%0 Journal Article
%A Jing Zhao
%A Jing Li
%A Qinhuo Liu
%A Wenjie Fan
%A Bo Zhong
%A Shanlong Wu
%A Le Yang
%A Yelu Zeng
%A Baodong Xu
%A Gaofei Yin
%+ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;;Joint Center for Global Change Studies, Beijing 100875, China;;Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China;;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
%T Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China
%J Remote Sensing
%D 2015
%N 6
%V 7
%K multi-sensor dataset;the middle reach of the Heihe River Basin;leaf area index;HJ1/CCD;Landsat8/OLI
%X The primary restriction on high resolution remote sensing data is the limit observation frequency. Using a network of multiple sensors is an efficient approach to increase the observations in a specific period. This study explores a leaf area index (LAI) inversion method based on a 30 m multi-sensor dataset generated from HJ1/CCD and Landsat8/OLI, from June to August 2013 in the middle reach of the Heihe River Basin, China. The characteristics of the multi-sensor dataset, including the percentage of valid observations, the distribution of observation angles and the variation between different sensor observations, were analyzed. To reduce the possible discrepancy between different satellite sensors on LAI inversion, a quality control system for the observations was designed. LAI is retrieved from the high quality of single-sensor observations based on a look-up table constructed by a unified model. The averaged LAI inversion over a 10-day period is set as the synthetic LAI value. The percentage of valid LAI in...
%W CNKI