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

**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

**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, Annual Frequency Statistics, Calendar Year (Landsat, Collection 3, 30 m, WO-STATS-ANNUAL, 3.1.6).** The DEA Annual Water Observation Statistic is a set of calendar year statistical summaries of the DEA Water Observations product that combines satellite observations, that occur within each calendar year from 1986 to present, 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, Multi Year Frequency Statistics, 1986 to near present (Landsat, Collection 3, 30 m, WO-STATS, Frequency, 3.1.6).** The DEA Multi Year Water Observation Statistic is a statistical summary that combines all years (1986 to near present) of the DEA Water Observations product and helps the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations that were observed over the specifed 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

Water Observations from Space Filtered Statistics 25m 2.1.5 (Landsat, Filtered) Water Observations from Space (WOfS) Filtered Statistics helps provide the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WOfS Filtered Statistics consists of a Confidence layer that compares the WOfS Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WOfS Statistics water summary where Confidence is low. This layer is Filtered Water Summary: A simplified version of the Water Summary, showing the frequency of water observations where the Confidence is above a cutoff level. No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. The Filtered Water Summary layer is a noise-reduced view of surface water across Australia. Even though confidence filtering is applied to the Filtered Water Summary, some cloud and shadow, and sensor noise does persist. For more information please see: https://data.dea.ga.gov.au/?prefix=WOfS/filtered_summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au

Water Observations from Space Statistics 25m 2.1.5 (Landsat, Wet) Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products which help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This layer contains Wet Count: how many times water was detected in observations that were clear. No clear observations of water causes an area to appear transparent, 1-50 total clear observations of water correlate with red and yellow colours, 100 clear observations of water correlate with green, 200 clear observations of water correlates with light blue, 300 clear observations of water correlates to deep blue and 400 and over observations of clear water correlate to purple. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au

Intertidal Extents Model 25m 2.0.0 (Extents) The Intertidal Extents Model (ITEM v2.0) product analyses GA’s historic archive of satellite imagery to derive a model of the spatial extents of the intertidal zone throughout the tidal cycle. The model can assist in understanding the relative elevation profile of the intertidal zone, delineating exposed areas at differing tidal heights and stages. The product differs from previous methods used to map the intertidal zone which have been predominately focused on analysing a small number of individual satellite images per location (e.g Ryu et al., 2002; Murray et al., 2012). By utilising a full 30 year time series of observations and a global tidal model (Egbert and Erofeeva, 2002), the methodology enables us to overcome the requirement for clear, high quality observations acquired concurrent to the time of high and low tide. *Accuracy and limitations* Due the sun-synchronous nature of the various Landsat sensor observations; it is unlikely that the full physical extents of the tidal range in any cell will be observed. Hence, terminology has been adopted for the product to reflect the highest modelled tide observed in a given cell (HOT) and the lowest modelled tide observed (LOT) (see Sagar et al. 2017). These measures are relative to Mean Sea Level, and have no consistent relationship to Lowest (LAT) and Highest Astronomical Tide (HAT). The inclusion of the lowest (LMT) and highest (HMT) modelled tide values for each tidal polygon indicates the highest and lowest tides modelled for that location across the full time series by the OTPS model. The relative difference between the LOT and LMT (and HOT and HMT) heights gives an indication of the extent of the tidal range represented in the Relative Extents Model. As in ITEM v1.0, v2.0 contains some false positive land detection in open ocean regions. These are a function of the lack of data at the extremes of the observed tidal range, and features like glint and undetected cloud in these data poor regions/intervals. Methods to isolate and remove these features are in development for future versions. Issues in the DEA archive and data noise in the Esperance, WA region off Cape Le Grande and Cape Arid (Polygons 236,201,301) has resulted in significant artefacts in the model, and use of the model in this area is not recommended. The Confidence layer is designed to assess the reliability of the Relative Extent Model. Within each tidal range percentile interval, the pixel-based standard deviation of the NDWI values for all observations in the interval subset is calculated. The average standard deviation across all tidal range intervals is then calculated and retained as a quality indicator in this product layer. The Confidence Layer reflects the pixel based consistency of the NDWI values within each subset of observations, based on the tidal range. Higher standard deviation values indicate water classification changes not based on the tidal cycle, and hence lower confidence in the extent model. Possible drivers of these changes include: Inadequacies of the tidal model, due perhaps to complex coastal bathymetry or estuarine structures not captured in the model. These effects have been reduced in ITEM v2.0 compared to previous versions, through the use of an improved tidal modelling frameworkChange in the structure and exposure of water/non-water features NOT driven by tidal variation. For example, movement of sand banks in estuaries, construction of man-made features (ports etc.).Terrestrial/Inland water features not influenced by the tidal cycle. File naming: THE RELATIVE EXTENTS MODEL v2.0 ITEM_REL_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE> TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file LONGITUDE is the longitude of the centroid of the tidal polygon LATITUDE is the latitude of the centroid of the tidal polygon THE CONFIDENCE LAYER v2.0 ITEM_STD_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE> TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file LONGITUDE is the longitude of the centroid of the tidal polygon LATITUDE is the latitude of the centroid of the tidal polygon *Overview* The Intertidal Extents Model product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range (Sagar et al. 2017).The current version (2.0) utilises all Landsat observations (5, 7, and 8) for Australian coastal regions (excluding off-shore Territories) between 1986 and 2016 (inclusive). ITEM v2.0 has implemented an improved tidal modelling framework (see Sagar et al. 2018) over that utilised in ITEM v1.0. The expanded Landsat archive within the Digital Earth Australia (DEA) has also enabled the model extent to be increased to cover a number of offshore reefs, including the full Great Barrier Reef and southern sections of the Torres Strait Islands. The DEA archive and new tidal modelling framework has improved the coverage and quality of the ITEM v2.0 relative extents model, particularly in regions where AGDC cell boundaries in ITEM v1.0 produced discontinuities or the imposed v1.0 cell structure resulted in poor quality tidal modelling (see Sagar et al. 2017). https://docs.dea.ga.gov.au/data/product/dea-intertidal-extents-landsat/ For service status information, see https://status.dea.ga.gov.au