An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data

被引:116
|
作者
Cao, Ruyin [1 ]
Chen, Jin [2 ,3 ]
Shen, Miaogen [4 ]
Tang, Yanhong [1 ]
机构
[1] Natl Inst Environm Studies, Tsukuba, Ibaraki 3058506, Japan
[2] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
[3] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[4] Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Alpine Ecol & Biodivers, Beijing 100101, Peoples R China
基金
中国国家自然科学基金;
关键词
Climate change; Green up; Inner Mongolia; Logistic fitting; Precipitation; Start of the growing season; TIBETAN PLATEAU; GROWING-SEASON; GREEN-UP; CLIMATE-CHANGE; NDVI DATA; CHINA; LAND; TEMPERATURE; RESOLUTION; RESPONSES;
D O I
10.1016/j.agrformet.2014.09.009
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
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.
引用
收藏
页码:9 / 20
页数:12
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