期刊论文详细信息
Remote Sensing
Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites
Stephan Paul1  Sascha Willmes2  Oliver Gutjahr2  Andreas Preu෾r2  Günther Heinemann2  Richard Müller2 
[1] Department of Environmental Meteorology, University of Trier, Behringstraße 21, 54296 Trier, Germany;
关键词: MODIS;    polynyas;    sea ice;    clouds;    gap filling;   
DOI  :  10.3390/rs70505042
来源: mdpi
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【 摘 要 】

The opacity of clouds is the main problem for optical and thermal space-borne sensors, like the Moderate-Resolution Imaging Spectroradiometer (MODIS). Especially during polar nighttime, the low thermal contrast between clouds and the underlying snow/ice results in deficiencies of the MODIS cloud mask and affected products. There are different approaches to retrieve information about frequently cloud-covered areas, which often operate with large amounts of days aggregated into single composites for a long period of time. These approaches are well suited for static-nature, slow changing surface features (e.g., fast-ice extent). However, this is not applicable to fast-changing features, like sea-ice polynyas. Therefore, we developed a spatial feature reconstruction to derive information for cloud-covered sea-ice areas based on the surrounding days weighted directly proportional with their temporal proximity to the initial day of interest. Its performance is tested based on manually-screened and artificially cloud-covered case studies of MODIS-derived polynya area data for the polynya in the Brunt Ice Shelf region of Antarctica. On average, we are able to completely restore the artificially cloud-covered test areas with a spatial correlation of 0.83 and a mean absolute spatial deviation of 21%.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.

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