An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

Abstract

Soil moisture monitoring using Global Navigation Satellite System (GNSS) multipath signals has gained continuous interests in recent years. However, traditional GNSS-interferometric reflectometry (GNSS-IR) soil moisture retrieval methods generally utilize a single frequency or single satellite, which fail to take full advantage of different and complementary of satellite signals with different frequencies. An improved algorithm for soil moisture retrieval based on principal component analysis (PCA) and entropy method using multi-frequency amplitude and phase offset fusion data was proposed in this research. The performance of the proposed soil moisture retrieval method was evaluated using data recorded by Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China. The results from GPS and BeiDou both showed that the retrieved soil moisture has a stronger correlation with in situ soil moisture, which can better reflect the fluctuation of ground truth measurements. Compared with the traditional method, the retrieval accuracy of the proposed method in terms of root-mean-square error (RMSE) was improved by 50.93%, and the average correlation coefficient were increased by 11.71%. This research proved that the proposed method could effectively improve retrieval accuracy due to the increasing number of frequencies and tracks clustering. Moreover, this study has illustrated the feasibility of BeiDou signals to precisely estimate surface soil moisture.

Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network

“…Figure 6 shows the results of the validation of the inversion model of SSM in vegetated areas. As can be seen from Figure 6, the validation accuracy for SSM in vegetation cover and the in situ SSM is high (R = 0.855; RMSE = 0.024 cm 3 /cm 3 ) and uniformly distributed on both sides of a 1:1 straight line, which indicates that it is feasible and accurate to utilize the joint inversion of SSM using the WCM and the RBF neural network model in vegetated areas, which is in line with the results of a previous study [26]. Meanwhile, adding the NDVI to the input layer of the RBF neural network model can improve the accuracy of the inversion.…”

“…Compared with Sentinel-1 VH polarization, VV polarization is more advantageous in SSM inversion, with an R of 0.911 and an RMSE of 0.053 cm 3 /cm 3 between the model inversion values and the in situ values. In bare soil, Chen et al [26] proposed an improved algorithm based on the fusion data of multi-frequency amplitude and phase migration based on principal component analysis (PCA) and the entropy method for SSM inversion. Compared with the inversion of SSM by GPS and the Beidou satellite, the inversion of SSM obtained by this method had a stronger correlation with the in situ SSM.…”

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

“…The B1 and B2 from the BDS also reflected well the trend of SM [48] (Yang et al, 2019). Compared with a single satellite, SM retrieved by GNSS multisatellite fusion has higher stability and accuracy [49]. In addition, the SM retrieved by multisatellite dual-frequency combined multipath error can effectively improve the time resolution of SM [50].…”

Sensor Networks, Data Processing, and Inference: The Hydrology Challenge

“…[81], [153], [154] [87], [93], [95], [96], [98], [99], [100], [138], [139], [140] [121], [122] [21], [22], [33], [129] [41], [143], [144] [63]…”

“…Satellite images have provided accurate measurement of many variables of great interest for hydrology as soil water content [16]- [22], evapotranspiration [23]- [25], streamflow [26]- [31] or precipitation [32]- [37]. In fact, satellites can deliver images with high spatial and temporal resolution.…”