作者:Xiao, ZQ (Xiao, Zhiqiang)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1,3 ] ; Wang, TT (Wang, Tongtong)[ 1 ] ; Liu, Q (Liu, Qiang)[ 2 ]

REMOTE SENSING

卷: 7  期: 8  页: 9844-9864

DOI: 10.3390/rs70809844

出版年: AUG 2015

摘要

Land-surface reflectance, estimated from satellite observations through atmospheric corrections, is an essential parameter for further retrieval of various high level land-surface parameters, such as leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface albedo. Although great efforts have been made, land-surface reflectance products still contain considerable noise caused by, e.g., cloud or mixed-cloud pixels, which results in temporal and spatial inconsistencies in subsequent downstream products. In this study, a new method is developed to remove the residual clouds in the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface reflectance product and reconstruct time series of surface reflectance for the red, near infrared (NIR), and shortwave infrared (SWIR) bands. A smoothing method is introduced to calculate upper envelopes of vegetation indices (VIs) from the surface reflectance data and the cloud contaminated reflectance data are identified using the time series VIs and the upper envelopes of the time series VIs. Surface reflectance was then reconstructed according to cloud-free surface reflectance by incorporating the upper envelopes of the time series VIs as constraint conditions. The method was applied to reconstruct time series of surface reflectance from MODIS/TERRA surface reflectance product (MOD09A1). Temporal consistency analysis indicates that the new method can reconstruct temporally-continuous time series of land-surface reflectance. Comparisons with cloud-free MODIS/AQUA surface reflectance product (MYD09A1) over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites in 2003 demonstrate that the new method provides better performance for the red band (R-2 = 0.8606 and RMSE = 0.0366) and NIR band (R-2 = 0.6934 and RMSE = 0.0519), than the time series cloud detection (TSCD) algorithm (R-2 = 0.5811 and RMSE = 0.0649; and R-2 = 0.5005 and RMSE = 0.0675, respectively).

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

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