Accurate identification of sea ice type from altimeter data is important for inversion of sea ice freeboard by radar altimeter, as well as for maritime navigation in polar regions. Previous sea ice classification studies have rarely involved multifrequency altimeter data. In this article, benefiting from the dual-frequency observation capability of the Chinese HY-2B altimeter, differences between waveform features of C- and Ku-band radar altimeter over different types of sea ice were investigated and analyzed for the first time. To validate and evaluate the sea ice classification capability of different feature combinations of the different bands, we extracted 14 features from the waveforms of C- and Ku-band HY-2B altimeter data acquired between December 2019 and April 2020. Our experiments demonstrated that the C- and Ku-band radar waveforms are sensitive to sea ice type, and that sea ice type classification could be improved by combining Ku- and C- band data. This work also proposed a sea ice classification methodology using C- and Ku-band altimeter waveforms. Comparison with results from previous studies indicated that the overall accuracy of the proposed method (up to 93.45%) is much higher than that of existing state-of-the-art methods.

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

The normalized radar cross section (NRCS) of microwave backscattering from a rough sea surface can be described by a composite Bragg scattering (CBS) model, which is a combination of a two-scale backscatter model and a geometric optics model. For small local radar incidence angles that are smaller than a given angle setting, the two-scale backscattering mechanism of the sea surface is replaced by a geometric optics solution for specular reflection. In this study, an optimized setting of 18 degrees for the small incidence angle is determined by comparing the NRCSs between 52 RADARSAT-2 (R2) synthetic aperture radar (SAR) images and the CBS model with simultaneous sea surface wind derived by meteorological mooring buoys located in the northern South China Sea (SCS). By using the CBS model with the optimized incidence angle setting of 18 degrees and the nearly simultaneous wind directions of the Advanced Scatterometer (ASCAT) as the input, sea surface wind speeds are retrieved from 46 R2 SAR images at C-band and from 2 COSMO-SkyMed (CSK) SAR images at X-band for the application study. The average root mean square error (RMSE) of the C-band R2 SAR retrieval results validated against nearly simultaneous measurements of ASCAT is 1.5 m/s, while the same value is 1.6 m/s for the X-band CSK SAR image. The results also indicate that the optimized small incidence angle setting of the CBS model found in this study is relatively reliable under sea surface wind speed conditions less than 14.0 m/s.