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

Richardson, Andrew D.; Hufkens, Koen; Milliman, Tom; Frolking, Steve

doi:10.1038/s41598-018-23804-6

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

Journal Article · 09 April 2018 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-018-23804-6 · OSTI ID:1499990

Richardson, Andrew D. ^[1]; Hufkens, Koen ^[2]; Milliman, Tom ^[3]; Frolking, Steve ^[3]

Northern Arizona Univ., Flagstaff, AZ (United States). School of Informatics, Computing and Cyber Systems. Center for Ecosystem Science and Society
National Inst. of Agricultural Research (INRA), Villenave d’Ornon (France)
Univ. of New Hampshire, Durham, NH (United States). Earth Systems Research Center

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.

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Research Organization:: 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 Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); United States Geological Survey (USGS); National Research Agency (ANR) (France)

Grant/Contract Number:: SC0016011; EF-1065029; EF-1702697; G10AP00129; G16AC00224; ANR-10-LABX-45

OSTI ID:: 1499990

Journal Information:: Scientific Reports, Vol. 8; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (79)

Real-time monitoring and short-term forecasting of land surface phenology White, Michael A.; Nemani, Ramakrishna R. Remote Sensing of Environment, Vol. 104, Issue 1 https://doi.org/10.1016/j.rse.2006.04.014	journal	September 2006
Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations Peng, Dailiang; Wu, Chaoyang; Li, Cunjun Ecological Indicators, Vol. 77 https://doi.org/10.1016/j.ecolind.2017.02.024	journal	June 2017
PhenoCam Dataset v1.0: Vegetation Phenology from Digital Camera Imagery, 2000-2015 Richardson, A. D.; Hufkens, K.; Milliman, T. ORNL Distributed Active Archive Center https://doi.org/10.3334/ORNLDAAC/1511	collection	January 2017
A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States Robinson, Nathaniel; Allred, Brady; Jones, Matthew Remote Sensing, Vol. 9, Issue 8 https://doi.org/10.3390/rs9080863	journal	August 2017
Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology Hufkens, Koen; Friedl, Mark; Sonnentag, Oliver Remote Sensing of Environment, Vol. 117 https://doi.org/10.1016/j.rse.2011.10.006	journal	February 2012
First operational BRDF, albedo nadir reflectance products from MODIS Schaaf, Crystal B.; Gao, Feng; Strahler, Alan H. Remote Sensing of Environment, Vol. 83, Issue 1-2 https://doi.org/10.1016/S0034-4257(02)00091-3	journal	November 2002
Interpreting canopy development and physiology using a European phenology camera network at flux sites Wingate, L.; Ogée, J.; Cremonese, E. Biogeosciences, Vol. 12, Issue 20 https://doi.org/10.5194/bg-12-5995-2015	journal	January 2015
Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat Melaas, Eli K.; Sulla-Menashe, Damien; Gray, Josh M. Remote Sensing of Environment, Vol. 186 https://doi.org/10.1016/j.rse.2016.09.014	journal	December 2016
Very high resolution interpolated climate surfaces for global land areas Hijmans, Robert J.; Cameron, Susan E.; Parra, Juan L. International Journal of Climatology, Vol. 25, Issue 15 https://doi.org/10.1002/joc.1276	journal	January 2005
Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest Bowling, David R.; Logan, Barry A.; Hufkens, Koen Agricultural and Forest Meteorology, Vol. 252 https://doi.org/10.1016/j.agrformet.2018.01.025	journal	April 2018
Monitoring vegetation phenology using an infrared-enabled security camera Petach, Anika R.; Toomey, Michael; Aubrecht, Donald M. Agricultural and Forest Meteorology, Vol. 195-196 https://doi.org/10.1016/j.agrformet.2014.05.008	journal	September 2014
On the relationship between continuous measures of canopy greenness derived using near-surface remote sensing and satellite-derived vegetation products Brown, Luke A.; Dash, Jadunandan; Ogutu, Booker O. Agricultural and Forest Meteorology, Vol. 247 https://doi.org/10.1016/j.agrformet.2017.08.012	journal	December 2017
Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem Browning, Dawn; Karl, Jason; Morin, David Remote Sensing, Vol. 9, Issue 10 https://doi.org/10.3390/rs9101071	journal	October 2017
Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics Shuai, Yanmin; Schaaf, Crystal; Zhang, Xiaoyang International Journal of Remote Sensing, Vol. 34, Issue 16 https://doi.org/10.1080/01431161.2013.803169	journal	May 2013
The 2007 Eastern US Spring Freeze: Increased Cold Damage in a Warming World? Gu, Lianhong; Hanson, Paul J.; Post, W. Mac BioScience, Vol. 58, Issue 3 https://doi.org/10.1641/B580311	journal	March 2008
Effects of forest tent caterpillar defoliation on carbon and water fluxes in a boreal aspen stand Stephens, Jilmarie J.; Black, T. Andrew; Jassal, Rachhpal S. Agricultural and Forest Meteorology, Vol. 253-254 https://doi.org/10.1016/j.agrformet.2018.01.035	journal	May 2018
MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets Friedl, Mark A.; Sulla-Menashe, Damien; Tan, Bin Remote Sensing of Environment, Vol. 114, Issue 1 https://doi.org/10.1016/j.rse.2009.08.016	journal	January 2010
NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types Filippa, Gianluca; Cremonese, Edoardo; Migliavacca, Mirco Agricultural and Forest Meteorology, Vol. 249 https://doi.org/10.1016/j.agrformet.2017.11.003	journal	February 2018
Warm spring reduced carbon cycle impact of the 2012 US summer drought Wolf, Sebastian; Keenan, Trevor F.; Fisher, Joshua B. Proceedings of the National Academy of Sciences, Vol. 113, Issue 21 https://doi.org/10.1073/pnas.1519620113	journal	April 2016
Monitoring vegetation phenology using MODIS Zhang, Xiaoyang; Friedl, Mark A.; Schaaf, Crystal B. Remote Sensing of Environment, Vol. 84, Issue 3 https://doi.org/10.1016/S0034-4257(02)00135-9	journal	March 2003
Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS Ahl, Douglas E.; Gower, Stith T.; Burrows, Sean N. Remote Sensing of Environment, Vol. 104, Issue 1 https://doi.org/10.1016/j.rse.2006.05.003	journal	September 2006
An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration Eklundh, Lars; Jin, Hongxiao; Schubert, Per Sensors, Vol. 11, Issue 8 https://doi.org/10.3390/s110807678	journal	August 2011
Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies Balzarolo, Manuela; Anderson, Karen; Nichol, Caroline Sensors, Vol. 11, Issue 8 https://doi.org/10.3390/s110807954	journal	August 2011
Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery Richardson, Andrew D.; Hufkens, Koen; Milliman, Tom Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.28	journal	March 2018
Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves Baumann, Matthias; Ozdogan, Mutlu; Richardson, Andrew D. International Journal of Applied Earth Observation and Geoinformation, Vol. 54 https://doi.org/10.1016/j.jag.2016.09.005	journal	February 2017
Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century Morisette, Jeffrey T.; Richardson, Andrew D.; Knapp, Alan K. Frontiers in Ecology and the Environment, Vol. 7, Issue 5 https://doi.org/10.1890/070217	journal	June 2009
Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data Melaas, Eli K.; Friedl, Mark A.; Zhu, Zhe Remote Sensing of Environment, Vol. 132 https://doi.org/10.1016/j.rse.2013.01.011	journal	May 2013
Optimal Detection of Changepoints With a Linear Computational Cost Killick, R.; Fearnhead, P.; Eckley, I. A. Journal of the American Statistical Association, Vol. 107, Issue 500 https://doi.org/10.1080/01621459.2012.737745	journal	September 2012
Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN) Nasahara, Kenlo Nishida; Nagai, Shin Ecological Research, Vol. 30, Issue 2 https://doi.org/10.1007/s11284-014-1239-x	journal	January 2015
Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography Klosterman, Stephen; Melaas, Eli; Wang, Jonathan A. Agricultural and Forest Meteorology, Vol. 248 https://doi.org/10.1016/j.agrformet.2017.10.015	journal	January 2018
Near-surface remote sensing of spatial and temporal variation in canopy phenology Richardson, Andrew D.; Braswell, Bobby H.; Hollinger, David Y. Ecological Applications, Vol. 19, Issue 6 https://doi.org/10.1890/08-2022.1	journal	September 2009
Digital repeat photography for phenological research in forest ecosystems Sonnentag, Oliver; Hufkens, Koen; Teshera-Sterne, Cory Agricultural and Forest Meteorology, Vol. 152 https://doi.org/10.1016/j.agrformet.2011.09.009	journal	January 2012
Use of digital webcam images to track spring green-up in a deciduous broadleaf forest Richardson, Andrew D.; Jenkins, Julian P.; Braswell, Bobby H. Oecologia, Vol. 152, Issue 2 https://doi.org/10.1007/s00442-006-0657-z	journal	March 2007
Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 White, Michael A.; de BEURS, Kirsten M.; Didan, Kamel Global Change Biology, Vol. 15, Issue 10 https://doi.org/10.1111/j.1365-2486.2009.01910.x	journal	October 2009
Using phenocams to monitor our changing Earth: toward a global phenocam network Brown, Tim B.; Hultine, Kevin R.; Steltzer, Heidi Frontiers in Ecology and the Environment, Vol. 14, Issue 2 https://doi.org/10.1002/fee.1222	journal	March 2016
Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements Moulin, S.; Kergoat, L.; Viovy, N. Journal of Climate, Vol. 10, Issue 6 https://doi.org/10.1175/1520-0442(1997)010<1154:GSAOVP>2.0.CO;2	journal	June 1997
Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology Elmore, Andrew; Stylinski, Cathlyn; Pradhan, Kavya Remote Sensing, Vol. 8, Issue 6 https://doi.org/10.3390/rs8060502	journal	June 2016
Cross-scalar satellite phenology from ground, Landsat, and MODIS data Fisher, Jeremy I.; Mustard, John F. Remote Sensing of Environment, Vol. 109, Issue 3 https://doi.org/10.1016/j.rse.2007.01.004	journal	August 2007
Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery Zhang, Xiaoyang; Jayavelu, Senthilnath; Liu, Lingling Agricultural and Forest Meteorology, Vol. 256-257 https://doi.org/10.1016/j.agrformet.2018.03.003	journal	June 2018
Monitoring multi-layer canopy spring phenology of temperate deciduous and evergreen forests using low-cost spectral sensors Ryu, Youngryel; Lee, Galam; Jeon, Soohyun Remote Sensing of Environment, Vol. 149 https://doi.org/10.1016/j.rse.2014.04.015	journal	June 2014
Overview of the radiometric and biophysical performance of the MODIS vegetation indices Huete, A.; Didan, K.; Miura, T. Remote Sensing of Environment, Vol. 83, Issue 1-2 https://doi.org/10.1016/S0034-4257(02)00096-2	journal	November 2002
Integrating Multiscale Seasonal Data for Resource Management Richardson, Andrew; Weltzin, Jake; Morisette, Jeffrey Eos https://doi.org/10.1029/2017EO065709	journal	January 2017
Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment Keenan, T. F.; Darby, B.; Felts, E. Ecological Applications, Vol. 24, Issue 6 https://doi.org/10.1890/13-0652.1	journal	September 2014
Ecological impacts of a widespread frost event following early spring leaf-out Hufkens, Koen; Friedl, Mark A.; Keenan, Trevor F. Global Change Biology, Vol. 18, Issue 7 https://doi.org/10.1111/j.1365-2486.2012.02712.x	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 Liu, Yan; Hill, Michael J.; Zhang, Xiaoyang Agricultural and Forest Meteorology, Vol. 237-238 https://doi.org/10.1016/j.agrformet.2017.02.026	journal	May 2017
Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery Klosterman, S. T.; Hufkens, K.; Gray, J. M. Biogeosciences, Vol. 11, Issue 16 https://doi.org/10.5194/bg-11-4305-2014	journal	January 2014
Evaluation of regression procedures for methods comparison studies Linnet, K. Clinical Chemistry, Vol. 39, Issue 3 https://doi.org/10.1093/clinchem/39.3.424	journal	March 1993
Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies Balzarolo, Manuela; Anderson, Karen; Nichol, Caroline Sensors, Vol. 11, Issue 8 https://doi.org/10.3390/s11087954	journal	August 2011
Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 White, M. A.; De Beurs, K. M.; Didan, K. Wiley-Blackwell https://doi.org/10.5167/uzh-19473	text	January 2009
Interpreting canopy development and physiology using a European phenology camera network at flux sites Wingate, Lisa; Ogée, Jérôme; Cremonese, Edoardo ETH Zurich https://doi.org/10.3929/ethz-b-000106268	text	January 2015
Optimal detection of changepoints with a linear computational cost Killick, R.; Fearnhead, P.; Eckley, I. A. arXiv https://doi.org/10.48550/arxiv.1101.1438	text	January 2011
Using phenocams to monitor our changing Earth: toward a global phenocam network Brown, Tim B.; Hultine, Kevin R.; Steltzer, Heidi Frontiers in Ecology and the Environment, Vol. 14, Issue 2 https://doi.org/10.1002/fee.1222	journal	March 2016
Use of digital webcam images to track spring green-up in a deciduous broadleaf forest Richardson, Andrew D.; Jenkins, Julian P.; Braswell, Bobby H. Oecologia, Vol. 152, Issue 2 https://doi.org/10.1007/s00442-006-0657-z	journal	March 2007
Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN) Nasahara, Kenlo Nishida; Nagai, Shin Ecological Research, Vol. 30, Issue 2 https://doi.org/10.1007/s11284-014-1239-x	journal	January 2015
Monitoring vegetation phenology using an infrared-enabled security camera Petach, Anika R.; Toomey, Michael; Aubrecht, Donald M. Agricultural and Forest Meteorology, Vol. 195-196 https://doi.org/10.1016/j.agrformet.2014.05.008	journal	September 2014
On the relationship between continuous measures of canopy greenness derived using near-surface remote sensing and satellite-derived vegetation products Brown, Luke A.; Dash, Jadunandan; Ogutu, Booker O. Agricultural and Forest Meteorology, Vol. 247 https://doi.org/10.1016/j.agrformet.2017.08.012	journal	December 2017
Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography Klosterman, Stephen; Melaas, Eli; Wang, Jonathan A. Agricultural and Forest Meteorology, Vol. 248 https://doi.org/10.1016/j.agrformet.2017.10.015	journal	January 2018
NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types Filippa, Gianluca; Cremonese, Edoardo; Migliavacca, Mirco Agricultural and Forest Meteorology, Vol. 249 https://doi.org/10.1016/j.agrformet.2017.11.003	journal	February 2018
Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest Bowling, David R.; Logan, Barry A.; Hufkens, Koen Agricultural and Forest Meteorology, Vol. 252 https://doi.org/10.1016/j.agrformet.2018.01.025	journal	April 2018
Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations Peng, Dailiang; Wu, Chaoyang; Li, Cunjun Ecological Indicators, Vol. 77 https://doi.org/10.1016/j.ecolind.2017.02.024	journal	June 2017
Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves Baumann, Matthias; Ozdogan, Mutlu; Richardson, Andrew D. International Journal of Applied Earth Observation and Geoinformation, Vol. 54 https://doi.org/10.1016/j.jag.2016.09.005	journal	February 2017
Real-time monitoring and short-term forecasting of land surface phenology White, Michael A.; Nemani, Ramakrishna R. Remote Sensing of Environment, Vol. 104, Issue 1 https://doi.org/10.1016/j.rse.2006.04.014	journal	September 2006
Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology Hufkens, Koen; Friedl, Mark; Sonnentag, Oliver Remote Sensing of Environment, Vol. 117 https://doi.org/10.1016/j.rse.2011.10.006	journal	February 2012
Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data Melaas, Eli K.; Friedl, Mark A.; Zhu, Zhe Remote Sensing of Environment, Vol. 132 https://doi.org/10.1016/j.rse.2013.01.011	journal	May 2013
Monitoring multi-layer canopy spring phenology of temperate deciduous and evergreen forests using low-cost spectral sensors Ryu, Youngryel; Lee, Galam; Jeon, Soohyun Remote Sensing of Environment, Vol. 149 https://doi.org/10.1016/j.rse.2014.04.015	journal	June 2014
Functional group analyses of herpetofauna in South Korea using a large dataset Jeon, Jong Yoon; Lee, Dong Kun; Kim, Jae Hyun Scientific Data, Vol. 10, Issue 1 https://doi.org/10.1038/s41597-022-01924-z	journal	January 2023
Warm spring reduced carbon cycle impact of the 2012 US summer drought Wolf, Sebastian; Keenan, Trevor F.; Fisher, Joshua B. Proceedings of the National Academy of Sciences, Vol. 113, Issue 21 https://doi.org/10.1073/pnas.1519620113	journal	April 2016
Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics Shuai, Yanmin; Schaaf, Crystal; Zhang, Xiaoyang International Journal of Remote Sensing, Vol. 34, Issue 16 https://doi.org/10.1080/01431161.2013.803169	journal	May 2013
Evaluation of regression procedures for methods comparison studies Linnet, K. Clinical Chemistry, Vol. 39, Issue 3 https://doi.org/10.1093/clinchem/39.3.424	journal	March 1993
Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 White, Michael A.; de BEURS, Kirsten M.; Didan, Kamel Global Change Biology, Vol. 15, Issue 10 https://doi.org/10.1111/j.1365-2486.2009.01910.x	journal	October 2009
Ecological impacts of a widespread frost event following early spring leaf-out Hufkens, Koen; Friedl, Mark A.; Keenan, Trevor F. Global Change Biology, Vol. 18, Issue 7 https://doi.org/10.1111/j.1365-2486.2012.02712.x	journal	May 2012
Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century Morisette, Jeffrey T.; Richardson, Andrew D.; Knapp, Alan K. Frontiers in Ecology and the Environment, Vol. 7, Issue 5 https://doi.org/10.1890/070217	journal	June 2009
Near-surface remote sensing of spatial and temporal variation in canopy phenology Richardson, Andrew D.; Braswell, Bobby H.; Hollinger, David Y. Ecological Applications, Vol. 19, Issue 6 https://doi.org/10.1890/08-2022.1	journal	September 2009
Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology Elmore, Andrew; Stylinski, Cathlyn; Pradhan, Kavya Remote Sensing, Vol. 8, Issue 6 https://doi.org/10.3390/rs8060502	journal	June 2016
A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States Robinson, Nathaniel; Allred, Brady; Jones, Matthew Remote Sensing, Vol. 9, Issue 8 https://doi.org/10.3390/rs9080863	journal	August 2017
Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem Browning, Dawn; Karl, Jason; Morin, David Remote Sensing, Vol. 9, Issue 10 https://doi.org/10.3390/rs9101071	journal	October 2017
An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration Eklundh, Lars; Jin, Hongxiao; Schubert, Per Sensors, Vol. 11, Issue 8 https://doi.org/10.3390/s110807678	journal	August 2011
Interpreting canopy development and physiology using a European phenology camera network at flux sites Wingate, L.; Ogée, J.; Cremonese, E. Biogeosciences, Vol. 12, Issue 20 https://doi.org/10.5194/bg-12-5995-2015	journal	January 2015
Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery Klosterman, S. T.; Hufkens, K.; Gray, J. M. Biogeosciences Discussions, Vol. 11, Issue 2 https://doi.org/10.5194/bgd-11-2305-2014	journal	January 2014

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