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Original Articles

Evaluation of land surface temperature and emissivities retrieved from MSG/SEVIRI data with MODIS land surface temperature and emissivity products

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Pages 3140-3152 | Received 16 Dec 2010, Accepted 04 May 2011, Published online: 18 Oct 2012
 

Abstract

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first geostationary satellite, Meteosat Second Generation (MSG), with Moderate Resolution Imaging Spectroradiometer (MODIS) LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control (QC) criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East.

Acknowledgements

The authors thank anonymous referees for their comments and suggestions that have significantly improved the article. This work was partly supported by the Hi-Tech Research and Development Programme of China (863 Plan Programme) under Grant 2008AA121805, the National Natural Science Foundation of China (41101330), the Key Laboratory of Spatial Data Mining & Information Sharing, the Ministry of Education, Fuzhou University, and the China Postdoctoral Science Foundation.

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