IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2022)

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

  • Zhizhou Guo,
  • Baojian Liu,
  • Wei Wan,
  • Feng Lu,
  • Xinliang Niu,
  • Rui Ji,
  • Cheng Jing,
  • Weiqiang Li,
  • Xiuwan Chen,
  • Jun Yang,
  • Zhaoguang Bai

DOI
https://doi.org/10.1109/JSTARS.2022.3179325
Journal volume & issue
Vol. 15
pp. 4680 – 4689

Abstract

Read online

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 cm3·cm−3. The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm3·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.

Keywords