Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface Clustering Algorithm

Abstract

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity (ESR) and SM. The global comparison between the results and SM product from the SMAP mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is <formula><tex>$0.070 cm^3.cm^-3$</tex></formula>. The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of<formula><tex>$0.036 cm^3.cm^-3$</tex></formula>. This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.