Sea ice type is one of the most sensitive variables in Arctic ice monitoring and detailed information about it is essential for ice situation evaluation, vessel navigation, and climate prediction. Many machine-learning methods including deep learning can be employed for ice-type detection, and most classifiers tend to prefer different feature combinations. In order to find the optimal classifier-feature assembly (OCF) for sea ice classification, it is necessary to assess their performance differences. The objective of this letter is to make a recommendation for the OCF for sea ice classification using Cryosat-2 (CS-2) data. Six classifiers including convolutional neural network (CNN), Bayesian, K nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were studied. CS-2 altimeter data of November 2015 and May 2016 in the whole Arctic were used. The overall accuracy was estimated using multivalidation to evaluate the performances of individual classifiers with different feature combinations. Overall, RF achieved a mean accuracy of 89.15%, followed by Bayesian, SVM, and BPNN (~86%), outperforming the worst (CNN and KNN) by 7%. Trailing-edge width (TeW) and leading-edge width (LeW) were the most important features, and feature combination of TeW, LeW, Sigma0, maximum of the returned power waveform (MAX), and pulse peakiness (PP) was the best choice. RF with feature combination of TeW, LeW, Sigma0, MAX, and PP was finally selected as the OCF for sea ice classification and the results that demonstrated this method achieved a mean accuracy of 91.45%, which outperformed the other state-of-art methods by 9%.

In recent years, Shanghai has entered a stage of microscale land subsidence, but the uneven subsidence is still significant, with long-term impacts on the operational safety of subways and other infrastructures. On the basis of 154 high-resolution Terra Synthetic Aperture Radar-X (TerraSAR-X) images captured from 2013 to 2020 and the time-series persistent scatterer-interferometric SAR (PS-InSAR) method, the land subsidence along the subways in Shanghai was acquired, and the levelling data of 56 benchmarks were used to validate the measurements derived by PS-InSAR. The results indicated that the two data sets agreed well, with a correlation coefficient of 0.9 and maximum D-value of 4.0 mm derived from six pairs of comparative data sequences. The proportion of PS points showing deformation rates between −3.0 mm/a and 3.0 mm/a reached 99.4%. These results indicated that the land subsidence trend along the subway was relatively stable overall, while significant deformation was distributed mainly along the suburban subways, especially the lines that were newly open to traffic, such as Line 5 and the Pujiang line (PJ Line); along these lines, the proportions of PS points with deformation rates exceeding ±3 mm/a were 7.2% and 7.6%, respectively, and the proportions were much smaller in the other lines. The maximum cumulative deformation (MCD) along the subways was located between Jiangchuan Road Station and Xidu Station of Line 5 with a value of −66.4 mm, while the second and third MCDs were −48.2 mm along Line 16 and −44.5 mm along PJ Line, respectively. Engineering constructions, such as human-induced ground loads, foundation pit constructions, and road constructions, were the main factors affecting local land subsidence. The analysis results also showed that land subsidence was relatively significant during the period before the subways were open to traffic due to subway construction, while land subsidence clearly slowed after the subway lines were open to traffic. This deceleration in land subsidence was closely related to the rise in the groundwater level.

Accurate multitemporal glacier change investigations and analyses are lacking on the southeastern Tibetan Plateau (SETP). A combination of photogrammetry, optical remote sensing and synthetic aperture radar (SAR) datasets can accurately identify large-scale glaciers; In the present study, glaciers in three periods on the SETP (1970s, 2000, 2020) were identified from multisource remote sensing data based on a deep learning method and manual visual interpretation, and multitemporal glacial inventory data from relatively high-frequency source imagery. Totals of 11648, 12993, and 11875 glaciers were identified in the 1970s, 2000, and 2020, with total areas of 13372.08 km ² , 11692.31 km ² , and 10612.94 km ² , respectively. The general distribution of SETP glaciers was identified to be typical of alpine glaciers dominated by small-sized glaciers. The average elevation of glaciers was approximately 5000 m; the slopes were mostly lower than 40°, and the main aspect was southeast, followed by south and southwest. The glaciers retreated from the 1970s to 2020, and a total glacier area of approximately 2759.14 km ² was degraded during this time, with an average annual melting rate of 0.45% yr ^-1 . Rising summer temperatures may be the driving force behind the continuous decline in the glacier area. Overall, the results obtained in this study showed relatively low uncertainty involved in the identification of glaciers compared to some previous studies. The results can provide accurate glacier information for glacier monitoring and modeling studies on the SETP.

The successful launch of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a new and advanced tool for sea ice thickness (SIT) estimations in the Arctic. However, the performance of ICESat-2 for SIT estimations still remains unknown. In the present study, SIT estimates derived from ICESat-2 are examined using three retrieval methods, namely, two buoyancy methods with the merged snow depth and empirical snow depth (BMA and BME, respectively) and one empirical estimation method (EEM), and these estimates are compared to near-simultaneous airborne measurements from the IceBird mission in April 2019. Overall, the ICESat-2 total freeboard registers quite well with that from the near-concurrent IceBird mission with a mean bias of 2.5 cm, which demonstrates the high reliability of ICESat-2 data for SIT estimation. However, the much more evident difference between SIT estimations than total freeboard from ICESat-2 and IceBird indicates that other parameters (e.g., snow depth and snow/ice densities) may bring increased uncertainties to the SIT estimation. Overall, BMA is the best method for SIT estimation and has the closest thickness distribution to that of IceBird data with a mean bias of 0.11 m, followed by the BME and EEM methods. The dominate error sources for SIT estimation using the buoyancy method are ice density and snow depth that require further investigation in future studies.

The seasonal and regional variability of Arctic sea ice area (SIA) and thickness (SIT) were investigated between 1979 and 2020 for the Atlantic sector (AS), Pacific sector (PS) and Barents–Kara Seas (BKSs). We applied the SIA data from remote sensing observations and SIT data from numerical model calculations. We found the large summer variability of SIA and SIT in AS and PS compared with those in winter. The opposite feature was seen in the BKSs. The annual declining rates of SIA and SIT were the largest in PS (−1.73 × 10⁴ km² yr⁻¹) and AS (−3.36 × 10⁻² m yr⁻¹), respectively. The SIA variability was modest for winter PS and the northern Canadian Arctic Archipelago of AS. The annual and winter SIA flux from PS to AS gradually increased in 1979–2020; the summer SIA flux accounted for 11% of the PS summer SIA decline. The annual and seasonal SIA outflow through the Fram Strait during 1979–2020 steadily increased while for annual and winter SIA export, the increase mainly occurred in 1979–2000; the summer SIA outflow was only 1.45% equivalent to the decrease in the entire Arctic summer SIA. We concluded that sea ice export was not a major impact factor on the seasonal and regional decline of SIA and SIT except for the individual years. The near surface air temperature (SAT) and sea surface temperature (SST) were responsible for the retreat and thinning of the sea ice. The dramatic increase in SAT in winter resulted in a strong decrease in winter sea ice in BKS. The outgoing longwave radiation had significant negative correlations with SIA and SIT and positive correlations with SAT and SST. The Atlantic multi-decadal oscillation, related to the North Atlantic Ocean’s SST anomalies, had significant negative correlations with SIA and SIT. The SIT had higher correlations with the atmospheric and oceanic factors compared with SIA, which indicates that SIT is important for predictions of Arctic sea ice and climate change.

Atmospheric rivers (ARs) can rapidly alter regional hydrothermal conditions and directly affect lake ice. This study presents the first assessment of AR impacts on lake ice phenology across the Northern Hemisphere. AR events were associated with positive anomalies in temperature, rainfall, and snowfall, as well as negative anomalies in solar radiation. These climate anomalies showed significant correlations with lake ice phenology. Between 1951 and 2022, AR‐associated climate anomalies explained on average 20%, 33%, and 30% of the long‐term variations in ice‐on, ice‐off, and ice duration, respectively, with stronger effects on North American lakes than on Eurasian lakes. Temperature anomalies contributed approximately half of the total explanatory power. Furthermore, extreme lake ice events corresponded closely to the frequency and intensity of AR events. These results indicate that ARs play a significant role in driving interannual variability in lake ice phenology across the Northern Hemisphere.