A GPS-IR Method for Retrieving NDVI From Integrated Dual-Frequency Observations

The global positioning system interferometric reflectometry (GPS-IR) method has the advantage of acquiring observations continuously in all weather conditions, which has great application potential in vegetation remote sensing. However, L-band electromagnetic wave signals are susceptible to various environmental factors, resulting in deviations in GPS-IR observation data at certain times. The accuracy and reliability of the vegetation index retrieving results are challenging to achieve by using single-frequency GPS data. This letter proposed a novel method to combine dual-frequency data for retrieving normalized difference vegetation index (NDVI). We integrated the multipath observations based on the theory of information entropy. Subsequently, a unary linear regression model was established to retrieve the NDVI from the calculated normalized microwave reflection index (NMRI). For validation purposes, the comparative analysis was conducted between the proposed model and the previous single-frequency NDVI retrieving model in terms of retrieval accuracy, based on the continuous observation data acquired by four GPS reference stations in the past five years. The experimental results indicated that the proposed model is available for retrieving the NDVI, with the correlation coefficient of 0.749-0.815 and the root mean square error (RMSE) of 0.056-0.081. Compared with the results acquired by the previous single-frequency NDVI retrieving model, the correlation coefficient of the retrieved NDVI was increased by an average of 18.5%, and the RMSE was reduced by 30.3%. The proposed method in this letter helps further improve the accuracy and continuity of NDVI observation data in some local areas, which contributes to grasping the growth status of vegetation comprehensively.