skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing

Abstract

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

Authors:
 [1];  [2];  [3];  [3]
  1. Northern Arizona Univ., Flagstaff, AZ (United States). School of Informatics, Computing and Cyber Systems. Center for Ecosystem Science and Society
  2. National Inst. of Agricultural Research (INRA), Villenave d’Ornon (France)
  3. Univ. of New Hampshire, Durham, NH (United States). Earth Systems Research Center
Publication Date:
Research Org.:
Northern Arizona Univ., Flagstaff, AZ (United States); Univ. of New Hampshire, Durham, NH (United States); National Inst. of Agricultural Research (INRA), Villenave d’Ornon (France)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); National Science Foundation (NSF); United States Geological Survey (USGS); National Research Agency (ANR) (France)
OSTI Identifier:
1499990
Grant/Contract Number:  
SC0016011; EF-1065029; EF-1702697; G10AP00129; G16AC00224; ANR-10-LABX-45
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 8; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; 54 ENVIRONMENTAL SCIENCES; ecological networks; phenology

Citation Formats

Richardson, Andrew D., Hufkens, Koen, Milliman, Tom, and Frolking, Steve. Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing. United States: N. p., 2018. Web. doi:10.1038/s41598-018-23804-6.
Richardson, Andrew D., Hufkens, Koen, Milliman, Tom, & Frolking, Steve. Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing. United States. doi:10.1038/s41598-018-23804-6.
Richardson, Andrew D., Hufkens, Koen, Milliman, Tom, and Frolking, Steve. Mon . "Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing". United States. doi:10.1038/s41598-018-23804-6. https://www.osti.gov/servlets/purl/1499990.
@article{osti_1499990,
title = {Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing},
author = {Richardson, Andrew D. and Hufkens, Koen and Milliman, Tom and Frolking, Steve},
abstractNote = {Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.},
doi = {10.1038/s41598-018-23804-6},
journal = {Scientific Reports},
number = ,
volume = 8,
place = {United States},
year = {2018},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 17 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1. Figure 1.: Climate space spanned by the 128 camera sites included in this analysis. Mean annual temperature and mean annual precipitation are from the WorldClim database. Symbols are colored according to a simplified IGBP land cover classification (Forest = IGBP 1, 2, 4, 5; Grassland and cropland = IGBP 10,more » 12, 14; Savanna = IGBP 8, 9; Shrubland = IGBP 7; Urban = IGBP 13).« less

Save / Share:

Works referenced in this record:

Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century
journal, June 2009

  • Morisette, Jeffrey T.; Richardson, Andrew D.; Knapp, Alan K.
  • Frontiers in Ecology and the Environment, Vol. 7, Issue 5
  • DOI: 10.1890/070217

Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data
journal, May 2013


Optimal Detection of Changepoints With a Linear Computational Cost
journal, September 2012

  • Killick, R.; Fearnhead, P.; Eckley, I. A.
  • Journal of the American Statistical Association, Vol. 107, Issue 500
  • DOI: 10.1080/01621459.2012.737745

Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN)
journal, January 2015


Real-time monitoring and short-term forecasting of land surface phenology
journal, September 2006


Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography
journal, January 2018


PhenoCam Dataset v1.0: Vegetation Phenology from Digital Camera Imagery, 2000-2015
collection, January 2017

  • Richardson, A. D.; Hufkens, K.; Milliman, T.
  • ORNL Distributed Active Archive Center
  • DOI: 10.3334/ORNLDAAC/1511

Near-surface remote sensing of spatial and temporal variation in canopy phenology
journal, September 2009

  • Richardson, Andrew D.; Braswell, Bobby H.; Hollinger, David Y.
  • Ecological Applications, Vol. 19, Issue 6
  • DOI: 10.1890/08-2022.1

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States
journal, August 2017

  • Robinson, Nathaniel; Allred, Brady; Jones, Matthew
  • Remote Sensing, Vol. 9, Issue 8
  • DOI: 10.3390/rs9080863

Digital repeat photography for phenological research in forest ecosystems
journal, January 2012


Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology
journal, February 2012


First operational BRDF, albedo nadir reflectance products from MODIS
journal, November 2002


Use of digital webcam images to track spring green-up in a deciduous broadleaf forest
journal, March 2007


Interpreting canopy development and physiology using a European phenology camera network at flux sites
journal, January 2015


Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat
journal, December 2016

  • Melaas, Eli K.; Sulla-Menashe, Damien; Gray, Josh M.
  • Remote Sensing of Environment, Vol. 186
  • DOI: 10.1016/j.rse.2016.09.014

Very high resolution interpolated climate surfaces for global land areas
journal, January 2005

  • Hijmans, Robert J.; Cameron, Susan E.; Parra, Juan L.
  • International Journal of Climatology, Vol. 25, Issue 15
  • DOI: 10.1002/joc.1276

Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest
journal, April 2018


Monitoring vegetation phenology using an infrared-enabled security camera
journal, September 2014


On the relationship between continuous measures of canopy greenness derived using near-surface remote sensing and satellite-derived vegetation products
journal, December 2017


Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006
journal, October 2009


Using phenocams to monitor our changing Earth: toward a global phenocam network
journal, March 2016

  • Brown, Tim B.; Hultine, Kevin R.; Steltzer, Heidi
  • Frontiers in Ecology and the Environment, Vol. 14, Issue 2
  • DOI: 10.1002/fee.1222

Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem
journal, October 2017

  • Browning, Dawn; Karl, Jason; Morin, David
  • Remote Sensing, Vol. 9, Issue 10
  • DOI: 10.3390/rs9101071

Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics
journal, May 2013

  • Shuai, Yanmin; Schaaf, Crystal; Zhang, Xiaoyang
  • International Journal of Remote Sensing, Vol. 34, Issue 16
  • DOI: 10.1080/01431161.2013.803169

Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements
journal, June 1997


The 2007 Eastern US Spring Freeze: Increased Cold Damage in a Warming World?
journal, March 2008

  • Gu, Lianhong; Hanson, Paul J.; Post, W. Mac
  • BioScience, Vol. 58, Issue 3
  • DOI: 10.1641/B580311

Effects of forest tent caterpillar defoliation on carbon and water fluxes in a boreal aspen stand
journal, May 2018


MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
journal, January 2010

  • Friedl, Mark A.; Sulla-Menashe, Damien; Tan, Bin
  • Remote Sensing of Environment, Vol. 114, Issue 1
  • DOI: 10.1016/j.rse.2009.08.016

Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology
journal, June 2016

  • Elmore, Andrew; Stylinski, Cathlyn; Pradhan, Kavya
  • Remote Sensing, Vol. 8, Issue 6
  • DOI: 10.3390/rs8060502

NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types
journal, February 2018


Warm spring reduced carbon cycle impact of the 2012 US summer drought
journal, April 2016

  • Wolf, Sebastian; Keenan, Trevor F.; Fisher, Joshua B.
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 21
  • DOI: 10.1073/pnas.1519620113

Cross-scalar satellite phenology from ground, Landsat, and MODIS data
journal, August 2007


Monitoring vegetation phenology using MODIS
journal, March 2003


Monitoring multi-layer canopy spring phenology of temperate deciduous and evergreen forests using low-cost spectral sensors
journal, June 2014


Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS
journal, September 2006

  • Ahl, Douglas E.; Gower, Stith T.; Burrows, Sean N.
  • Remote Sensing of Environment, Vol. 104, Issue 1
  • DOI: 10.1016/j.rse.2006.05.003

An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration
journal, August 2011

  • Eklundh, Lars; Jin, Hongxiao; Schubert, Per
  • Sensors, Vol. 11, Issue 8
  • DOI: 10.3390/s110807678

Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies
journal, August 2011

  • Balzarolo, Manuela; Anderson, Karen; Nichol, Caroline
  • Sensors, Vol. 11, Issue 8
  • DOI: 10.3390/s110807954

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery
journal, March 2018

  • Richardson, Andrew D.; Hufkens, Koen; Milliman, Tom
  • Scientific Data, Vol. 5, Issue 1
  • DOI: 10.1038/sdata.2018.28

Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves
journal, February 2017

  • Baumann, Matthias; Ozdogan, Mutlu; Richardson, Andrew D.
  • International Journal of Applied Earth Observation and Geoinformation, Vol. 54
  • DOI: 10.1016/j.jag.2016.09.005

Overview of the radiometric and biophysical performance of the MODIS vegetation indices
journal, November 2002


Integrating Multiscale Seasonal Data for Resource Management
journal, January 2017


Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment
journal, September 2014

  • Keenan, T. F.; Darby, B.; Felts, E.
  • Ecological Applications, Vol. 24, Issue 6
  • DOI: 10.1890/13-0652.1

Ecological impacts of a widespread frost event following early spring leaf-out
journal, May 2012


Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales
journal, May 2017


Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery
journal, January 2014


Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery
journal, June 2018


    Works referencing / citing this record:

    Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves
    journal, February 2017

    • Baumann, Matthias; Ozdogan, Mutlu; Richardson, Andrew D.
    • International Journal of Applied Earth Observation and Geoinformation, Vol. 54
    • DOI: 10.1016/j.jag.2016.09.005

    Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery
    journal, March 2018

    • Richardson, Andrew D.; Hufkens, Koen; Milliman, Tom
    • Scientific Data, Vol. 5, Issue 1
    • DOI: 10.1038/sdata.2018.28

    Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery
    journal, January 2014


    Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales
    journal, May 2017


    Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics
    journal, May 2013

    • Shuai, Yanmin; Schaaf, Crystal; Zhang, Xiaoyang
    • International Journal of Remote Sensing, Vol. 34, Issue 16
    • DOI: 10.1080/01431161.2013.803169

    Ecological impacts of a widespread frost event following early spring leaf-out
    journal, May 2012


    On the relationship between continuous measures of canopy greenness derived using near-surface remote sensing and satellite-derived vegetation products
    journal, December 2017


    Using phenocams to monitor our changing Earth: toward a global phenocam network
    journal, March 2016

    • Brown, Tim B.; Hultine, Kevin R.; Steltzer, Heidi
    • Frontiers in Ecology and the Environment, Vol. 14, Issue 2
    • DOI: 10.1002/fee.1222

    Digital repeat photography for phenological research in forest ecosystems
    journal, January 2012


    An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration
    journal, August 2011

    • Eklundh, Lars; Jin, Hongxiao; Schubert, Per
    • Sensors, Vol. 11, Issue 8
    • DOI: 10.3390/s110807678

    Very high resolution interpolated climate surfaces for global land areas
    journal, January 2005

    • Hijmans, Robert J.; Cameron, Susan E.; Parra, Juan L.
    • International Journal of Climatology, Vol. 25, Issue 15
    • DOI: 10.1002/joc.1276

    Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat
    journal, December 2016

    • Melaas, Eli K.; Sulla-Menashe, Damien; Gray, Josh M.
    • Remote Sensing of Environment, Vol. 186
    • DOI: 10.1016/j.rse.2016.09.014

    Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest
    journal, April 2018


    Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem
    journal, October 2017

    • Browning, Dawn; Karl, Jason; Morin, David
    • Remote Sensing, Vol. 9, Issue 10
    • DOI: 10.3390/rs9101071

    Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies
    journal, August 2011

    • Balzarolo, Manuela; Anderson, Karen; Nichol, Caroline
    • Sensors, Vol. 11, Issue 8
    • DOI: 10.3390/s110807954

    Optimal Detection of Changepoints With a Linear Computational Cost
    journal, September 2012

    • Killick, R.; Fearnhead, P.; Eckley, I. A.
    • Journal of the American Statistical Association, Vol. 107, Issue 500
    • DOI: 10.1080/01621459.2012.737745

    Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography
    journal, January 2018


    Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment
    journal, September 2014

    • Keenan, T. F.; Darby, B.; Felts, E.
    • Ecological Applications, Vol. 24, Issue 6
    • DOI: 10.1890/13-0652.1

    Real-time monitoring and short-term forecasting of land surface phenology
    journal, September 2006


    Near-surface remote sensing of spatial and temporal variation in canopy phenology
    journal, September 2009

    • Richardson, Andrew D.; Braswell, Bobby H.; Hollinger, David Y.
    • Ecological Applications, Vol. 19, Issue 6
    • DOI: 10.1890/08-2022.1

    A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States
    journal, August 2017

    • Robinson, Nathaniel; Allred, Brady; Jones, Matthew
    • Remote Sensing, Vol. 9, Issue 8
    • DOI: 10.3390/rs9080863

    Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery
    journal, June 2018


    Cross-scalar satellite phenology from ground, Landsat, and MODIS data
    journal, August 2007


    Warm spring reduced carbon cycle impact of the 2012 US summer drought
    journal, April 2016

    • Wolf, Sebastian; Keenan, Trevor F.; Fisher, Joshua B.
    • Proceedings of the National Academy of Sciences, Vol. 113, Issue 21
    • DOI: 10.1073/pnas.1519620113

    Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS
    journal, September 2006

    • Ahl, Douglas E.; Gower, Stith T.; Burrows, Sean N.
    • Remote Sensing of Environment, Vol. 104, Issue 1
    • DOI: 10.1016/j.rse.2006.05.003

    Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006
    journal, October 2009


    NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types
    journal, February 2018


    Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology
    journal, February 2012


    MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
    journal, January 2010

    • Friedl, Mark A.; Sulla-Menashe, Damien; Tan, Bin
    • Remote Sensing of Environment, Vol. 114, Issue 1
    • DOI: 10.1016/j.rse.2009.08.016

    Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data
    journal, May 2013


    Monitoring multi-layer canopy spring phenology of temperate deciduous and evergreen forests using low-cost spectral sensors
    journal, June 2014


    Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN)
    journal, January 2015


    Monitoring vegetation phenology using an infrared-enabled security camera
    journal, September 2014


    Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century
    journal, June 2009

    • Morisette, Jeffrey T.; Richardson, Andrew D.; Knapp, Alan K.
    • Frontiers in Ecology and the Environment, Vol. 7, Issue 5
    • DOI: 10.1890/070217

    Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology
    journal, June 2016

    • Elmore, Andrew; Stylinski, Cathlyn; Pradhan, Kavya
    • Remote Sensing, Vol. 8, Issue 6
    • DOI: 10.3390/rs8060502

    Use of digital webcam images to track spring green-up in a deciduous broadleaf forest
    journal, March 2007


    Interpreting canopy development and physiology using a European phenology camera network at flux sites
    journal, January 2015


    Effects of forest tent caterpillar defoliation on carbon and water fluxes in a boreal aspen stand
    journal, May 2018


    Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset
    journal, October 2019


      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.