• Login
    View Item 
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    An optimal method for validating satellite-derived land surface phenology using in-situ observations from national phenology networks

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    manuscriptRsprs_v3-tmc.pdf
    Size:
    3.680Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
    Download
    Author
    Ye, Yongchang
    Zhang, Xiaoyang
    Shen, Yu
    Wang, Jianmin
    Crimmins, Theresa
    Scheifinger, Helfried
    Affiliation
    School of Natural Resources and the Environment, University of Arizona
    Issue Date
    2022-12
    Keywords
    In-situ observations
    LSP validation
    PEP725
    Phenology
    USA-NPN
    VIIRS
    
    Metadata
    Show full item record
    Publisher
    Elsevier BV
    Citation
    Ye, Y., Zhang, X., Shen, Y., Wang, J., Crimmins, T., & Scheifinger, H. (2022). An optimal method for validating satellite-derived land surface phenology using in-situ observations from national phenology networks. ISPRS Journal of Photogrammetry and Remote Sensing, 194, 74–90.
    Journal
    ISPRS Journal of Photogrammetry and Remote Sensing
    Rights
    © 2022 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. 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
    Satellite-based land surface phenology (LSP) products play an important role in understanding atmosphere-vegetation carbon and energy exchanges. These products have been widely calculated from various satellite observations from local to global scales. However, the quality and accuracy of LSP products are often poorly quantified due to spatial mismatch between satellite observed pixels and in-situ observations. In the present study, we demonstrate an optimal algorithm leveraging the scalability, consistency, and representativeness of rich in-situ observations from national phenology networks to validate LSP products. Specifically, we demonstrate two approaches for validating the phenological timing of greenup onset in the operational Visible Infrared Imaging Radiometer Suite (VIIRS) LSP product developed at NASA using in-situ observations collected from the Pan European Phenological database (PEP725, 9664 site-years) and the USA National Phenology Network (USA-NPN, 3144 site-years) spanning 2013–2020. The first approach assumes that in-situ data contain observations of phenological transitions (e.g., leaf-out) that are directly comparable with satellite detections. Accordingly, in-situ data were aggregated using four upscaling methods (mean, median, 30th percentile, and minimum bias) to directly compare with VIIRS LSP. The second approach assumes that species-specific phenological timing in in-situ data is basically impossible to spatially reconcile VIIRS LSP, but phenological events in a local area are driven by the same or very similar weather conditions. Therefore, interannual variations and long-term trends were applied to compare VIIRS LSP with in-situ data. The result shows first that the 30th percentile method is more promising in aggregating in-situ observations than the commonly used mean method. Second, direct comparison indicates that VIIRS greenup onset has a mean absolute difference of 13.9 ± 9.8 days with PEP725 in-situ observations and 12.3 ± 10.9 days with USA-NPN observations in well-selected deciduous forest sites. Third, the interannual comparison reveals that VIIRS greenup onset exhibits the same directions of multi-year anomalies and long-term trends as those of both PEP725 and USA-NPN observations in over 70% of sample sites. These findings improve our understanding of the scale mismatch and sample representativeness of species-specific phenology and the uncertainties of long-term LSP detections from remote sensing data.
    Note
    24 month embargo; available online: 17 October 2022
    ISSN
    0924-2716
    DOI
    10.1016/j.isprsjprs.2022.09.018
    Version
    Final accepted manuscript
    Sponsors
    National Aeronautics and Space Administration
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.isprsjprs.2022.09.018
    Scopus Count
    Collections
    UA Faculty Publications

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.