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, 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

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

The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B2/B1 - Blue-cyan is goethite rich, - Green is hematite-goethite, - Red-yellow is hematite-rich Useful For: (1) Mapping transported materials (including palaeochannels) characterised by hematite (relative to geothite). Combine with AlOH composition to find co-located areas of hematite and poorly ordered kaolin to map transported materials; and (2) hematite-rish areas in drier conditions (eg above the water table) whereas goethite-rich in wetter conditions (eg at/below the water or areas recently exposed). May also be climate driven. For 'Ferric Oxide Composition' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74352 For service status information, see https://status.dea.ga.gov.au

The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: (B6+B8)/B7 - Blue is low content, - Red is high content (potentially includes: chlorite, epidote, jarosite, nontronite, gibbsite, gypsum, opal-chalcedony Useful for mapping: (1) jarosite (acid conditions) – in combination with ferric oxide content (high); (2) gypsum/gibbsite – in combination with ferric oxide content (low); (3) magnesite - in combination with ferric oxide content (low) and MgOH content (moderate-high); (4) chlorite (e.g. propyllitic alteration) – in combination with Ferrous in MgOH (high); and (5) epidote (calc-silicate alteration) – in combination with Ferrous in MgOH (low). For 'FeOH Group Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74358 For service status information, see https://status.dea.ga.gov.au

The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band ratio: B4/B3 - Blue is low abundance, - Red is high abundance Useful for: (1) Exposed iron ore (hematite-goethite). Use in combination with the “Opaques index” to help separate/map dark: (a) surface lags (e.g. maghemite gravels) which can be misidentified in visible and false colour imagery; and (b) magnetite in BIF and/or bedded iron ore; and (2) Acid conditions: combine with FeOH Group content to help map jarosite which will have high values in both products. Mapping hematite versus goethite mapping is NOT easily achieved as ASTER’s spectral bands were not designed to capture diagnostic iron oxide spectral behaviour. However, some information on visible colour relating in part to differences in hematite and/or goethite content can be obtained using a ratio of B2/B1 especially when this is masked using a B4/B3 to locate those pixels with sufficient iro oxide content. For 'Ferric Oxide Content' dataset information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74351 For service status information, see https://status.dea.ga.gov.au

The National ASTER Map of Australia is the parent datafile of a dataset that comprises a set of 14+ geoscience products made up of mosaiced ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) scenes across Australia. ASTER calibration, processing and standardisation approaches have been produced as part of a large multi-agency project to facilitate uptake of these techniques and make them easily integrated with other datasets in a GIS. Collaborative research, undertaken by Geoscience Australia, the Commonwealth Scientific Research Organisation (CSIRO) and state and industry partners, on the world-class Mt Isa mineral province in Queensland was completed in 2008 as a test-case for these new methods. For parent datafile information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/74347 Band Ratio: (B10+B12)/B11 - Blue is low gypsum content, - Red is high gypsum content Useful for mapping: (1) evaporative environments (e.g. salt lakes) and associated arid aeolian systems (e.g. dunes); (2) acid waters (e.g. from oxidising sulphides) invading carbonate rich materials including around mine environments; and (3) hydrothermal (e.g. volcanic) systems. For service status information, see https://status.dea.ga.gov.au

Weathering Intensity Weathering intensity or the degree of weathering is an important characteristic of the earth’s surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. For service status information, see https://status.dea.ga.gov.au