Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the   Heihe River Basin, China

doi:10.3390/rs70606862

CORC > 北京大学 > 地球与空间科学学院

	Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China
	Zhao, Jing ; Li, Jing ; Liu, Qinhuo ; Fan, Wenjie ; Zhong, Bo ; Wu, Shanlong ; Yang, Le ; Zeng, Yelu ; Xu, Baodong ; Yin, Gaofei
刊名	REMOTE SENSING
	2015
关键词	SATELLITE IMAGERY MISR DATA PART 1 VEGETATION ALGORITHM RESOLUTION PRODUCTS MODIS LAI PRINCIPLES
DOI	10.3390/rs70606862
英文摘要	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 inversions increases significantly from 6.4% to 49.7% for single-sensors to 75.9% for multi-sensors. LAI retrieved from the multi-sensor dataset show good agreement with the field measurements. The correlation coefficient (R-2) is 0.90, and the average root mean square error (RMSE) is 0.42. The network of multiple sensors with 30 m spatial resolution can generate LAI products with reasonable accuracy and meaningful temporal resolution.; National High Technology Research and Development Program of China [2012AA12A304]; Chinese Academy of Sciences Action Plan for West Development Project [KZCX2-XB3-15-2]; National Natural Science Foundation of China [41271366, 91325105, 41401393]; CAS/SAFEA International Partnership Program for Creative Research Teams [KZZD-EW-TZ-09]; Key Laboratory of Satellite Mapping Technology and Application, National Administration of Surveying, Mapping and Geoinformation [KLAMTA-201409]; SCI(E); EI; ARTICLE; zhaojing1@radi.ac.cn; lijing01@radi.ac.cn; liuqh@radi.ac.cn; fanwj@pku.edu.cn; zhongbo@radi.ac.cn; wsl0579@163.com; yangle@radi.ac.cn; zengyelu@163.com; xubd@radi.ac.cn; coffing@163.com; 6; 6862-6885; 7
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/419657]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Zhao, Jing,Li, Jing,Liu, Qinhuo,et al. Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China[J]. REMOTE SENSING,2015.
APA	Zhao, Jing.,Li, Jing.,Liu, Qinhuo.,Fan, Wenjie.,Zhong, Bo.,...&Yin, Gaofei.(2015).Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China.REMOTE SENSING.
MLA	Zhao, Jing,et al."Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China".REMOTE SENSING (2015).