Daytime Arctic Cloud Detection based on Multi-angle Satellite Data with Case Studies
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چکیده
Global climate models predict that the strongest dependences of surface air temperatures on increasing atmospheric carbon dioxide levels will occur in the northern high latitudes of Alaska, Greenland, and eastern Siberia (Giorgi and Bi, 2005). A systematic study of these dependences requires accurate Arctic-wide measurements, especially of cloud coverage as gains or losses in cloud coverage can, in turn, affect surface air temperatures. Cloud detection in the Arctic is extremely important for regional climate studies across the Arctic, but it is also quite challenging because of the similar remote sensing characteristics of clouds, iceand snow-covered surfaces. This paper proposes two new operational arctic cloud detection algorithms using Multi-angle Imaging SpectroRadiometer (MISR) imagery. The key idea embedded in these algorithms is to search for iceand snow-covered surface pixels in the MISR imagery instead of cloudy pixels directly, as in the MISR operational algorithms. Through extensive exploratory data analysis, three physically useful features have been identified that differentiate well surface pixels from cloudy pixels for MISR images with different sunlight and weather conditions. They are the correlation of MISR images of the same scene from different MISR viewing directions, the standard deviation of pixel values across a scene, and a Normalized Differential Angular Index (NDAI) that characterizes the changes is a scene with changes in the MISR view direction. The first algorithm based on these three features, Enhanced Linear Correlation Matching (ELCM), thresholds the three features with two fixed cut-off values for the correlation and standard deviation features and one data-adaptive value for the NDAI feature. 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, simple, and fast for operational processing of MISR data. The best available validation data, expert labels, are used to test the two algorithms. Based on five million test pixels ELCM results are significantly better both in terms of accuracy (92%) and coverage (100%) when compared with two MISR operational algorithms,
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تاریخ انتشار 2006