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

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 ΔΓ is modeled as a function of both variations in SM and VOD (ΔSM and Δτ). 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 Δτ 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 cm3/cm3 are obtained based on models using the SMAP and simulated Δτ, respectively. The model without τ generates an r of 0.95 and an RMSE of 0.031 cm3/cm3. 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 Δτ 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 Δτ approximation function into a bilinear regression model to obtain SM results.