/autumn and lower values in winter, and spatial distribution revealed elevated moisture levels in the southwestern and eastern margins compared to the central and northern regions. These findings provide critical scientific insights for water resource management, agricultural productivity optimization, and ecological conservation in semiarid environments.