NDLI: Soil Moisture Retrieval from the CyGNSS Data Based on a Bilinear Regression

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Soil Moisture Retrieval from the CyGNSS Data Based on a Bilinear Regression

Content Provider	MDPI
Author	Chen, Sizhe Yan, Qingyun Jin, Shuanggen Huang, Weimin Chen, Tiexi Jia, Yan Liu, Shuci Cao, Qing
Copyright Year	2022
Abstract	Soil moisture (SM) has normally been estimated based on a linear relationship between SM and the surface reflectivity ( $Γ$ ) from the spaceborne Global Navigation Satellite System (GNSS)-Reflectometry, while it usually relies on inputs of SM data without considering vegetation optical depth (VOD/ $τ$ ) effects. In this study, a new scheme is proposed for retrieving soil moisture from the Cyclone GNSS (CyGNSS) data. The variation of CyGNSS-derived $Δ Γ$ is modeled as a function of both variations in SM and VOD ( $Δ S M$ and $Δ τ$ ). For retrieving SM, ancillary $τ$ data can be obtained from the Soil Moisture Active Passive (SMAP) mission. In addition to this option, a model for simulating $Δ τ$ is suggested as an alternative. Experimental evaluation is performed for the time span from August 2019 to July 2021. Excellent agreements between the final retrievals and referenced SMAP SM products are achieved for both training (1-year period) and test (1-year duration) sets. On the whole, overall correlation coefficients (r) of 0.97 and 0.95 and root-mean-square errors (RMSEs) of 0.024 and 0.028 cm $^{3}$ /cm $^{3}$ are obtained based on models using the SMAP and simulated $Δ τ$ , respectively. The model without $τ$ generates an r of 0.95 and an RMSE of 0.031 cm $^{3}$ /cm $^{3}$ . The efficiency and necessity of considering $τ$ are thus confirmed by its enhancement based on correlation and RMSE against the one without $τ$ , and the usefulness of approximating $Δ τ$ by sinusoidal functions is also validated. Influences of SM statistics in terms of mean and variance on the retrieval accuracy are evaluated. This work unveils the interaction between CyGNSS data, SM, and $τ$ and demonstrates the feasibility of integrating the $Δ τ$ approximation function into a bilinear regression model to obtain SM results.
Starting Page	1961
e-ISSN	20724292
DOI	10.3390/rs14091961
Journal	Remote Sensing
Issue Number	9
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2022-04-19
Access Restriction	Open
Subject Keyword	Remote Sensing Imaging Science Gnss-reflectometry Cygnss Soil Moisture Smap Vegetation Optical Depth
Content Type	Text
Resource Type	Article

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