An integrated phenology modelling framework in R
HUFKENS, Koen
Interactions Sol Plante Atmosphère [UMR ISPA]
Department of Applied Ecology and Environmental Biology
Department of Organismic and Evolutionary Biology
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Interactions Sol Plante Atmosphère [UMR ISPA]
Department of Applied Ecology and Environmental Biology
Department of Organismic and Evolutionary Biology
HUFKENS, Koen
Interactions Sol Plante Atmosphère [UMR ISPA]
Department of Applied Ecology and Environmental Biology
Department of Organismic and Evolutionary Biology
Interactions Sol Plante Atmosphère [UMR ISPA]
Department of Applied Ecology and Environmental Biology
Department of Organismic and Evolutionary Biology
RICHARDSON, Andrew D.
Department of Organismic and Evolutionary Biology
School of Informatics, Computing, and Cyber Systems [SICCS]
Northern Arizona University [Flagstaff]
< Reduce
Department of Organismic and Evolutionary Biology
School of Informatics, Computing, and Cyber Systems [SICCS]
Northern Arizona University [Flagstaff]
Language
en
Article de revue
This item was published in
Methods in Ecology and Evolution. 2018, vol. 9, n° 5, p. 1276-1285
Wiley
English Abstract
<strong>1.</strong> 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 ...Read more >
<strong>1.</strong> 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. <strong>2.</strong> 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. <strong>3.</strong> 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. <strong>4.</strong> 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.Read less <
Keywords
phenology
USA-NPN
English Keywords
modelling
MODIS land surface phenology
PEP725
PHENOCAM
R package
Origin
Hal imported