GNSS-IR Soil Moisture Estimation by Track Clustering and Multi-characteristic Parameter Fusion Using Entropy Method

Objectives Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) technology, as an emerging approach in near-Earth remote sensing, has become a research hotspot in recent years in the area of soil moisture monitoring owing to its low cost and high precision.

Methods To enhance the accuracy of GNSS-IR technology in soil moisture retrieval, GNSS observations from a self-built station in Lishui District, Nanjing City, China were used. First, feature parameters including multipath coherent phase, amplitude, and frequency of signal-to-noise ratio (SNR) observations from GPS, BDS, GLONASS and Galileo systems were extracted. Then, based on analysis of characteristics of SNR changes with soil moisture in different systems, frequency bands and orbits, this paper proposed a multi GNSS -system feature-level data fusion inversion method that accounts for differences of satellite trajectory. Trajectory clustering-based fusion was then performed according to orbital types and frequency bands for the observation data from multiple GNSS systems. After determing the weight using entropy method, soil moisture inversion was carried out. The results of multi-system trajectory fusion inversion were compared with those traditional average-weight fusion method and the multiple linear regression method.

Results The soil moisture fusion inversion result of the combination of feature parameters including phase, amplitude, and frequency of SNR is better than the inversion results of a single phase feature parameter and the combination of two feature parameters including phase and amplitude. Multi-system trajectory fusion generally improves the inversion performance compared to single system trajectory fusion, with an average correlation coefficient increasing by 4.0% and root mean square error （RMSE) decreasing by 22.8% to 39.9%. Compared to traditional average-weight fusion method, multiple linear regression method and weighted fusion method, the multi-system trajectory clustering fusion soil moisture inversion method based on entropy method reduces RMSE by 34.0%, 25.6% and 29.5%, respectively.

Conclusions The proposed method can provide long-term and accurate soil moisture inversion results.