Estimating the Hemispherical Broadband Longwave Emissivity of Global Vegetated Surfaces Using a Radiative Transfer Model

作者:Cheng, J (Cheng, Jie)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1,2 ] ; Verhoef, W (Verhoef, Wout)[ 3 ] ; Shi, LP (Shi, Linpeng)[ 1 ] ; Liu, Q (Liu, Qiang)[ 4 ]

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

卷: 54  期: 2  页: 905-917

DOI: 10.1109/TGRS.2015.2469535

出版年: FEB 2016

摘要

Current satellite broadband emissivity (BBE) products do not correctly characterize the seasonal variation of vegetation abundance. This paper proposes a new method to estimate the BBE of vegetated surfaces to better describe the seasonal variation of vegetation abundance. The method takes advantage of the radiative transfer models' ability to calculate multiple scattering with a physical basis and uses the 4SAIL model to construct a lookup table (LUT) of BBE for vegetated surfaces. The BBE of the vegetated surface was derived from the LUT using three inputs: leaf BBE, soil BBE, and leaf area index (LAI). The validation results show that the accuracy of the new method exceeds 0.005 over fully vegetated surfaces. As a case study, this method was applied to data from 2003 to generate global vegetated surface BBE products for that year. An analysis of the results indicated that the derived BBE can correctly reflect seasonal variations in vegetation abundance that the data converted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS spectral emissivity products have been unable to reveal. The new method was also compared to the vegetation cover method (VCM). The VCM can correctly characterize seasonal variations in vegetation abundance. However, the classification of bare soil and vegetation in the VCM may produce step discontinuity in the calculated BBE. The new method is being implemented to produce a new version of the Global LAnd Surface Satellite (GLASS) BBE product over vegetated surfaces.

通讯作者地址: Cheng, J (通讯作者)

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

地址:

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

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

[ 3 ] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7514 AE Enschede, Netherlands

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