This study exploits the unique capabilities of the FY-3E WindRAD scatterometer, the first spaceborne dual-frequency (Ku- and C-band) and dual-polarization (hhandvv) rotating fan-beam scanning measurements, to investigate the backscatter characteristics of open water (OW), first-year ice (FYI), and multi-year ice (MYI) under different seasonal, wavelength, and polarization conditions throughout 2022 in the Arctic. Four types of feature parameters were defined for systematic analysis based on WindRAD swath data. It is concluded that the mean backscatter coefficient σp,λand the wavelength gradient ratioGRpare key indicators for distinguishing between FYI and MYI, with the Ku-band exhibiting superior performance outside the melt season due to enhanced volume scattering from desalinated ice and bubble structures. During melting, however, both ice types become indistinguishable as meltwater increases dielectric loss and reduces penetration depth. Furthermore, the standard deviation of the backscatter coefficient Δσp,λand the polarization ratio γλprove highly effective in separating sea ice from OW with the C-band showing particular advantage owing to a wider incidence angle range and stronger angular sensitivity of Bragg scattering over water. The γλapproaches 1 for both FYI and MYI due to depolarizing rough surfaces, whereas OW exhibits lower values dominated by Bragg scattering. This study provides a systematic observational basis for exploring the benefits of dual-frequency joint detection in enhancing sea ice monitoring capabilities, providing vital support for the development and refinement of algorithms for FY-3E WindRAD operational sea ice products.

Sea surface wind speed (SSWS) is an important oceanic dynamical parameter, extensively utilized in numerical simulations of climate change and weather forecasts, as well as in storm intensity assessment. Microwave radiometers onboard sun-synchronous satellites can provide a large amount of SSWS data globally. Atmospheric attenuation caused by large raindrops and rainwater contamination can both lead to estimation errors, especially for the sensors without L-band and C-band channels such as the Microwave Radiation Imager (MWRI) sensor onboard the Fengyun-3D (FY3D) satellite. To investigate the potential of MWRI in SSWS detection under bad weather conditions, a lightweight deep neural network (LWDNN) is used based on the Global Change Observation Mission First-Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2) all-weather SSWS product in 2021. The SSWS product from AMSR2, soil moisture active passive (SMAP), buoys, and the ERA5 reanalysis data have been utilized to validate the LWDNN under all weather conditions. The overall root mean square error (RMSE) of MWRI SSWS is less than 2.0 m/s under all weather conditions and less than 1.5 m/s in the clear-sky region. In order to test the retrieval effectiveness of LWDNN in cyclone regions, the scenes of cyclones are collected. Results show that the RMSEs of the MWRI maximum wind speed (VMAX) product relative to the AMSR2 and SMAP data are 6.31 and 6.66 m/s, respectively, in the wind speed range of 20–70 m/s, and there are no systematic biases. The RMSEs of the MWRI SSWS relative to the Stepped-Frequency Microwave Radiometer (SFMR) is 5.21 m/s in the wind speed range of 6–56 m/s, and the SSWS of MWRI and SFMR is generally consistent.

Fengyun-3 (FY-3) series are operational satellite missions that can provide global GNSS-R observations using multiple GNSS systems. This abstract highlights the advancements in GNSS-R product development from FY-3E, FY-3F, and FY-3G. Currently available to the public are operational Level 1 and Level 2 wind products with a 25-km resolution. Additionally, a raw intermediate frequency product is accessible for scientific research. Upcoming product developments include the release of Level 2 12.5-km wind, Level 2 land soil moisture, Level 2 sea ice thickness, and Level 3 wind. Their algorithms and scientific impact will be discussed.

Antarctic sea ice has experienced unique and complex changes in the past decades, the sea ice extent of which reaches the lowest record in February 2023. There are few studies on Antarctic sea ice classification since it is more difficult to be identified due to its characteristics of being younger and more dynamic compared to Arctic sea ice. This letter presents a classification algorithm for Antarctic sea ice based on the first-ever spaceborne dual-frequency scatterometer called WindRAD on board Fengyun-3E (FY-3E). The feature parameters are first extracted based on WindRAD orbital data. Then the$k$-means method with an optimized feature vector is used for sea ice classification retrieval. Finally, suspicious multiyear ice (MYI) is corrected based on an image dilation algorithm. The intercomparison of WindRAD Antarctic sea ice classification results with other sea ice type products shows quite good consistency not only in the spatial distribution characteristics, but also in the time series of MYI extent, verifying the capability of FY-3E WindRAD in monitoring Antarctic sea ice type.

Reflected global navigation satellite system (GNSS) signals from Earth surface can be received by receivers at low Earth orbit for the remote sensing of geophysical parameters. While the technique has been studied for around 30 years, most early spaceborne GNSS reflectometry missions only adapted to receive GPS signals and the studies of reflected Galileo (GAL) signals in space are limited. The Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the FY-3E satellite is the first mission that can operationally receive reflected GPS, BeiDou (BDS), and GAL signals at the same time. This letter presents the GAL reflectometry measurements from GNOS-II together with their calibration and wind speed (WS) retrieval methods. Results show that while GAL has a different signal modulation, the observables can be used to retrieve WSs using the same geophysical model functions (GMFs) of GPS after a dedicated calibration. The retrieved WSs from GAL also have a comparable accuracy as those from GPS and BDS.