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.

Depth hoar (DH) forms when there is a strong temperature gradient in the snowpack. In the Antarctic ice sheet (AIS), DH mostly forms during summer insolation. The seasonal regularity provides an age marker for each internal snow/firn layer, which is necessary for estimating surface mass balance (SMB) using ice-penetrating radar (IPR) and ice core. However, little is known about DH inside the AIS because ice drill and snow pit observations are inefficient and sparse. The deployment of the Operation IceBridge (OIB) airborne snow radar has significantly enhanced the field observation dataset. So far, the spatial distribution of DH remains unclear due to the lack of DH extraction over the AIS from OIB snow radar. Based on instance segmentation SOLOv2 and self-attention mechanism Swin Transformer, a DH layer automatic extraction algorithm ST-SOLOv2 is proposed, with AP50 of 0.9 and F1-score of 0.83, outperforms other commonly used instance segmentation networks (SOLOv2, Mask R-CNN, BlendMask, YOLACT, and CondInst). After the proposed preprocessing pipeline, we conduct the ST-SOLOv2 to locate each DH near the surface. Our results suggest DH number increases with elevation and decreases with slope angle from coast to inland. And DH number decreases with surface melting days as melting/freezing cycles obscure snow layering. We present a method for efficiently monitoring the DH distribution that can be used in further studies of snow radar SMB estimation.

The Antarctic blue ice can be classified into wind- and melt-induced based on their origins. They play a different role in the development of surface water systems, the surface energy balance and the infrastructure. Currently, visible light remote sensing is the most effective method for mapping blue ice. However, optical imagery faces difficulties in classifying blue ice accurately, and it is also greatly influenced by weather conditions. Synthetic Aperture Radar (SAR) images have the potential to map blue ice under all weather conditions, but it is difficult to distinguish blue ice from other similar weak microwave reflecting surfaces. In this study, by employing a segmentation method based on band ratios of the Sentinel-2 images, we delineated the geographical distribution of blue ice area (BIA) in the Lambert Glacier Basin. Taking advantage of the disparity in coherence levels between melt-induced and wind-induced blue ice, we performed blue ice classification in the Lambert Glacier Basin using Sentinel-1 images. The proposed method achieves an overall accuracy of 0.91 and F1-score of 0.91 and provides blue ice types with a spatial resolution of 10 m. The total area of blue ice in the Lambert Basin was estimated to be approximately 1.986 × 104km2. Among them, the area of melt-induced blue ice was approximately 1.276 × 104km2, while the wind-induced blue ice covered around 0.710 × 104km2. Melt-induced BIA was predominantly distributed in low-altitude coastal areas and downstream of glaciers, exhibiting higher surface temperatures compared to wind-induced BIA. Wind-induced BIA, on the other hand, was mainly found near nunataks and exposed rocks, displaying higher albedo than melt-induced BIA.

Surface meltwater critically impacts the Antarctic mass balance and global sea level rise. Quantifying the extent of surface meltwater in Antarctica on a large scale is a challenging task. Traditional methods, such as thresholding, have many limitations. We used a deep learning method, the U-Net with attention blocks, to automatically extract surface meltwater from Sentinel-2 images. We inserted attention mechanism blocks into U-Net to assign different weights to all pixels and channels to utilize the high resolution and multiple channels of Sentinel-2 images. The model was used to map surface water bodies in Sentinel-2 images, and the average accuracy reached 0.9969 on the test dataset. In East Antarctica, the Amery Ice Shelf (AIS) exhibits the largest surface meltwater area. Studying surface meltwater dynamics on the AIS is useful for understanding the East Antarctic mass balance and demonstrating the model performance. We analyzed the classification results for surface water bodies on the AIS from January 2017-2022. Spatially, 96% of surface water bodies are concentrated inland of the AIS from 70-73°S and account for 93% of the region 20 km from the coastline of the AIS. Temporally, the water body area varies considerably in different years, with a maximum in 2017 (932.54 km2) and a minimum in 2021 (58.34 km2). The spatial distribution of surface water body on the AIS is controlled by the firn air content, katabatic winds, bare rocks and blue ice. The interannual variability is associated with complex climate factors, including temperature, surface net solar radiation, snowfall, and snowmelt, among which temperature and snowfall show strong correlation.

Surface snowmelt is a vital environmental parameter that affects energy exchanges between polar ice sheets and the atmosphere. Due to the difficulties of continuous in-situ measurements, passive microwave remote sensing technology has become a major method for obtaining ice sheet surface snowmelt states over large areas. Feng Yun-3 (FY-3) series satellites, the second generation of Chinese polar-orbiting meteorological satellite missions, have great potential for providing long-term polar ice sheet surface snowmelt state products. In this study, we establish a monthly inter-calibration model to synergize brightness temperatures from the Microwave Radiation Imager (MWRI) aboard different FY-3 satellites. Based on the calibrated continuous brightness temperature record, an improved snowmelt algorithm is proposed by using an adaptive thresholding method, which does not rely on in-situ observation data. After inter-calibration, the consistency of the melt extent obtained by different sensors is considerably better than before, with the bias decreasing from 85 pixels to 3 pixels in the Greenland Ice Sheet (GrIS) and from 16 pixels to 6 pixels in the Antarctic Ice Sheet (AIS). Evaluation of the snowmelt result is conducted with the automatic weather station (AWS) air temperature, and a promising accuracy is found with an overall accuracy above 92% in the AIS and approximately 86% in the GrIS. This study provides new possibilities for a long-term continuous snowmelt product by connecting FY-3B, FY-3C, FY-3D, and its successors FY-3F and FY-3G. The inter-calibration coefficients and FY-3 crossing times are available at https://doi.org/10.6084/m9.figshare.20657712.v1.