Comparison and Analysis of GNSS-MR Vegetation Index Inversion Characteristics Based on Two Multipath Parameters

Objectives The normalized difference vegetation index (NDVI) is one of the important parameters for monitoring vegetation changes. With the continuous development of the global navigation satellite system (GNSS), GNSS multipath reflectometry technology has been proven to have the potential for monitoring vegetation changes. This paper aims to explore the correlations between NDVI and the normalized amplitude ($A_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}$) and normalized microwave reflectance index (NMRI) obtained from GNSS interferometry reflectometry to assess the application potential of GNSS signals in vegetation monitoring.

Methods The study used GNSS signal-to-noise ratio data to calculate $A_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}$ and NMRI, analyzing their correlations with NDVI and change trends. In addition, total precipitation data from the European centre for medium-range weather forecasting reanalysis v5 and drought severity data were utilized to explore the relationship between precipitation/drought and $A_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}$ and NMRI. Further analysis was conducted on the correlation between NMRI parameters and NDVI at different frequency points under the GPS, GLONASS, and Galileo systems.

Results Analysis of experimental data from three plate boundary observatory sites (P042, P052, and P675) reveal strong linear correlations between $A_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}$ and NDVI, as well as between NMRI and NDVI, with correlation coefficients ranging from 0.58 to 0.86. When the surrounding vegetation types were grassland and crops, NMRI at different frequency points across all three GNSS systems demonstrate good correspondence with NDVI. Notably, among the three frequency bands, the L1-band NMRI exhibites the strongest correlation with NDVI, with coefficients ranging from 0.46 to 0.89, which may be attributed to the shortest wavelength of the L1 signal.

Conclusions The study shows that multi-constellation and multi-frequency GNSS signals can effectively monitor vegetation indices, which validates the application potential of GNSS interferometric reflectometry technology for monitoring vegetation changes and provides important data support for future research.