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RSS FeedsRemote Sensing, Vol. 13, Pages 3725: An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data (Remote Sensing)

 
 

17 september 2021 14:37:32

 
Remote Sensing, Vol. 13, Pages 3725: An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data (Remote Sensing)
 


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.


 
53 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 13, Pages 3733: Indoor Experiments of Bistatic/Multistatic GB-SAR with One-Stationary and One-Moving Antennae (Remote Sensing)
Remote Sensing, Vol. 13, Pages 3735: The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings (Remote Sensing)
 
 
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