RESEARCH ON MULTI-STAR NONLINEAR REGRESSION ESTIMATION MODEL OF SOIL MOISTURE BASED ON WAVELET ANALYSIS
- 1College of Geomatics Engineering and Geoinformation, Guilin University of Technology, 541006 Guilin, China
- 2Guangxi Key Laboratory of Spatial Information and Geomatics, 541006 Guilin, China
Keywords: GPS-IR, Wavelet Analysis, Soil Moisture, SNR, Multi-star Nonlinear Regression
Abstract. Soil moisture is an important parameter for studying meteorological, hydrological, and agricultural research. How to obtain accurate soil moisture has become a key issue. The traditional method of obtaining soil moisture has the disadvantages of high cost, complicated operation and limited application range. In recent years, using GPS-IR (GPS-Interferometric Reflectometry) technology to estimate soil moisture has become a Research hot. Traditional GPS-IR technology uses low-order polynomial to separate GPS satellite direct and reflected signals, but the separation effect is poor. And single-star inversion accuracy is low. In order to solve these problems, this paper proposes a multi-star nonlinear regression model based on wavelet analysis.Firstly, the satellite direct reflection signal is fitted by wavelet analysis, then the relative phase delay is solved by the nonlinear least squares method. Finally, the multi-star nonlinear regression model is established to estimate the soil moisture. The experiment used the observation data of the P041 station provided by the US Plate Edge Observation Program PBO in 2012. The results show that the wavelet analysis separates the reflected signal better than the low-order polynomial. The model can fully combine the advantages of wavelet analysis and multi-star fusion inversion, and effectively improve the abnormal jumping phenomenon of single-star. The inversion result is significantly upgrade than the traditional method. When the model combination reached double-star and triple-star, the better results were obtained. The R reached 0.922 and 0.948, respectively. The test results increased by 18.6% and 20.9% compared with the traditional method.