# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 1 Introduction  
Soil moisture is a crucial factor that affects the transfer of water and heat energy between the surface of the soil and the boundary layer atmosphere through the processes of evaporation and transpiration by plants [1, 2]. Assessing surface soil moisture is critical for refining short- and medium-term meteorological models, monitoring plant growth, and assisting in the early warning for natural calamities such as floods and droughts [3, 4]. Remote sensing of soil moisture from space enables measurements with global coverage. Observations at low microwave frequencies are optimal for soil moisture monitoring because the signals are unaffected by weather conditions and less energy is attenuated by biomass. Both SMOS (soil moisture and ocean salinity) and SMAP (soil moisture active passive) are typical passive microwave spaceborne missions operating at the L-band (1.4 GHz), offering global soil moisture measurements at a resolution of 36-40 km, with a revisit period of about 3 days [5, 6]. Global Navigation Satellite System Reflectometry (GNSS-R) is an emerging technique using navigation signals that are reflected off the earth surface for remote sensing applications. In 2003, the first remote sensing observations utilizing GNSS reflected signals from an orbit of 680 km were carried out by the United Kingdom Disaster Monitoring Constellation (UK-DMC) [7]. Subsequently, the United Kingdom TechDemoSat-1 (UK TDS-1) was launched in 2014 [8], with an orbit altitude of 635 km and an inclination of 98.4 degrees, providing worldwide GNSS-R measurements of the sea surface, land and cryosphere. To prioritize cyclone monitoring observations between \(\pm 38^{\circ}\) latitude, NASA launched the Cyclone Global Navigation Satellite System (CYGNSS) in 2016. This constellation consisting of eight tiny satellites has an orbit altitude of 510 km and an inclination of 35 degrees [9]. Though numerous GNSS-R missions have been reported in the literature, there is little investigation on the spaceborne GNSS-R data from BeiDou (BDS) and Galileo (GAL) navigation satellite systems. The Fengyun-3E (FY-3E) satellite, carrying the Global Navigation Satellite System Occultation Sounder II (GNOS-II), was launched on 5 July 2021 [10]. The satellite orbits at an altitude of 836 km and has an inclination of 98.75 degrees, providing global coverage between \(\pm 85\) degrees for ionosphere, neutral atmosphere, and earth surface monitoring. As the upgraded version of the GNOS onboards the FY-3C and FY-3D, GNOS-II combines GNSS radio occultation and GNSS reflectometry. Meanwhile, the GNOS-II has eight reflection channels, allowing it to simultaneously track eight different navigation satellites. After one year of in-orbit testing with GPS and BDS signals, the Galileo signal acquisition was enabled on 7 July 2022 by switching one BDS channel to GAL channel. The GNOS-II payload will also be onboard FY-3F and FY-3G to construct a GNSS-R observation constellation, providing Earth observations with greater spatial coverage and temporal resolution [11]. The GNOS-II receiver is able to collect 300,000 reflections each day, of which approximately 27% are from land surfaces. With a similar frequency to SMOS and SMAP, GNSS reflectometry shows great potential for land applications including monitoring of soil moisture [12; 13; 14; 15; 16], vegetation biomass [17; 18], inland waterbodies [19; 20] and freeze-thaw [21; 22]. Meanwhile, GNSS-R has the potential for a better spatial resolution than the radiometer, and the observations are not affected by surface temperature. Using GNSS-R for land soil moisture retrieval was first demonstrated by several aircraft experiments [23; 24]. There are also several approaches for GNSS-R soil moisture retrieval from space, including empirical change detection [13], multivariate regression [15] and neural network approach [16]. The empirical change detection is similar to the ASCAT soil moisture algorithm [25], and it was used for CYGNSS soil moisture product generation. This paper is also based on this method.