孟祥臣等:Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor-Application to Landsat 8 TIRS10 Data
被阅读 53 次
2018-06-25
Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor-Application to Landsat 8 TIRS10 Data
作者:Meng, XC (Meng, Xiangchen)[ 1,2 ] ; Cheng, J (Cheng, Jie)[ 1,2,3 ]
REMOTE SENSING
卷: 10  期: 3
文献号: 474
DOI: 10.3390/rs10030474
出版年: MAR 2018
文献类型:Article
 
摘要
Global reanalysis products have been widely used for correcting the atmospheric effects of thermal infrared data, but their performances have not been comprehensively evaluated. In this paper, we evaluate eight global reanalysis products (NCEP/FNL; NCEP/DOE Reanalysis2; MERRA-3; MERRA-6; MERRA2-3; MERRA2-6; JRA-55; and ERA-Interim) commonly used in the atmospheric correction of Landsat 8 TIRS10 data by referencing global radiosonde observations collected from 163 stations. The atmospheric parameters (atmospheric transmittance, upward radiance, and downward radiance) simulated with MERRA-6 and ERA-Interim were accurate than those simulated with other reanalysis products for different water vapor contents and surface elevations. When global reanalysis products were applied to retrieve land surface temperature (LST) from simulated Landsat 8 TIRS10 data, ERA-Interim and MERRA-6 were accurate than other reanalysis products. The overall LST biases and RMSEs between the retrieved LSTs and LSTs that were used to generate the top-of-atmosphere radiances were less than 0.2 K and 1.09 K, respectively. When eight reanalysis products were used to estimate LSTs from thirty-two Landsat 8 TIRS10 images covering the Heihe River basin in China, the various reanalysis products showed similar validation accuracies for LSTs with low water vapor contents. The biases ranged from 0.07 K to 0.24 K, and the STDs (RMSEs) ranged from 1.93 K (1.93 K) to 2.02 K (2.04 K). Considering the above evaluation results, MERRA-6 and ERA-Interim are recommended for thermal infrared data atmospheric corrections.
 
通讯作者地址: Cheng, J (通讯作者)
Chinese Acad Sci, State Key Lab Remote Sensing Sci, Beijing Normal Univ & Inst Remote Sensing & Digit, 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.
通讯作者地址: Cheng, J (通讯作者)
ARS, USDA, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA.
地址:
[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Beijing Normal Univ & Inst Remote Sensing & Digit, Beijing 100875, Peoples R China
[ 2 ] Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing 100875, Peoples R China
[ 3 ] ARS, USDA, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA