• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine

    Author(s)
    Wang, Mengmeng
    Zhang, Zhengjia
    Hu, Tian
    Wang, Guizhou
    He, Guojin
    Zhang, Zhaoming
    Li, Hua
    Wu, Zhijie
    Liu, Xiuguo
    Griffith University Author(s)
    Hu, Tian
    Year published
    2020
    Metadata
    Show full item record
    Abstract
    A long time-series land surface temperature (LST) product is useful for ecological and environmental studies. However, current LST products cannot provide a global coverage at a fine spatial resolution (∼100 m) over a long period (>30 years). Landsat series satellites that have been launched since 1972 provide a unique opportunity to fill the gap. Here, we proposed a single-channel framework for producing global long time-series Landsat LST retrievals on a Google earth engine (GEE) cloud computing platform. This framework unifies the LST, land surface emissivity (LSE) and atmospheric water vapor (AWV) estimation algorithms, ...
    View more >
    A long time-series land surface temperature (LST) product is useful for ecological and environmental studies. However, current LST products cannot provide a global coverage at a fine spatial resolution (∼100 m) over a long period (>30 years). Landsat series satellites that have been launched since 1972 provide a unique opportunity to fill the gap. Here, we proposed a single-channel framework for producing global long time-series Landsat LST retrievals on a Google earth engine (GEE) cloud computing platform. This framework unifies the LST, land surface emissivity (LSE) and atmospheric water vapor (AWV) estimation algorithms, as well as the emissivity and atmospheric input data for the Landsat LST retrievals from the entire Landsat thermal infrared image archive. In situ LST measurements and the MODIS LST products were employed to evaluate Landsat LST retrievals using the proposed framework over land and water surfaces, respectively. In total, 1317 clear-sky LST samples were collected from the Landsat 5–8 series after spatiotemporal registration with seven sites, and the average bias and root-mean-square error (RMSE) were 0.33 and 2.01 K, respectively. Intercomparison between Landsat and MODIS LST retrievals based on 100 clear-sky scenes over 12 inland lakes showed an average bias of 0.17 K and RMSE of 1.11 K. We conclude that the proposed single-channel framework can produce Landsat LST with high accuracy following a simple yet robust way. Implementation of the single-channel method on GEE shows promise in providing the community with freely accessible and global long time-series (>30 years) LST data.
    View less >
    Journal Title
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
    Volume
    13
    DOI
    https://doi.org/10.1109/JSTARS.2020.3014586
    Subject
    Artificial intelligence
    Physical geography and environmental geoscience
    Geomatic engineering
    Science & Technology
    Physical Sciences
    Engineering, Electrical & Electronic
    Geography, Physical
    Publication URI
    http://hdl.handle.net/10072/397832
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander