DEA Surface Reflectance NBART (Landsat 9)
DEA Surface Reflectance NBART (Landsat 9)
Geoscience Australia Landsat 9 OLI-TIRS NBART Collection 3
- Version:
3.0.0 (Latest)
- Product types:
Baseline, Raster
- Time span:
31 Oct 2021 – Present
- Update frequency:
Daily
- Product ID:
ga_ls9c_ard_3
About
DEA Surface Reflectance NBART (Landsat 9) is part of a suite of Digital Earth Australia (DEA)’s Surface Reflectance datasets that represent the vast archive of images captured by the US Geological Survey (USGS) Landsat and European Space Agency (ESA) Sentinel-2 satellite programs, validated, calibrated, and adjusted for Australian conditions — ready for easy analysis.
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Key details
Collection |
Geoscience Australia Landsat Collection 3 |
DOI |
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Licence |
Cite this product
Data citation |
Alam, I., Chopra, A., Gray, D., Hooke, J., Li, F., Ly, L., Mettes, J., Miller, J., Norton, A., OHehir, A. 2023. Geoscience Australia Landsat 9 OLI TIRS Analysis Ready Data Collection 3. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/148693
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Publications
Li, F., Jupp, D. L. B., Reddy, S., Lymburner, L., Mueller, N., Tan, P., & Islam, A. (2010). An evaluation of the use of atmospheric and BRDF correction to standardize Landsat data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3), 257–270. https://doi.org/10.1109/JSTARS.2010.2042281
Background
Sub-product
This is a sub-product of DEA Surface Reflectance (Landsat 9). See the parent product for more information.
Radiance data collected by Landsat 9 sensors can be affected by atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect.
Surfaces with varying terrain can introduce inconsistencies to optical satellite images through irradiance and bidirectional reflectance distribution function (BRDF) effects. For example, slopes facing the sun appear brighter compared with those facing away from the sun. Likewise, many surfaces on Earth are anisotropic in nature, so the signal picked up by a satellite sensor may differ depending on the sensor’s position.
These need to be reduced or removed to ensure the data is consistent and can be compared over time.
What this product offers
This product takes Landsat 9 imagery captured over the Australian continent and corrects the inconsistencies across land and coastal fringe. It achieves this using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR).
However, in addition, this product applies terrain illumination correction to correct for varying terrain.
The resolution is a 30 m grid based on the USGS Landsat Collection 2 archive, or a 15 m grid for the panchromatic band.
Technical information
Radiance measurements
Landsat’s Earth Observation (EO) sensors measure radiance (brightness of light), which is a composite of:
surface reflectance
atmospheric condition
interaction between surface land cover, solar radiation and sensor view angle
land surface orientation relative to the imaging sensor
It has been traditionally assumed that Landsat imagery displays negligible variation in sun and sensor view angles. However, these can vary significantly both within and between scenes, especially in different seasons and geographic regions (Li et al. 2012).
Surface reflectance correction models
This product represents standardised optical surface reflectance using robust physical models to correct for variations and inconsistencies in image radiance values.
It delivers modelled surface reflectance from Landsat 9 OLI-TIRS data using physical rather than empirical models. This ensures that the reflective value differences between imagery acquired at different times by different sensors will be primarily due to on-ground changes in biophysical parameters rather than artefacts of the imaging environment.
This product is created using a physics-based, coupled Bidirectional Reflectance Distribution Function (BRDF) and atmospheric correction model that can be applied to both flat and inclined surfaces (Li et al. 2012). The resulting surface reflectance values are comparable both within individual images and between images acquired at different times.
For more information on the BRDF/Albedo Model Parameters product, see NASA MODIS BRDF/Albedo parameter and MCD43A1 BRDF/Albedo Model Parameters Product.
This product also corrects for differences in terrain using a Digital Elevation Model (DEM).
Landsat archive
To improve access to Australia’s archive of Landsat TM/ETM+/OLI data, several collaborative projects have been undertaken in conjunction with industry, government and academic partners. These projects have enabled implementation of a more integrated approach to image data correction that incorporates normalising models to account for atmospheric effects, BRDF and topographic shading (Li et al. 2012). The approach has been applied to Landsat TM/ETM+ and OLI imagery to create baseline surface reflectance products.
The advanced supercomputing facilities provided by the National Computational Infrastructure (NCI) at the Australian National University (ANU) have been instrumental in handling the considerable data volumes and processing complexities involved with the production of this product.
Bands Included in the Analysis Ready Data
The product contains 8 bands
Band number |
Band name |
Wave length |
Resolution |
|---|---|---|---|
Band 1 |
Visible |
0.43 - 0.45 µm |
30 m |
Band 2 |
Visible |
0.45 - 0.51 µm |
30 m |
Band 3 |
Visible |
0.53 - 0.59 µm |
30 m |
Band 4 |
Red |
0.64 - 0.67 µm |
30 m |
Band 5 |
Near-Infrared |
0.85 - 0.88 µm |
30 m |
Band 6 |
SWIR 1 |
1.57 - 1.65 µm |
30 m |
Band 7 |
SWIR 2 |
2.11 - 2.29 µm |
30 m |
Band 8 |
Panchromatic (PAN) |
0.50 - 0.68 µm |
15 m |
Image format specifications
band01, band02, band03, band04, band05, band06, band07
Format |
GeoTIFF |
Resolution |
30m |
Datatype |
Int16 |
No data value |
-999 |
Valid data range |
[1,10000] |
Tiled with X and Y block sizes |
512x512 |
Compression |
Deflate, Level 6, Predictor 2 |
Pyramids |
Levels: [8,16,32] |
Contrast stretch |
None |
Output CRS |
As specified by source dataset; source is UTM with WGS84 as the datum |
band08
Format |
GeoTIFF |
Resolution |
15m |
Datatype |
Int16 |
No data value |
-999 |
Valid data range |
[1,10000] |
Tiled with X and Y block sizes |
512x512 |
Compression |
Deflate, Level 6, Predictor 2 |
Pyramids |
None |
Contrast stretch |
None |
Output CRS |
As specified by source dataset; source is UTM with WGS84 as the datum |
thumbnail
Format |
JPEG |
RGB combination |
Red: band 4 |
Resolution |
X and Y directions each resampled to 10% of the original size |
Compression |
JPEG, Quality 75 (GDAL default) |
Contrast stretch |
Linear |
Output CRS |
Geographics (Latitude/Longitude) WGS84 |
Lineage
This product is derived from the USGS Landsat Level 1 Collection 2 archive.
The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset was provided by the National Aeronautics and Space Administration (NASA). It was produced daily using 16 days of Terra and Aqua MODIS data at 500 m resolution. See USGS: MCD43A1, NASA: MODIS BRDF / Albedo Parameter, Schaaf et al. (2002)
The ozone data was provided by Environment Canada. See Environment Canada: Global Ozone Maps
The Aerosol Optical Thickness data was provided by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). See Qin et al. (2015)
The Precipitable Water for Entire Atmosphere data was provided by the National Oceanic and Atmospheric Administration (NOAA) / Earth System Research Laboratory (ESRL) / Physical Sciences Division (PSD). See Kalnay et al. (1996)
The baseline Digital Surface Model (DSM) data produced from the Shuttle Radar Topography Mission (SRTM) was provided by the National Geospatial-Intelligence Agency (NGA). See NGA: SRTM, NASA: SRTM
Level 1 Collection 2 data was provided by the United States Geological Survey (USGS)’s Earth Resources Observation and Science (EROS) Center. See USGS: EROS, USGS: Landsat Collection 2
Processing steps
References
Li, F., Jupp, D. L. B., Reddy, S., Lymburner, L., Mueller, N., Tan, P., & Islam, A. (2010). An evaluation of the use of atmospheric and BRDF correction to standardize Landsat data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3), 257–270. https://doi.org/10.1109/JSTARS.2010.2042281
Li, F., Jupp, D. L. B., Thankappan, M., Lymburner, L., Mueller, N., Lewis, A., & Held, A. (2012). A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain. Remote Sensing of Environment, 124, 756–770. https://doi.org/10.1016/j.rse.2012.06.018
Accuracy
Atmospheric correction accuracy depends on the quality of aerosol data available to determine the atmospheric profile at the time of image acquisition.
BRDF correction is based on low resolution imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), which is assumed to be relevant to medium resolution imagery such as that captured by Landsat 9 OLI-TIRS. BRDF correction is applied to each whole Landsat 9 OLI-TIRS scene and does not account for changes in land cover. It also excludes effects due to topographic shading and local BRDF.
The algorithm assumes that BRDF effect for inclined surfaces is modelled by the surface slope and does not account for land cover orientation relative to gravity (as occurs for some broadleaf vegetation with vertical leaf orientation).
The algorithm also depends on several auxiliary data sources:
Availability of relevant MODIS BRDF data
Availability of relevant aerosol data
Availability of relevant water vapour data
Availability of relevant DEM data
Availability of relevant ozone data
Improved or more accurate sources for any of the above listed auxiliary dependencies will also improve the surface reflectance result.
Quality assurance
Results from the DEA Cal/Val workflow over 17 data takes from 9 field sites were created based on both BRDF Collections 5 and 6.
The results for each collection were averaged and then compared. The comparison showed small changes in individual field sites, but overall there was no significant difference in the average results over all field sites to within 1% at most.
The technical report containing the data summary for the Phase 1 DEA Surface Reflectance Validation is available: DEA Analysis Ready Data Phase 1 Validation Project : Data Summary
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Old versions
No old versions available.
Frequently asked questions
Why doesn’t DEA make Landsat thermal bands available to users?
Landsat satellite sensors not only collect data in the short-wave spectrum but also collect data into the thermal infrared bands. The USGS makes this data available in the form of a surface temperature and emissivity product: Landsat Collection 2 Surface Temperature. It provides this separately to the surface reflectance products.
This USGS Surface Temperature product is a global product that uses global datasets to perform corrections on the data that are collected by the satellite sensors. The land surface temperature outputs are very sensitive to the atmospheric profile data that is used to perform the correction. For the global analysis, the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA) atmospheric data is used to provide values for height, air temperature, and humidity. While this dataset works well for a global correction, studies over Australia have shown that the correction can be improved when higher resolution datasets are considered (Li et al, 2015).
DEA’s ARD product provides high-quality data corrections for Australian conditions. At present, we do not produce a custom land surface temperature dataset for Australia, and so we have not included the thermal bands in our ARD package.
If you would like to use USGS Landsat thermal data directly, we provide a Jupyter notebook that shows you how to combine DEA ARD data with USGS thermal data.
Acknowledgments
This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government.
Landsat level 0 and level 1 data courtesy of the U.S. Geological Survey.
The authors would like to thank the following organisations:
National Aeronautics and Space Administration (NASA)
Environment Canada
The Commonwealth Scientific and Industrial Research Organisation (CSIRO)
National Oceanic and Atmospheric Administration (NOAA) / Earth System Research Laboratories (ESRL) / Physical Sciences Laboratory (PSD)
The National Geospatial-Intelligence Agency (NGA)
The United States Geological Survey (USGS) / Earth Resources Observation and Science (EROS) Center
Spectral Sciences Inc.
License and copyright
© Commonwealth of Australia (Geoscience Australia).
Released under Creative Commons Attribution 4.0 International Licence.