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A Study of GNSS-IR Soil Moisture Inversion Algorithms Integrating Robust Estimation with Machine Learning

Author

Listed:
  • Rui Ding

    (Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China
    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Nanshan Zheng

    (Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China
    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Hao Zhang

    (Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China)

  • Hua Zhang

    (Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China)

  • Fengkai Lang

    (Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China)

  • Wei Ban

    (Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China)

Abstract

Soil moisture monitoring is widely used in agriculture, water resource management, and disaster prevention, which is of great significance for sustainability. The global navigation satellite system interferometric reflectometry (GNSS-IR) technology provides a supplementary method for soil moisture monitoring. However, due to the quality of the signal-to-noise ratio (SNR) measurements and the complex surface environment, inevitable outliers in multipath interference signal metrics (amplitude, frequency, and phase) were used as modeling variables to inverse GNSS-IR soil moisture. Besides, it is hard to use the univariate model to comprehensively analyze the relationship between the various factors, due to the poor fitting effect and weak generalization ability of the model. In this paper, the minimum covariance determinant (MCD) robust estimation and machine learning algorithms are adopted. The MCD robust estimation can eliminate outliers of the multipath signal metrics and machine learning algorithms, including the back propagation neural network (BPNN), Gaussian process regression (GPR), and random forest (RF), and can comprehensively establish nonlinear GNSS-IR soil moisture inversion models using multipath interference signal metrics. Moreover, the study of the modeling parameter selection for the three machine learning algorithms and the inversion results for single satellite and all satellites are also carried out to make the algorithms more generalizable. The results show that the correlation coefficients (R) and the root mean square error (RMSE) of the machine learning models for all satellite tracks are increased by 4.3~86.6% and reduced by 2.8~30%, respectively, compared with the MCD multiple regression model. The RF model with 80 decision trees and 1 node shows the clearest improvement. The total model using all satellite data has more generalization ability than the single satellite model but causes some loss of accuracy.

Suggested Citation

  • Rui Ding & Nanshan Zheng & Hao Zhang & Hua Zhang & Fengkai Lang & Wei Ban, 2023. "A Study of GNSS-IR Soil Moisture Inversion Algorithms Integrating Robust Estimation with Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6919-:d:1127899
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