A Multi-scale topographic position image of Australia has been generated by combining a topographic position index and topographic ruggedness. Topographic Position Index (TPI) measures the topographic slope position of landforms. Ruggedness informs on the roughness of the surface and is calculated as the standard deviation of elevations. Both these terrain attributes are therefore scale dependent and will vary according to the size of the analysis window. Based on an algorithm developed by Lindsay et al. (2015) we have generated multi-scale topographic position model over the Australian continent using 3 second resolution (~90m) DEM derived from the Shuttle Radar Topography Mission (SRTM). The algorithm calculates topographic position scaled by the corresponding ruggedness across three spatial scales (window sizes) of 0.2-8.1 Km; 8.2-65.2 Km and 65.6-147.6 Km. The derived ternary image captures variations in topographic position across these spatial scales (blue local, green intermediate and red regional) and gives a rich representation of nested landform features that have broad application in understanding geomorphological and hydrological processes and in mapping regolith and soils over the Australian continent. Lindsay, J, B., Cockburn, J.M.H. and Russell, H.A.J. 2015. An integral image approach to performing multi-scale topographic position analysis, Geomorphology 245, 51–61.

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: Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest - Dale Roberts, John Wilford and Omar Ghattas Ghattas (2019). Nature Communications, DOI: 10.1038/s41467-019-13276-1. https://www.nature.com/articles/s41467-019-13276-1

This collection contains Earth Observations from space created by Geoscience Australia. This collection specifically is focused on data and derived data from the European Commission's Copernicus Programme. Example products include: Sentinel-1-CSAR-SLC, Sentinel-2-MSI-L1C, Sentinel-3-OLCI etc.

This collection contains Earth Observations from space created by Geoscience Australia. This collection specifically is focused on RADAR and Synthetic Aperture Radar (SAR) data. Example products include: ALOS SLC, ENVISAT raw etc.

1. 3 band RGB composite Red: B3/B2 Green: B3/B7 Blue: B4/B7 (white = green vegetation) Use this image to help interpret (1) the amount of green vegetation cover (appears as white); (2) basic spectral separation (colour) between different regolith and geological units and regions/provinces; and (3) evidence for unmasked cloud (appears as green).

1. Band ratio: B5/B4 Blue is low abundance, Red is high abundance This product can help map exposed "fresh" (un-oxidised) rocks (warm colours) especially mafic and ultramafic lithologies rich in ferrous silicates (e.g. actinolite, chlorite) and/or ferrous carbonates (e.g. ferroan dolomite, ankerite, siderite). Applying an MgOH Group content mask to this product helps to isolate ferrous bearing non-OH bearing minerals like pyroxenes (e.g. jadeite) from OH-bearing or carbonate-bearing ferrous minerals like actinolite or ankerite, respectively. Also combine with the FeOH Group content product to find evidence for ferrous-bearing chlorite (e.g. chamosite).

This collection contains Earth Observations from space created by Geoscience Australia. This collection specifically is focused on optical data. Example products include: Landsat NBAR Surface Reflectance, and Landsat pixel quality, etc.

This collection contains Earth Observations from space created by Geoscience Australia. This collection specifically is focused on derived or value-added products. Example products include: Fractional Cover (FC), Australian Geographic Reference Image (AGRI), and InterTidal Extents Model (ITEM) etc.

<b>Please Note:</b> The data related to this Abstract can be obtained by contacting <a href = "mailto: clientservices@ga.gov.au">Manager Client Services</a> and quoting Catalogue number 144231. The data are arranged by regions, so please download the Data Description document found in the Downloads tab to determine your area of interest. Remotely sensed datasets provide fundamental information for understanding the chemical, physical and temporal dynamics of the atmosphere, lithosphere, biosphere and hydrosphere. Satellite remote sensing has been used extensively in mapping the nature and characteristics of the terrestrial land surface, including vegetation, rock, soil and landforms, across global to local-district scales. With the exception of hyper-arid regions, mapping rock and soil from space has been problematic because of vegetation that either masks the underlying substrate or confuses the spectral signatures of geological materials (i.e. diagnostic mineral spectral features), making them difficult to resolve. As part of the Exploring for the Future program, a new barest earth Landsat mosaic of the Australian continent using time-series analysis significantly reduces the influence of vegetation and enhances mapping of soil and exposed rock from space. Here, we provide a brief background on geological remote sensing and describe a suite of enhanced images using the barest earth Landsat mosaic for mapping surface mineralogy and geochemistry. These geological enhanced images provide improved inputs for predictive modelling of soil and rock properties over the Australian continent. In one case study, use of these products instead of existing Landsat TM band data to model chromium and sodium distribution using a random forest machine learning algorithm improved model performance by 28–46%.

This 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. The individual geoscience products are a combination of bands and band ratios to highlight different mineral groups and parameters including: False colour composite CSIRO Landsat TM Regolith Ratios Green vegetation content Ferric oxide content Ferric oxide composition Ferrous iron index Opaque index AlOH group content AlOH group composition Kaolin group index FeOH group content MgOH group content MgOH group composition Ferrous iron content in MgOH/carbonate Surface mineral group distribution (relative abundance and composition)