Sea ice concentration (SIC) has been monitored with passive microwave (PM) observations for decades. Various techniques have been developed for its improvement. While techniques such as weather filters are commonly used, the necessity of combing radiative transfer model (RTM)-based atmospheric correction and dynamic tie points (DTP) remains an open question, particularly for near-90 GHz algorithm. This study investigates their respective influence on SIC retrieval using the FY-3D Microwave Radiation Imager (MWRI) data in 2019. The original and atmospherically corrected Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) algorithm (ASI and ASI2, respectively) are used in combination with fixed tie points (FTP) and DTP, resulting in four sets of ice concentration retrievals, namely ASI-FTP, ASI-DTP, ASI2-FTP, and ASI2-DTP. They are inter-compared with three PM-based ice concentration products and evaluated with a synthetic aperture radar (SAR)-based ice/water classification product and 20 clear-sky Moderate Resolution Imaging Spectroradiometer (MODIS) images from February to July 2019. The ASI2-based ice concentrations are overall higher and perform better, with the root mean square error (RMSE) and bias reduced by 5.4%–7.4% and 7.2%–8.0%, respectively. In comparison, the use of DTP has varying performances depending on the tie points extraction procedure. Good tie points work similarly to the atmospheric correction in mitigating SIC underestimations. The combined use of both varies substantially with seasons. During summer, it well captures the seasonal variability of tie points and effectively mitigates the atmospheric influence, thus significantly improving the retrievals. This highlights the necessity of combining both techniques for near-90 GHz algorithm, especially for summer.

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.

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.

Integrated retrieval using the optimal estimation (OE) method iteratively finds a set of geographical parameters that best match the observations. However, this method becomes more challenging over the ice surface due to the highly sensitive parameters such as sea ice concentration (SIC) and multiyear ice concentration (MYIC). In this study, a new time constraint that captures the distinct temporal characteristics of SIC and MYIC is incorporated into the OE method. The integrated retrievals, using both the original and time-constraint OE method (referred to as OE and OE-Z, respectively), were conducted based on FengYun-3D (FY-3D) microwave radiation imager (MWRI) data. Compared to other radiometer-based SIC and MYIC products, OE-Z outperforms OE, with the correlations increasing from 0.91 to 0.96 for SIC and from 0.41 to 0.49 for MYIC. The time constraint in OE-Z effectively mitigates the anomalous retrievals in SIC and MYIC, resulting in smoother and more reasonable time series than OE. Improvements in SIC and MYIC lead to enhanced simulation of surface microwave emission, thus improving the retrieval of atmospheric parameters. In comparison with the MOSAiC total water vapor (TWV) measurements, the RMSE in OE-Z reduces from 1.72 to 1.66 kg/m2, and the correlation increases from 0.46 to 0.50. The simulated brightness temperature (TB) biases in OE-Z reduce from 0.71 to 0.31 K at 36 GHz and from −8.95 to −7.72 K at 89 GHz. This emphasizes the importance of imposing suitable constraints on highly sensitive parameters in integrated retrieval.

Passive microwave (PM) and synthetic aperture radar (SAR) observations are essential tools for providing long time series of sea-ice cover information, including sea-ice concentration (SIC) and sea-ice extent (SIE). Large uncertainties have been revealed in PM SIC/SIE products in the marginal ice zone (MIZ) and during the melting season, where fusion with SAR data could be effective for improving accuracy due to its high spatial resolution and ability to preserve detailed ice distributions. A comprehensive comparison of PM and SAR ice cover products is needed for better data fusion. This study evaluates one of the PM SIE products, the advanced microwave scanning radiometer 2 (AMSR2) SIE product retrieved with the arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) algorithm, using a neural-network-based SAR SIE product throughout the year 2019. First, we present key results of three assessment parameters, including the overall accuracy (OA), error-of-ice (EI), and ice edge location distance (LD), and then estimate the optimal SIC segmentation threshold for AMSR2 ASI SIE. Based on OA and EI, the annual average SIC threshold of 12.24%, winter average of 9.25%, and summer average of 16.43% are obtained and regarded as optimal by excluding cases with large uncertainties. Second, the AMSR2 ASI SIE product is found to perform better in identifying thin ice and melt ponds, while the SAR NN SIE product has better detection of brash ice and frazil ice. We introduce a parameter of sea-ice fragmentation fraction (IFF) to analyze the primary impact factors behind the different performances. It is found that the ratio of LD to IFF could distinguish the aforementioned different ice conditions, thus providing hints for combining the complementary advantages of the two SIE products during data fusion.

As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in the Arctic. Microwave scatterometers in the Ku-band and C-band can provide daily observations of sea ice type. However, their comparative capabilities in mapping ice type have not been thoroughly evaluated. We present a systematic intercomparison of the backscatter signature in VV polarization (${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$) and the sea ice classification from three scatterometer systems using the same ice classification approach. The systems are the Ku-band quick scatterometer (QSCAT) and the newly launched Chinese rotating fan-beam scatterometer (RFSCAT) and the C-band advanced scatterometer (ASCAT). Three freezing seasons are used, i.e., 2007/08 and 2008/09 for the QSCAT/ASCAT comparison and 2019/20 for the RFSCAT/ASCAT comparison. With reference to ASCAT,${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$bias between QSCAT and RFSCAT results from their different incidence angles. A continuous declining trend of${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$from MYI and FYI is observed during winter, with a greater difference between MYI and FYI in the Ku-band. The MYI and FYI extent derived from QSCAT/RFSCAT is highly consistent with that derived from ASCAT, with a difference less than 7% and 3% for MYI and FYI, respectively. The overall accuracy (OA) is around 77% and 80% for the RFSCAT results and ASCAT results, respectively, compared with Sentinel-1 SAR images. The classification results show high consistency (81%–89%) with ice charts from the Canadian Ice Service. The incorporation of${\mathrm {Tb}}_{36\mathrm {h}}$from AMSR-E/AMSR2 improves the OA of the classification when using ASCAT or RFSCAT by 7%–11%.

The EONav ship routing service uses near real time observations of ocean surface currents, waves, wind and sea ice conditions, in conjunction with forecasts from numerical weather and ocean models, to plan the optimal route for a vessel. Products from the Copernicus Marine Environment Monitoring Service (CMEMS) and several national weather services are used together with in-house products developed for Synthetic Aperture Radar (SAR) data from satellites such as Sentinel-1, Radarsat-2 and COSMO-SkyMed. A novel data selection algorithm automatically ranks the various data sets and combines them to provide the most reliable met-ocean information. A sail plan optimized e.g. to reduce fuel consumption is determined based on the met-ocean information and is communicated to the ships to assist captains in selecting the best route and speed pattern to their destination.

Multiyear ice (MYI) characteristics can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combine both observations to identify partial concentrations of ice types (including MYI) is the Environment Canada Ice Concentration Extractor (ECICE). However, cycles of warm-cold air temperature trigger wet-dry cycles of the snow cover on MYI surface. Under wet snow conditions, anomalous brightness temperature and backscatter, similar to those of first-year ice (FYI), are observed. This leads to misidentification of MYI as being FYI, hence decreasing the estimated MYI concentration suddenly. The purpose of this paper is to introduce a correction scheme to restore the MYI concentration under this condition. The correction is based on air temperature records. It utilizes the fact that the warm spell in autumn lasts for a short period of time (a few days). The correction is applied to MYI concentration retrievals from ECICE using an input of combined QuikSCAT and AMSR-E data, acquired over the Arctic region in a series of autumn seasons from 2003 to 2008. The correction works well by replacing anomalous MYI concentrations with interpolated ones. For September of the six years, it introduces over 0.1×106km2MYI area, except for 2005. Due to the regional effect of warm air spells, the correction could be important in the operational applications where ice concentrations are crucial on small scale and mesoscale.

We developed a new method to monitor the ice sheet melting periods with passive microwave measurements. Our method combined the original brightness temperature time series with those simulated by time series simulation software package TIMESAT in order to obtain a time series with similar behaviour, but less noise. A generalized Gaussian model was used to classify the pixels to wet and dry snow. Based on the steep rise and drop of the new time series, we detected the onset and end of the Antarctic ice sheet melting from 1988 to 2008. The results indicated that the whole Antarctic experienced a deceasing melting during the 20 years. The Antarctic Peninsula region exhibited a long and stable melt occurrence, in comparison with which the Ross Ice Shelf has the nearly shortest melt duration and the largest variability in melt extent.