Paper Info
Title
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 and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm(3)center dot cm(-3). The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm(3)center dot cm(-3). 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.
Year
DOI
Venue
2022
10.1109/JSTARS.2022.3179325
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Keywords
DocType
Volume
Land surface, Sea surface, Surface roughness, Rough surfaces, Satellite broadcasting, Clustering algorithms, Vegetation mapping, Bufeng-1 (BF-1), global navigation satellite system reflectometry (GNSS-R), land surface clustering, soil moisture (SM)
Journal
15
ISSN
Citations 
PageRank 
1939-1404
0
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Zhizhou Guo100.68
Baojian Liu215.10
Wei Wan301.01
Feng Lu401.35
Xinliang Niu500.34
Rui Ji600.34
Jing Cheng75014.53
Weiqiang Li800.34
Xiuwan Chen93318.04
Jun Yang1063.59
Zhaoguang Bai1100.34