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Sensors 2015, 15(5), 9942-9961; doi:10.3390/s150509942

Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds

1
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China
5
Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
6
ICube, UdS, CNRS, 300 Bld Sebastien Brant, CS10413, 67412 Illkirch, France
*
Authors to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 9 March 2015 / Accepted: 23 April 2015 / Published: 28 April 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [1580 KB, uploaded 22 May 2015]   |  

Abstract

Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 μm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 μm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31–34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies. View Full-Text
Keywords: cirrus clouds; error correction; generalized split-window algorithm; land surface temperature retrieval; MODIS cirrus clouds; error correction; generalized split-window algorithm; land surface temperature retrieval; MODIS
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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

Fan, X.; Tang, B.-H.; Wu, H.; Yan, G.; Li, Z.-L. Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds. Sensors 2015, 15, 9942-9961.

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