**1. Introduction**

Soil moisture is of great value in understanding plant physiological activities, hydrometeorological processes, global energy exchange and agricultural production [1–3]. The distribution information of accurate soil moisture is not only of great significance for scientific research, but can also serve a number of practical applications. Spaceborne GNSS Reflectometry (GNSS-R) is an emerging remote sensing technology for reflecting soil moisture over a large area due to its advantages of a wide signal source, large data volume, short revisit time, low cost and low power consumption, etc. Its frequency band and high spatial-temporal resolution can effectively compensate for the shortcomings of optical remote sensing, which is easily obscured by clouds, and the low spatial resolution of microwave remote sensing products [4–10].

Since the UK TechDemoSat-1 (TDS-1) and Cyclone Global Navigation Satellite System (CYGNSS) satellites provide spaceborne GNSS-R data for free, the retrieval of soil moisture for spaceborne GNSS-R has gradually become a research hotspot. In order to effectively characterize the relationship between the surface reflectivity of CYGNSS and soil moisture, and thus obtain high accuracy retrieval results for soil moisture, a large number of modeling

**Citation:** Wang, Q.; Sun, J.; Chang, X.; Jin, T.; Shang, J.; Liu, Z. The Correction Method of Water and Fresnel Reflection Coefficient for Soil Moisture Retrieved by CYGNSS. *Remote Sens.* **2023**, *15*, 3000. https:// doi.org/10.3390/rs15123000

Academic Editors: Hugo Carreno-Luengo, Dallas Masters, Chun-Liang Lin and Nereida Rodriguez-Alvarez

Received: 8 April 2023 Revised: 20 May 2023 Accepted: 6 June 2023 Published: 8 June 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

algorithms have been applied, such as linear models [11–14], machine learning [15–17] and deep learning [18], etc.

The reflection signal carrying water body information will weaken the sensitivity of the surface reflectivity of CYGNSS to soil moisture, and thus reduce the accuracy of soil moisture retrieval. Related studies have shown that a 25 m wide body of water can significantly affect the surface reflectivity of CYGNSS [11]. Therefore, Chew et al. [19], Wan et al. [20] and Zhu et al. [21] performed the removal of the water body effect on CYGNSS observations by means of external data sources such as the Global Surface Water Explorer (GSWE) and the water data of SMAP. The Fresnel reflection coefficient is one of the major component variables of CYGNSS surface reflectivity, and is directly related to the angle of incidence and the soil dielectric constant. The influence of the correlation parameters in the Fresnel reflection coefficient on CYGNSS surface reflectivity can be effectively weakened, which can improve the accuracy of soil moisture retrieval [22]. Al-Khaldi et al. [23] proposed the normalization method of the incident angle to correct the surface reflectivity of CYGNSS, and the results proved that the method could attenuate the effect of the incident angle in the Fresnel reflection coefficient. In addition, this method was also applied to the soil moisture product algorithm of CYGNSS developed by Chew et al. [11,23].

As the sampling frequency of CYGNSS increases, its spatial resolution changes and thus the original method for removing the influence of water bodies will mistakenly pick data carrying valid information. The Fresnel reflection coefficient is composed of the incident angle and the soil dielectric constant, which is directly related to the microwave frequency, soil temperature, soil type composition and soil moisture [24]. Current research has focused on attenuating the effects of incident angle and soil moisture on the Fresnel reflection coefficient, further resulting in a lack of complete analysis of the relevant influences in the Fresnel reflection coefficient and the establishment of a unified model to attenuate the effects of these parameters. Therefore, this paper first improved the removal method of observations affected by water bodies based on the analysis of CYGNSS data. Then, the variable response of the Fresnel reflection coefficient was analyzed in detail and the normalization method was proposed. Finally, the accuracy of soil moisture retrieval using CYGNSS was improved by combining the method of soil moisture retrieval with a linear model. After the introduction, Section 2 describes an overview of the study area and the adopted dataset. Section 3 presents proposed methods and the retrieval method of soil moisture. The results of the proposed method and soil moisture retrieval are displayed and appraised in Section 5. The discussion for results of the study is given in Section 5. Finally, the main conclusions for this study are given in Section 6.
