气候变化领域集成服务门户(CCPortal): An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data

CCPortal

DOI	10.1016/j.agrformet.2014.09.009
	An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data
	Cao, Ruyin; Chen, Jin; Shen, Miaogen; Tang, Yanhong
通讯作者	Chen, J (通讯作者)
发表日期	2015
ISSN	0168-1923
EISSN	1873-2240
起始页码	9
结束页码	20
卷号	200
英文摘要	Satellite-derived greenness vegetation indices provide a valuable data source for characterizing spring vegetation phenology over regional or global scales. A logistic function has been widely used to fit time series of vegetation indices to estimate green-up date (GUD), which is currently being used for generating the global phenological product from the Enhanced Vegetation Index (EVI) time-series data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, we address a violation of the basic assumption of the logistic fitting method that arises from the fact that vegetation growth under natural conditions is controlled by multiple environmental factors and often does not follow a well-defined S-shaped logistic temporal profile. We developed the adaptive local iterative logistic fitting method (ALILF) to analyze the local range (i.e., the range of data points where the values in the time series begin to increase rapidly) in the MODIS EVI profile in which GUD is found. The new method adopts an iterative procedure and an adaptive temporal window to properly simulate the trajectory of EVI time series in the local range, and can determine GUD more accurately. GUD estimated by ALILF almost match the date of the onset of the greenness increase well while the traditional logistic fitting method shows errors of even more than 1 month in the same cases. ALILF is a more general form of the logistic fitting method that can estimate GUD both from well-defined S-shaped time series and from non-logistic ones. Besides, it is resistant to a range of noise levels added on the time-series data (Gaussian noise with a mean value of zero and standard deviations ranging from 0% to 15% of the EVI value). These advantages mean ALILF may be widely used for monitoring spring vegetation phenology from greenness vegetation indices. (C) 2014 Elsevier B.V. All rights reserved.
关键词	TIBETAN PLATEAU GROWING-SEASON GREEN-UP CLIMATE-CHANGE NDVI DATA CHINA LAND TEMPERATURE RESOLUTION RESPONSES
英文关键词	Climate change; Green up; Inner Mongolia; Logistic fitting; Precipitation; Start of the growing season
语种	英语
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000347582300002
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/258504
推荐引用方式 GB/T 7714	Cao, Ruyin,Chen, Jin,Shen, Miaogen,et al. An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data[J]. 中国科学院青藏高原研究所,2015,200.
APA	Cao, Ruyin,Chen, Jin,Shen, Miaogen,&Tang, Yanhong.(2015).An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data.AGRICULTURAL AND FOREST METEOROLOGY,200.
MLA	Cao, Ruyin,et al."An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data".AGRICULTURAL AND FOREST METEOROLOGY 200(2015).