High-resolution CubeSat imagery and machine learning for detailed snow-covered area
High-resolution CubeSat imagery and machine learning for detailed snow-covered area
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DOI：
10.1016/j.rse.2021.112399
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发表时间：
2021-06
影响因子：
13.5
通讯作者：
A. Cannistra;D. Shean;N. Cristea
中科院分区：
文献类型：
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作者：
A. Cannistra;D. Shean;N. Cristea
积雪影响着各种各样的物理、生态和社会系统。因此，光学遥感积雪面积测量技术的发展使许多研究领域取得了进展。然而，在许多情况下，目前可用的遥感SCA产品的空间和时间分辨率不足以在与精细尺度空间异质现象研究相关的空间和时间分辨率上捕捉SCA演变。我们开发了一种基于卷积神经网络的方法来识别积雪覆盖区域，该方法使用了每天覆盖近全球的~3米4波段PlanetScope光学卫星图像数据集。通过将我们的模型性能与两个北美站点（美国加利福尼亚州内华达山脉和美国科罗拉多州落基山脉）的高分辨率机载激光雷达差分深度测量数据和卫星平台得出的积雪范围进行比较，我们表明，尽管辐射带宽和频带放置有限，但这些新兴图像档案在高时空分辨率下具有巨大的潜力，可以准确观测积雪覆盖区域。我们在训练盆地中获得了平均雪分类f分数0.73，在气候不同的样本外盆地中获得了0.67，这表明模型具有可转移性。我们还评估了这些数据在森林地区的表现，提出了进一步研究的途径。立方体卫星图像无与伦比的空间和时间覆盖范围为卫星遥感积雪提供了极好的机会，对生态和水资源应用具有实际意义。
Snow cover affects a diverse array of physical, ecological, and societal systems. As such, the development of optical remote sensing techniques to measure snow-covered area (SCA) has enabled progress in a wide variety of research domains. However, in many cases, the spatial and temporal resolutions of currently available remotely sensed SCA products are insufficient to capture SCA evolution at spatial and temporal resolutions relevant to the study of fine-scale spatially heterogeneous phenomena. We developed a convolutional neural network-based method to identify snow covered area using the ~3 m, 4-band PlanetScope optical satellite image dataset with ~daily, near-global coverage. By comparing our model performance to snow extent derived from high-resolution airborne lidar differential depth measurements and satellite platforms in two North American sites (Sierra Nevada, CA, USA and Rocky Mountains, CO, USA), we show that these emerging image archives have great potential to accurately observe snow-covered area at high spatial and temporal resolutions despite limited radiometric bandwidth and band placement. We achieve average snow classification F-Scores of 0.73 in our training basin and 0.67 in a climatically-distinct out-of-sample basin, suggesting opportunities for model transferability. We also evaluate the performance of these data in forested regions, suggesting avenues for further research. The unparalleled spatial and temporal coverage of CubeSat imagery offers an excellent opportunity for satellite remote sensing of snow, with real implications for ecological and water resource applications.