| Summary: | Soil moisture (SM) has normally been estimated based on a linear relationship between SM and the surface reflectivity (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>Γ</mo></semantics></math></inline-formula>) from the spaceborne Global Navigation Satellite System (GNSS)-Reflectometry, while it usually relies on inputs of SM data without considering vegetation optical depth (VOD/<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>τ</mo></semantics></math></inline-formula>) effects. In this study, a new scheme is proposed for retrieving soil moisture from the Cyclone GNSS (CyGNSS) data. The variation of CyGNSS-derived <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mo>Γ</mo></mrow></semantics></math></inline-formula> is modeled as a function of both variations in SM and VOD (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mi mathvariant="normal">S</mi><mi mathvariant="normal">M</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mo>τ</mo></mrow></semantics></math></inline-formula>). For retrieving SM, ancillary <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>τ</mo></semantics></math></inline-formula> data can be obtained from the Soil Moisture Active Passive (SMAP) mission. In addition to this option, a model for simulating <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mo>τ</mo></mrow></semantics></math></inline-formula> 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 (<i>r</i>) of 0.97 and 0.95 and root-mean-square errors (RMSEs) of 0.024 and 0.028 cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>/cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula> are obtained based on models using the SMAP and simulated <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mo>τ</mo></mrow></semantics></math></inline-formula>, respectively. The model without <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>τ</mo></semantics></math></inline-formula> generates an <i>r</i> of 0.95 and an RMSE of 0.031 cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>/cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>. The efficiency and necessity of considering <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>τ</mo></semantics></math></inline-formula> are thus confirmed by its enhancement based on correlation and RMSE against the one without <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>τ</mo></semantics></math></inline-formula>, and the usefulness of approximating <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mo>τ</mo></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>τ</mo></semantics></math></inline-formula> and demonstrates the feasibility of integrating the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mo>τ</mo></mrow></semantics></math></inline-formula> approximation function into a bilinear regression model to obtain SM results.
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