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In some areas of Antarctica, blue‐colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi‐sensor remote sensing data (MODIS, RADARSAT‐2, and TanDEM‐X) in a deep learning framework, we map blue ice across the continent at 200‐m resolution. We use a novel methodology for image segmentation with "noisy" labels to learn an underlying "clean" pattern with a neural network. In total, BIAs cover ca. 140,000 km2 (∼1%) of Antarctica, of which nearly 50% located within 20 km of the grounding line. There, the low albedo of blue ice enhances melt‐water production and its mapping is crucial for mass balance studies that determine the stability of the ice sheet. Moreover, the map provides input for fieldwork missions and can act as constraint for other geophysical mapping efforts. Plain Language Summary: While most of the continent of Antarctica is covered by snow, in some areas, ice is exposed at the surface, with a typical blue color. At lower elevations, blue ice enhances melt‐water production, which is important for studying the future of the ice sheet. Moreover, scientific teams frequently visit blue ice areas (BIAs) as they act as traps for meteorites and very old ice. In this study, we map the extent and the exact location of BIAs using various satellite observations. These diverse observations are efficiently combined in an artificial intelligence algorithm. We develop the algorithm so that it can learn to map blue ice even though existing training labels, which teach the algorithm what blue ice looks like, are imperfect. We quantify that the new map scores better on various performance metrics compared to the current most‐used blue ice map. Moreover, for the first time, we estimate uncertainties of the detection of blue ice. The map indicates that ca. 1% of the surface of Antarctica exposes blue ice and will be important for fieldwork missions and understanding surface processes leading to melt and potential sea level rise. Key Points: We map blue ice areas in Antarctica by combining multi‐sensor satellite observations in a convolutional neural networkBlue ice covers ca. 140,000 km2 (∼1%) of Antarctica, of which ca. 50% located in the grounding zoneOur map will improve mass balance estimates and studies on ice‐shelf stability, and will support searches for meteorites or old ice [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]