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

Remote Sensing

A-Ra Cho³  Youn-Young Choi²  Myoung-Seok Suh²  Markus Neteler¹ 

[1] Department of Climate and Air Quality Research, National Institute of Environmental Research, Hwangyeong-ro, Kyungseo-dong, Incheon 404-708, Korea; E-Mail;Department of Atmospheric Science, Kongju National University, 56, Gongjudaehak-ro, Gongju-si, Chungcheongnam-do 314-701, Korea; E-Mail:;Department of Climate and Air Quality Research, National Institute of Environmental Research, Hwangyeong-ro, Kyungseo-dong, Incheon 404-708, Korea; E-Mail:

关键词: land surface temperature;    split-window algorithm;    COMS;    MODIS;   

DOI  :  10.3390/rs70201777

来源: mdpi

【 摘 要 】

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).

【 授权许可】

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