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    Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data

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    Name:
    Yan_RSE_2019.pdf
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    4.706Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
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    Author
    Yan, D.
    Scott, R.L.
    Moore, D.J.P.
    Biederman, J.A.
    Smith, W.K.
    Affiliation
    Univ Arizona, Sch Nat Resources & Environm
    Issue Date
    2019-03-15
    Keywords
    Drylands
    Greenness indices
    Gross primary productivity
    PhenoCam
    Landsat
    MODIS
    
    Metadata
    Show full item record
    Publisher
    ELSEVIER SCIENCE INC
    Citation
    Yan, D., Scott, R. L., Moore, D. J. P., Biederman, J. A., & Smith, W. K. (2019). Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data. Remote Sensing of Environment, 223, 50-62.
    Journal
    REMOTE SENSING OF ENVIRONMENT
    Rights
    © 2019 Elsevier Inc. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Drylands account for approximately 40% of the global land surface and play a dominant role in the trend and variability of terrestrial carbon uptake and storage. Gross ecosystem photosynthesis termed gross primary productivity (GPP) is a critical driver of terrestrial carbon uptake and remains challenging to be observed directly. Currently, vegetation indices that largely capture changes in greenness are the most commonly used datasets in satellite-based GPP modeling. However, there remains significant uncertainty in the spatiotemporal relationship between greenness indices and GPP, especially for relatively heterogeneous dryland ecosystems. In this paper, we compared vegetation greenness indices from PhenoCam and satellite (Landsat and MODIS) observations against GPP estimates from the eddy covariance technique, across three representative ecosystem types of the southwestern United States. We systematically evaluated the changes in the relationship between vegetation greenness indices and GPP: i) across spatial scales of canopy-level, 30-meter, and 500-meter resolution; and ii) across temporal scale of daily, 8-day, 16-day, and monthly resolution. We found that greenness-GPP relationships were independent of spatial scales as long as land cover type and composition remained relatively constant. We also found that the greenness-GPP relationships became stronger as the time interval increased, with the strongest relationships observed at the monthly resolution. We posit that the greenness-GPP relationship breaks down at short timescales because greenness changes more slowly than plant physiological function, which responds rapidly to changes in key biophysical drivers. These findings provide insights into the potential for and limitations of modeling GPP using remotely sensed greenness indices across dryland ecosystem types.
    Note
    24 month embargo; published online: 17 January 2019
    ISSN
    00344257
    DOI
    10.1016/j.rse.2018.12.029
    Version
    Final accepted manuscript
    Sponsors
    Strategic Environmental Research and Development Program (SERDP) [RC18-1322]; United States Department of Agriculture (USDA) [58-0111-17-013]; United States Department of Energy (DOE) [DE-SC0016011]; DOE's Ameriflux Management Program; Northeastern States Research Cooperative; NSF's Macrosystems Biology Program [EF-1065029, EF-1702697]; DOE's Regional and Global Climate Modeling program [DE-SC0016011]; US National Park Service Inventory and Monitoring Program; USA National Phenology Network from the United States Geological Survey [G10AP00129]; USA National Phenology Network; North Central Climate Science Center from the United States Geological Survey [G16AC00224]; PhenoCam; USDA Agricultural Research Service; University of Arizona
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0034425718305881
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.rse.2018.12.029
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