Estimation of the Soil Moisture Content in a Desert Steppe on the Mongolian Plateau Based on Ground-Penetrating Radar

Desert grasslands are a crucial component of terrestrial ecosystems that play vital roles in regional and global hydrological cycling, climate change, and ecosystem balance through variations in their soil moisture content (SMC). Despite this, current research on the SMC of desert grasslands remains...

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Bibliographic Details
Published inSustainability Vol. 16; no. 19; p. 8558
Main Authors Li, Kaixuan, Liao, Zilong, Ji, Gang, Liu, Tiejun, Yu, Xiangqian, Jiao, Rui
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 02.10.2024
Subjects
Agricultural production
Climate change
Ecosystems
Grasslands
Hydrology
Livestock
Methods
Mixed media
Moisture content
Precipitation
Radar
Remote sensing
Resource management
River ecology
Sustainable development
Mongolia
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Summary:Desert grasslands are a crucial component of terrestrial ecosystems that play vital roles in regional and global hydrological cycling, climate change, and ecosystem balance through variations in their soil moisture content (SMC). Despite this, current research on the SMC of desert grasslands remains insufficient, with many areas remaining underexplored. In this study, we focused on a typical desert grassland located in the northern foothills of the Yinshan Mountains. Ground-penetrating radar (GPR) exploration and soil sampling were used to test existing mixed-media models, and a new mixed-media model was calibrated using cross-validation methods. Among the three general mixed-media models, the Topp and Roth models yielded more accurate SMC estimates for the study area, with root mean square errors of 0.0091 g/cm3 and 0.0054 g/cm3, respectively, and mean absolute percentage errors of 25.86% and 19.01%, respectively, demonstrating their high precision. A comparison of the calibrated and original mixed-media models revealed that the estimation accuracy was significantly improved after parameter calibration. After parameter calibration, the Ferre model achieved an accuracy comparable to that of the Topp model. Parameter-calibrated models can be used to estimate the SMC using GPR data, offering a higher precision than general models and possessing greater suitability for the study area. The soil in the study area is primarily composed of sand particles and is therefore more compatible with the parameters of the Topp model, whereas the Ferre model requires further parameter calibration to achieve effective application.
ISSN:2071-1050
2071-1050
DOI:10.3390/su16198558