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Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites
Wu, Chaoyang1; Peng, Dailiang2; Soudani, Kamel3; Siebicke, Lukas4; Gough, Christopher M.5; Arain, M. Altaf6; Bohrer, Gil7; Lafleur, Peter M.8; Peichl, Matthias9; Gonsamo, Alemu10,11; Xu, Shiguang12; Fang, Bin13; Ge, Quansheng1
2017-02-15
Source PublicationAGRICULTURAL AND FOREST METEOROLOGY
ISSN0168-1923
Volume233Pages:171-182
Corresponding AuthorWu, Chaoyang(wucy@igsnrr.ac.cn) ; Peng, Dailiang(pengdl@radi.ac.cn)
AbstractPhenology is an important indicator of annual plant growth and is also widely incorporated in ecosystem models to simulate interannual variability of ecosystem productivity under climate change. A comprehensive understanding of the potentials of current algorithms to detect the start and end for growing season (SOS and EOS) from remote sensing is still lacking. This is particularly true when considering the diverse interactions between phenology and climate change among plant functional types as well as potential influences from different sensors. Using data from 60 flux tower sites (376 site-years in total) from the global FLUXNET database, we applied four algorithms to extract plant phenology from time series of normalized difference vegetation index (NDVI) from both MODIS and SPOT-VGT sensors. Results showed that NDVI-simulated phenology had overall low correlation (R-2 <0.30) with flux-derived SOS/EOS observations, but this predictive strength substantially varied by fitting algorithm, sensor and plant functional type. Different fitting algorithms can produce significantly different phenological estimates, but this difference can also be influenced by sensor type. SPOT-VGT simulated better EOS but no difference in the accuracy of SOS was found with different sensors. It may be due to increased frequency of data sampling.(1 0 days for SPOT-VGT vs. 16 days for MODIS) during spring season when rapid plant growth does not help SPOT-VGT more sensitive to growth. In contrast, more frequent data acquisition favors better modeling of plant growth in autumn when a gradual decrease in photosynthesis occurs. Our study results highlight that none of these algorithm can provide consistent good accuracy in modeling SOS and EOS with respect to both plant functional types and sensors. More importantly, a rigorous validation of phenology modeling against ground data is necessary before applying these algorithms at regional or global scales and consequently previous conclusions on regional SOS/EOS trends should be viewed with caution if independent validation is lacking. (C) 2016 Elsevier B.V. All rights reserved.
KeywordPhenology SOS/EOS Remote sensing NDVI MODIS SPOT-VGT Forest
DOI10.1016/j.agrformet.2016.11.193
WOS KeywordDECIDUOUS BROADLEAF FOREST ; TIME-SERIES DATA ; NET ECOSYSTEM PRODUCTIVITY ; DIGITAL REPEAT PHOTOGRAPHY ; SPRING PHENOLOGY ; INTERANNUAL VARIATIONS ; SEASONAL-VARIATIONS ; DIFFERENT BIOMES ; FALL PHENOLOGY ; CARBON-DIOXIDE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41371013] ; National Natural Science Foundation of China[41522109] ; key Research Program of Frontier Sciences, CAS[QYZDB-SSW-DQC011] ; International Postdoctoral Fund[2015PE030]
Funding OrganizationNational Natural Science Foundation of China ; key Research Program of Frontier Sciences, CAS ; International Postdoctoral Fund
WOS Research AreaAgriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS SubjectAgronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS IDWOS:000393259400016
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64936
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Chaoyang; Peng, Dailiang
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth, Beijing 100101, Peoples R China
3.Univ Paris 11, Fac Sci Orsay, CNRS, AgroParisTech,Lab Ecol Systemat & Evolut, Orsay, France
4.Univ Goettingen, Fac Forest Sci & Forest Ecol, Bioclimatol Dept, Gottingen, Germany
5.Virginia Commonwealth Univ, Dept Biol, Richmond, VA 23284 USA
6.McMaster Univ, Sch Geog & Earth Sci, Hamilton, ON L8S 4K1, Canada
7.Ohio State Univ, Dept Civil Environm & Geodet Engn, Columbus, OH 43210 USA
8.Trent Univ, Dept Geog, Peterborough, ON K9J 7B8, Canada
9.Swedish Univ Agr Sci, Dept Forest Ecol & Management, S-90183 Umea, Sweden
10.Univ Toronto, Dept Geog, 100 St George St, Toronto, ON M5S 3G3, Canada
11.Univ Toronto, Program Planning, 100 St George St, Toronto, ON M5S 3G3, Canada
12.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
13.Columbia Univ, Dept Earth & Environm Engn, 500 W 120th St, New York, NY 10027 USA
Recommended Citation
GB/T 7714
Wu, Chaoyang,Peng, Dailiang,Soudani, Kamel,et al. Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites[J]. AGRICULTURAL AND FOREST METEOROLOGY,2017,233:171-182.
APA Wu, Chaoyang.,Peng, Dailiang.,Soudani, Kamel.,Siebicke, Lukas.,Gough, Christopher M..,...&Ge, Quansheng.(2017).Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites.AGRICULTURAL AND FOREST METEOROLOGY,233,171-182.
MLA Wu, Chaoyang,et al."Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites".AGRICULTURAL AND FOREST METEOROLOGY 233(2017):171-182.
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