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Regional-scale phenology modeling based on meteorological records and remote sensing observations

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dc.contributor.author Yang, Xi
dc.contributor.author Mustard, John F.
dc.contributor.author Tang, Jianwu
dc.contributor.author Hong, Xu
dc.date.accessioned 2012-10-22T18:53:20Z
dc.date.available 2013-03-14T08:46:29Z
dc.date.issued 2012-09-14
dc.identifier.citation Journal of Geophysical Research 117 (2012): G03029 en_US
dc.identifier.uri http://hdl.handle.net/1912/5482
dc.description Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 117 (2012): G03029, doi:10.1029/2012JG001977. en_US
dc.description.abstract Changes of vegetation phenology in response to climate change in the temperate forests have been well documented recently and have important implications on the regional and global carbon and water cycles. Predicting the impact of changing phenology on terrestrial ecosystems requires an accurate phenology model. Although species-level phenology models have been tested using a small number of vegetation species, they are rarely examined at the regional level. In this study, we used remotely sensed phenology and meteorological data to parameterize the species-level phenology models. We used a remotely sensed vegetation index (Two-band Enhanced Vegetation Index, EVI2) derived from the Moderate Resolution Spectroradiometer (MODIS) 8-day reflectance product from 2000 to 2010 of New England, United States to calculate remotely sensed vegetation phenology (start/end of season, or SOS/EOS). The SOS/EOS and the daily mean air temperature data from weather stations were used to parameterize three budburst models and one senescence model. We compared the relative strengths of the models to predict vegetation phenology and selected the best model to reconstruct the “landscape phenology” in New England from year 1960 to 2010. Of the three budburst models tested, the spring warming model showed the best performance with an averaged Root Mean Square Deviation (RMSD) of 4.59 days. The Akaike Information Criterion supported the spring warming model in all the weather stations. For senescence modeling, the Delpierre model was better than a null model (the averaged phenology of each weather station, averaged model efficiency = 0.33) and has a RMSD of 8.05 days. A retrospective analysis using the spring warming model suggests a statistically significant advance of SOS in New England from 1960 to 2010 averaged as 0.143 days per year (p = 0.015). EOS calculated using the Delpierre model and growing season length showed no statistically significant advance or delay between 1960 and 2010 in this region. These results suggest the applicability of species-level phenology models at the regional level (and potentially terrestrial biosphere models) and the feasibility of using these models in reconstructing and predicting vegetation phenology. en_US
dc.description.sponsorship This research was supported by the Brown University–Marine Biological Laboratory graduate program in Biological and Environmental Sciences, Brown–ECI phenology working group, and Brown Office of International Affairs Seed Grant on phenology. en_US
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.publisher American Geophysical Union en_US
dc.relation.uri http://dx.doi.org/10.1029/2012JG001977
dc.subject Budburst/senescence en_US
dc.subject Chilling en_US
dc.subject Growing season length en_US
dc.subject Phenology model en_US
dc.subject Photoperiod en_US
dc.subject Remote sensing en_US
dc.title Regional-scale phenology modeling based on meteorological records and remote sensing observations en_US
dc.type Article en_US
dc.description.embargo 2013-03-14 en_US
dc.identifier.doi 10.1029/2012JG001977


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