气候变化领域集成服务门户(CCPortal): High-resolution CubeSat imagery and machine learning for detailed snow-covered area

CCPortal

DOI	10.1016/j.rse.2021.112399
	High-resolution CubeSat imagery and machine learning for detailed snow-covered area
	Cannistra A.F.; Shean D.E.; Cristea N.C.
发表日期	2021
ISSN	00344257
卷号	258
英文摘要	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. © 2021 The Author(s)
英文关键词	Airborne lidar; Machine learning; Planet; PlanetScope; Seasonal snow; Snow covered area; Supervised classification
语种	英语
scopus关键词	Earth (planet); Ecology; Machine learning; Neural networks; Optical radar; Remote sensing; Satellite imagery; Small satellites; Water resources; Airborne LiDAR; Cubesat; High resolution; Machine-learning; Planetscope; Seasonal snow; Snow covered area; Snow covers; Spatial and temporal resolutions; Supervised classification; Snow; accuracy assessment; airborne survey; artificial neural network; data set; image classification; lidar; machine learning; remote sensing; satellite altimetry; satellite imagery; snow cover; spatial resolution; spatiotemporal analysis; California; Rocky Mountains; Sierra Nevada [California]; United States; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178878
作者单位	Department of Biology, University of Washington, Seattle, WA, United States; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
推荐引用方式 GB/T 7714	Cannistra A.F.,Shean D.E.,Cristea N.C.. High-resolution CubeSat imagery and machine learning for detailed snow-covered area[J],2021,258.
APA	Cannistra A.F.,Shean D.E.,&Cristea N.C..(2021).High-resolution CubeSat imagery and machine learning for detailed snow-covered area.Remote Sensing of Environment,258.
MLA	Cannistra A.F.,et al."High-resolution CubeSat imagery and machine learning for detailed snow-covered area".Remote Sensing of Environment 258(2021).