DEA Surface Reflectance NBAR (Landsat 9)

DEA Surface Reflectance NBAR (Landsat 9)

Geoscience Australia Landsat 9 OLI-TIRS NBAR 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

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About

DEA Surface Reflectance NBAR (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

10.26186/148693

Licence

Creative Commons Attribution 4.0 International 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

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. 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 fringes using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR). This consistency over time and space is instrumental in identifying and quantifying environmental change.

The resolution is a 30 m grid based on the USGS Landsat Collection 2 archive, or a 15 m grid for the panchromatic band.

This product does not apply terrain illumination correction. See the sibling product DEA Surface Reflectance NBART (Landsat 9).

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.

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]
Compression: deflate
Resampling: GDAL default (nearest)
Overview X&Y block sizes: 512x512

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
Green: band 3
Blue: band 2

Resolution

X and Y directions each resampled to 10% of the original size

Compression

JPEG, Quality 75 (GDAL default)
PHOTOMETRIC colour model: YCBCR

Contrast stretch

Linear
Input minimum: 10
Input maximum: 3500
Output minimum: 0
Output maximum: 255

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

  1. Longitude and Latitude Calculation

  2. Satellite and Solar Geometry Calculation

  3. Aerosol Optical Thickness Retrieval

  4. BRDF Shape Function Retrieval

  5. Ozone Retrieval

  6. Incidence and Azimuthal Incident Angles Calculation

  7. Exiting and Azimuthal Exiting Angles Calculation

  8. MODTRAN

  9. Atmospheric Correction Coefficients Calculation

  10. Bilinear Interpolation of Atmospheric Correction Coefficients

  11. Surface Reflectance Calculation (NBAR)

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.