Accurate mapping of snow amount in the mountains is critical as mountain snowpacks are water supply for millions of people. Satellite remote sensing has been largely unable to reliably detect the amount of snowpack in these areas. Recently, C-band Synthetic Aperture Radar (SAR) data from the Sentinel-1 (S1) satellites have shown potential for measuring snow depth in the mountains. However, their spatiotemporal coverage is incomplete, and their evaluation with robust, aerial snow depth data is limited. Here, we evaluate two S1 snowpack datasets with some of the best available gridded snowpack data over the Colorado Rockies and Sierra Nevada mountains in the western US: the Airborne Snow Observatory (ASO) and the University of Arizona (UA) snowpack datasets. Compared to ASO and UA data, the S1 data are biased high when snow is shallow, and biased low when snow is deep (particularly later in spring when there is wet snow), though these biases are reduced for deep snow areas when wet snow pixels are removed. We then apply corrections based on machine learning that account for physiographic characteristics to improve the accuracy of the S1 data. Furthermore, we fill gaps in the S1 data by using snow persistence, but also account for potential snow accumulation and ablation, to generate temporally complete snow depth maps over mountainous areas. Corrected and gap-filled S1 snow depth mapping could be especially important for snow monitoring in remote mountain areas where other techniques for snow mapping do not work or are logistically infeasible or cost-prohibitive.

中文翻译：

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利用 Sentinel-1、机载积雪观测站和亚利桑那大学积雪数据的机器学习改进山地积雪估计

准确绘制山区积雪量至关重要，因为山区积雪是数百万人的水源。卫星遥感基本上无法可靠地检测这些地区的积雪量。最近，来自 Sentinel-1 (S1) 卫星的 C 波段合成孔径雷达 (SAR) 数据显示出测量山区积雪深度的潜力。然而，它们的时空覆盖并不完整，并且利用可靠的航空雪深数据进行的评估也很有限。在这里，我们使用美国西部科罗拉多洛基山脉和内华达山脉的一些最佳可用网格积雪数据来评估两个 S1 积雪数据集：机载积雪观测站 (ASO) 和亚利桑那大学 (UA) 积雪数据集。与 ASO 和 UA 数据相比，S1 数据在雪浅时偏高，在雪深时偏低（特别是在春季晚些时候，有湿雪时），尽管在湿雪像素时，对于深雪区域，这些偏差会减小被删除。然后，我们应用基于机器学习的修正来考虑地理学特征，以提高 S1 数据的准确性。此外，我们通过使用积雪持久性来填补 S1 数据中的空白，而且还考虑了潜在的积雪和消融，以生成山区上暂时完整的积雪深度图。校正和填补空白的 S1 雪深测绘对于偏远山区的雪监测尤其重要，因为在这些地区，其他雪测图技术不起作用或在逻辑上不可行或成本过高。