Global warming is causing glaciers in the Caucasus Mountains and around the world to lose mass at an accelerated pace. As a result of this rapid retreat, significant parts of the glacierized surface area can be covered with debris deposits, often making them indistinguishable from the surrounding land surface by optical remote-sensing systems. Here, we present the DebCovG-carto toolbox to delineate debris-covered and debris-free glacier surfaces from non-glacierized regions. The algorithm uses synthetic aperture radar-derived coherence images and the normalized difference snow index applied to optical satellite data. Validating the remotely-sensed boundaries of Ushba and Chalaati glaciers using field GPS data demonstrates that the use of pairs of Sentinel-1 images (2019) from identical ascending and descending orbits can substantially improve debris-covered glacier surface detection. The DebCovG-carto toolbox leverages multiple orbits to automate the mapping of debris-covered glacier surfaces. This new automatic method offers the possibility of quickly correcting glacier mapping errors caused by the presence of debris and makes automatic mapping of glacierized surfaces considerably faster than the use of other subjective methods.

中文翻译：

---

多传感器遥感绘制乔治亚州大高加索地区冰川碎片覆盖图

全球变暖正在导致高加索山脉和世界各地的冰川加速流失。由于这种快速撤退，冰川化表面区域的重要部分可能被碎屑沉积物覆盖，通常使光学遥感系统无法将它们与周围的地表区分开来。在这里，我们展示了 DebCovG-carto 工具箱，用于从非冰川化区域描绘覆盖碎片和无碎片的冰川表面。该算法使用合成孔径雷达衍生的相干图像和应用于光学卫星数据的归一化差异雪指数。使用现场 GPS 数据验证 Ushba 和 Chalaati 冰川的遥感边界表明，使用来自相同上升和下降轨道的 Sentinel-1 图像对（2019 年）可以显着改善碎片覆盖的冰川表面检测。DebCovG-carto 工具箱利用多个轨道自动绘制碎片覆盖的冰川表面。这种新的自动方法提供了快速纠正由碎片存在引起的冰川映射错误的可能性，并且使冰川表面的自动映射比使用其他主观方法快得多。