2.0.0: DEA Surface Reflectance NBAR (Landsat)
2.0.0: DEA Surface Reflectance NBAR (Landsat)
Surface Reflectance NBAR 25m 2.0.0
2.0.0 (See latest version)
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This is an old version of the product. See the latest version.
This product has been deprecated and is superseded by these products:
Geoscience Australia Landsat Collection 2
This product has been deprecated and is superseded by these products:
The light reflected from the Earth’s surface (surface reflectance) is important for monitoring environmental resources – such as agricultural production and mining activities – over time.
We need to make accurate comparisons of imagery acquired at different times, seasons and geographic locations. However, inconsistencies can arise due to variations in 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 Surface Reflectance product has been corrected to account for variations caused by atmospheric properties, sun position and sensor view angle at time of image capture.
These corrections have been applied to all satellite imagery in the Landsat archive since 1987. This is undertaken to allow comparison of imagery acquired at different times, by different sensors, in different seasons and in different geographic locations. These products also indicate where the imagery has been affected by cloud or cloud shadow, contains missing data or has been affected in other ways.
The Surface Reflectance products are useful as a fundamental starting point for any further analysis, and underpin all other optical derived Digital Earth Australia products.
This product eliminates pre-processing requirements for a wide range of land and coastal monitoring applications and renders more accurate results from analyses, particularly those utilising time series data.
Such applications include various forms of change detection, including:
Monitoring of urban growth, coastal habitats, mining activities, and agricultural production
Scientific research emergency management
Surface Reflectance (SR) is a suite of Earth Observation (EO) products from GA. The SR product suite provides standardised optical surface reflectance datasets using robust physical models to correct for variations in image radiance values due to atmospheric properties, and sun and sensor geometry. The resulting stack of surface reflectance grids are consistent over space and time which is instrumental in identifying and quantifying environmental change. SR is based on radiance data from the Landsat TM/ETM+ and OLI sensors.
The standardised SR data products deliver calibrated optical surface reflectance data across land and coastal fringes. SR is a medium resolution (~25 m) grid based on the Landsat TM/ETM+/OLI archive and presents surface reflectance data in 25 m² grid cells.
Radiance measurements from EO sensors do not directly quantify the surface reflectance of the Earth. Such measurements are modified by variations in atmospheric properties, sun position, sensor view angle, surface slope and surface aspect. To obtain consistent and comparable measures of Earth surface reflectance from EO, these variations need to be reduced or removed from the radiance measurements (Li et al., 2010). This is especially important when comparing imagery acquired in different seasons and geographic regions.
The SR product is created using a physics-based, coupled 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 and/or with different sensors.
This product does not include terrain illumination reflectance correction (see Surface Reflectance NBART 2 (Landsat)).
GA has acquired Landsat imagery over Australia since 1979, including TM, ETM+ and OLI imagery. While this data has been used extensively for numerous land and coastal mapping studies, its utility for accurate monitoring of environmental resources has been limited by the processing methods that have been traditionally used to correct for inherent geometric and radiometric distortions in EO imagery. 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 the SR 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 production of this product.
Surface Reflectance correction models
Image radiance values recorded by passive EO sensors are a composite of:
interaction between surface land cover, solar radiation and sensor view angle; and
land surface orientation relative to the imaging sensor.
It has been traditionally assumed that Landsat imagery display 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). The SR product delivers modeled surface reflectance from Landsat TM/ETM+/OLI/ data using physical rather than empirical models. Accordingly, this product will ensure that 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 artifacts of the imaging environment.
Integrated time series data
Once consistent and comparable measures of surface reflectance have been retrieved from EO data, it is possible to quantify changes in Earth surface features through time. Given the growing time series of EO imagery, this landmark facility will streamline the process of reliably monitoring long-term changes in land and water resources.
Extract metadata from data sources
Calculate sun and sensor angles per pixel (Vincenty, 1975; Edberg and Oliver, 2013)
Determine values for six base atmospheric parameters across each image scene
Divide scene into quarters and select the nine unique points which form the corners of these quadrants
Compute the six parameters across optical spectrum at each of the nine points using a Radiative Transfer Model (Modtran5) and atmospheric state data
Accumulate values for the six parameters at each of the nine points to correspond to Landsat bands using Landsat spectral response function
Interpolate accumulated values for the six parameters across image scene using the bilinear method.
Derive normalised surface reflectance for sun angle of 45° Use interpolated, accumulated values for the six base atmospheric parameters to compute the atmospheric and BRDF correction for each pixel and output the normalised surface reflectance for sun angle of 45°.
Ortho-processing using DSM
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
Atmospheric correction accuracy is dependent on the quality of aerosol data available to determine the atmospheric profile at time of image acquisition.
BRDF correction is based on low resolution imagery (MODIS) which is assumed to be relevant to medium resolution imagery such as Landsat TM/ETM+/OLI. BRDF correction is applied to each whole Landsat TM/ETM+/OLI scenes and does not account for changes in land cover. It also excludes effects due to topographic shading and local BRDF. This algorithm is dependent on the availability of relevant MODIS BRDF data.
Topographic shading correction algorithm is designed for medium resolution imagery and assumes that hill slopes are resolved by the sensor system (Li et al., 2012). The algorithm assumes that: a. 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 broad leaf vegetation with vertical leaf orientation).
Validate combined atmospheric and surface BRDF correction using field reflectance measurements at two very different sites, Gwydir, NSW, and Lake Frome, SA; correlation (measured as r) between corrected image values and field data was > 0.95.
Validate surface BRDF correction using data from image overlap areas of adjacent paths acquired one week apart in northeast Queensland - normalised reflectance factor was very close in corrected images, but not in original images, and difference in reflectance factor values between corrected and uncorrected images can be > 0.05.
Cross-validate Landsat TM data for accuracy of spectral reflectance using the MODIS reflectance product for Lake Frome; correlation (measured as r2) between corrected Landsat TM image values and MODIS reflectance product was 0.93-0.97 in all bands except Landsat TM band 5, which was 0.90.
Topographic Correction As detailed in Li et al. (2012), two high relief areas in southeast Australia (Australian Alps in northeast Victoria and the Blue Mountains in NSW) were used to test the algorithm against eight Landsat images with varying solar angles (seasons), and terrain types.
Visual assessment showed that the algorithm removed much of the topographic effect and detected deep shadows in all eight images. An indirect validation based on the change in correlation between the data and terrain slope showed that the correlation coefficient between the surface reflectance factor and the cosine of the incident (sun) angle reduced dramatically after the topographic correction algorithm was applied. The correlation coefficient typically reduced from 0.80-0.70 to 0.05 in areas of significant relief. It was also shown how the corrected surface reflectance can provide suitable input data for multi-temporal land cover classification in areas of high relief based on spectral signatures and spectral albedo, while the products based only on BRDF and atmospheric correction cannot. To provide comparison with previous work and to validate the proposed algorithm, two empirical methods based on the C-correction were used as well as the established sun-canopy-sensor SCS-method to provide benchmarks. The proposed method was found to achieve the same measures of shade reduction without empirical regression.
The Geometric QA software utilises an area based image-to-image correlation technique to assess and compare the difference between the target image and the reference image at regular gridded QA points. The residual of each QA point will be derived and scene statistics such as number of valid QA points, mean residual X/Y, STD residual X/Y and CEP90 will be recorded in v2 AGDC.
Because each scene recorded in v2 AGDC will have a GQA assessment result, the minimum GCP number threshold has been lowered to enable more products processed to Ortho level, especially for coastal scenes where only a very small portion of the image contains land for GCP identification purpose.
Product generation process is fully automated and there are checks in place to ensure that each step results in output meeting relevant product specification criteria. For example, the production system performs geometry QA before generating the final version of the product. Failed processes are rerun according to set up routines to ensure completeness of data. A sample of final data is verified manually for conformance to product specification.
At the end of the process, the system generates a companion dataset, the Pixel Quality assessment (PQ25). The PQ25 is a classification that represents an assessment of whether an image pixel represents an unobscured unsaturated observation of the Earth’s surface and whether the pixel is represented in each spectral band. In particular, whether a pixel contains:
Per band saturation assessment
Cloud shadow estimation
The PQ25 product allows users to produce masks which can be used to exclude affected pixels which don’t meet quality criteria from further analysis.
You can find the history in the latest version of the product.
Landsat data is provided by the United States Geological Survey (USGS) through direct reception of the data at Geoscience Australia’s satellite reception facility or download.
License and copyright
© Commonwealth of Australia (Geoscience Australia).
Released under Creative Commons Attribution 4.0 International Licence.