Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds.
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.
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. fanxw.11b@igsnrr.ac.cn.|University of Chinese Academy of Sciences, Beijing 100049, China. fanxw.11b@igsnrr.ac.cn.
Xiwei
X
Fan
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. tangbh@igsnrr.ac.cn.|Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China. tangbh@igsnrr.ac.cn.
Bo-Hui
BH
Tang
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. wuhua@igsnrr.ac.cn.|Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China. wuhua@igsnrr.ac.cn.
Hua
H
Wu
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. gjyan@bnu.edu.cn.
Guangjian
G
Yan
Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China. lizhaoliang@caas.cn.|ICube, UdS, CNRS, 300 Bld Sebastien Brant, CS10413, 67412 Illkirch, France. lizhaoliang@caas.cn.
Zhao-Liang
ZL
Li