气候变化领域集成服务门户(CCPortal): Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions

CCPortal

DOI	10.1016/j.rse.2020.111999
	Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions
	Poulsen C.; Egede U.; Robbins D.; Sandeford B.; Tazi K.; Zhu T.
发表日期	2020
ISSN	00344257
卷号	248
英文摘要	A Feed Forward Neural Net (NN) approach to distinguish between clouds and the surface has been applied to the Sea and Land Surface Temperature Radiometer in polar regions. The masking algorithm covers the Arctic, Antarctic and regions typically classified as the cryosphere such as northern hemisphere permafrost. The mask has been trained using collocations with the CALIOP active lidar, which in narrow strips provide more accurate detection of cloud, and was subsequently evaluated as a function of cloud type and surface type. The mask was compared with the existing operational Bayesian and Empirical cloud masks by eye and also statistically using CALIOP data. It was found to perform exceptionally well in the polar regions. The Kuiper skill score improved from 0.28, for the operational Bayesian and 0.17 for the Empirical masks to 0.77 for the NN. The NN algorithm also has a much more homogeneous performance over all surface types. The key improvement came from better identification of clear scenes; for the NN mask, the same performance in terms of contamination of cloudy pixels in the sample of identified clear pixels can be achieved while retaining 40% of the clear pixels compared with 10% for the operational cloud identification. The algorithm performed with almost the same skill over sea and land. The best performance was achieved for opaque clouds while transparent and broken clouds showed slightly reduced accuracy. © 2020 Elsevier Inc.
英文关键词	Cloud mask; Neural net; Poles; Sentinel-3; SLSTR
语种	英语
scopus关键词	Feedforward neural networks; Land surface temperature; Pixels; Cloud identification; Cloud masks; Cloudy pixels; Feed forward; Narrow strips; NN algorithms; Northern Hemispheres; Polar Regions; Machine learning; accuracy assessment; algorithm; artificial neural network; CALIOP; comparative study; cryosphere; identification method; land surface; lidar; machine learning; permafrost; pixel; polar region; radiometer; satellite data
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/179183
作者单位	Monash University, Melbourne, Australia; Imperial College London, London, United Kingdom; RAL Space, STFC, United Kingdom
推荐引用方式 GB/T 7714	Poulsen C.,Egede U.,Robbins D.,et al. Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions[J],2020,248.
APA	Poulsen C.,Egede U.,Robbins D.,Sandeford B.,Tazi K.,&Zhu T..(2020).Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions.Remote Sensing of Environment,248.
MLA	Poulsen C.,et al."Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions".Remote Sensing of Environment 248(2020).