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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.


Target area used for comparing the ST retrieved by our method from Metop-A IASI data with Metop-A AVHRR SST product.
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sensors-15-13406-f010: Target area used for comparing the ST retrieved by our method from Metop-A IASI data with Metop-A AVHRR SST product.

Mentions: Our developed method is applied to Metop-A IASI data observed over the Mediterranean Sea. The Mediterranean Sea is in the region with a latitude of 30° N–43° N and a longitude of 12° E–32° E shown in Figure 10.


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)

Target area used for comparing the ST retrieved by our method from Metop-A IASI data with Metop-A AVHRR SST product.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-13406-f010: Target area used for comparing the ST retrieved by our method from Metop-A IASI data with Metop-A AVHRR SST product.
Mentions: Our developed method is applied to Metop-A IASI data observed over the Mediterranean Sea. The Mediterranean Sea is in the region with a latitude of 30° N–43° N and a longitude of 12° E–32° E shown in Figure 10.

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.