TY  - JOUR
AU  - Yin
AU  - Li
AU  - Liu
AU  - Zhong
PY  - 2016
DA  - 2016//
TI  - Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data
JO  - Remote Sensing Letters
VL  - 7
IS  - 8
AB  - 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...
SN  - 2150-704X
ID  - Yin2016
ER  -