Abstract
The Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven as a promising remote sensing technique. The Cyclone Global Navigation Satellite System (CYGNSS) was launched in December 2016, which provide a great opportunity to remotely sense the Earth’s surface geophysical parameters with unprecedented spatial and temporal resolution. However, it is still under-developing and testing for land surface soil moisture (SM) retrieval. In this paper, we gridded CYGNSS individual DDM-derived reflectivity into the Equal-Area Scalable Earth Grid 2 (EASE-Grid 2) projection, which is aligned with SMAP SM products, to establish a GNSS-R SM retrieval model in Southeast China. In order to refine the SM inversion algorithm, we also adopt the vegetation opacity and roughness coefficient data to mitigate the attenuation effect of vegetation and surface roughness. The accuracy of the CYGNSS derived SM is evaluated and the characteristics of the gridded retrieval SM time series in southeastern China are analyzed.
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