Seasonal snow plays a crucial role in society and understanding trends in snow depth and mass is essential for making informed decisions about water resources and adaptation to climate change. However, quantifying snow depth in remote, mountainous areas with complex topography remains a significant challenge. The increasing availability of high-resolution synthetic aperture radar (SAR) observations from active microwave satellites has prompted opportunistic use of the data to retrieve snow depth remotely across large spatial and frequent temporal scales and at a high spatial resolution. Nevertheless, these novel SAR-based snow depth retrieval methods face their own set of limitations, including challenges for shallow snowpacks, high vegetation cover, and wet snow conditions. In response, here we introduce a machine learning approach to enhance SAR-based snow depth estimation over the European Alps. By integrating Sentinel-1 SAR imagery, optical snow cover observations, and topographic, forest cover and snow class information, our machine learning retrieval method more accurately estimates snow depth at independent in-situ measurement sites than current methods. Further, our method provides estimates at 100 m horizontal resolution and is capable of better capturing local-scale topography-driven snow depth variability. Through detailed feature importance analysis, we identify optimal conditions for SAR data utilization, thereby providing insight into future use of C-band SAR for snow depth retrieval.

中文翻译：

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一种根据 Sentinel-1 图像估计欧洲阿尔卑斯山积雪深度的机器学习方法


季节性降雪在社会中发挥着至关重要的作用，了解积雪深度和质量的趋势对于就水资源和适应气候变化做出明智的决策至关重要。然而，量化地形复杂的偏远山区的积雪深度仍然是一个重大挑战。来自主动微波卫星的高分辨率合成孔径雷达（SAR）观测的可用性不断增加，促使人们利用这些数据在大空间和频繁时间尺度上以高空间分辨率远程检索雪深。然而，这些基于 SAR 的新型积雪深度反演方法面临着自身的一系列局限性，包括浅积雪、高植被覆盖和湿雪条件的挑战。为此，我们在此介绍一种机器学习方法，以增强欧洲阿尔卑斯山基于 SAR 的雪深估计。通过集成Sentinel-1 SAR图像、光学积雪观测以及地形、森林覆盖和积雪等级信息，我们的机器学习检索方法比现有方法更准确地估计独立原位测量地点的积雪深度。此外，我们的方法提供了 100 m 水平分辨率的估计，并且能够更好地捕获局部规模地形驱动的雪深变化。通过详细的特征重要性分析，我们确定了 SAR 数据利用的最佳条件，从而为未来使用 C 波段 SAR 进行雪深反演提供了见解。