Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China

作者:He, YQ (He, Yaqian)[ 1,2,3,4 ] ; Bo, YC (Bo, Yanchen)[ 1,2,3 ] ; de Jong, R (de Jong, Rogier)[ 5 ] ; Li, AH (Li, Aihua)[ 1,2,3 ] ; Zhu, YX (Zhu, Yuxin)[ 1,2,3 ] ; Cheng, JH (Cheng, Jiehai)[ 1,2,3 ]

INTERNATIONAL JOURNAL OF REMOTE SENSING

卷: 36  期: 1  页: 300-317

DOI: 10.1080/01431161.2014.994719

出版年: 2015

摘要

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.

通讯作者地址: Bo, YC (通讯作者)

Beijing Normal Univ, Res Ctr Remote Sensing, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Res Ctr Remote Sensing, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, GIS, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China

[ 4 ] W Virginia Univ, Dept Geol & Geog, Morgantown, WV 26506 USA

[ 5 ] Univ Zurich, Dept Geog, Remote Sensing Labs, CH-8057 Zurich, Switzerland