Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data
	Yin, Gaofei1 ; Li, Jing 2,3; Liu, Qinhuo 2,3; Zhong, Bo 2; Li, Ainong1
刊名	REMOTE SENSING LETTERS
	2016-08-01
卷号	7 期号:8 页码:771-780
关键词	MODIS
ISSN号	2150-704X
通讯作者	Li, Jing ; Liu, Qinhuo
英文摘要	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 which are characterized by high frequency of cloud coverage; (2) the improvement in spatial continuity is obvious as can be seen from the increase of retrieval ratio, defined as the ratio of the number of retrieved pixels to the total number of pixels, from 0.78 for GEOV1 LAI product, and 0.88 for MOD15A2 LAI product to 0.98 for multi-sensor LAI product.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Remote Sensing ; Imaging Science & Photographic Technology
研究领域[WOS]	Remote Sensing ; Imaging Science & Photographic Technology
关键词[WOS]	LEAF-AREA INDEX ; ESSENTIAL CLIMATE VARIABLES ; TIME-SERIES ; GLOBAL PRODUCTS ; GEOV1 LAI ; DATA SETS ; VEGETATION ; RETRIEVAL ; ALGORITHM ; FAPAR
收录类别	SCI
语种	英语
WOS记录号	WOS:000378625600006
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/17485]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心
作者单位	1.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu, Peoples R China 2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 3.JCGCS, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yin, Gaofei,Li, Jing,Liu, Qinhuo,et al. Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data[J]. REMOTE SENSING LETTERS,2016,7(8):771-780.
APA	Yin, Gaofei,Li, Jing,Liu, Qinhuo,Zhong, Bo,&Li, Ainong.(2016).Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data.REMOTE SENSING LETTERS,7(8),771-780.
MLA	Yin, Gaofei,et al."Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data".REMOTE SENSING LETTERS 7.8(2016):771-780.