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

Author

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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)

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.

Suggested Citation

  • Weitao Lv & Xiasong Hu & Xilai Li & Jimei Zhao & Changyi Liu & Shuaifei Li & Guorong Li & Haili Zhu, 2024. "Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 16(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3509-:d:1380736
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    References listed on IDEAS

    as
    1. Khan, Nasir M. & Rastoskuev, Victor V. & Sato, Y. & Shiozawa, S., 2005. "Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 96-109, August.
    2. Jushuang Qin & Menglu Ma & Jiabin Shi & Shurui Ma & Baoguo Wu & Xiaohui Su, 2023. "The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China," IJERPH, MDPI, vol. 20(1), pages 1-18, January.
    3. Axel Bronstert & Benjamin Creutzfeldt & Thomas Graeff & Irena Hajnsek & Maik Heistermann & Sibylle Itzerott & Thomas Jagdhuber & David Kneis & Erika Lück & Dominik Reusser & Erwin Zehe, 2012. "Potentials and constraints of different types of soil moisture observations for flood simulations in headwater catchments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(3), pages 879-914, February.
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