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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]
+ Show Author Affiliations
  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); 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.
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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. https://doi.org/10.1038/s41598-018-23804-6
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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. https://doi.org/10.1038/s41598-018-23804-6. https://www.osti.gov/servlets/purl/1499990.
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@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}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s41598-018-23804-6
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Citation Metrics:
Cited by: 85 works
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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
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    Works referencing / citing this record:

    Testing Hopkins’ Bioclimatic Law with PhenoCam data
    journal, March 2019

    • Richardson, Andrew D.; Hufkens, Koen; Li, Xiaolu
    • Applications in Plant Sciences, Vol. 7, Issue 3
    • DOI: 10.1002/aps3.1228

    Comparison of large‐scale citizen science data and long‐term study data for phenology modeling
    journal, December 2018

    • Taylor, Shawn D.; Meiners, Joan M.; Riemer, Kristina
    • Ecology, Vol. 100, Issue 2
    • DOI: 10.1002/ecy.2568

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

    Comparison of Grassland Phenology Derived from MODIS Satellite and PhenoCam Near-Surface Remote Sensing in North America
    journal, September 2019

    Rethinking false spring risk
    journal, May 2019

    • Chamberlain, Catherine J.; Cook, Benjamin I.; García de Cortázar-Atauri, Iñaki
    • Global Change Biology
    • DOI: 10.1111/gcb.14642

    Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery
    journal, December 2018

    Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions
    journal, November 2018

    • Wang, Siyu; Lu, Xinchen; Cheng, Xiao
    • Remote Sensing, Vol. 10, Issue 11
    • DOI: 10.3390/rs10111784

    Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions
    journal, March 2019

    • Stendardi, Laura; Karlsen, Stein; Niedrist, Georg
    • Remote Sensing, Vol. 11, Issue 5
    • DOI: 10.3390/rs11050542

    Comparison Of Large-Scale Citizen Science Data And Long-Term Study Data For Phenology Modeling
    text, January 2018

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