@Article{Yin2016,
author="{Yin}
and {Li}
and {Liu}
and {Zhong}",
title="Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data",
journal="Remote Sensing Letters",
year="2016",
volume="7",
number="8",
abstract="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...",
issn="2150-704X"
}