Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset

• Published in: Scientific Data
• Main Authors: Seyednasrollah, Bijan, Young, Adam M., Hufkens, Koen, Milliman, Tom, Friedl, Mark A., Frolking, Steve, Richardson, Andrew D.
• Other Authors: School of Informatics, Computing, and Cyber Systems (SICCS), Northern Arizona University Flagstaff, Department of Organismic and Evolutionary Biology, Harvard University Cambridge, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Faculty of Bioscience Engineering, Universiteit Gent = Ghent University Belgium (UGENT), University of New Hampshire (UNH), Department of Earth and Environment Boston, Boston University Boston (BU)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2019
• Subjects: [SDV]Life Sciences [q-bio]; [SDE]Environmental Sciences; Tundra
• Online Access: https://hal.inrae.fr/hal-02619925; https://hal.inrae.fr/hal-02619925/document; https://hal.inrae.fr/hal-02619925/file/2019_Seyednasrollah_SD_1.pdf; https://doi.org/10.1038/s41597-019-0229-9

International audience Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9913694