An integrated phenology modelling framework in r
Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climat...
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| Vydáno v: | Methods in ecology and evolution Ročník 9; číslo 5; s. 1276 - 1285 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
John Wiley & Sons, Inc
01.05.2018
Wiley Wiley-Blackwell |
| Témata: | |
| ISSN: | 2041-210X, 2041-210X |
| On-line přístup: | Získat plný text |
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| Abstract | Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system.
Here, we present the phenor r package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA‐NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data.
We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework.
In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products. |
|---|---|
| AbstractList | Abstract
Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system.
Here, we present the
phenor r
package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the
<styled-content style='fixed-case'>USA</styled-content>
National Phenology Network (
<styled-content style='fixed-case'>USA</styled-content>
‐
<styled-content style='fixed-case'>NPN</styled-content>
), the Pan European Phenology Project (
<styled-content style='fixed-case'>PEP</styled-content>
725),
<styled-content style='fixed-case'>MODIS</styled-content>
phenology (
<styled-content style='fixed-case'>MCD</styled-content>
12Q2) combined with (global) retrospective and projected climate data.
We show an example analysis, using the
phenor
modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (
<styled-content style='fixed-case'>RMSE</styled-content>
) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework.
In conclusion, we hope the
phenor
phenology modelling framework in the
r
language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products. Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system.Here, we present the phenor r package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA‐NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data.We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework.In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products. Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. Here, we present the phenor r package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA‐NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data. We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products. 1. Phenology is a first-order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. 2. Here, we present the PHENOR R package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA-NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data. 3. We show an example analysis, using the PHENOR modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. 4. In conclusion, we hope the PHENOR phenology modelling framework in the R language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products. Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. Here, we present the phenor r package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network ( USA ‐ NPN ), the Pan European Phenology Project ( PEP 725), MODIS phenology ( MCD 12Q2) combined with (global) retrospective and projected climate data. We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared ( RMSE ) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products. |
| Author | Milliman, Tom Melaas, Eli K. Hufkens, Koen Basler, David Richardson, Andrew D. Goslee, Sarah |
| Author_xml | – sequence: 1 givenname: Koen orcidid: 0000-0002-5070-8109 surname: Hufkens fullname: Hufkens, Koen email: koen.hufkens@gmail.com organization: Harvard University – sequence: 2 givenname: David surname: Basler fullname: Basler, David organization: Harvard University – sequence: 3 givenname: Tom surname: Milliman fullname: Milliman, Tom organization: University of New Hampshire – sequence: 4 givenname: Eli K. surname: Melaas fullname: Melaas, Eli K. organization: Boston University – sequence: 5 givenname: Andrew D. surname: Richardson fullname: Richardson, Andrew D. organization: Northern Arizona University – sequence: 6 givenname: Sarah surname: Goslee fullname: Goslee, Sarah |
| BackLink | https://hal.inrae.fr/hal-02622109$$DView record in HAL https://www.osti.gov/biblio/1420342$$D View this record in Osti.gov |
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| Cites_doi | 10.1046/j.1365-2486.1999.00281.x 10.1111/1365-2435.12309 10.1007/978-94-007-6925-0_15 10.5194/bg-9-2063-2012 10.18637/jss.v042.i11 10.1111/j.1365-2486.2009.01910.x 10.1002/qj.828 10.1111/j.1365-3040.1989.tb01938.x 10.1016/S0378-4371(96)00271-3 10.1093/treephys/26.9.1165 10.1016/j.ecolmodel.2005.10.020 10.1111/gcb.13122 10.1016/S0168-1923(96)02359-3 10.2307/2403139 10.1016/j.agrformet.2008.08.013 10.1038/nclimate2942 10.1111/gcb.13326 10.1111/gcb.12360 10.1111/j.1365-2486.2006.01311.x 10.2307/2404093 10.1016/j.agrformet.2011.09.009 10.1016/j.agrformet.2012.09.012 10.1890/13-0652.1 10.1093/oxfordjournals.aob.a084891 10.1046/j.1469-8137.1999.00445.x 10.1016/j.rse.2015.02.003 10.1006/jtbi.2000.2178 10.2307/2404467 10.1111/geb.12206 10.1029/2005EO510005 10.1890/14-0005.1 10.1093/aob/mcv055 10.1038/sdata.2018.28 10.1016/j.agrformet.2012.05.001 10.1016/j.rse.2009.08.016 10.1098/rstb.2010.0102 10.1029/2008JD010201 10.2307/1931815 10.1002/ece3.1273 10.1111/j.1365-2486.2011.02562.x 10.21273/HORTSCI.22.3.371 10.14214/sf.313 10.1016/j.envsoft.2014.09.005 10.1111/nph.12680 10.1038/432289a 10.32614/RJ-2013-002 10.1371/journal.pone.0057373 10.1016/S0034-4257(02)00135-9 10.1016/j.agrformet.2015.11.007 10.1007/s00442-009-1363-4 10.5194/bgd-11-2305-2014 10.2307/2404609 10.1006/anbo.1996.0321 10.1007/s00484-003-0171-5 10.1046/j.1365-3040.1999.00395.x |
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| References_xml | – volume: 41 start-page: 167 year: 2007 end-page: 205 article-title: A framework for modelling the annual cycle of trees in boreal and temperate regions publication-title: Silva Fennica – volume: 22 start-page: 792 year: 2016 end-page: 805 article-title: Multiscale modeling of spring phenology across deciduous forests in the Eastern United States publication-title: Global Change Biology – volume: 42 start-page: 1 year: 2011 end-page: 26 article-title: Genetic optimization using derivatives: The rgenoud package for R publication-title: Journal of Statistical Software – start-page: 275 year: 2013 end-page: 293 – volume: 23 start-page: 1245 year: 2014 end-page: 1254 article-title: Macroscale prediction of autumn leaf coloration throughout the continental United States publication-title: Global Ecology and Biogeography – volume: 18 start-page: 566 year: 2011 end-page: 584 article-title: Terrestrial biosphere models need better representation of vegetation phenology: Results from the North American Carbon Program publication-title: Global Change Biology – volume: 1735 start-page: 545 year: 1735 end-page: 576 article-title: Observations du thermometre, faites a Paris pendant l′annee 1735 comparees avec celles qui onte faites sous la ligne et al′Ile de France,a Alger et en quelques‐unes de nos ıles de l′Amerique publication-title: Memoires de l'Academie Royale des Sciences de Paris – start-page: 79 year: 2007 end-page: 131 – volume: 79 start-page: 133 year: 1997 end-page: 137 article-title: Changing environmental effects on frost hardiness of scots pine during dehardening publication-title: Annals of Botany – volume: 4 start-page: 4658 year: 2014 end-page: 4668 article-title: MODISTools ‐ downloading and processing MODIS remotely sensed data in R publication-title: Ecology and Evolution – volume: 24 start-page: 1478 year: 2014 end-page: 1489 article-title: Tracking forest phenology and seasonal physiology using digital repeat photography: A critical assessment publication-title: Ecological Applications. – volume: 1 start-page: 1 year: 2012 end-page: 7 article-title: A New estimate of the average earth surface land temperature spanning 1753 to 2011 publication-title: Geoinformatic & Geostatistics: An Overview – volume: 164 start-page: 10 year: 2012 end-page: 19 article-title: Shortcomings of classical phenological forcing models and a way to overcome them publication-title: Agricultural and Forest Meteorology – volume: 84 start-page: 471 year: 2003 end-page: 475 article-title: Monitoring vegetation phenology using MODIS publication-title: Remote Sensing of Environment – volume: 143 start-page: 339 year: 1999 end-page: 349 article-title: Climatic determinants of budburst seasonality in four temperate‐zone tree species publication-title: New Phytologist – volume: 31 start-page: 172 year: 1994 end-page: 181 article-title: Selecting a model to predict the onset of growth of publication-title: Journal of Applied Ecology – volume: 25 start-page: 99 year: 2015 end-page: 115 article-title: Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy‐scale photosynthesis publication-title: Ecological Applications – volume: 11 start-page: 2305 year: 2014 end-page: 2342 article-title: Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery publication-title: Biogeosciences Discussions – volume: 207 start-page: 337 year: 2000 end-page: 347 article-title: A unified model for budburst of trees publication-title: Journal of theoretical biology – volume: 86 start-page: 538 year: 2005 article-title: Implementing a U.S. National Phenology Network publication-title: Eos, Transactions American Geophysical Union – volume: 161 start-page: 187 year: 2009 end-page: 198 article-title: Responses of canopy duration to temperature changes in four temperate tree species: Relative contributions of spring and autumn leaf phenology publication-title: Oecologia – volume: 194 start-page: 256 year: 2006 end-page: 265 article-title: Modelling of weather variability effect on fitophenology publication-title: Ecological Modelling – volume: 41 start-page: 785 year: 1960 end-page: 790 article-title: A critique of the heat unit approach to plant response studies publication-title: Ecology – volume: 5 start-page: 891 year: 1999 end-page: 902 article-title: Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest publication-title: Global Change Biology – volume: 12 start-page: 235 year: 1989 end-page: 247 article-title: Foliar stage in wheat correlates better to photothermal time than to thermal time publication-title: Plant, Cell & Environment – volume: 62 start-page: 385 year: 2014 end-page: 398 article-title: Plant modelling framework: Software for building and running crop models on the APSIM platform publication-title: Environmental Modelling and Software – volume: 116 start-page: 875 year: 2015 end-page: 888 article-title: Changes in autumn senescence in northern hemisphere deciduous trees: A meta‐analysis of autumn phenology studies publication-title: Annals of Botany – volume: 213 start-page: 1 year: 1990 end-page: 47 article-title: Modelling bud dormancy release in trees from cool and temperate regions publication-title: Acta Forestalia Fennica – volume: 202 start-page: 350 year: 2014 end-page: 355 article-title: Does humidity trigger tree phenology? Proposal for an air humidity based framework for bud development in spring publication-title: New Phytologist – volume: 161 start-page: 63 year: 2015 end-page: 77 article-title: Modeling grassland spring onset across the Western United States using climate variables and MODIS‐derived phenology metrics publication-title: Remote Sensing of Environment – volume: 15 start-page: 2335 year: 2009 end-page: 2359 article-title: Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982‐2006 publication-title: Global Change Biology – volume: 217 start-page: 10 year: 2016 end-page: 21 article-title: Evaluating phenological models for the prediction of leaf‐out dates in six temperate tree species across central Europe publication-title: Agricultural and Forest Meteorology – volume: 152 start-page: 159 year: 2012 end-page: 177 article-title: Digital repeat photography for phenological research in forest ecosystems publication-title: Agricultural and Forest Meteorology – volume: 137 start-page: 553 year: 2011 end-page: 597 article-title: The ERA‐interim reanalysis: configuration and performance of the data assimilation system publication-title: Quarterly Journal of the Royal Meteorological Society – volume: 22 start-page: 1 year: 1999 end-page: 13 article-title: Selecting models to predict the timing of flowering of temperate trees: Implications for tree phenology modelling publication-title: Plant, Cell and Environment – volume: 28 start-page: 1344 year: 2014 end-page: 1355 article-title: Tree phenology responses to winter chilling, spring warming, at north and south range limits publication-title: Functional Ecology – volume: 26 start-page: 693 year: 1989 end-page: 700 article-title: Date of budburst of fifteen tree species in Britain following climatic warming publication-title: Journal of Applied Ecology – volume: 365 start-page: 3227 year: 2010 end-page: 3246 article-title: Influence of spring and autumn phenological transitions on forest ecosystem productivity publication-title: Philosophical transactions of the Royal Society of London Series B, Biological Sciences – volume: 84 start-page: 273 year: 1997 end-page: 284 article-title: Detecting leaf area and surface resistance during transition seasons publication-title: Agricultural and Forest Meteorology – volume: 26 start-page: 1165 year: 2006 end-page: 1172 article-title: Models of the spring phenology of boreal and temperate trees: Is there something missing? publication-title: Tree physiology – volume: 432 start-page: 289 year: 2004 end-page: 290 article-title: Historical phenology: Grape ripening as a past climate indicator publication-title: Nature – year: 2016 – volume: 169 start-page: 156 year: 2013 end-page: 173 article-title: Climate change, phenology, and phenological control of vegetation feedbacks to the climate system publication-title: Agricultural and Forest Meteorology – volume: 8 start-page: e57373 year: 2013 article-title: Predicting climate change impacts on the amount and duration of autumn colors in a New England forest publication-title: PLoS ONE – volume: 29 start-page: 597 year: 1992 end-page: 604 article-title: Predicting the timing of budburst in temperate trees publication-title: Journal of Applied Ecology – year: 2018 article-title: Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery publication-title: Scientific Data – volume: 13 start-page: 707 year: 2007 end-page: 721 article-title: Phenology model from surface meteorology does not capture satellite‐based greenup estimations publication-title: Global Change Biology – volume: 20 start-page: 951 year: 1983 end-page: 963 article-title: Thermal time, chill days and prediction of budburst in Picea sitchensis publication-title: Journal of applied Ecology – volume: 114 start-page: 168 year: 2010 end-page: 182 article-title: MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets publication-title: Remote Sensing of Environment – volume: 22 start-page: 371 year: 1987 end-page: 377 article-title: Endo‐, para‐, and ecodormancy: Physiological terminology and classification for dormancy research publication-title: HortScience – volume: 233 start-page: 395 year: 1996 end-page: 406 article-title: Generalized Simulated Annealing publication-title: Physica A – volume: 47 start-page: 193 year: 2003 end-page: 201 article-title: Physiology‐based phenology models for forest tree species in Germany publication-title: International journal of biometeorology – volume: 20 start-page: 170 year: 2013 end-page: 182 article-title: Chilling outweighs photoperiod in preventing precocious spring development publication-title: Global Change Biology – volume: 5 start-page: 13 year: 2013 end-page: 28 article-title: Generalized simulated annealing for global optimization: The GenSA Package publication-title: R Journal – volume: 38 start-page: 1013 year: 1974 end-page: 1023 article-title: Apple fruit bud development and growth; analysis and an empirical model publication-title: Annals of Botany – volume: 113 start-page: D20119 year: 2008 article-title: A European daily high‐resolution gridded data set of surface temperature and precipitation for 1950‐2006 publication-title: Journal of Geophysical Research Atmospheres – volume: 9 start-page: 2063 year: 2012 end-page: 2083 article-title: On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model publication-title: Biogeosciences – volume: 22 start-page: 3675 year: 2016 end-page: 3688 article-title: A new seasonal‐deciduous spring phenology submodel in the Community Land Model 4.5: Impacts on carbon and water cycling under future climate scenarios publication-title: Global Change Biology – year: 2017 – volume: 149 start-page: 256 year: 2009 end-page: 262 article-title: Predicting the start and peak dates of the Poaceae pollen season in Spain using process‐based models publication-title: Agricultural and Forest Meteorology – volume: 6 start-page: 10 year: 2016 end-page: 14 article-title: Productivity of North American grasslands is increased under future climate scenarios despite rising aridity publication-title: Nature Climate Change. – volume: 1735 start-page: 545 year: 1735 ident: e_1_2_9_16_1 article-title: Observations du thermometre, faites a Paris pendant l′annee 1735 comparees avec celles qui onte faites sous la ligne et al′Ile de France,a Alger et en quelques‐unes de nos ıles de l′Amerique publication-title: Memoires de l'Academie Royale des Sciences de Paris – ident: e_1_2_9_25_1 doi: 10.1046/j.1365-2486.1999.00281.x – ident: e_1_2_9_14_1 doi: 10.1111/1365-2435.12309 – volume: 213 start-page: 1 year: 1990 ident: e_1_2_9_22_1 article-title: Modelling bud dormancy release in trees from cool and temperate regions publication-title: Acta Forestalia Fennica – ident: e_1_2_9_12_1 doi: 10.1007/978-94-007-6925-0_15 – ident: e_1_2_9_41_1 doi: 10.5194/bg-9-2063-2012 – ident: e_1_2_9_39_1 doi: 10.18637/jss.v042.i11 – ident: e_1_2_9_59_1 doi: 10.1111/j.1365-2486.2009.01910.x – ident: e_1_2_9_17_1 doi: 10.1002/qj.828 – ident: e_1_2_9_38_1 doi: 10.1111/j.1365-3040.1989.tb01938.x – start-page: 79 volume-title: Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change year: 2007 ident: e_1_2_9_49_1 – ident: e_1_2_9_55_1 doi: 10.1016/S0378-4371(96)00271-3 – ident: e_1_2_9_37_1 doi: 10.1093/treephys/26.9.1165 – ident: e_1_2_9_15_1 doi: 10.1016/j.ecolmodel.2005.10.020 – ident: e_1_2_9_40_1 doi: 10.1111/gcb.13122 – ident: e_1_2_9_50_1 doi: 10.1016/S0168-1923(96)02359-3 – ident: e_1_2_9_7_1 doi: 10.2307/2403139 – ident: e_1_2_9_20_1 doi: 10.1016/j.agrformet.2008.08.013 – ident: e_1_2_9_26_1 doi: 10.1038/nclimate2942 – ident: e_1_2_9_8_1 doi: 10.1111/gcb.13326 – ident: e_1_2_9_34_1 doi: 10.1111/gcb.12360 – ident: e_1_2_9_18_1 doi: 10.1111/j.1365-2486.2006.01311.x – ident: e_1_2_9_42_1 doi: 10.2307/2404093 – ident: e_1_2_9_52_1 doi: 10.1016/j.agrformet.2011.09.009 – volume: 1 start-page: 1 year: 2012 ident: e_1_2_9_48_1 article-title: A New estimate of the average earth surface land temperature spanning 1753 to 2011 publication-title: Geoinformatic & Geostatistics: An Overview – ident: e_1_2_9_47_1 doi: 10.1016/j.agrformet.2012.09.012 – ident: e_1_2_9_29_1 doi: 10.1890/13-0652.1 – ident: e_1_2_9_32_1 doi: 10.1093/oxfordjournals.aob.a084891 – ident: e_1_2_9_10_1 doi: 10.1046/j.1469-8137.1999.00445.x – ident: e_1_2_9_61_1 doi: 10.1016/j.rse.2015.02.003 – ident: e_1_2_9_9_1 doi: 10.1006/jtbi.2000.2178 – ident: e_1_2_9_27_1 doi: 10.2307/2404467 – ident: e_1_2_9_28_1 doi: 10.1111/geb.12206 – ident: e_1_2_9_4_1 doi: 10.1029/2005EO510005 – ident: e_1_2_9_54_1 doi: 10.1890/14-0005.1 – ident: e_1_2_9_21_1 doi: 10.1093/aob/mcv055 – ident: e_1_2_9_46_1 doi: 10.1038/sdata.2018.28 – ident: e_1_2_9_5_1 doi: 10.1016/j.agrformet.2012.05.001 – ident: e_1_2_9_19_1 doi: 10.1016/j.rse.2009.08.016 – ident: e_1_2_9_45_1 doi: 10.1098/rstb.2010.0102 – ident: e_1_2_9_24_1 doi: 10.1029/2008JD010201 – ident: e_1_2_9_58_1 doi: 10.2307/1931815 – volume-title: R: A language and environment for statistical computing year: 2016 ident: e_1_2_9_43_1 – ident: e_1_2_9_56_1 doi: 10.1002/ece3.1273 – ident: e_1_2_9_44_1 doi: 10.1111/j.1365-2486.2011.02562.x – ident: e_1_2_9_33_1 doi: 10.21273/HORTSCI.22.3.371 – ident: e_1_2_9_23_1 doi: 10.14214/sf.313 – ident: e_1_2_9_6_1 doi: 10.1016/j.envsoft.2014.09.005 – ident: e_1_2_9_35_1 doi: 10.1111/nph.12680 – ident: e_1_2_9_13_1 doi: 10.1038/432289a – ident: e_1_2_9_60_1 doi: 10.32614/RJ-2013-002 – ident: e_1_2_9_2_1 doi: 10.1371/journal.pone.0057373 – ident: e_1_2_9_62_1 doi: 10.1016/S0034-4257(02)00135-9 – ident: e_1_2_9_3_1 doi: 10.1016/j.agrformet.2015.11.007 – ident: e_1_2_9_57_1 doi: 10.1007/s00442-009-1363-4 – ident: e_1_2_9_30_1 doi: 10.5194/bgd-11-2305-2014 – ident: e_1_2_9_31_1 doi: 10.2307/2404609 – ident: e_1_2_9_36_1 doi: 10.1006/anbo.1996.0321 – ident: e_1_2_9_51_1 doi: 10.1007/s00484-003-0171-5 – ident: e_1_2_9_11_1 doi: 10.1046/j.1365-3040.1999.00395.x – ident: e_1_2_9_53_1 |
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| Snippet | Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and... 1. Phenology is a first-order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and... Abstract Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and... |
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| SubjectTerms | Albedo Biosphere Climate models Climate system Climatic data Data collection Environmental Sciences Evapotranspiration Life Sciences modelling MODIS land surface phenology PEP725 PhenoCam Phenology r package Reproducibility Surface roughness USA‐NPN Vegetation |
| Title | An integrated phenology modelling framework in r |
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