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Global 30-m seamless data cube (2000-2022) of land surface reflectance generated from Landsat-5,7,8,9 and MODIS Terra constellations

 

Description:

    The global 30-m Seamless Data Cube (SDC) surface reflectance dataset (2000-2022) is generated by combining multi-sensor observations from the Landsat 5, 7, 8, 9 and MODIS Terra products. Compared to other Landsat-based surface reflectance datasets, the SDC exhibits enhanced capabilities for monitoring land changes and robust consistency in both spatial and temporal dimensions (without missing values), which are important for land cover and land use change analyses over large geographical extents.

 

Data Archive:

New-version:

https://data-starcloud.pcl.ac.cn/iearthdata/, see (http://sdc.iearth.cloud/data/) for instructions.

    A web-based interface is provided for researchers to freely access the SDC dataset. The entrance is at https://data-starcloud.pcl.ac.cn/aiforearth.

    Some sample data of the SDC are also available in the “Downloads” section of this page.

    If you need continental or global-scale SDC dataset, please feel free to contact us. We are open to all forms of cooperation. (schen17 at connect.hku.hk).

 

Instructions on using the web-based interface:

  1. visit: https://data-starcloud.pcl.ac.cn/aiforearth;
  2. choose “Customized data”;
  3. choose “30m Seamless Data Cube”;
  4. set order mode: Quick (800×800) or  Standard (3661×3661, longer wait time).
  5. select Which year of SDC to be generated: 2000-2022, (1985-1999 is on the way) 
  6. select the Spatial range of SDC to be generated: click the blue icon first, then click and choose the MGRS tile or sub-tile on the right side.
  7. Click “Submit” to submit the order, and wait for the customized SDC data to be retrieved.
  8. View the SDC data online, or download it to your local PCs.

 

Tiling System:

    The UTM-based Military Grid Reference System (MGRS) was chosen as the projection system for the SDC product, which was also adopted by ESA’s Sentinel-2 products and NASA’s HLS products (Claverie et al., 2018). It is noteworthy that our adopted grid slightly deviates from the Sentinel-2 grid. Since the original Landsat coordinate system exhibits a half-pixel (15 meters) offset relative to the Sentinel-2 grid, we expanded and shifted the original MGRS grid by 15 meters in each direction to align with the Landsat coordinate system. The SDC product is gridded into this modified MGRS system, with a tile size of 109.83×109.83 km (3661×3661 Landsat pixels).

 

Data Format:

    Following are the components of a typical CSDC file name:

            CSDC30_TTTTT_YYYYMMDD.tif

            (e.g., CSDC30_10SEH_20150101.tif)

            TTTTT  MGRS tile name

            YYYY   Year

            MM       Month

            DD        Day

    

The default file format is Cloud-Optimized GeoTIFF (COG).

Band IndexBand NameWavelengths (micrometers)Data TypeUnitsData Range
1Blue0.45 - 0.51UINT16Reflectance0 - 10000
2Green0.53 - 0.59UINT16Reflectance0 - 10000
3Red0.64 - 0.67UINT16Reflectance0 - 10000
4NIR0.85 - 0.88UINT16Reflectance0 - 10000
5SWIR11.57 - 1.65UINT16Reflectance0 - 10000
6SWIR22.11 - 2.29UINT16Reflectance0 - 10000

    In the SDC data files, six data bands are stacked as a three-dimensional array (Bands * Height * Width).

 

FROM-GLC30 land cover products

    Based on the SDC, we produced global anuual land cover maps (2000-2022) using the FROM-GLC classification system, which are also available in the “Download” section below (path: SDC/fromglc30, updating). 

Naming:

            fromglc30_TTTTT_YYYY.tif

            (e.g., fromglc30_10SEH_2001.tif)

            TTTTT  MGRS tile name

            YYYY   Year

Known Issues

    Our enhanced cloud masking approach identifies most of the previously undetected clouds. Nonetheless, there are still certain thin aerosols and cloud shadows in Landsat imagery that may undermine the data quality of the SDC dataset. We are considering adding a QA band for the SDC dataset.

    The website server cannot correctly display all the land cover files (fromglc30_TTTTT_YYYY.tif). If you are unable to find the land cover files for your Area of Interest (AOI), please contact us. We apologize for the inconvenience and are working to resolve this issue and reconfigure the website.

 

论文引用
Chen, S., Wang, J., & Gong, P. (2023). ROBOT: A spatiotemporal fusion model toward seamless data cube for global remote sensing applications. Remote Sensing of Environment, 294, 113616. https://doi.org/10.1016/j.rse.2023.113616
Chen, S., Wang, J., Liu, Q., Liang, X., Liu, R., Qin, P., Yuan, J., Wei, J., Yuan, S., Huang, H., and Gong, P.: Global 30 m seamless data cube (2000–2022) of land surface reflectance generated from Landsat 5, 7, 8, and 9 and MODIS Terra constellations, Earth Syst. Sci. Data, 16, 5449–5475, https://doi.org/10.5194/essd-16-5449-2024, 2024.
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上次更新时间: 9/13/2023, 7:43:52 AM
  • SDC
    • SDC_V002
    • SDC_withHLS
    • experimental_data
    • fromglc30_2000
    • fromglc30_2001
    • fromglc30_2002
    • fromglc30_2003
    • fromglc30_2004
    • fromglc30_2005
    • fromglc30_2006
    • fromglc30_2007
    • fromglc30_2008
    • fromglc30_2009
    • fromglc30_2010
    • fromglc30_2011
    • fromglc30_2012
    • fromglc30_2013
    • fromglc30_2014
    • fromglc30_2015
    • fromglc30_2016
    • fromglc30_2017
    • fromglc30_2018
    • fromglc30_2019
    • fromglc30_2020
    • fromglc30_2021
    • fromglc30_2022
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