GNSS-IR Soil Moisture Estimation Based on Track Clustering and Multi Characteristic Parameter Fusion Using Entropy Method

Objectives: GNSS-IR technology, as an emerging tool for near-Earth remote sensing, has become a research hotspot in recent years in the area of soil moisture monitoring, with its low cost and high precision. Methods: In order to improve the accuracy of GNSS-IR technology in retrieving soil moisture, a self-built GNSS station located in Lishui District, Nanjing City was selected as the research data source, Firstly, feature parameters such as multipath coherent phase, amplitude, and frequency of Signal-to-Noise Ratio (SNR) observation data from GPS, BDS, GLONASS, Galileo, and other systems are extracted. Based on the analysis of the characteristics of GNSS observation SNR changes with soil moisture in different systems, different frequency bands and different orbits, a multi GNSS system feature data fusion inversion method considering satellite trajectory differences was proposed. For the observation data of multiple GNSS systems, trajectory clustering fusion is carried out according to different orbits and frequency bands. After confirming the weight using entropy method, soil moisture inversion is carried out. The results of multi-system trajectory fusion inversion are compared with traditional average weight fusion methods and multiple linear regression methods. Results: The combination of SNR's phase, amplitude, and frequency feature parameters for soil moisture fusion inversion is better than the inversion results of a single phase feature parameter and a combination of two feature parameters (phase, 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 RMSE decreasing by 22.8% to 39.9%; 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 compared to traditional average weight fusion method, multiple linear regression method and weighted fusion method. Conclusions: The proposed method can provide long-term and accurate soil moisture inversion results.