Daytime Arctic cloud detection based on multi-angle satellite data with case studies

• 成果类型: Article
• 署名作者: Shi, Tao; Yu, Bin; Clothiaux, Eugene E.; Braverman, Amy J.
• 署名单位: University System of Ohio; Ohio State University; University of California System; University of California Berkeley; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); California Institute of Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214507000001283
• 发表日期: 2008
• 页码: 584-593
• 关键词: misr

摘要

Global climate models predict that the strongest dependences of surface air temperatures on increasing atmospheric carbon dioxide levels will occur in the Arctic. A systematic study of these dependences requires accurate Arctic-wide measurements, especially of cloud coverage. Thus cloud detection in the Arctic is extremely important, but it is also challenging because of the similar remote sensing characteristics of clouds and ice- and snow-covered surfaces. This article proposes two new operational Arctic cloud detection algorithms using Multiangle Imaging SpectroRadiometer (MISR) imagery. The key idea is to identify cloud-free surface pixels in the imagery instead of cloudy pixels as in the existing MISR operational algorithms. Through extensive exploratory data analysis and using domain knowledge, three physically useful features to differentiate surface pixels from cloudy pixels have been identified. The first algorithm, enhanced linear correlation matching (ELCM), thresholds the features with either fixed or data-adaptive cutoff values. Probability labels are obtained by using ELCM labels as training data for Fisher's quadratic discriminant analysis (QDA), leading to the second (ELCM-QDA) algorithm. Both algorithms are automated and computationally efficient for operational processing of the massive MISR data set. Based on 5 million expert-labeled pixels, ELCM results are significantly in terms of both accuracy (92%) and coverage (100%) compared with two MISR operational algorithms, one with an accuracy of 80% and coverage of 27% and the other with an accuracy of 83% and a coverage of 70%. The ELCM-QDA probability prediction is also consistent with the expert labels and is more informative. In conclusion, ELCM and ELCM-QDA provide the best performance to date among all available operational algorithms using MISR data.