The global positioning system (GPS) can provide the daily coordinate time series to help geodesy and geophysical studies. However, due to logistics and malfunctioning, missing values are often “seen” in GPS time series, especially in polar regions. Acquiring a consistent and complete time series is the prerequisite for accurate and reliable statical analysis. Previous imputation studies focused on the temporal relationship of time series, and only a few studies used spatial relationships and/or were based on machine learning methods. In this study, we impute 20 Greenland GPS time series using missForest, which is a new machine learning method for data imputation. The imputation performance of missForest and that of four traditional methods are assessed, and the methods’ impacts on principal component analysis (PCA) are investigated. Results show that missForest can impute more than a 30-day gap, and its imputed time series has the least influence on PCA. When the gap size is 30 days, the mean absolute value of the imputed and true values for missForest is 2.71 mm. The normalized root mean squared error is 0.065, and the distance of the first principal component is 0.013. missForest outperforms the other compared methods. missForest can effectively restore the information of GPS time series and improve the results of related statistical processes, such as PCA analysis.

Arctic sea ice variations are sensitive to Arctic environmental changes and global changes. Freeboard and thickness are two important parameters in sea ice change research. Satellite altimetry can provide long-time and large-scale sea ice monitoring. We estimated the Arctic sea ice freeboard and its variations for the period from 2002 to 2012 from Envisat satellite altimetry data. To remove geoid undulations, we reprocessed the Envisat data using a newly developed mean sea surface (MSS) model, named DTU18. Residuals in the static geoid were removed by using the moving average technique. We then determined the local sea surface height and sea ice freeboard from the Envisat elevation profiles. We validated our freeboard estimates using two radar freeboard products from the European Space Agency (ESA) Climate Change Initiative (CCI) and the Alfred Wegener Institute (AWI), as well as the Operation IceBridge (OIB) sea ice freeboard product. The overall differences between our estimates and the CCI and AWI data were 0.11 ± 0.14 m and 0.12 ± 0.14 m, respectively. Our estimates show good agreement with the three products for areas of freeboard larger than 0.2 m and smaller than 0.3 m. For areas of freeboard larger than 0.3 m, our estimates correlate better with OIB freeboard than with CCI and AWI. The variations in the Arctic sea ice thickness are discussed. The ice freeboard reached its minimum in 2008 during the research period. Sharp decreases were found in the winters of 2005 and 2007.

Satellite altimeters can be used to derive long-term and large-scale sea ice thickness changes. Sea ice thickness retrieval is based on measurements of freeboard, and the conversion of freeboard to thickness requires knowledge of the snow depth and snow, sea ice, and sea water densities. However, these parameters are difficult to be observed concurrently with altimeter measurements. The uncertainties in these parameters inevitably cause uncertainties in sea ice thickness estimations. This paper introduces a new method based on least squares adjustment (LSA) to estimate Arctic sea ice thickness with CryoSat-2 measurements. A model between the sea ice freeboard and thickness is established within a 5 km × 5 km grid, and the model coefficients and sea ice thickness are calculated using the LSA method. Based on the newly developed method, we are able to derive estimates of the Arctic sea ice thickness for 2010 through 2019 using CryoSat-2 altimetry data. Spatial and temporal variations of the Arctic sea ice thickness are analyzed, and comparisons between sea ice thickness estimates using the LSA method and three CryoSat-2 sea ice thickness products (Alfred Wegener Institute (AWI), Centre for Polar Observation and Modelling (CPOM), and NASA Goddard Space Flight Centre (GSFC)) are performed for the 2018–2019 Arctic sea ice growth season. The overall differences of sea ice thickness estimated in this study between AWI, CPOM, and GSFC are 0.025 ± 0.640 m, 0.143 ± 0.640 m, and −0.274 ± 0.628 m, respectively. Large differences between the LSA and three products tend to appear in areas covered with thin ice due to the limited accuracy of CryoSat-2 over thin ice. Spatiotemporally coincident Operation IceBridge (OIB) thickness values are also used for validation. Good agreement with a difference of 0.065 ± 0.187 m is found between our estimates and the OIB results.

Sea ice plays an important role in global climate system. Operation IceBridge (OIB) was launched in 2009 to bridge the gap in polar observations between the Ice, Cloud, and Land Elevation Satellite (ICESat) and ICESat-2 missions. OIB flew more than 1000 aircraft surveys, providing annual mapping of sea ice, glaciers, and ice sheets in the Antarctic and Arctic. This study utilizes altimetry data and synchronized optical images from OIB to derive Arctic sea ice freeboard estimates for the period from 2009 to 2019. Lead detection is important in determining sea ice freeboard. We developed an improved approach by combining optical images, OIB laser pulse apparent reflectivity, and L1B elevation profiles to detect leads. We then determined the local sea surface height (SSH) with a lowest elevation method from the L1B elevation profile of leads. Thus, sea ice freeboard was calculated by the difference between L2 relative elevation and corresponding local SSH. We derived the estimates of the Arctic sea ice freeboard from 2009 to 2019. Spatial and temporal variations of the freeboard were analyzed based on the estimates. The Arctic annual average sea ice freeboard showed a decreasing trend from OIB observations. However, an obvious increase in multiyear ice freeboard was observed in 2014. We validated our freeboard estimates with OIB freeboard product from the National Snow and Ice Data Center and coincident ICESat-2 freeboard. The comparison shows that our freeboard estimates generally compare well with the ICESat-2 freeboard, and our method is more sensitive to thin ice.

Polar sea ice monitoring is critical for global climate research and Arctic ship navigation, yet traditional satellite remote sensing struggles to resolve micro-scale ice features due to resolution and weather constraints. This study presents a novel sea ice classification method integrating UAV-derived high-resolution optical imagery from the 2024 “Sun Yat-sen University Polar Vessel” Arctic expedition with deep learning. The dataset, comprising four types (sea ice, melt ponds, open water, ships), was constructed through geometric correction, and semi-automated annotation using the ISAT-SAM approach. The proposed Ice-Unet model, optimized via transfer learning from pre-trained weights, employs a VGG16-inspired backbone with multi-scale feature fusion and downscaling mechanisms for semantic segmentation. Comparative evaluations against DeepLabV3+ (Xception/MobileNet), PSPNet (ResNet50), and HRNet demonstrate Ice-Unet’s superiority, achieving leading IoU of 95.07% and recall rates of 97.11%, particularly excelling in melt pond detection and ice lead morphology characterization. Case analysis reveals precise quantification of melt pond spatial patterns and predictive capability for their evolution into sea ice leads. The research empirically confirms the operational synergy between UAV platforms and deep learning architectures in monitoring transient sea ice dynamics, establishing a sub-meter resolution methodology that significantly enhances the precision of sea ice classification based ship navigation through near-real-time geospatial analytics.