Consistent estimation of multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model

作者:Shi, HY (Shi, Hanyu)[ 1 ] ; Xiao, ZQ (Xiao, Zhiqiang)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1,2 ] ; Zhang, XT (Zhang, Xiaotong)[ 1 ]

REMOTE SENSING OF ENVIRONMENT

卷: 184  页: 40-57

DOI: 10.1016/j.rse.2016.06.008

出版年: OCT 2016

摘要

Traditional methods usually estimate individual land surface parameters separately from surface reflectance data, a practice which requires atmospheric correction for top of atmosphere (TOA) reflectance and may result in physical inconsistency among parameters. This paper proposes a new method for consistent estimation of multiple land surface parameters and aerosol optical depth (ACID) from Moderate Resolution Imaging Spectroradiometer (MODIS) TOA reflectance data. Soil, vegetation canopy, and atmospheric radiative transfer models were coupled, and a global sensitivity analysis of the coupled model was performed to identify the influential parameters from satellite observations. The most influential parameters, including leaf area index (LAI) and aerosol optical depth (AOD), were simultaneously retrieved from MODIS TOA reflectance data and then provided as input to the coupled soil-canopy-atmosphere radiative transfer model to calculate land surface reflectance, incident photosynthetically active radiation (PAR), land surface albedo, and the fraction of absorbed photosynthetically active radiation (FAPAR). The retrieved land surface parameters and AOD were compared with the corresponding MODIS, Global Land Surface Satellite (GLASS), GEOV1, and Multi-angle Imaging Spectroradiometer (MISR) products and validated by ground measurements from seven sites with different vegetation types. The results demonstrate that the new inversion method can effectively produce multiple physically consistent parameters with accuracy comparable to that of existing satellite products over the select sites. (C) 2016 Elsevier Inc. All rights reserved.

通讯作者地址: Xiao, ZQ (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