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A Multi-Channel Method for Retrieving Surface Temperature for High-Emissivity Surfaces from Hyperspectral Thermal Infrared Images.

Zhong X, Labed J, Zhou G, Shao K, Li ZL - Sensors (Basel) (2015)

Bottom Line: With an assumption of land surface emissivity (LSE) of 1, ST is proposed as a function of 10 brightness temperatures measured at the top of atmosphere by a radiometer having a spectral interval of 800-1200 cm(-1) and a spectral sampling frequency of 0.25 cm(-1).The results indicated that the parameters in the developed function are dependent on the spectral sampling frequency and that ST of high-emissivity surfaces can be accurately retrieved by the proposed method if appropriate values are used for each spectral sampling frequency.The results also showed that the accuracy of the retrieved ST is of the order of magnitude of the instrumental noise and that the root mean square error (RMSE) of the ST retrieved from satellite data is 0.43 K in comparison with the AVHRR SST product.

View Article: PubMed Central - PubMed

Affiliation: ICube, UdS, CNRS, 300 Bld Sebastien Brant, CS10413, 67412 Illkirch, France. x.zhong@unistra.fr.

ABSTRACT
The surface temperature (ST) of high-emissivity surfaces is an important parameter in climate systems. The empirical methods for retrieving ST for high-emissivity surfaces from hyperspectral thermal infrared (HypTIR) images require spectrally continuous channel data. This paper aims to develop a multi-channel method for retrieving ST for high-emissivity surfaces from space-borne HypTIR data. With an assumption of land surface emissivity (LSE) of 1, ST is proposed as a function of 10 brightness temperatures measured at the top of atmosphere by a radiometer having a spectral interval of 800-1200 cm(-1) and a spectral sampling frequency of 0.25 cm(-1). We have analyzed the sensitivity of the proposed method to spectral sampling frequency and instrumental noise, and evaluated the proposed method using satellite data. The results indicated that the parameters in the developed function are dependent on the spectral sampling frequency and that ST of high-emissivity surfaces can be accurately retrieved by the proposed method if appropriate values are used for each spectral sampling frequency. The results also showed that the accuracy of the retrieved ST is of the order of magnitude of the instrumental noise and that the root mean square error (RMSE) of the ST retrieved from satellite data is 0.43 K in comparison with the AVHRR SST product.

No MeSH data available.


The bottom temperatures as a function of the total precipitable water vapour for the 139 atmospheric profiles and other 39 atmospheric profiles.
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sensors-15-13406-f001: The bottom temperatures as a function of the total precipitable water vapour for the 139 atmospheric profiles and other 39 atmospheric profiles.

Mentions: We have selected typical profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database for simulation [38,39] in two steps. First, we have classified the 2311 clear-sky TIGR profiles into six groups according the concentration of water vapour. The total precipitable water-vapour ranges of the six groups are between 0 and 1 g/cm2, between 1 and 2 g/cm2, between 2 and 3 g/cm2, between 3 and 4 g/cm2, between 4 to 5 g/cm2, and between 5 and 6 g/cm2, respectively. After that, we have randomly selected nearly 23 profiles from each group to make sure the selected profiles were representative. Each atmospheric profile has 40 layers from 1013 hPa to 0.05 hPa. The air mass types for the selected atmospheric profiles are tropical, temperate, cold temperate and summer polar, cold polar, and winter polar types. The total precipitable water vapours of the selected atmospheric profiles range from 0 g/cm2 to 6 g/cm2. The variation of bottom temperature with the total precipitable water vapour for the 139 atmospheric profiles is presented in Figure 1. The method for determining clear-sky atmospheric profiles from the TIGR database has been detailed by [40].


A Multi-Channel Method for Retrieving Surface Temperature for High-Emissivity Surfaces from Hyperspectral Thermal Infrared Images.

Zhong X, Labed J, Zhou G, Shao K, Li ZL - Sensors (Basel) (2015)

The bottom temperatures as a function of the total precipitable water vapour for the 139 atmospheric profiles and other 39 atmospheric profiles.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507689&req=5

sensors-15-13406-f001: The bottom temperatures as a function of the total precipitable water vapour for the 139 atmospheric profiles and other 39 atmospheric profiles.
Mentions: We have selected typical profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database for simulation [38,39] in two steps. First, we have classified the 2311 clear-sky TIGR profiles into six groups according the concentration of water vapour. The total precipitable water-vapour ranges of the six groups are between 0 and 1 g/cm2, between 1 and 2 g/cm2, between 2 and 3 g/cm2, between 3 and 4 g/cm2, between 4 to 5 g/cm2, and between 5 and 6 g/cm2, respectively. After that, we have randomly selected nearly 23 profiles from each group to make sure the selected profiles were representative. Each atmospheric profile has 40 layers from 1013 hPa to 0.05 hPa. The air mass types for the selected atmospheric profiles are tropical, temperate, cold temperate and summer polar, cold polar, and winter polar types. The total precipitable water vapours of the selected atmospheric profiles range from 0 g/cm2 to 6 g/cm2. The variation of bottom temperature with the total precipitable water vapour for the 139 atmospheric profiles is presented in Figure 1. The method for determining clear-sky atmospheric profiles from the TIGR database has been detailed by [40].

Bottom Line: With an assumption of land surface emissivity (LSE) of 1, ST is proposed as a function of 10 brightness temperatures measured at the top of atmosphere by a radiometer having a spectral interval of 800-1200 cm(-1) and a spectral sampling frequency of 0.25 cm(-1).The results indicated that the parameters in the developed function are dependent on the spectral sampling frequency and that ST of high-emissivity surfaces can be accurately retrieved by the proposed method if appropriate values are used for each spectral sampling frequency.The results also showed that the accuracy of the retrieved ST is of the order of magnitude of the instrumental noise and that the root mean square error (RMSE) of the ST retrieved from satellite data is 0.43 K in comparison with the AVHRR SST product.

View Article: PubMed Central - PubMed

Affiliation: ICube, UdS, CNRS, 300 Bld Sebastien Brant, CS10413, 67412 Illkirch, France. x.zhong@unistra.fr.

ABSTRACT
The surface temperature (ST) of high-emissivity surfaces is an important parameter in climate systems. The empirical methods for retrieving ST for high-emissivity surfaces from hyperspectral thermal infrared (HypTIR) images require spectrally continuous channel data. This paper aims to develop a multi-channel method for retrieving ST for high-emissivity surfaces from space-borne HypTIR data. With an assumption of land surface emissivity (LSE) of 1, ST is proposed as a function of 10 brightness temperatures measured at the top of atmosphere by a radiometer having a spectral interval of 800-1200 cm(-1) and a spectral sampling frequency of 0.25 cm(-1). We have analyzed the sensitivity of the proposed method to spectral sampling frequency and instrumental noise, and evaluated the proposed method using satellite data. The results indicated that the parameters in the developed function are dependent on the spectral sampling frequency and that ST of high-emissivity surfaces can be accurately retrieved by the proposed method if appropriate values are used for each spectral sampling frequency. The results also showed that the accuracy of the retrieved ST is of the order of magnitude of the instrumental noise and that the root mean square error (RMSE) of the ST retrieved from satellite data is 0.43 K in comparison with the AVHRR SST product.

No MeSH data available.