作者:Meng, XC (Meng, Xiangchen)[ 1,2,3 ] ; Cheng, J (Cheng, Jie)[ 1,2,3 ] ; Liang, SL (Liang, Shunlin)[ 1,2,3,4 ]

REMOTE SENSING

卷: 9  期: 12

文献号: 1247

DOI: 10.3390/rs9121247

出版年: DEC 2017

摘要

Land surface temperature (LST) is a key parameter for a wide number of applications, including hydrology, meteorology and surface energy balance. In this study, we first proposed a new land surface emissivity (LSE) scheme, including a lookup table-based method to determine the vegetated surface emissivity and an empirical method to derive the bare soil emissivity from the Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product. Then, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data and the Feng Yun-3C/Medium Resolution Spectral Imager (FY-3C/MERSI) precipitable water vapor product were used to correct the atmospheric effects. After resolving the land surface emissivity and atmospheric effects, the LST was derived in a straightforward manner from the FY-3C/MERSI data by the radiative transfer equation algorithm and the generalized single-channel algorithm. The mean difference between the derived LSE and field-measured LSE over seven stations is approximately 0.002. Validation of the LST retrieved with the LSE determined by the new scheme can achieve an acceptable accuracy. The absolute biases are less than 1 K and the STDs (RMSEs) are less than 1.95 K (2.2 K) for both the 1000 m and 250 m spatial resolutions. The LST accuracy is superior to that retrieved with the LSE determined by the commonly used Normalized Difference Vegetation Index (NDVI) threshold method. Thus, the new emissivity scheme can be used to improve the accuracy of the LSE and further the LST for sensors with broad spectral ranges such as FY-3C/MERSI.

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

[ 4 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA