作者:Cheng, J (Cheng, Jie)[ 1,2,3 ] ; Liang, SL (Liang, Shunlin)[ 1,2,3 ] ; Nie, AX (Nie, Aixiu)[ 4 ] ; Liu, Q (Liu, Qiang)[ 4 ]

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING

卷: 46  期: 4  页: 591-596

DOI: 10.1007/s12524-017-0713-7

出版年: APR 2018

文献类型:Article

摘要

For a satellite sensor with only one or two thermal infrared channels, it is difficult to retrieve the surface emissivity from the received emissive signal. Empirical linear relationship between surface emissivity and red reflectance are already established for deriving emissivity, but the inner physical mechanism remains unclear. The optical constants of various minerals that cover the spectral range from 0.44 to 13.5 mu m in conjunction with modern radiative transfer models were used to produce corresponding surface reflectance and emissivity spectra. Compared to the commonly used empirical linear relationship, a more accurate multiple linear relationship between Landsat TM5 emissivity and optical reflectances was derived using the simulated data, which indicated the necessity of replacing the empirical relationship with the new one for improving surface emissivity estimate in the single channel algorithm. The significant multiple linear relationship between broadband emissivity (BBE, 8-13.5 mu m) and MODIS spectral albedos was also derived using the same data. This paper demonstrates that there is a physical linkage between surface emissive and reflective variables, and provides a theoretical perspective on estimating surface emissivity for sensors with only one or two thermal infrared channels.

通讯作者地址: Cheng, J (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Cheng, J (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China.

通讯作者地址: Cheng, J (通讯作者)

Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing 100875, Peoples R China

[ 4 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China