Multiscale Estimation of Leaf Area Index from Satellite Observations Based on an Ensemble Multiscale Filter

作者:Jiang, JY (Jiang, Jingyi)[ 1,2 ] ; Xiao, ZQ (Xiao, Zhiqiang)[ 1 ] ; Wang, JD (Wang, Jindi)[ 1 ] ; Song, JL (Song, Jinling)[ 1 ]

REMOTE SENSING

卷: 8  期: 3

DOI: 10.3390/rs8030229

出版年: MAR 2016

摘要

Currently, multiple leaf area index (LAI) products retrieved from remote sensing data are widely used in crop growth monitoring, land-surface process simulation and studies of climate change. However, most LAI products are only retrieved from individual satellite observations, which may result in spatial-temporal discontinuities and low accuracy in these products. In this paper, a new method was developed to simultaneously retrieve multiscale LAI data from satellite observations with different spatial resolutions based on an ensemble multiscale filter (EnMsF). The LAI average values corresponding to the date of satellite observations were calculated from the multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and were used as a priori knowledge for LAI in order to construct an initial ensemble multiscale tree (EnMsT). Satellite observations obtained at different spatial resolutions were then applied to update the LAI values at each node of the EnMsT using a two-sweep filtering procedure. Next, the retrieved LAI values at the finest scale were used as a priori knowledge for LAI for the new round of construction and updating of the EnMsT, until the sum of the difference of LAI values at each node of the EnMsT between two adjacent updates is less than a given threshold. The method was tested using Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and MODIS surface reflectance data from five sites that have different vegetation types. The results demonstrate that the retrieved LAI values for each spatial resolution were in good agreement with the aggregated LAI reference map values for the corresponding spatial resolution. The retrieved LAI values at the coarsest scale provided better accuracy with the aggregated LAI reference map values (root mean square error (RMSE) = 0.45) compared with that obtained from the MODIS LAI values (RMSE = 1.30).

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 ] INRA EMMAH UMR 1114, F-84914 Avignon, France