{"title":"A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery","authors":"","doi":"10.1016/j.rse.2024.114369","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S003442572400395X/pdfft?md5=e1cd445e0123b69e2281c8def9aa4e64&pid=1-s2.0-S003442572400395X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S003442572400395X","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
季节性积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变化趋势对于做出有关水资源和适应气候变化的明智决策至关重要。然而,对地形复杂的偏远山区的积雪深度进行量化仍然是一项重大挑战。有源微波卫星提供的高分辨率合成孔径雷达(SAR)观测数据越来越多,这促使人们不失时机地利用这些数据,以高空间分辨率远程检索大空间尺度和频繁时间尺度的积雪深度。然而,这些基于合成孔径雷达的新型雪深检索方法也面临着自身的一系列局限性,包括对浅积雪、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,以增强基于合成孔径雷达的欧洲阿尔卑斯山雪深估算。通过整合 Sentinel-1 SAR 图像、光学积雪观测数据以及地形、森林覆盖和积雪等级信息,我们的机器学习检索方法能比现有方法更准确地估算出独立原地测量点的积雪深度。此外,我们的方法还能提供 100 米水平分辨率的估算值,并能更好地捕捉局部尺度地形导致的雪深变化。通过详细的特征重要性分析,我们确定了利用合成孔径雷达数据的最佳条件,从而为未来利用 C 波段合成孔径雷达进行雪深检索提供了启示。
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.