Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms

Weitao Lv, Xiasong Hu (), Xilai Li, Jimei Zhao, Changyi Liu, Shuaifei Li, Guorong Li and Haili Zhu
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Weitao Lv: School of Geological Engineering, Qinghai University, Xining 810016, China
Xiasong Hu: School of Geological Engineering, Qinghai University, Xining 810016, China
Xilai Li: College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
Jimei Zhao: College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
Changyi Liu: School of Geological Engineering, Qinghai University, Xining 810016, China
Shuaifei Li: School of Geological Engineering, Qinghai University, Xining 810016, China
Guorong Li: School of Geological Engineering, Qinghai University, Xining 810016, China
Haili Zhu: School of Geological Engineering, Qinghai University, Xining 810016, China

Sustainability, 2024, vol. 16, issue 9, 1-21

Abstract: Soil moisture plays an important role in maintaining ecosystem stability and sustainable development, especially for the upper reaches of the Yellow River region. Therefore, accurately and conveniently monitoring soil moisture has become the focus of scholars. This study combines three machine learning algorithms: random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN)—with the traditional monitoring of soil moisture using remote sensing indices to construct a more accurate soil moisture inversion model. To enhance the accuracy of the soil moisture inversion model, 27 environmental variables were screened and grouped, including vegetation index, salinity index, and surface temperature, to determine the optimal combination of variables. The results show that screening the optimal independent variables in the Xijitan landslide distribution area lowered the root mean square error (RMSE) of the RF model by 16.95%. Of the constructed models, the combined model shows the best applicability, with the highest R 2 of 0.916 and the lowest RMSE of 0.877% with the test dataset; the further research shows that the BPNN model achieved higher overall accuracy than the other two individual models, with the test set R 2 being 0.809 and the RMSE 0.875%. The results of this study can provide a theoretical reference for the effective use of Landsat satellite data to monitor the spatial and temporal distribution of and change in soil water content on the two sides of the upper Yellow River basin under vegetation cover.

Keywords: soil moisture inversion; random forest; support vector machine; neural network; combined modeling method; optimal combination of variables (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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