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Remote Sens. 2015, 7(2), 1777-1797;

Improvements of a COMS Land Surface Temperature Retrieval Algorithm Based on the Temperature Lapse Rate and Water Vapor/Aerosol Effect

Department of Climate and Air Quality Research, National Institute of Environmental Research, Hwangyeong-ro, Kyungseo-dong, Incheon 404-708, Korea
Department of Atmospheric Science, Kongju National University, 56, Gongjudaehak-ro, Gongju-si, Chungcheongnam-do 314-701, Korea
Author to whom correspondence should be addressed.
Academic Editors: Markus Neteler and Prasad S. Thenkabail
Received: 7 November 2014 / Accepted: 2 February 2015 / Published: 5 February 2015
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The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during validation with Moderate Resolution Imaging Spectroradiometer (MODIS) LST because of the too simplified LST algorithm. In this study, six types of LST retrieval equations (CSW_v2.0) were developed to upgrade the CSW_v1.0. These methods were developed by classifying “dry,” “normal,” and “wet” cases for day and night and considering the relative sizes of brightness temperature difference (BTD) values. Similar to CSW_v1.0, the LST retrieved by CSW_v2.0 had a correlation coefficient of 0.99 with the prescribed LST and a slightly larger bias of −0.03 K from 0.00K; the root mean square error (RMSE) improved from 1.41 K to 1.39 K. In general, CSW_v2.0 improved the retrieval accuracy compared to CSW_v1.0, especially when the lapse rate was high (mid-day and dawn) and the water vapor content was high. The spatial distributions of LST retrieved by CSW_v2.0 were found to be similar to the MODIS LST independently of the season, day/night, and geographic locations. The validation using one year’s MODIS LST data showed that CSW_v2.0 improved the retrieval accuracy of LST in terms of correlations (from 0.988 to 0.989), bias (from −1.009 K to 0.292 K), and RMSEs (from 2.613 K to 2.237 K). View Full-Text
Keywords: land surface temperature; split-window algorithm; COMS; MODIS land surface temperature; split-window algorithm; COMS; MODIS

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Cho, A.-R.; Choi, Y.-Y.; Suh, M.-S. Improvements of a COMS Land Surface Temperature Retrieval Algorithm Based on the Temperature Lapse Rate and Water Vapor/Aerosol Effect. Remote Sens. 2015, 7, 1777-1797.

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