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

View Article: PubMed Central - PubMed

ABSTRACT

On the basis of the radiative transfer theory, this paper addressed the estimate of Land Surface Temperature (LST) from the Chinese first operational geostationary meteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1, 10.3-11.3 μm and IR2, 11.5-12.5 μm), using the Generalized Split-Window (GSW) algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithm corresponding to a series of overlapping ranging of the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST were derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer 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 algorithm with the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with the Viewing 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 the GSW 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 to the land surface classification or using the method proposed by Jiang et al. (2006); and the WVC could be obtained from MODIS total precipitable water product MOD05, or be retrieved using Li et al.' method (2003). The sensitivity and error analyses in term of the uncertainty of the LSE and WVC as well as the instrumental noise were performed. In addition, 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 the same simulated FY-2C data. The result of the intercomparsion showed that most of the algorithms give comparable results.

No MeSH data available.


Histogram of the difference between the actual and estimated Ts for the overlap water vapor content WVC ∈ [1.0, 1.5] using the coefficients of different sub-ranges.
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f8-sensors-08-00933: Histogram of the difference between the actual and estimated Ts for the overlap water vapor content WVC ∈ [1.0, 1.5] using the coefficients of different sub-ranges.

Mentions: Figure 8 gives an example of the uncertainty of the WVC. From figure 8 one can see that the overlap water vapor content WVC ∈ [1.0, 1.5] falling into two sub-ranges WVC ∈ [0.0, 1.5] and WVC ∈ [1.0, 2.5]. When we estimate the LST with the water vapor content WVC ∈ [1.0, 1.5] using the coefficients corresponding to the sub-range ε ∈ [0.94, 1.0], WVC ∈ [0.0, 1.5], and Ts ∈ [290K, 310K], the RMSE between the actual and the estimated Ts is 0.18 K, while using the coefficients corresponding to the sub-range ε ∈ [0.94, 1.0], WVC ∈ [1.0, 2.5], and Ts ∈ [290K, 310K], the RMSE is 0.43 K.


Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data
Histogram of the difference between the actual and estimated Ts for the overlap water vapor content WVC ∈ [1.0, 1.5] using the coefficients of different sub-ranges.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927530&req=5

f8-sensors-08-00933: Histogram of the difference between the actual and estimated Ts for the overlap water vapor content WVC ∈ [1.0, 1.5] using the coefficients of different sub-ranges.
Mentions: Figure 8 gives an example of the uncertainty of the WVC. From figure 8 one can see that the overlap water vapor content WVC ∈ [1.0, 1.5] falling into two sub-ranges WVC ∈ [0.0, 1.5] and WVC ∈ [1.0, 2.5]. When we estimate the LST with the water vapor content WVC ∈ [1.0, 1.5] using the coefficients corresponding to the sub-range ε ∈ [0.94, 1.0], WVC ∈ [0.0, 1.5], and Ts ∈ [290K, 310K], the RMSE between the actual and the estimated Ts is 0.18 K, while using the coefficients corresponding to the sub-range ε ∈ [0.94, 1.0], WVC ∈ [1.0, 2.5], and Ts ∈ [290K, 310K], the RMSE is 0.43 K.

View Article: PubMed Central - PubMed

ABSTRACT

On the basis of the radiative transfer theory, this paper addressed the estimate of Land Surface Temperature (LST) from the Chinese first operational geostationary meteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1, 10.3-11.3 μm and IR2, 11.5-12.5 μm), using the Generalized Split-Window (GSW) algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithm corresponding to a series of overlapping ranging of the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST were derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer 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 algorithm with the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with the Viewing 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 the GSW 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 to the land surface classification or using the method proposed by Jiang et al. (2006); and the WVC could be obtained from MODIS total precipitable water product MOD05, or be retrieved using Li et al.' method (2003). The sensitivity and error analyses in term of the uncertainty of the LSE and WVC as well as the instrumental noise were performed. In addition, 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 the same simulated FY-2C data. The result of the intercomparsion showed that most of the algorithms give comparable results.

No MeSH data available.