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    程洁等:Estimating global land surface broadband thermal-infrared emissivity using advanced very high resolution radiometer optical data

    作者:来源:发布时间:2014-08-22

    Estimating global land surface broadband thermal-infrared emissivity using advanced very high resolution radiometer optical data
    作者:Cheng, J (Cheng, Jie)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1,2 ]
    INTERNATIONAL JOURNAL OF DIGITAL EARTH
    卷: 6  页: 34-49  增刊: 1  特刊: SI
    DOI: 10.1080/17538947.2013.783129
    出版年: DEC 9 2013

    摘要
    An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE) from advanced very high resolution radiometer (AVHRR) visible and near-infrared data from 1981 through 1999 was presented. Land surface was divided into three types according to its normalized difference vegetation index (NDVI) values: bare soil, vegetated area, and transition zone. For each type, BBE at 8-13.5 mu m was formulated as a nonlinear function of AVHRR reflectance for Channels 1 and 2. Given difficulties in validating coarse emissivity products with ground measurements, the algorithm was cross-validated by comparing retrieved BBE with BBE derived through different methods. Retrieved BBE was initially compared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square error were less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 for transition zones, and -0.002 and 0.005 for vegetated areas. Retrieved BBE was also compared with BBE obtained through the NDVI threshold method. The proposed algorithm was better than the NDVI threshold method, particularly for bare soil. Finally, retrieved BBE and BBE derived from MODIS data were consistent, as were the two BBE values.

    通讯作者地址: Cheng, J (通讯作者)
    Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
    地址:
    [ 1 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
    [ 2 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

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