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 8 OLI/TIRS) A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. 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 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. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. Mosaics are available for the following years: Landsat 8: 2013 to 2017; 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

**Geoscience Australia Water Observations (Landsat, Collection 3, 30 m, Individual Observations, 3.1.6).** Water Observations are the principal Digital Earth Australia (DEA) Water product (previously known as Water Observations from Space (WOfS)). This product shows where surface water was observed within each individual Landsat (5, 7 and 8) satellite image on each particular day since mid 1986. These daily data layers are termed Water Observations (WOs). WOs show the extent of water in a corresponding Landsat scene, along with the degree to which the scene was obscured by clouds, shadows or where sensor problems cause parts of a scene to not be observable. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://docs.dea.ga.gov.au/data/product/dea-water-observations-landsat/ For service status information, see https://status.dea.ga.gov.au

Tasseled Cap Indices Percentiles 30m 1.0.0 The Tasseled Cap Indices Percentiles provide an annual summary of landscape wetness, greenness and brightness indices that can be used to identify wetlands and groundwater ecosystems. They provide annual statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap indices. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. Geoscience Australia Landsat Collection 3 Tasseled Cap Indices Percentiles, 30 metre, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Areas that are partially covered in water, or where water is mixed with vegetation when viewed from above, provide habitat for a wide range of aquatic organisms. The ability to map partial inundation is also crucial to understand patterns of human water use. We need to be able to identify potential wetlands and groundwater dependent ecosystems on the Australian continent so that they can be monitored and managed. The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. This product provides valuable discrimination for characterising: - vegetated wetlands, - salt flats, - salt lakes, - coastal land cover classes The Tasseled Cap indices transform translates the six spectral bands of Landsat into a single wetness index. The wetness index can be used to identify areas in the landscape that are potentially wetlands or groundwater dependent ecosystems. The Tasseled Cap Indices Percentiles capture how the wetness, greeness and brightness index behaves over time. The percentiles are well suited to characterising wetlands, salt flats/salt lakes and coastal ecosystems. However, care should be applied when analysing the wetness index, as soil colour and fire scars can cause misleading results. In areas of high relief caused by cliffs or steep terrain, terrain shadows can cause false positives (a falsely high wetness index). The 10th, 50th and 90th percentiles of the Tasseled Cap Indices are intended to capture the extreme (10th and 90th percentile) values and long-term average (50th percentile) values of the indices. Percentiles are used in preference to minimum, maximum and mean, as the min/max/mean statistical measures are more sensitive to undetected cloud/cloud shadow, and can be misleading for non-normally distributed data. The Tasseled Cap Indices Percentiles are intended to complement the Water Observations (WO) algorithm. WO is designed to discriminate open water, but the Tasseled Cap wetness index identifies areas of water and areas where water and vegetation are mixed together; i.e. mangroves and palustrine wetlands. If you are interested in terrestrial vegetation (where water in the pixel is not a factor), use the Fractional Cover product, which provides a better biophysical characterisation of green vegetation fraction, dry vegetation fraction and bare soil vegetation fraction. In terms of limitations, caution should be used, especially with the Tasseled Cap Indices results in areas where residual terrain shadow, or dark soils can cause high 'wetness' index values. One of the limitations of using the Tasseled Cap wetness index is that it will identify all 'wet' things, including potential wetlands, groundwater dependent ecosystems, irrigated crops/pasture, man-made water storages and sewerage treatment, and does not discriminate between these. As such it should be used in conjunction with other contextual data to ensure that features identified using the Tasseled Cap Wetness Percentiles are features of interest rather than false positives. We used the Tasseled Cap transforms described in Crist et al. (1985). Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3), 301–306. https://doi.org/10.1016/0034-4257(85)90102-6 For service status information, see https://status.dea.ga.gov.au

Tasseled Cap Wetness Percentiles 25m 2.0.0 The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. Geoscience Australia Landsat Collection 2 Tasseled Cap Wetness Percentiles 1986-2018, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Areas that are partially covered in water, or where water is mixed with vegetation when viewed from above, provide habitat for a wide range of aquatic organisms. The ability to map partial inundation is also crucial to understand patterns of human water use. We need to be able to identify potential wetlands and groundwater dependent ecosystems on the Australian continent so that they can be monitored and managed. The Tasseled Cap Wetness Percentiles provide a multi-decadal summary of landscape wetness that can be used to identify wetlands and groundwater ecosystems. They provide statistical summaries (10th, 50th and 90th percentiles) of the Tasseled Cap wetness index from 1987 to 2017. They are intended for use as inputs into classification algorithms to identify potential wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms. This product provides valuable discrimination for characterising: - vegetated wetlands, - salt flats, - salt lakes, - coastal land cover classes The Tasseled Cap wetness transform translates the six spectral bands of Landsat into a single wetness index. The wetness index can be used to identify areas in the landscape that are potentially wetlands or groundwater dependent ecosystems. The Tasseled Cap Wetness Percentiles capture how the wetness index behaves over time. The percentiles are well suited to characterising wetlands, salt flats/salt lakes and coastal ecosystems. However, care should be applied when analysing the wetness index, as soil colour and fire scars can cause misleading results. In areas of high relief caused by cliffs or steep terrain, terrain shadows can cause false positives (a falsely high wetness index). The 10th, 50th and 90th percentiles of the Tasseled Cap wetness index are intended to capture the extreme (10th and 90th percentile) values and long-term average (50th percentile) values of the wetness index. Percentiles are used in preference to minimum, maximum and mean, as the min/max/mean statistical measures are more sensitive to undetected cloud/cloud shadow, and can be misleading for non-normally distributed data. The Tasseled Cap Wetness Percentiles are intended to complement the Water Observations from Space (WOfS) algorithm. WOfS is designed to discriminate open water, but the Tasseled Cap wetness index identifies areas of water and areas where water and vegetation are mixed together; i.e. mangroves and palustrine wetlands. If you are interested in terrestrial vegetation (where water in the pixel is not a factor), use the Fractional Cover product, which provides a better biophysical characterisation of green vegetation fraction, dry vegetation fraction and bare soil vegetation fraction. In terms of limitations, caution should be used, especially with the Tasseled Cap wetness index results in areas where residual terrain shadow, or dark soils can cause high 'wetness' index values. One of the limitations of using the Tasseled Cap wetness index is that it will identify all 'wet' things, including potential wetlands, groundwater dependent ecosystems, irrigated crops/pasture, man-made water storages and sewerage treatment, and does not discriminate between these. As such it should be used in conjunction with other contextual data to ensure that features identified using the Tasseled Cap Wetness Percentiles are features of interest rather than false positives. We used the Tasseled Cap transforms described in Crist et al. (1985). Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3), 301–306. https://doi.org/10.1016/0034-4257(85)90102-6 For service status information, see https://status.dea.ga.gov.au

**Prototype Geoscience Australia Sentinel 2 Near Real Time Water Observations Provisional Collection 3** This product is the implementation of the DEA Water Observations (DEA WO) product (previously known as Water Observations from Space, or WOfS) on the Geoscience Australia Sentinel-2 Near Real Time surface reflectance product. This is a rapid, provisional, product. It has not been validated and is of unknown accuracy. The Provisional Digital Earth Australia Water Observations (Sentinel-2) product shows where surface water was observed by the Sentinel 2A and Sentinel 2B satellites on any particular day over the most recent 3 months. The surface water observations are derived from Geoscience Australia Sentinel-2 Near Real Time surface reflectance imagery for all of Australia. The provisional, Near Real Time product is available for a rolling window of the most recent three months of data, and is produced within 24 hours of the satellite passing over an area. For more information, see https://docs.dea.ga.gov.au/data/product/dea-water-observations-provisional-sentinel-2-nrt/

**Geoscience Australia Water Observations, Seasonal Frequency Statistics, November to March (Landsat, Collection 3, 30 m, WO-STATS-NOV-MAR, 3.1.6).** The DEA Seasonal Water Observation (November to March) Statistic is a set of seasonal statistical summaries of the DEA Water Observations product. The product combines satellite observations, that occur during November to March, into summary products that help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; and the percentage of wet observations that were observed over the specified time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://docs.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au

**Geoscience Australia Water Observations, Seasonal Frequency Statistics, April to October (Landsat, Collection 3, 30 m, WO-STATS-APR-OCT, 3.1.6).** The DEA Seasonal Water Observation (April to October) Statistic is a set of seasonal statistical summaries of the DEA Water Observations product. The product combines satellite observations, that occur during April to October within each year, into summary products that help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; and the percentage of wet observations that were observed over the specified time period in the landscape. **What this product offers** Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected in observations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. For more information, see https://docs.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/ For service status information, see https://status.dea.ga.gov.au