Spaceborne GNSS-R retrieving on global soil moisture approached by support vector machine learning

GNSS Reflectometry system is an excellent to sense soil moisture content. In recent, GNSS-R technique could be aided to detect soil moisture contents but still have many difficulities issues, most especially vegetation impact. Soil moisture observing is a major concept for enhancing the sustainability of the earth's system and process. On retrieving soil moisture from spaceborne GNSS-R technology has been challenging to the system, retrieving model and geophysical parameters. In this research, we use the Support Vector Machine (SVM) method to retrieve global soil moisture, the TDS-1 Delay Doppler Map (DDM) and the AVHRR Normalized Difference Vegetation Index (NDVI) imagery as inputs and the Soil Moisture and Ocean Salinity (SMOS) soil moisture data as a reference to retrieve global SM daily basis. The results have shown that the squared correlation coefficient (R) values are much higher in TDS-1 fused with NDVI than using DDM alone, which indicates that vegetation impact has effectively weakened. The feasibility of this approach could provide the performance for spaceborne GNSS-R retrieving to soil moisture analysis.