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It is of great significance to timely and accurately monitor the vegetation water content (VWC) for the study of plant growth status, drought assessment, and fire risk forecasting. Global Positioning System Interferometric Reflectometry (GPS-IR) is a new type of remote sensing technology. The signal-to-noise ratio recorded by the measurement receiver can be used to retrieve VWC effectively. Although studies have fully proved that the change of VWC can be effectively reflected by the normalized microwave reflectance index (NMRI), the NMRI based on a single ground-based GPS-IR cannot achieve spatial continuity. This paper makes full use of the advantages of multiple ground-based GPS-IR combined with vegetation index, weather, topography and other related elements, and a nonlinear inversion method of high spatial resolution VWC based on multiple ground-based GPS-IR is proposed. Firstly, First, various elements are acquired through Google Earth Engine, and the resolution is unified through ArcGIS; Then, the relationship model between the multiple ground-based GPS-IR and related elements is established through the least square support vector machine (LS-SVM), and the modeling effects of different model input variables are compared and analyzed, and the best modeling solution is selected; Finally, after model testing, the accurate inversion of VWC is realized. Taking a certain area of the United States as the research object, experiments show that: 1) Different fac-tors have different influences on modeling accuracy. Three vegetation indices of NDVI, GPP and LAI > topography > longitude and latitude of the station > daily average rainfall and daily average temperature. Among them, NDVI is a key element that affects modeling accuracy. 2) Using LS-SVM can effectively integrate multiple ground-based GPS-IR and related elements, and the model fitting process is relatively stable; the 16Day/500 m resolution NMRI image obtained by model inversion can better reflect regional VWC changes. 3) The model inversion error is relatively stable. The maximum error is only 0.031, and the root mean square error is only 0.040.

Obtaining high-precision, long-term sequences of vegetation water content (VWC) is of great significance for assessing surface vegetation growth, soil moisture, and fire risk. In recent years, the global navigation satellite system-interferometric reflection (GNSS-IR) has become a new type of remote sensing technology with low cost, all-weather capability, and a high temporal resolution. It has been widely used in the fields of snow depth, sea level, soil moisture content, and vegetation water content. The normalized microwave reflectance index (NMRI) based on GNSS-IR technology has been proven to be effective in monitoring changes in VWC. This paper considers the advantages and disadvantages of remote sensing technology and GNSS-IR technology in estimating VWC. A point-surface fusion method of GNSS-IR and MODIS data based on the GA–BP neural network is proposed to improve the accuracy of VWC estimation. The vegetation index products (NDVI, GPP, LAI) and the NMRI that unified the temporal and spatial resolution were used as the input and output data of the training model, and the GA–BP neural network was used for training and modeling. Finally, a spatially continuous NMRI product was generated. Taking a particular area of the United States as a research object, experiments show that (1) a neural network can realize the effective fusion of GNSS-IR and MODIS products. By comparing the GA–BP neural network, BP neural network, and multiple linear regression (MLR), the three models fusion effect. The results show that the GA–BP neural network has the best modeling effect, and the r and RMSE between the model estimation result and the reference value are 0.778 and 0.0332, respectively; this network is followed by the BP neural network, in which the r and RMSE are 0.746 and 0.0465, respectively. MLR has the poorest effect, with r and RMSE values of 0.500 and 0.0516, respectively. (2) The spatiotemporal variation in the 16 days/500 m resolution NMRI product obtained by GA–BP neural network fusion is consistent with that in the experimental area. Through the testing of GNSS stations that did not participate in the modeling, the r between the estimated value of the NMRI and the reference value is greater than 0.87, and the RMSE is less than 0.049. Therefore, the method proposed in this paper is optional and effective. The spatially continuous NMRI products obtained by fusion can reflect the changes in VWC in the experimental area more intuitively.

The research on the inversion of soil moisture by Global Navigation Satellite Signal-Interferometer and Reflectometry (GPS-IR) has become a hot research field in recent years. In the past few years, ground-based experiments using the US Panel Observation Program (PBO) have confirmed that GPS receivers primarily used for measurements can be used to measure changes in surface physical parameters. In this paper, we study the improvement of the separation model of satellite reflected signal in GPS signal to noise ratio (SNR) observation, and the inversion model of GPS-IR remote sensing soil moisture. Firstly, wavelet analysis is used to effectively separate satellite reflected signals. Further use Least squares support vector machine (LS-SVM) rolling predictive model for estimating soil moisture. Soil moisture was estimated using the GPS SNR provided by the P038 station of the PBO observation network. Comparative analysis of the feasibility and effectiveness of single- and multiple-GPS satellites for soil moisture rolling estimation. Theoretical analysis and experiments show that wavelet analysis can effectively improve the separation accuracy of satellite reflected signals. The LS-SVM rolling estimation results are highly consistent with the soil moisture verification data. This model fully exploits the advantages of LS-SVM and effectively integrates the satellites. Performance, improved the use of a single satellite for soil moisture estimation, the results are prone to abnormal jumps; the model requires less modeling data, the use of rolling can achieve long-term estimation, the estimation error is more stable.

Global Positioning System Interferometric Reflectance (GPS-IR) is a new remote sensing technique, which can be used to estimate soil moisture content near the surface. From the view of multi-satellite fusion, an estimating method of surface soil water content based on multi-satellite fusion is proposed. Firstly, the direct and reflected signals of multiple satellites are separated by low order polynomial fitting, and then the sinusoidal fitting model of the reflected signals is established to obtain the relative delay phase. Secondly, the multiple linear regression inversion model of soil moisture is established, and the input variable set of the model is determined by the correlation coefficient of each satellite. Finally, the advantage of multi-satellite fusion is brought into full play to retrieve soil moisture. The feasibility and effectiveness of single satellite and multiple satellite fusion inversion are compared and analyzed through the monitoring data provided by the Plate Boundary Observation (PBO). The experimental results show that the multiple linear regression model can realize the effective fusion of multiple satellites. Compared with the single satellite, the inversion accuracy is higher and the inversion error is more stable.