Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain
被引:54
作者:
Lu, Linlin
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机构:
Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R ChinaChinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R China
Lu, Linlin
[1
]
Wang, Cuizhen
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机构:
Univ Missouri, Dept Geog, Columbia, MO 65211 USAChinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R China
Wang, Cuizhen
[2
]
Guo, Huadong
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机构:
Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R ChinaChinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R China
Guo, Huadong
[1
]
Li, Qingting
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机构:
Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R ChinaChinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R China
Li, Qingting
[1
]
机构:
[1] Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R China
[2] Univ Missouri, Dept Geog, Columbia, MO 65211 USA
winter wheat;
phenology;
SPOT-VEGETATION;
time series;
TIME-SERIES;
AVHRR;
LANDSAT;
GROWTH;
D O I:
10.1080/10106049.2012.760004
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat-maize and winter wheat-cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement with in situ observations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region.