Currently, Arctic sea ice thickness (SIT) data with extensive spatiotemporal coverage primarily comes from satellite observations, including CryoSat-2, Soil Moisture and Ocean Salinity (SMOS), and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The studies of the intercomparison and evaluation of multi-source satellite products in recent years are limited. In this study, three latest version products of ICESat-2, CryoSat-2, and CS2SMOS (a merged product of CryoSat-2 and SMOS) were examined from October to April, between 2018 and 2022. Three types of observation including airborne data from the Operation IceBridge (OIB) and IceBird, and in situ data from Beaufort Gyre Exploration Project (BGEP) are selected as the reference in the evaluation. The intercomparison results show that the mean SIT is generally largest in ICESat-2, second largest in CryoSat-2, and smallest in CS2SMOS. The SIT in CryoSat-2 is closer to the SIT in ICESat-2. The thickness displayed by the three satellite products starts to increase at different freezing months, varying between October and November. The three satellite products demonstrated the strongest agreements in SIT in the Beaufort Sea and Central Arctic regions, and exhibited the most distinct differences in the Barents Sea. In the evaluation with OIB data, three satellite-derived SIT were generally underestimated and CS2SMOS demonstrates the closest match. The evaluation using IceBird data indicates an underestimation for all satellites, with CryoSat-2 showing the best agreement. In the assessment with BGEP data, ICESat-2 displayed a more pronounced degree of overestimation or underestimation compared to the other two satellites, and CS2SMOS exhibited the optimal agreement. Based on the comprehensive consideration, CS2SMOS demonstrated the best performance with the airborne and in situ observational data, followed by CryoSat-2 and ICESat-2. The intercomparison and evaluation results of satellite products can contribute to a further understanding of the accuracies and uncertainties of the latest version SIT retrieval and the appropriate selection and utilization of satellite products.

The accuracy and reliability of the latest version of multisource satellite derived Arctic sea ice thickness (SIT) in thinner ice regions are currently uncertain. This study integrated a comprehensive comparison and assessment of Arctic SIT derived from CryoSat-2, Soil Moisture and Ocean Salinity (SMOS), fusions of CryoSat-2 and SMOS (CS2SMOS), and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) during 2011–2022. The focus was on the region with mean SIT less than 1 m. The five datasets from the Operation IceBridge L4 and Quick Look, IceBird campaign, CryoSat Validation Experiment, and the Canadian Arctic Archipelago stations were utilized as the reference data in the assessment. The four satellite products could capture similar major spatiotemporal variations in SIT. During 2011–2022, CryoSat-2 generally derived the largest multiyear mean SIT, followed by CS2SMOS, and SMOS exhibited the smallest mean. During 2018–2022, ICESat-2 recorded the largest mean SIT and the rankings of the other three satellite products remained consistent. The comparison and assessment results indicated that all four satellite products generally exhibited some underestimations of SIT. During 2011–2022, the comprehensive results highlighted CryoSat-2 as the best overall performance product, exhibiting optimal agreement with all five reference datasets. During 2018–2022, CryoSat-2 consistently demonstrated the best overall performance. CS2SMOS exhibited a performance similar to CryoSat-2 in the two selected periods. This study contributes to further understandings of reliabilities and potential disparities among the latest versions of multisource satellite products in the thinner ice region.

Currently, the performance of sea ice thickness (SIT) data retrieved from multisource satellite products in the Arctic seasonal ice zones remains unclear. This study presented the spatiotemporal intercomparison and evaluation of satellite data, including the latest versions of Soil Moisture and Ocean Salinity (SMOS), CryoSat-2, combined CryoSat-2 and SMOS (CS2SMOS), and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), specifically focusing on area with mean SIT below 0.5 m. Five evaluation datasets were used. During 2010–2023, SMOS had the smallest mean SIT, with CryoSat-2 showing the largest mean SIT. During 2018–2023, with the inclusion of ICESat-2, SMOS still showed the smallest mean SIT. CryoSat-2 exhibited the largest mean SIT, followed by ICESat-2, CS2SMOS ranked third. Evaluation results indicated that four satellite products generally underestimated SIT. In two periods, SMOS consistently exhibited the weakest performance, which showed a large gap from what was expected in previous studies. In contrast, CS2SMOS demonstrated the highest alignment with five evaluation datasets during 2010–2023, indicating the best overall performance. During 2018–2023, ICESat-2 exhibited the best overall performance with two evaluation datasets. This study refreshes previous knowledge about SMOS in the seasonal ice zones and contributes to further improvements in SIT retrieval.

Accurate estimates of snow depth on sea ice are essential for monitoring sea ice mass balance and quantifying heat exchange between the atmosphere and sea ice. However, the seasonal evolution of pan-Arctic snow depths throughout the freezing season (October–April) remains poorly understood. In this study, we developed an Adaptive Seasonal Algorithm for Snow Depth retrieval over Arctic sea ice (AdaSA-SD) based on the Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements, which incorporates time-varying and ice-type dependent algorithmic coefficients. A daily pan-Arctic snow depth dataset with a spatial resolution of 25 km × 25 km was then generated for the freezing seasons from 2012 to 2023. Our results accurately reproduce the observed spatial patterns and seasonal and interannual variability of Arctic snow depths, with mean biases ranging from 0.5 to 3.0 cm and mean absolute percentage errors between 10% and 30% when validated against airborne, buoy, and transect data. The seasonal cycle of our estimated snow depths also agrees well with simulated results from state-of-the-art snow models. Furthermore, the uncertainties and limitations associated with the snow depth retrievals are thoroughly discussed. The approach and dataset proposed in this study will improve our understanding of the changing state of the Arctic snow-sea ice system across the freezing season.

Satellite retrievals of Arctic sea ice thickness typically assume a constant sea ice bulk density (IBD), overlooking its seasonal variations influenced by ice internal texture and contaminants. This study unveils the initial insights into the seasonal evolution and parameterization of IBD during the Arctic freezing season from October to April. To retrieve IBD, we combined in situ observations obtained from ice mass balance buoys, snow pits, and snow transects during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, as well as laser freeboard data derived from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Assuming hydrostatic equilibrium, local-scale IBDs for the level ice component of the MOSAiC ice floes, predominantly consisting of second-year ice, were obtained at a spatial scale of approximately 50 km. The results indicated a statistically significant seasonal decreasing trend in IBD at a rate of ~16 kg m⁻³ per month (P < 0.001) from mid-October to mid-January, likely attributable to increased internal porosity as the sea ice aged. This was followed by a relatively stable period from mid-January to mid-April, with an average IBD of ~897 ± 11 kg m⁻³. Core-based IBDs from eight MOSAiC sites showed a similar seasonal pattern, but with a narrower range of variation and an earlier onset of the relatively stable period, possibly owing to the spatial heterogeneity of the MOSAiC ice floes. Based on regression analyses, we developed updated parameterizations for IBD that are anticipated to be applicable throughout the freezing season, encompassing both first- and second-year ice. In particular, the ice draft-to-thickness ratio emerged as the most efficient parameter for determining IBD (R² = 0.99, RMSE = 1.62 kg m⁻³), with potential application to multi-year ice and deformed ice as well. Our updated parameterizations have the potential to optimize basin-scale satellite-derived sea ice thickness, thereby contributing to more accurate monitoring of changes in sea ice volume.