Geoscience Australia Landsat 8 OLI-TIRS Analysis Ready Data Collection 3 This product takes Landsat 8 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 8. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 8 OLI-TIRS) https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-8-oli-tirs/ DEA Surface Reflectance NBART (Landsat 8 OLI-TIRS) https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-8-oli-tirs/ DEA Surface Reflectance OA (Landsat 8 OLI-TIRS) https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-8-oli-tirs/ The resolution is a 30 m grid based on the USGS Landsat Collection 1 and 2 archive. https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-8-oli-tirs/ For service status information, see https://status.dea.ga.gov.au

Geoscience Australia Landsat 5 TM Analysis Ready Data Collection 3 This product takes Landsat 5 Thematic Mapper (TM) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. It contains three sub-products that provide corrections or attribution information: DEA Surface Reflectance NBAR (Landsat 5 TM) https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-nbar-landsat-5-tm/ DEA Surface Reflectance NBART (Landsat 5 TM) https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-landsat-5-tm/ DEA Surface Reflectance OA (Landsat 5 TM) https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-landsat-5-tm/ The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive. https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-landsat-5-tm/ For service status information, see https://status.dea.ga.gov.au

DEA Land Cover Calendar Year (Landsat) Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://docs.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au

DEA Land Cover Environmental Descriptors Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps the understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time allows the observation of the state of land cover at specific times and therefore the way that land cover changes. For more information, see https://docs.dea.ga.gov.au/data/product/dea-land-cover-landsat/ For service status information, see https://status.dea.ga.gov.au

Geoscience Australia Landsat Fractional Cover Collection 3 Fractional Cover (FC), developed by the Joint Remote Sensing Research Program, is a measurement that splits the landscape into three parts, or fractions: green (leaves, grass, and growing crops) brown (branches, dry grass or hay, and dead leaf litter) bare ground (soil or rock) DEA uses Fractional Cover to characterise every 30 m square of Australia for any point in time from 1987 to today. https://docs.dea.ga.gov.au/data/product/dea-fractional-cover-landsat/ For service status information, see https://status.dea.ga.gov.au

Geoscience Australia Sentinel-2B MSI Analysis Ready Data Collection 3 This product takes Sentinel-2B imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2B. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. It contains two sub-products that provide corrections or attribution information: * DEA Surface Reflectance NBART (Sentinel-2B MSI) https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-nbart-sentinel-2b-msi/ * DEA Surface Reflectance OA (Sentinel-2B MSI) https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-oa-sentinel-2b-msi/ The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. Full product description: https://docs.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi/ For service status information, see https://status.dea.ga.gov.au

Fractional Cover 30m Percentiles 3.0.0 (Landsat, Annual) Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 30m x 30m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program. This contains the percentage of green vegetation, non-green vegetation and bare soil per pixel at the 10th, 50th (median) and 90th percentiles respectively for observations acquired in each full calendar year (1st of January - 31st December) from 1987 to the most recent full calendar year. Fractional Cover products use Water Observations (WO) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 3 clear observations over the year. https://docs.dea.ga.gov.au/data/product/dea-fractional-cover-percentiles-landsat/ For service status information, see https://status.dea.ga.gov.au

GA Barest Earth (Landsat) An estimate of the spectra of the barest state (i.e., least vegetation) observed from imagery of the Australian continent collected by the Landsat 5, 7, and 8 satellites over a period of more than 30 years (1983 - 2018). The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. This product complements the Landsat-8 Barest Earth which is based on the same algorithm but just uses Landsat8 satellite imagery from 2013-2108. Landsat-8's OLI sensor provides improved signal-to-noise radiometric (SNR) performance quantised over a 12-bit dynamic range compared to the 8-bit dynamic range of Landsat-5 and Landsat-7 data. However the Landsat 30+ Barest Earth has a greater capacity to find the barest ground due to the greater temporal depth. Reference: Roberts, D., Wilford, J., Ghattas, O. (2019). Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest. Nature Communications. Mosaics are available for the following years: Landsat 5 / Landsat 7 / Landsat 8 - 1983 to 2018; For service status information, see https://status.dea.ga.gov.au

GA Barest Earth (Sentinel-2) Abstract The Sentinel-2 Bare Earth thematic product provides the first national scale mosaic of the Australian continent to support improved mapping of soil and geology. The bare earth algorithm using all available Sentinel-2 A and Sentinel-2 B observations up to September 2020 preferentially weights bare pixels through time to significantly reduce the effect of seasonal vegetation in the imagery. The result are image pixels that are more likely to reflect the mineralogy and/or geochemistry of soil and bedrock. The algorithm uses a high-dimensional weighted geometric median approach that maintains the spectral relationships across all Sentinel-2 bands. A similar bare earth algorithm has been applied to Geoscience Australia’s deeper Landsat time series archive (please search for "Landsat barest Earth". Both bare earth products have spectral bands in the visible near infrared and shortwave infrared region of the electromagnetic spectrum. However, the main visible and near-infrared Sentinel-2 bands have a spatial resolution of 10 meters compared to 30m for the Landsat TM equivalents. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. Not all the sentinel-2 bands have been processed - we have excluded atmospheric bands including 1, 9 and 10. The remaining bands have been re-number 1-10 and these bands correlate to the original bands in brackets below: 1 = blue (2) , 2 = green (3) , 3 = red (4), 4 = vegetation red edge (5), 5 = vegetation red edge (6), 6= vegetation red edge (7), 7 = NIR(8), 8 = Narrow NIR (8a), 9 = SWIR1 (11) and 10 = SWIR2(12). All 10 bands have been resampled to 10 meters to facilitate band integration and use in machine learning. Pixel quality and the degree of barest recorded on the ground will largely depend on the number of ‘clean’ cloud free bare earth observation. We have noticed some image artefacts near the vicinity of Lake Eyre where highly reflective surface materials are not being well separated by the cloud filter – these artefacts will be address in a future improved version of the bare earth model. Lineage Statement Large-scale image composites are increasingly important for a variety of applications such as land cover mapping, soil and bedrock mapping, change detection, and the generation of high-quality data to parameterise and validate bio-physical and geophysical models. A number of compositing methodologies are being used in remote sensing in general, however challenges such as maintaining the spectral relationship between bands, mitigating against boundary artifacts due to mosaicking scenes from different epochs, and ensuring spatial regularity across the mosaic image still exist. The creation of good composite images is a particularly important technology since the opening of the Landsat archive by the United States Geological Survey. The greater availability of satellite imagery has resulted in demand to provide large regional mosaics that are representative of conditions over specific time periods while also being free of clouds and other unwanted image noise. One approach is the stitching together of a number of clear images. Another is the creation of mosaics where pixels from different epochs are combined based on some algorithm from a time series of observations. This ‘pixel composite’ approach to mosaic generation provides a more consistent result compared with stitching clear images due to the improved color balance created by the combining of one-by-one pixel representative images. Another strength of pixel-based composites is their ability to be automated for application to very large data collections and time series such as national satellite data archives. The Bare Earth pixel composite mosaic (BE-PCM) provides an approach that leverages high-dimensional statistical theory to deliver a spectrally consistent, artefact-free pixel composite product that is representative of the barest (i.e., least vegetation) state at each pixel over the specific time period. The BE-PCM is derived from Sentinel-2 A and Sentinel-2 B observations from 2014 to September 2020 corrected to measurements of NBAR surface reflectance (e.g., SR-N_25_2.0.0 or SR-NT_25_2.0.0). The data are masked for cloud, shadows and other image artefacts using the pixel quality product (PQ_25_2.0.0) to help provide as clear a set of observations as possible from which to calculate the BE-PCM. The BE-PCM methodology and algorithm is given in Roberts, Wilford, Ghattas (2018). The technology builds on the earlier work of Roberts et al. (2017) where a method for producing cloud-free pixel composite mosaics using ‘geometric medians’ was proposed. Note: The constituent pixels in the BE-PCM pixel composite mosaics are synthetic, meaning that the pixels have not been physically observed by the satellite. Rather they are the computed high-dimensional median of a time series of pixels which gives a robust estimate of the median state of the Earth at its barest (i.e., least vegetation). References Roberts, D., Wilford, J., Ghattas, O. (2018). Revealing the Australian Continent at its Barest. Submitted and under review. Roberts, D., Mueller, N., Mcintyre, A. (2017). High-dimensional pixel composites from earth observation time series. IEEE Transactions on Geoscience and Remote Sensing 55 (11), 6254-6264 For service status information, see https://status.dea.ga.gov.au

Mangrove Canopy Cover 30m 3.0.0 (Landsat, Collection 3) Mangrove canopy cover version 3.0.0, 30 metre. Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. The mangrove canopy cover product provides valuable information about the extent and canopy density of mangroves for each year between 1987 and 2021 for the entire Australian coastline. The canopy cover classes are: 20-50% (pale green), 50-80% (mid green), 80-100% (dark green). The product consists of a sequence (one per year) of 30 meter resolution maps that are generated by analysing the Landsat fractional cover (https://doi.org/10.6084/m9.figshare.94250.v1) developed by the Joint Remote Sensing Research Program and the Global Mangrove Watch layers (https://doi.org/10.1071/MF13177) developed by the Japanese Aerospace Exploration Agency. https://docs.dea.ga.gov.au/data/product/dea-mangrove-canopy-cover-landsat/ For service status information, see https://status.dea.ga.gov.au