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

Concept and initial realization of PHYSER — A PHYsics-based framework for SpacebornE GNSS-R soil moisture retrieval with accurate soil reflectivity

“…Compared to ocean surface modeling (Zavorotny et al, 2000;Zavorotny et al, 2014), conducting GNSS-R forward modeling for land surfaces involves complexities associated with computational electromagnetics and microwave radiation transfer. Moreover, forward modeling is not always reversible for retrieval because of the extensive computational resources required (Loria et al, 2020;Liu et al, 2022). Even if it is possible to retrieve geophysical parameters by finding optimum cost function values between model prediction and observation, the hypersensitivity to various environmental variables could complicate the problem.…”

“…The LSC method has been proven to be a practical way for spaceborne GNSS-R SM retrieval because it can take advantage of the similarity of land surface physical properties in different regions and minimizes the influence of different land types (Jia et al, 2021;Guo et al, 2022;Jia et al, 2022).…”

“…In this study, the method proposed by Guo et al, 2022 is introduced to derive the land type clusters, which are used as the basis to correct the Γ cal . This study assumes that, for one specific grid, the environmental variables are similar on a monthly scale; thus, monthly averaged values are used to determine the land clusters.…”

“…In recent years, several spaceborne GNSS-R missions have been successively launched, e.g., the TechDemoSat-1 (TDS-1) launched by the U.K. in 2014, the Cyclone Global Navigation Satellite System (CYGNSS) launched by the National Aeronautics and Space Administration (NASA) in 2016, the BuFeng-1 (BF-1) A/B twin satellites launched by China in 2019, and the GNSS Occultation Sounder II (GNOS-II) onboard the Fengyun-3E satellite launched by China in 2021. The GNSS-R missions have been used to observe not only the ocean surface, e.g., wind speed, ocean altimetry, sea ice, and sea surface salinity (Clarizia et al, 2016;Li et al, 2019;Rodriguez-Alvarez et al, 2019;Liu et al, 2020), but also the land surface, e.g., SM, soil freeze-thaw, inland waterbody, flood inundation, wetland, and biomass (Chew and Small., 2018;Gerlein-Safdi et al, 2019;Morris et al, 2019;Chew and Small., 2020;Carreno-Luengo and Ruf., 2021;Yang et al, 2022). Several future GNSS-R missions are dedicated to monitoring land water cycle components, e.g., the HydroGNSS and the Signals of Opportunity P-band Investigation (SNOOPI), which shows a broad prospect for developing spaceborne GNSS-R technique.…”

“…This study aims to define a PHYsics-based SpacebornE GNSS-R SM retrieval framework (PHYSER) and proposes initial strategies to realize it. The idea of this study is a continuation of two previous studies of our group, i.e., Wan et al (2020) and Yang et al (2022), which have made initial attempts to correct the CYGNSS SuR. However, the corrected SuRs of these two studies contributed to empirical and semi-empirical modeling because they did not achieve the finalized accurate SoR, which could be directly used to retrieve soil permittivity.…”