%0 Journal Article
%A Yin
%A Li
%A Liu
%A Zhong
%A Li
%+ Institute of Mountain Hazards and Environment , Chinese Academy of Sciences , Chengdu , China;;State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth , Chinese Academy of Sciences , Beijing , China;;Joint Center for Global Change Studies (JCGCS) , Beijing , China
%T Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data
%J Remote Sensing Letters
%D 2016
%N 8
%V 7
%X ABSTRACT(#br)Spatio-temporally continuous leaf area index (LAI) is required for surface process simulation, climate modelling and global change study. As a result of cloud contamination and other factors, the current LAI products are spatially and temporally discontinuous. A multi-sensor integration method was proposed in this paper to combine Terra-Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua-MODIS, FY (FengYun) 3A-MEdium Resolution Spectrum Imager (MERSI) and FY3B-MERSI data to improve LAI spatio-temporal continuity. It consists of a normalization algorithm to eliminate the difference between MODIS and MERSI data in spatial and spectral aspects, a daily LAI retrieval algorithm based on neural networks and a maximum value compositing algorithm. The feasibility of our LAI retrieval method to improve continuity was assessed at national scale (in China). Results show that (1) the combination of multi-sensor data can significantly improve LAI temporal continuity, especially for mountainous regions...
%@ 2150-704X
%W CNKI