We provide a new sea ice and water classification product with high spatial and high temporal coverage using Sentinel-1 Synthetic Aperture Radar (SAR) data. The classification is applied in the Fram Strait region in the Arctic during melting seasons, when the contrast between backscatter intensities of different ice types observed by SAR is reduced due to the melted ice surface and wet snow on sea ice. The wet or melted snow strongly reduces the SAR penetration depth and thus suppresses the volume scattering contribution of sea ice. Furthermore, within the marginal sea ice zone (MIZ)ambiguities between ice and water can result from the effects of winds and ocean currents on the ocean SAR backscatter. On the other hand, under calm conditions the contrast between thin ice and flat open water can be reduced, and thusdecrease the separability of some ice. In summary, the melting season represents the most challenging time of the year forreliable ice-water classification from SAR data. We propose here a new approach to overcome these problems by using amixture statistical distribution based conditional random fields (MSTA-CRF) model. To obtain reliable ice-waterclassification whilst maintaining a fast computation time suitable for operational applications, the MSTA-CRF adopts a superpixel approach in the fully connected CRF model. The MSTA-CRF is a semantic model, which integrates statisticaldistributions (Gamma, Weibull, Alpha-Stable, etc.) to model the backscatters of ice and water and overcome the effects ofspeckle noise and wind-roughened water. Dual-polarization Extended Wide (EW) mode Sentinel-1A/1B SAR data with40 m spatial resolution is available several times per day within the Fram Strait region. Observations from June toSeptember during the six years 2015–2020 are collected and classified into ice and water categories. The classification performance of algorithm is evaluated using ice charts from the Ice Service at the Norwegian Meteorological Institute(MET Norway). The methods of training sample selection, and their application to processing large data volumes andautomatic classification of ice-water are discussed. In the experiment part, we demonstrate that the MSTA-CRF can providea good performance with about 90 % accuracy for ice-water classification, which is better than most of other state-of-theart algorithms. Compared with the 89 GHz microwave radiometer ASI sea ice concentration product, the sea ice extent in Fram Strait derived from MSTA-CRF algorithm is lower during melting seasons from 2015 to 2020, and the monthly Juneto September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.

This work introduces an innovative radiometric terrain correction algorithm using PolInSAR imagery for improving forest vertical structure parameter estimation. The variance of radar backscattering caused by terrain undulation has been considered in this research by exploiting an iteration optimization procedure to improve the backscattering estimation for a Synthetic Aperture Radar (SAR) image. To eliminate the variance of backscatter coefficients caused by the local incident angle, a radiometric normalization algorithm has been investigated to compensate the influence of terrain on backscattering values, which hinders forest vertical parameter estimation. In vertical parameter estimation, species diversity and the spatial distribution of different vegetation have been modeled. Then, a combination of Fisher’s Alpha-Diversity model parameter estimation and the three-stage inversion method was designed for the vertical structure parameter. To demonstrate the efficiency of the proposed method in forest height estimation, the classical phase difference and three-stage inversion approach have been performed for the purpose of comparison. The proposed algorithm is tested on ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 (Radio Direction and Range Satellite 2) data sets for the Great Xing’an Mountain area and BioSAR (Biomass Synthetic Aperture Radar) 2007 data sets for the Remningstorp area. Height estimation results have also been validated using in-situ measurements. Experiments indicate the proposed method has the ability to compensate the influence of terrain undulation and improving the accuracy of forest vertical structure parameter estimation.

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.

High spatial resolution sea ice concentration (SIC) is crucial for global climate and marine activity. However, retrieving high spatial resolution SIC from passive microwave sensors is challenging due to the trade-off between spatial resolution and atmospheric contamination. Our study develops the Ice-WaterNet framework, a novel superpixel-based deep learning model that integrates Conditional Random Fields (CRF) with a dual-attention U-Net to enhance ice–water classification in Synthetic Aperture Radar (SAR) imagery. The Ice-WaterNet model has been extensively tested on 2735 Sentinel-1 dual-polarized SAR images from 2021 to 2023, covering both winter and summer seasons in the Fram Strait. To tackle the complex surface features during the melt season, wind-roughened open water, and varying ice floe sizes, a superpixel strategy is employed to efficiently reduce classification uncertainty. Uncertain superpixels identified by CRF are iteratively refined using the U-Net attention mechanism. Experimental results demonstrate that Ice-WaterNet achieves significant improvements in classification accuracy, outperforming CRF and U-Net by 3.375% in Intersection over Union (IoU) and 3.09% in F1-score during the melt season, and by 1.96 in IoU and 1.75 in F1-score during the freeze season. The derived high-resolution SIC products, updated every two days, were evaluated against Met Norway ice charts and compared with ASI from AMSR-2 and SSM/I, showing a substantial reduction in misclassification in marginal ice zones, particularly under melting conditions. These findings underscore the potential of Ice-WaterNet in supporting precise sea ice monitoring and climate change research.

The Amery Ice Shelf (AIS) dynamics and mass balance caused by iceberg calving and basal melting are significant in the ocean climate system. Using satellite imagery from Sentinel-1 SAR, we monitored the temporal and spatial variability of the frontal positions on the Amery Ice Shelf, Antarctica, from 2015 to 2021. In this paper, we propose an automatic algorithm based on the SO-CFAR strategy and a profile cumulative method for frontal line extraction. To improve the accuracy of the extracted frontal lines, we developed a framework combining the Constant False Alarm Rate (CFAR) and morphological image-processing strategies. A visual comparison between the proposed algorithm and state-of-the-art algorithm shows that our algorithm is effective in these cases including rifts, icebergs, and crevasses as well as ice-shelf surface structures. We present a detailed analysis of the temporal and spatial variability of fronts on AIS that we find, an advance of the AIS frontal line before the D28 calving event, and a continuous advance after the event. The study reveals that the AIS extent has been advanced at the rate of 1015 m/year. Studies have shown that the frontal location of AIS has continuously expanded. From March 2015 to May 2021, the frontal location of AIS expanded by 6.5 km; while the length of the AIS frontal line is relatively different after the D28 event, the length of the frontal line increased by about 7.5% during 2015 and 2021 (255.03 km increased to 273.5 km). We found a substantial increase in summer advance rates and a decrease in winter advance rates with the seasonal characteristics. We found this variability of the AIS frontal line to be in good agreement with the ice flow velocity.