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REMOTE SENSING FOR LAND & RESOURCES    2012, Vol. 24 Issue (4) : 16-20     DOI: 10.6046/gtzyyg.2012.04.03
Technology and Methodology |
Discussions on Using Channels of Split-window Algorithm to Retrieve Earth Surface Temperature
MENG Peng1,2, HU Yong1, GONG Cai-lan1, LI Lin1,2
1. Shanghai Institute of Technical Physics, CAS, Shanghai 200083, China;
2. Graduate School of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  Being simple and effective, the split-window algorithm based on thermal infrared remote sensing is widely used to retrieve surface temperature. The method mainly uses thermal infrared bands in 10~13.3 μm(1 000~750 cm-1) range, neglecting bands in 8~9.09 μm (1 250~1 100 cm-1) range. This paper analyzes the process of deriving the formula of the split-window algorithm, summarizes the problems associated with the channel setting and makes numerical simulation analysis in the 10~13.3 μm (1 000~750 cm-1) and 8~9.09 μm (1 250~1 100 cm-1) ranges to solve the problems. The results show that split-window algorithm derived on the basis of this approach has similar performance in both 10~13.3 μm (1 000~750 cm-1) and 8~9.09 μm (1 250~1 100 cm-1) spectral ranges. Therefore, it can be concluded that the spectral range in 8~9.09 μm (1 250~1 100 cm-1) range can also be used to derive split-window algorithm for thermal remote sensing.
Keywords object-oriented      lake information extraction      eCognition      TM image     
: 

TP 79

 
Issue Date: 13 November 2012
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SHEN Jin-xiang
YANG Liao
CHEN Xi
LI Jun-li
PENG Qing-qing
HU Ju
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SHEN Jin-xiang,YANG Liao,CHEN Xi, et al. Discussions on Using Channels of Split-window Algorithm to Retrieve Earth Surface Temperature[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 16-20.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.04.03     OR     https://www.gtzyyg.com/EN/Y2012/V24/I4/16
[1] Schott J R,Volchok W J.Thematic Mapper Thermal Infrared Calibration[J].Photogrametric Engineering and Remote Sensing,1985,51(9):1351-1357.

[2] 覃志豪,Zhang M H,Karnieli A,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456-466.

Qin Z H,Zhang M H,Karnieli A,et al.Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM6 Data[J].Acta Geographica Sinica,2001,56(4):456-466(in Chinese with English Abstract).

[3] Price J C.Land Surface Temperature Meas urements from the Split Window Channels of the NOAA7 Advanced Very High Resolution Radiometer[J].Journal of Gephysical Research,1984,89(D5):7231-7237.

[4] Gillespie A,Rokugawa S,Matsunaga T,el al.Temperature and Emissivity Seperation Algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)Images[J].IEEE Transactions on Geoscience and Remote Sensing,1998,36(4):1113-1126.

[5] Wan Z M,Li Z L.A Physics-based Algorithm for Retrieving Land Surface Emissivity and Temperature from EOS/MODIS Data[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(4):980-996.

[6] Planck M.On the Law of Distribution of Energy in the Normal Spectrum[J].Annalender Physik,1901,4:553.

[7] Michels T E.Planck Functions and Integrals:Methods of Computation[R].Washingtion D C:National Aeronautics and Space Administration,1968.

[8] Chandrasekhar S.Radiative Transfer[M].New York:Dover Publication,1960.

[9] Mcmillin L.Estimation of Sea Surface Temperatures from Two Infrared Window M-easures with Different Absorption[J].Joural of Geophysical Research,1975,80(C36):5113-5117.

[10] CNES and EUMETSAT.IASI Program[EB/OL].http://smsc.cnes.fr/IASI/.
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