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Remote Sens. 2015, 7(5), 5042-5056; doi:10.3390/rs70505042

Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites

Department of Environmental Meteorology, University of Trier, Behringstraße 21, 54296 Trier, Germany
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Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 23 February 2015 / Revised: 10 April 2015 / Accepted: 16 April 2015 / Published: 23 April 2015
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Abstract

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%. View Full-Text
Keywords: MODIS; polynyas; sea ice; clouds; gap filling MODIS; polynyas; sea ice; clouds; gap filling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Paul, S.; Willmes, S.; Gutjahr, O.; Preußer, A.; Heinemann, G. Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites. Remote Sens. 2015, 7, 5042-5056.

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