PhenoCam Dataset v1.0: Vegetation Phenology from Digital Camera Imagery, 2000-2015

Authors: RICHARDSON, A - Northern Arizona University; HUFKENS, K - Harvard University; MILLIMAN, T - University Of New Hampshire; AUBRECHT, D - Harvard University; CHEN, M - Harvard University; GRAY, J - Boston University; JOHNSTON, M - Non ARS Employee; KEENAN, T - Non ARS Employee; KLOSTERMAN, S - Harvard University; KOSMALA, M - Harvard University; MELAAS, E - Boston University; FRIEDL, M - Boston University; FROLKING, S - University Of New Hampshire; ABRAHA, M - Non ARS Employee; ALBER, M - Non ARS Employee; APPLE, M - Non ARS Employee; LAW, B - Non ARS Employee; BLACK, T - Non ARS Employee; BLAKEN, P - Non ARS Employee; Browning, Dawn; BRET-HARTE, S - Non ARS Employee; BRUNSELL, N - University Of Kansas; BURNS, S - Non ARS Employee; CREMONESE, E - Non ARS Employee; DESAI, A - Non ARS Employee; DUNN, A - Non ARS Employee; EISSENSTAT, D - Pennsylvania State University; EUSKIRCHEN, S - Non ARS Employee; FLANAGAN, L - Non ARS Employee; FORSYTHE, B - Non ARS Employee; GALLAGHER, J - Non ARS Employee; GU, L - Non ARS Employee; HOLLINGER, D - Non ARS Employee; JONES, J - Non ARS Employee; KING, J - Non ARS Employee; LANGVALL, O - Non ARS Employee; MCCAUGHEY, J - Queen'S University - Canada; MCHALE, P - Non ARS Employee; MEYER, G - Non ARS Employee; MITCHELL, M - Non ARS Employee; MIGLIAVACCA, M - Max Planck Institute For Biogeochemistry; NESIC, Z - Non ARS Employee; NOORMETS, A - Texas A&M University; NOVICK, K - Indiana University; O'CONNELL, J - Non ARS Employee; OISHI, A - Forest Service (FS); OSWALD, W - Non ARS Employee; PERKINS, T - Non ARS Employee; PHILLIPS, R - Non ARS Employee; SCHWARTZ, M - Non ARS Employee; Scott, Russell - Russ; SONNENTAG, O - University Of Montreal; THOM, J - Non ARS Employee

Submitted to: Ag Data Commons
Publication Type: Other
Publication Acceptance Date: 12/27/2017
Publication Date: 12/27/2017
Citation: Richardson, A.D., Hufkens, K., Milliman, T., Aubrecht, D.M., Chen, M., Gray, J.M., Johnston, M.R., Keenan, T.F., Klosterman, S.T., Kosmala, M., Melaas, E.K., Friedl, M.A., Frolking, S., Abraha, M., Alber, M., Apple, M., Law, B.E., Black, T.A., Blaken, P., Browning, D.M., Bret-Harte, S., Brunsell, N., Burns, S.P., Cremonese, E., Desai, A.R., Dunn, A.L., Eissenstat, D.M., Euskirchen, S.E., Flanagan, L.B., Forsythe, B., Gallagher, J., Gu, L., Hollinger, D.Y., Jones, J.W., King, J., Langvall, O., Mccaughey, J.H., Mchale, P.J., Meyer, G.A., Mitchell, M.J., Migliavacca, M., Nesic, Z., Noormets, A., Novick, K., O'Connell, J., Oishi, A.C., Oswald, W.W., Perkins, T.D., Phillips, R.P., Schwartz, M.D., Scott, R.L., Sonnentag, O., Thom, J.E. 2017. PhenoCam Dataset v1.0: Vegetation Phenology from Digital Camera Imagery, 2000-2015. Ag Data Commons. https://doi.org/10.3334/ORNLDAAC/1511.

Interpretive Summary: Agricultural ecosystems differ in their ability to withstand drought and increases in temperature forecasted changes in climate and climate variability. Data metrics reflecting seasonal patterns in primary production (known as plant phenology) and links to environmental and or soil-related factors that drives those patterns are increasing in importance for sustaining agricultural production. What prompts crops and other vegetation to initiate growth? Are environmental drivers changing? If so, how might we adapt agricultural production to meet growing demands amidst increasing variability in climate? Standardized data metrics from widely distributed observations offer an approach to answering these questions. Digital cameras mounted on towers (hereafter “phenocams) provide detailed information about vegetation greenness. Data derived from phenocam images (e.g., dates for start of growth and start of senescence) fill a gap for agro-ecosystems nationwide and facilitate linkages between pasture-level field observations of crop status and landscape greenness and metrics derived from satellite remote sensing. The dataset published herein spans 133 sites across diverse ecosystems from 2000-2015 and provides data that can be used by scientists, land owners, and decision makers in conjunction with gridded environmental data products to better understand how ecosystems will respond to prolonged drought and/or changes in temperature, thereby informing management decisions.

Technical Abstract: This data set provides a time series of vegetation phenological observations for 133 sites across diverse ecosystems of North America and Europe from 2000-2015. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site. From each acquired image, RGB (red, green, blue) color channel information was extracted and means and other statistics calculated for a region-of-interest (ROI) that delineates an area of specific vegetation type. From the high-frequency (typically, 30 minute) imagery collected over several years, time series characterizing vegetation color, including canopy greenness, plus greenness rising and greenness falling transition dates, were summarized over 1- and 3-day intervals. The data products, consisting of almost 750 site-years of observations, can be used for phenological model validation and development, evaluation of satellite remote sensing data products, to understand relationships between canopy phenology and ecosystem processes, to study the seasonal changes in leaf-level physiology that are associated with changes in leaf color, for benchmarking earth system models, and for studies of climate change impacts on terrestrial ecosystems.