Surface melt is an important driver of ice shelf disintegration and its consequent mass loss over the Antarctic Ice Sheet. Monitoring surface melt using satellite remote sensing can enhance our understanding of ice shelf stability. However, the sensors do not measure the actual physical process of surface melt, but rather observe the presence of liquid water. Moreover, the sensor observations are influenced by the sensor characteristics and surface properties. Therefore, large inconsistencies can exist in the derived melt estimates from different sensors. In this study, we apply state-of-the-art melt detection algorithms to four frequently used remote sensing sensors: two active microwave sensors, ASCAT (Advanced Scatterometer) and Sentinel-1, a passive microwave sensor SSMIS (Special Sensor Microwave Imager/Sounder), and an optical sensor MODIS (Moderate Resolution Imaging Spectroradiometer). We intercompare the melt detection results over the entire Antarctic Ice Sheet and four selected study regions for the melt seasons 2015-2020. Our results show large spatiotemporal differences in detected melt between the sensors, with particular disagreement in blue ice areas, in aquifer regions, and during wintertime surface melt. We discuss that discrepancies between sensors are mainly due to (1) cloud obstruction and polar darkness, (2) frequency-dependent penetration of satellite signals, (3) temporal resolution, and (4) spatial resolution, as well as (5) the applied melt detection methods. Nevertheless, we argue that different sensors can complement each other, enabling improved detection of surface melt over the Antarctic Ice Sheet.

In the Alps, snow cover dynamics can be monitored using Earth observation (EO). However, low revisit frequency and cloud cover pose a challenge to long‐term time series analysis using high spatial resolution EO images. In this study, we applied the random forest regression to model regional snowline elevations (RSEs). In this manner, daily snowline dynamics and their long‐term trends can be derived, despite the aforementioned challenges. Of the six investigated Alpine catchments between 1984 and 2018, a significant increasing trend of RSEs is shown in four catchments in the early ablation seasons (between 5.38 ± 2.64 and 11.29 ± 4.79 m·a⁻¹) and five catchments in the middle ablation seasons (between 4.17 ± 2.62 and 8.76 ± 4.42 m·a⁻¹). On average, the random forest regression models can explain 75% of the RSE variations. Furthermore, air temperature was found influential in snow persistence especially during middle and late ablation seasons.

Accurately estimating the surface melt volume of the Antarctic Ice Sheet is challenging and has hitherto relied on climate modeling or observations from satellite remote sensing. Each of these methods has its limitations, especially in regions with high surface melt. This study aims to demonstrate the potential of improving surface melt simulations with a regional climate model by deploying a deep learning model. A deep-learning-based framework has been developed to correct surface melt from the regional atmospheric climate model version 2.3p2 (RACMO2), using meteorological observations from automatic weather stations (AWSs) and surface albedo from satellite imagery. The framework includes three steps: (1) training a deep multilayer perceptron (MLP) model using AWS observations, (2) correcting Moderate Resolution Imaging Spectroradiometer (MODIS) albedo observations, and (3) using these two to correct the RACMO2 surface melt simulations. Using observations from three AWSs at the Larsen B and C ice shelves, Antarctica, cross-validation shows a high accuracy (root-mean-square error of 0.95 mm w.e. d⁻¹, mean absolute error of 0.42 mm w.e. d⁻¹, and a coefficient of determination of 0.95). Moreover, the deep MLP model outperforms conventional machine learning models and a shallow MLP model. When applying the trained deep MLP model over the entire Larsen Ice Shelf, the resulting corrected RACMO2 surface melt shows a better correlation with the AWS observations for two out of three AWSs. However, for one location (AWS 18), the deep MLP model does not show improved agreement with AWS observations; this is likely because surface melt is largely driven by factors (e.g., air temperature, topography, katabatic wind) other than albedo within the corresponding coarse-resolution model pixels. Our study demonstrates the opportunity to improve surface melt simulations using deep learning combined with satellite albedo observations. However, more work is required to refine the method, especially for complicated and heterogeneous terrains.

Snowmelt in the mid-latitude European mountains is undergoing significant spatiotemporal changes. Regional snow line elevation (RSLE) is an appropriate indicator for assessing snow cover variations in mountain areas. To derive regional snow line dynamics during the ablation seasons 1984–2018, the present study unprecedentedly introduced a readily applicable framework. The framework constitutes four steps: atmospheric and topographic correction, snow classification, RSLE retrieval, and regional snow line retreat curve (RSLRC) derivation. The developed framework has been successfully applied to 8641 satellite images acquired by Landsat, ASTER, and Sentinel-2. The results of the intra-annual regional snow line variations show that: (1) regional snow lines in the Alpine catchments preserve the longest; (2) RSLEs are lower in the northern Pyrenees than in the southern part; (3) regional snow lines persist the shortest in the Carpathian catchments; and (4) during the end of the ablation season 2018, intermediate snowfall events in the catchments Adda, Tagliamento, and Uzh are observed. In terms of the long-term inter-annual variations, significantly accelerating snow line recession is detected in the northern Pyrenean catchment Ariege. In the Alpine catchment Alpenrhein and Drac, RSLRCs are shifting towards lower accumulated air-temperature (AT) significantly, with the magnitude of −3.77 °C·a⁻¹ (Alpenrhein) and −3.99 °C·a⁻¹ (Drac).

Cold regions affect global, regional and local climate; oftentimes they are relevant for water supply, host valuable ecosystems, and support human livelihood. They are thus eminently important for human society. In the context of ongoing climate change, monitoring and understanding cold region land surface dynamics is essential for environmental scientists, stakeholders and decision makers. However, the definition of cold regions remains inexplicit, and no up-to-date cold region maps or overarching spatial analyses exist. For example, Europe has densely populated cold regions, but hardly an article exists that provides a solid overview of Earth Observation (EO) based applications assessing cold region land surface dynamics in Europe. With this review article we aim at closing this gap by providing an overview of EO-based techniques for cold region observation in Europe, focusing on the dynamics of glaciers and snow. We present a novel spatial delineation of cold regions for Europe before analyzing the benefits and limitations of different EO sensor types and data processing methods for EO based cold region research. Furthermore, we identify research gaps and discuss challenges for future studies.