Sensors 2008, 8(2), 933-951; doi:10.3390/s8020933
Article

Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data

1 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China 2 TRIO/LSIIT(UMR7005 CNRS)/ENSPS, Bld Sebastien Brant, BP10413, 67412 Illkirch, France 3 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100086, China
* Author to whom correspondence should be addressed.
Received: 3 January 2008; Accepted: 31 January 2008 / Published: 14 February 2008
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
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Abstract: On the basis of the radiative transfer theory, this paper addressed the estimate ofLand Surface Temperature (LST) from the Chinese first operational geostationarymeteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1,10.3-11.3 μ m and IR2, 11.5-12.5 μ m ), using the Generalized Split-Window (GSW)algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithmcorresponding to a series of overlapping ranging of the mean emissivity, the atmosphericWater Vapor Content (WVC), and the LST were derived using a statistical regressionmethod from the numerical values simulated with an accurate atmospheric radiativetransfer model MODTRAN 4 over a wide range of atmospheric and surface conditions.The simulation analysis showed that the LST could be estimated by the GSW algorithmwith the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with theViewing Zenith Angle (VZA) less than 30° or for the sub-rangs with VZA less than 60°and the atmospheric WVC less than 3.5 g/cm2 provided that the Land Surface Emissivities(LSEs) are known. In order to determine the range for the optimum coefficients of theGSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according tothe land surface classification or using the method proposed by Jiang et al. (2006); and theWVC could be obtained from MODIS total precipitable water product MOD05, or beretrieved using Li et al.’ method (2003). The sensitivity and error analyses in term of theuncertainty of the LSE and WVC as well as the instrumental noise were performed. Inaddition, in order to compare the different formulations of the split-window algorithms,several recently proposed split-window algorithms were used to estimate the LST with thesame simulated FY-2C data. The result of the intercomparsion showed that most of thealgorithms give comparable results
Keywords: Land surface temperature; FY-2C data; Split-window algorithm.

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MDPI and ACS Style

Tang, B.; Bi, Y.; Li, Z.-L.; Xia, J. Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data. Sensors 2008, 8, 933-951.

AMA Style

Tang B, Bi Y, Li Z-L, Xia J. Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data. Sensors. 2008; 8(2):933-951.

Chicago/Turabian Style

Tang, Bohui; Bi, Yuyun; Li, Zhao-Liang; Xia, Jun. 2008. "Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data." Sensors 8, no. 2: 933-951.

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