The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 605–610, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-605-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 605–610, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-605-2020

  21 Aug 2020

21 Aug 2020

SPACEBORNE GNSS-R RETRIEVING ON GLOBAL SOIL MOISTURE APPROACHED BY SUPPORT VECTOR MACHINE LEARNING

A. Lwin1,2, D. Yang1, X. Hong1, S. Cheraghi Shamsabadi1, and W. A. Ahmed1 A. Lwin et al.
  • 1School of Electronic and Information Engineering, Beihang University, 100083, China
  • 2Remote Sensing and GIS Research Centre, Yangon Technological University, Myanmar

Keywords: TDS-1, DDM, NDVI, SMOS, SVM, Soil Moisture

Abstract. 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.