Work

Multi-source remote sensing retrieval and spatiotemporal distribution characteristics of soil moisture content in typical karst farmlands of southwestern China

Year: 2025

Type: article

Abstract: Study Region Southwest karst region,China Study Focus In karst regions characterized by high vegetation coverage and complex topography, indirect prediction methods utilizing natural environmental factors—including vegetation, topography, and meteorology—are essential for estimating farmland soil moisture content (SMC). Identifying optimal combinations of environmental factors and machine learning algorithms is fundamental to developing effective SMC prediction models. Focusing on Qiannan Prefecture in Guizhou Province—a core karst area of southwestern China—this study established an optimal selection mechanism for input variables and machine learning algorithms through environmental factor importance analysis and optimization experiments. New Hydrological Insights for the Region This study developed a methodology for constructing optimal soil moisture content (SMC) prediction models through systematic evaluation of environmental factors and machine learning algorithms. Applied in Qiannan Prefecture, the approach produced a farmland SMC20 prediction model integrating eight key environmental variables with XGBoost algorithm. Validation demonstrated high accuracy within the study area ( R² =0.85, RMSE =0.057, Bia s = -0.013, KGE =0.887). Compared to conventional models using fixed algorithms and variables, this method ensures relative optimality through scientific screening. Transferability tests in Guangxi (China) and Baena (Spain) revealed decreasing accuracy with increasing distance from the study area, prompting discussion on model generalization. Temporally, farmland SMC20 peaked in June and reached its lowest in January. Spatially, after excluding transitional zones, SMC20 showed gradual decrease with distance from rivers in karst basins. These findings provide quantitative data and technical support for mechanistic studies, temporal forecasting, and drought warnings in karst farmland systems.

Source: Journal of Hydrology Regional Studies

DOI: 10.1016/j.ejrh.2025.103052

Authors: Zan Zhu, Dongdong Liu, Xuyang Guo, Ya Yang, Shimei Yang, Lianrui Wang

Institutions: Guizhou University, Guilin University of Aerospace Technology

Language: English

Topic: Soil Moisture and Remote Sensing

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Climate action, Life on land

Open Access status: gold

Estimated APC paid: $2,600