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Guo Fei, Chen Weijie, Zhu Yifan, Zhang Xiaohong. A GNSS-IR soil moisture inversion method integrating phase, amplitude and frequency[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210644
Citation: Guo Fei, Chen Weijie, Zhu Yifan, Zhang Xiaohong. A GNSS-IR soil moisture inversion method integrating phase, amplitude and frequency[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210644

A GNSS-IR soil moisture inversion method integrating phase, amplitude and frequency

doi: 10.13203/j.whugis20210644
Funds:

National Natural Science Foundation of China (Grant No.42074029),Natural Science Foundation of Hubei Province for Distinguished Young Scholars (Grant No.2021CFA039)

  • Received Date: 2022-06-23
  • Objectives:GNSS-IR (Global Interferometric Reflectometry) is a new passive remote sensing technique for determining surface environment parameters, which places an important part in the inversion of earth's surface properties, such as soil moisture monitoring, snow parameter retrieval, and vegetation remote sensing, etc. The GNSS-IR offers several benefits over the traditional soil moisture inversion approach, including all-weather capability, high temporal precision, and cheap cost. Method:Considering the fact that the existing soil moisture inversion algorithms only utilize one single feature of GNSS reflected signal and from the perspective of increasing data availability, this paper proposes a GNSS-IR soil moisture inversion approach that integrates multi-type feature data by utilizing phase, amplitude, and frequency extracted GNSS signals reflected by soil. The main work is to effectively filter all available features extracted from the original GNSS SNR observations. The feasibility and effect of the suggested method are compared and evaluated using three machine learning models, including LSSVM (Least Squares Support Vector Machine), RF (Random Forest), and BPNN (Back Propagation Neural Network). Results:Comparing the inversion effects of the above three models, the BP neural network model has the best inversion effect, followed by the random forest (RF) model, and the least squares support vector machine (LSSVM) model is the worst. The results showed that the correlation coefficients between the reference value and soil moisture inversed by the multi-feature fusion method LSSVM, RF, And BPNN models are 0.823, 0.944, and 0.955, respectively, and the corresponding root mean square errors (RMSE) are 0.045, 0.035 and 0.032cm3·cm-3. Conclusions:Compared with the single feature inversion method, the soil moisture inversion accuracy is increased by 6-14%, and the correlation coefficient is increased by 2-7%. The results demonstrated that the proposed method has higher inversion accuracy and reliability than the single feature inversion method.
  • [1] Small E E, Larson K M, Chew C C, et al.Validation of GPS-IR Soil Moisture Retrievals:Comparison of Different Algorithms to Remove Vegetation Effects[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(10):4759-4770
    [2] Larson K M, Braun J J, Small E E, et al.GPS Multipath and Its Relation to NearSurface Soil Moisture Content[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(1):91-99
    [3] Km l, Ee s, Ed g, et al.Use of GPS Receivers as a Soil Moisture Network for Water Cycle Studies[J].Geophysical Research Letters, 2008, 35(24):L24405-1
    [4] Vey S, Güntner A, Wickert J, et al.LongTerm Soil Moisture Dynamics Derived from GNSS Interferometric Reflectometry:A Case Study for Sutherland, South Africa[J].GPS Solutions, 2016, 20(4):641-654
    [5] Chew C, Small E E, Larson K M.An Algorithm for Soil Moisture Estimation Using GPS-Interferometric Reflectometry for Bare and Vegetated Soil[J].GPS Solutions, 2016, 20(3):525-537
    [6] Chew C C, Small E E, Larson K M, et al.Effects of Near-Surface Soil Moisture on GPS SNR Data:Development of a Retrieval Algorithm for Soil Moisture[J].IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1):537-543
    [7] Zhang S B, Roussel N, Boniface K, et al.Use of Reflected GNSS SNR Data to Retrieve either Soil Moisture or Vegetation Height from a Wheat Crop[J].Hydrology and Earth System Sciences, 2017, 21(9):4767-4784
    [8] Ren C, Liang Y J, Lu X J, et al.Research on the Soil Moisture Sliding Estimation Method Using the LS-SVM Based on Multi-Satellite Fusion[J].International Journal of Remote Sensing, 2019, 40(5/6):2104-2119
    [9] Suykens J, Lukas L, Van Dooren P.Least Squares Support Vector Machine Classifiers:A Large Scale Algorithm[J].Euro Conf Circ Theory Design (ECCTD'99), 1999
    [10] Suykens J A K, De Brabanter J, Lukas L, et al.Weighted Least Squares Support Vector Machines:Robustness and Sparse Approximation[J].Neurocomputing, 2002, 48(1/2/3/4):85-105
    [11] Liang Y J, Ren C, Wang H Y, et al.Research on Soil Moisture Inversion Method Based on GA-BP Neural Network Model[J].International Journal of Remote Sensing, 2019, 40(5/6):2087-2103
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A GNSS-IR soil moisture inversion method integrating phase, amplitude and frequency

doi: 10.13203/j.whugis20210644
Funds:

National Natural Science Foundation of China (Grant No.42074029),Natural Science Foundation of Hubei Province for Distinguished Young Scholars (Grant No.2021CFA039)

Abstract: Objectives:GNSS-IR (Global Interferometric Reflectometry) is a new passive remote sensing technique for determining surface environment parameters, which places an important part in the inversion of earth's surface properties, such as soil moisture monitoring, snow parameter retrieval, and vegetation remote sensing, etc. The GNSS-IR offers several benefits over the traditional soil moisture inversion approach, including all-weather capability, high temporal precision, and cheap cost. Method:Considering the fact that the existing soil moisture inversion algorithms only utilize one single feature of GNSS reflected signal and from the perspective of increasing data availability, this paper proposes a GNSS-IR soil moisture inversion approach that integrates multi-type feature data by utilizing phase, amplitude, and frequency extracted GNSS signals reflected by soil. The main work is to effectively filter all available features extracted from the original GNSS SNR observations. The feasibility and effect of the suggested method are compared and evaluated using three machine learning models, including LSSVM (Least Squares Support Vector Machine), RF (Random Forest), and BPNN (Back Propagation Neural Network). Results:Comparing the inversion effects of the above three models, the BP neural network model has the best inversion effect, followed by the random forest (RF) model, and the least squares support vector machine (LSSVM) model is the worst. The results showed that the correlation coefficients between the reference value and soil moisture inversed by the multi-feature fusion method LSSVM, RF, And BPNN models are 0.823, 0.944, and 0.955, respectively, and the corresponding root mean square errors (RMSE) are 0.045, 0.035 and 0.032cm3·cm-3. Conclusions:Compared with the single feature inversion method, the soil moisture inversion accuracy is increased by 6-14%, and the correlation coefficient is increased by 2-7%. The results demonstrated that the proposed method has higher inversion accuracy and reliability than the single feature inversion method.

Guo Fei, Chen Weijie, Zhu Yifan, Zhang Xiaohong. A GNSS-IR soil moisture inversion method integrating phase, amplitude and frequency[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210644
Citation: Guo Fei, Chen Weijie, Zhu Yifan, Zhang Xiaohong. A GNSS-IR soil moisture inversion method integrating phase, amplitude and frequency[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210644
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