Extracting grassland vegetation phenology in North China based on cumulative SPOT-VEGETATION NDVI data
作者:Hou, XH (Hou, Xuehui)[ 1,2 ] ; Gao, S (Gao, Shuai)[ 1 ] ; Niu, Z (Niu, Zheng)[ 1 ] ; Xu, ZG (Xu, Zhigang)[ 1 ]
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷: 35  期: 9  页: 3316-3330
DOI: 10.1080/01431161.2014.903437
出版年: MAY 3 2014

摘要
Plant phenology is one of the main indicators of climate or other environmental processes. This paper assesses the detection accuracy of start of season (SOS) and end of season (EOS) for grassland vegetation in north China from 2001 to 2010 using SPOT-VEGETATION normalized difference vegetation index (NDVI) data sets and in situ observations. The cumulative NDVI is calculated and fitted using a logistic model to identify phenological transition dates. The curvature of the fitted logistic models predicts phenological transition dates that correspond to the times at which the curvature in the yearly integrated NDVI exhibits local minimums or maximums. Validating with in situ observations, phenological dates are extracted from satellite time series data and are accurate to within 10 days. The spatial trends of SOS and EOS are very similar for 2001-2010. SOS mainly occurs from the day of year (DOY) 110 to DOY 170, and EOS occurs from DOY 240 to DOY 300. SOS displays a marked delay from south to north, while EOS gradually advances, indicating regional differences in climate and terrain. However, the effect of latitude and longitude on the average EOS of alpine grasslands is not significantly different, while SOS at low latitude and high longitude is 10 days earlier than at high-latitude and high-longitude regions. We detected an overall advance in SOS of 3.1 days over 10 years, and a 1.3-day delay in EOS. However, the amplitude is low (about 5 days) and the changes in most regions are not significant (close to zero). The results in this paper are concordant with many reported studies that explored the phenology of grasslands in North China, which is an important component of global grasslands science.

通讯作者地址: Xu, ZG (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 2 ] Chinese Acad Sci, Beijing 100049, Peoples R China