PhenoCam Dataset v3.0: Digital Camera Imagery from the PhenoCam Network, 2000-2023

This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 738 sites across diverse ecosystems of the world (mostly North America) from 2000-2023. Vegetation types such as deciduous broadleaf forests, grasslands, evergreen needleleaf forests, and agriculture are the best-represented. The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v3.0. The images are provided in JPEG format organized in compressed tar.gz archives by site and date. Site locations and basic metadata are included in a GeoJSON file.

There are 49,462 files included in this dataset; 49,461 tar.gz files holding PhenoCam images, in JPEG (*.jpg) format, are packaged by Phenocam site, year, and month. There is also one GeoJSON file holding locations and basic information about each site. 

This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 738 sites across diverse ecosystems of the world (mostly North America) from 2000-2023. Vegetation types such as deciduous broadleaf forests, grasslands, evergreen needleleaf forests, and agriculture are the best-represented.The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v3.0. The images are provided in JPEG format organized in compressed tar.gz archives by site and date.  Site locations and basic metadata are included in a GeoJSON file.

Related Publications:

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.  2018. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data 180028. DOI: https://doi.org/10.1038/sdata.2018.28

• This paper provides an in-depth description of the methods used for PhenoCam image analysis and data processing. Users are encouraged to cite this paper along with the current dataset.

Seyednasrollah, B., A.M. Young, K. Hufkens, T. Milliman, M.A. Friedl, S. Frolking, and A.D. Richardson. 2019. Tracking vegetation phenology across diverse biomes using PhenoCam imagery: The PhenoCam Dataset v2.0. Scientific Data 6:222. https://doi.org/10.1038/s41597-019-0229-9

Young, A.M., T. Milliman, K. Hufkens, K. Ballou, C. Coffey, K. Begay, M. Fell, M. Javadian, A.K. Post, C. Schädel, Z. Vladich, O. Zimmerman, D.M. Browning, C.R. Florian, M.A. Friedl, M. Moon, M.D. SanClements, B. Seyednasrollah, and A.D. Richardson. 2025. Tracking vegetation phenology across diverse biomes using PhenoCam imagery: The PhenoCam Dataset v3.0. Submitted to Earth System Science Data.

Related Dataset: 

Seyednasrollah, B., et al. 2019. PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1674

• Version 2 of this dataset.

Zimmerman, O., et.al. 2024. PhenoCam Dataset v3.0: Vegetation Phenology from Digital Camera Imagery, 2000-2023. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2389

• Zimmerman et al. (2024) provides measures of vegetation phenology derived from images in this dataset.

Acknowledgments:

The development of PhenoCam has been supported by the Northeastern States Research Cooperative, the National Science Foundation, the Long-Term Agroecosystem Research (LTAR) network supported by the United States Department of Agriculture, the National Ecological Observatory Network (NEON) program sponsored by the National Science Foundation, the U.S.Department of Energy, Oak Ridge National Laboratory, the U.S. National Park Service Inventory and Monitoring Program, the USA National Phenology Network, and the North Central Climate Science Center of the United States Geological Survey.

Spatial Coverage: Multiple points mostly over North America, Europe, and other continents

Spatial Resolution: Point

Temporal Resolution: Variable. Generally every 30 minutes, but in some cases only once-daily.

Temporal Coverage: 2000-02-01 to 2023-12-31

Spatial Extent: (All latitude and longitude given in decimal degrees)

Sites	Westernmost Longitude	Easternmost Longitude	Northernmost Latitude	Southernmost Latitude
Global	-158.1701	172.3502	71.2825	-45.0384

Data File Information

There are 49,462 files included in this dataset; 49,461 tar.gz files holding PhenoCam images, in JPEG (*.jpg) format, are packaged by Phenocam site, year, and month. There is also one GeoJSON file holding locations and basic information about each site. 

The naming convention for *.tar.gz files is <site>_<yyyy>_<mm>.tar.gz, where 

• <site> = site name
• <yyyy> = year
• <mm> = month 

Example file name for data from the ARM site in Oklahoma from May 2004: armoklahoma_2004_05.tar.gz

Once uncompressed, each tar.gz file contains a series of nested directories for the site, year, and month, and then the series of JPEG (*.jpg) visible-wavelength images for that month.

The visible-wavelength jpg images are named as:  <site>_<yyyy>_<mm>_<dd>_hhmmss.jpg, where

• <dd> = day
• <hhmmss> = time in the local standard time at the site

For sites using the StarDot NetCam SC IR camera and following the standard PhenoCam configuration protocol, the tar.gz file also includes a series of monochrome JPEG (*.jpg) visible+infrared images, as well as metadata files (flat text) paired with each image. 

The visible+infrared jpg images are named  <site>_IR_<yyyy>_<mm>_<dd>_hhmmss.jpg.

The metadata files for visible-wavelength images are named  <site>_<yyyy>_<mm>_<dd>_hhmmss.meta.

The  metadata files for visible+infrared images are named <site>_IR_<yyyy>_<mm>_<dd>_hhmmss.meta.

(See the related dataset at https://doi.org/10.3334/ORNLDAAC/2389 for information on local time offset from UTC time.)

The locations of PhenoCam sites are included as point locations in a GeoJSON file: phenocam_images_sites_v3.geojson. 

Table 1: Point attributes for PhenoCam locations in phenocam_sites_v3.geojson.

Attribute	Units	Description
sitename	-	the name of the camera site, e.g. “coweeta,” used to designate all images and products associated with site
long_name	-	a more descriptive name for each camera site
lat	degrees north	the latitude (in decimal degrees) of the camera itself
lon	degrees east	the longitude (in decimal degrees) of the camera itself
elevation	m	the elevation of the ground surface (m above sea level) at the camera site
date_start	YYYY-MM-DD	the date of the first image in the archive for this site
date_end	YYYY-MM-DD	the date of the last image in the archive for this site
site_years	y	number years site has operated
site_type	-	the site class (Type I, II, or III)
infrared_enabled	-	whether camera is infrared enabled: Yes (Y), No (N)
utc_offset	h	difference between UTC time and local standard time at site
camera_description	-	the brand and model of the camera being used
camera_orientation	-	the compass direction in which the camera is pointing
flux_data	-	“True” if eddy covariance flux measurements were made at the site
flux_networks	-	name of flux network if the site belongs to a network (e.g., AmeriFlux, Fluxnet-Canada)
flux_sitenames	-	FLUXNET site code, if applicable
ecoregion	-	numeric code identifying the site’s EPA Ecoregion
MAP_site	mm	mean annual precipitation as reported by site personnel
MAP_daymet	mm	mean annual precipitation from the Daymet
MAP_worldclim	mm	mean annual precipitation from WorldClim
MAT_site	degree C	mean annual temperature as reported by site personnel
MAT_daymet	degree C	mean annual temperature from the Daymet
MAT_worldclim	degree C	mean annual temperature from WorldClim
primary_veg_type	-	abbreviation for the primary vegetation type at the site (see Table 2)
secondary_veg_type	-	abbreviation for the secondary vegetation type at the site (see Table 2)
dominant_species	-	Latin binomials for the dominant species at each site, as reported by site personnel
landcover_igbp	-	numeric code corresponding to the land cover classification scheme of the International Geosphere-Biosphere Programme, derived from MODIS imagery (Friedl et al., 2010; Channan et al., 2014)
wwf_biome	-	numeric code corresponding to the biome classification scheme of the World Wildlife Fund (Olson et al., 2001)
koeppen_geiger	-	climate classification according to the Köppen-Geiger system (Kottek et al, 2006)

Table 2. Vegetation types for PhenoCam sites. Site-years and number of sites pertains to the primary vegetation type at sites.

Abbreviation	Description	Site-years	Number of sites
AG	agriculture	602.6	136
DB	deciduous broadleaf	1204.1	168
DN	deciduous needleleaf	12.4	1
EB	evergreen broadleaf	116.6	25
EN	evergreen needleleaf	608.5	100
GR	grassland	710.6	138
NV	non-vegetated	1.4	1
SH	shrubs	297.2	61
TN	tundra	144.9	18
UN	forest understory	14.2	3
WL	wetland	162.9	31
Total		4172.2	738

Data derived from PhenoCam imagery can be used for phenological model validation and development, evaluation of satellite remote sensing data products, understanding relationships between canopy phenology and ecosystem processes, study of the seasonal changes in leaf-level physiology that are associated with changes in leaf color, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems (Richardson et al., 2018; Seyednasrollah et al. 2019).

Quality assurance was conducted by quantitative analysis through automated quality control routines (e.g. filtering and outlier detection) and a visual evaluation of each time series to examine consistency and overall quality (Richardson et al., 2018; Seyednasrollah et al. 2019; Young et al., 2025).

PhenoCam Network

The PhenoCam network is a cooperative network, established in 2008, that uses digital camera imagery to monitor ecosystem dynamics over time. It serves as a long-term, continental-scale, phenological observatory with cameras deployed within North America, from Alaska to Texas, and from Maine to Hawaii, and some on other continents.

The data included here are visible-wavelength, automated digital camera imagery from 738 camera sites, together totaling 4172.2 site-years of data across different ecoregions, climate zones, and vegetation types. Vegetation types such as deciduous broadleaf forests (1204 site-years of data in the dataset), grasslands (710 site-years), evergreen needleleaf forests (608 site-years), and agriculture (603 site-years) are the best-represented.

Figure 2. Global distribution of PhenoCam sites represented in this dataset. There are 624 sites in North America, 85 in Europe, 7 in Oceania, 3 in South America, 2 in Africa, and 2 in Asia.

Image Analysis for Phenology

These images were used to track vegetation phenology (Richardson et al., 2018) and results are available in Zimmerman et al. (2024). For each image, summary statistics for RGB (red, green, blue) color channels were calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 minute) imagery, time series characterizing vegetation color, including canopy greenness (canopy greenness index -- the green chromatic coordinate, Gcc) and red chromatic coordinate (Rcc) were derived for 1- and 3-day intervals. For cameras recording visible+infrared images, the cameraNDVI index is calculated using methods described in Young et al. (2025).

See Seyednasrollah et al. (2019a, 2019b),  Zimmerman et al. (2024), and Young et al. (2025) for more details.

Channan, S., Collins, K. and Emanuel, W.R., 2014. Global mosaics of the standard MODIS land cover type data. University of Maryland and the Pacific Northwest National Laboratory, College Park, Maryland, USA, 30.

Friedl, M.A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168-182. https://doi.org/10.1016/j.rse.2009.08.016

Kottek, M., Grieser, J., Beck, C., Rudolf, B. and Rubel, F., 2006. World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift 15:259-263. https://doi.org/10.1127/0941-2948/2006/0130

Richardson, A.D., K. Hufkens, T. Milliman, D.M. Aubrecht, M. Chen, J.M. Gray, M. R. Johnston, T.F. Keenan, S.T. Klosterman, M. Kosmala, E.K. Melaas, M.A. Friedl, and S. Frolking. 2018. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data 5:80028. https://doi.org/10.1038/sdata.2018.28

Seyednasrollah, B., A.M. Young, K. Hufkens, T. Milliman, M.A. Friedl, S. Frolking, and A.D. Richardson. 2019a. Tracking vegetation phenology across diverse biomes using PhenoCam imagery: The PhenoCam Dataset v2.0. Scientific Data 6:222. https://doi.org/10.1038/s41597-019-0229-9

Seyednasrollah, B., A.M. Young, K. Hufkens, T. Milliman, M.A. Friedl, S. Frolking, A.D. Richardson, M. Abraha, D.W. Allen, M. Apple, M.A. Arain, J. Baker, J.M. Baker, D. Baldocchi, C.J. Bernacchi, J. Bhattacharjee, P. Blanken, D.D. Bosch, R. Boughton, E.H. Boughton, R.F. Brown, D.M. Browning, N. Brunsell, S.P. Burns, M. Cavagna, H. Chu, P.E. Clark, B.J. Conrad, E. Cremonese, D. Debinski, A.R. Desai, R. Diaz-Delgado, L. Duchesne, A.L. Dunn, D.M. Eissenstat, T. El-Madany, D.S.S. Ellum, S.M. Ernest, A. Esposito, L. Fenstermaker, L.B. Flanagan, B. Forsythe, J. Gallagher, D. Gianelle, T. Griffis, P. Groffman, L. Gu, J. Guillemot, M. Halpin, P.J. Hanson, D. Hemming, A.A. Hove, E.R. Humphreys, A. Jaimes-Hernandez, A.A. Jaradat, J. Johnson, E. Keel, V.R. Kelly, J.W. Kirchner, P.B. Kirchner, M. Knapp, M. Krassovski, O. Langvall, G. Lanthier, G.l. Maire, E. Magliulo, T.A. Martin, B. McNeil, G.A. Meyer, M. Migliavacca, B.P. Mohanty, C.E. Moore, R. Mudd, J.W. Munger, Z.E. Murrell, Z. Nesic, H.S. Neufeld, T.L. O'Halloran, W. Oechel, A.C. Oishi, W.W. Oswald, T.D. Perkins, M.L. Reba, B. Rundquist, B.R. Runkle, E.S. Russell, E.J. Sadler, A. Saha, N.Z. Saliendra, L. Schmalbeck, M.D. Schwartz, R.L. Scott, E.M. Smith, O. Sonnentag, P. Stoy, S. Strachan, K. Suvocarev, J.E. Thom, R.Q. Thomas, A.K. Van den berg, R. Vargas, J. Verfaillie, C.S. Vogel, J.J. Walker, N. Webb, P. Wetzel, S. Weyers, A.V. Whipple, T.G. Whitham, G. Wohlfahrt, J.D. Wood, S. Wolf, J. Yang, X. Yang, G. Yenni, Y. Zhang, Q. Zhang, and D. Zona. 2019b. PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1674

Young, A.M., T. Milliman, K. Hufkens, K. Ballou, C. Coffey, K. Begay, M. Fell, M. Javadian, A.K. Post, C. Schädel, Z. Vladich, O. Zimmerman, D.M. Browning, C.R. Florian, M.A. Friedl, M. Moon, M.D. SanClements, B. Seyednasrollah, and A.D. Richardson. 2025. Tracking vegetation phenology across diverse biomes using PhenoCam imagery: The PhenoCam Dataset v3.0. Submitted to Earth System Science Data. 

Zimmerman, O., A.M. Young, T. Milliman, K. Hufkens, K. Ballou, C. Coffey, K. Begay, M. Fell, M. Javadian, A.K. Post, C. Schadel, Z. Vladich, D.M. Browning, C.R. Florian, M.A. Friedl, M. Moon, M.D. SanClements, B. Seyednasrollah, A.D. Richardson, M. Abraha, M. Alber, D.W. Allen, M. Apple, M.A. Arain, A.C. Axel, P.H.J. Badiou, M. Bahn, J. Baker, D. Baldocchi, S.L.J. Bayliss, N. Behrens, C.M. Beier, C.J. Bernacchi, D. Berveiller, J. Bhattacharjee, S.C. Biraud, J.J. Blais, P. Blanken, M. Bonfim, D.D. Bosch, R. Boughton, E.H. Boughton, R.F. Brown, N. Brunsell, S.P. Burns, M.S. Carbone, B.R. Carlson, A. Carrara, M. Cavagna, W.S. Chan, J. Chen, A. Christen, N. Chrysoulakis, H. Chu, P.E. Clark, B.J. Conrad, M.R. Cortese, R.M. Crandall, E. Cremonese, H. Crepaz, F.W. Davis, D. Debinski, A.R. Desai, M. Detto, R. Diaz-Delgado, L. Duchesne, A. Dunn, K.J. Easterday, D.M. Eissenstat, T. El-madany, D.S.S. Ellum, S.K.M. Ernest, A. Esposito, E.S. Euskirchen, L. Fenstermaker, L.F. Filchev, J.M. Fill, L.B. Flanagan, B. Forsythe, J.M. Frank, A.L. Freestone, J. Gallagher, M. García , D. Garcia-diaz, M. Gharun, D. Gianelle, J.M. Gonet, S.C. Goslee, T. Griffis, P. Groffman, L. Gu, J. Guillemot, I.M. Halm, M. Halpin, A. Hammerle, P.J. Hanson, C.V. Hanson, Z. Harlow, K.M. Hart, C.E. Hatch, P.L. Heinrich, D. Hemming, M. Heskel, A.C. Hill, A.A. Hove, T. Hubner, D. Hui, E.R. Humphreys, P.C. Inkenbrandt, A. Jaimes-hernandez, R. Jensen, R.L. Jiménez, J. Johnson, B.G. Johnson, L.S.K. Joseph, E. Keel, V.R. Kelly, K.W. King, J.W. Kirchner, P.B. Kirchner, M.A. Kline, S.H. Knox, M. Krassovski, K.L. Ladig, P.E. Laerke, O. Langvall, G. Lanthier, N.K. Lany, G.l. Maire, H. Liu, Y. Luo, A. Manco, J. Marrs, T.A. Martin, M. Mauritz, G. McCarty, B. McNeil, F. Meier, M. Mensinger, G. Meyer, M. Migliavacca, T. Miura, B.P. Mohanty, C.E. Moore, R. Mudd, J.W. Munger, Z.E. Murrell, Z. Nesic, H.S. Neufeld, J. Norris, J. O'Connell, T.L. O'Halloran, W. Oechel, A.C. Oishi, B. Oryan, W.W. Oswald, D.M.P. Peltier, J. Perez-Quezada, T.D. Perkins, C. Phillips, M. Pizarro, D. Poursanidis, J.W.M. Pullens, P. Ramanamandimby, M.P. Rao, R.L. Ray, M.L. Reba, C. Redmond, A. Reinmann, D.A. Roberts, G.P. Robertson, M. Rodriguez-Caton, K. Rumora, B.C. Rundquist, B.R.K. Runkle, T. Sachs, E.J. Sadler, A. Saha, N.Z. Saliendra, T.S. Sankey, H. Scheifinger, L. Schmalbeck, M.R. Schuppenhauer, M.D. Schwartz, R.L. Scott, S.D. Sebestyen, D. Smale, E.M. Smith, J. Smith, O. Sonnentag, P. Stoy, S. Strachan, K. Suvocarev, D. Szutu, F. Tekinay, J.E. Thom, R.Q. Thomas, C. Trauernicht, D. Trejo, D. Tsirantonakis, A. Valero, A.K. Van den berg, M.K. Van der molen, R. Vargas, M. Vellend, J. Verfaillie, C.S. Vogel, A. Vrieling, J.J. Walker, J.R. Ward, D.A. Watts, N. Webb, P. Wetzel, S. Weyers, A.V. Whipple, C.W. Whippo, W.A. White, T.G. Whitham, C. Wille, A. Wingler, G. Wohlfahrt, S. Wolf, J.D. Wood, A.L. Woodley, J. Yang, X. Yang, G. Yenni, A.J. Zambrano-olimpo, A. Zandonai, Y. Zhang, and D. Zona. 2025. PhenoCam Dataset v3.0: Vegetation Phenology from Digital Camera Imagery, 2000-2023. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2389