1. Band ratio: B13/B10 Blue is low silica content Red is high silica content (potentially includes Si-rich minerals, such as quartz, feldspars, Al-clays) Geoscience Applications: Broadly equates to the silica content though the intensity (depth) of this reststrahlen feature is also affected by particle size <250 micron. Useful product for mapping: (1) colluvial/alluvial materials; (2) silica-rich (quartz) sediments (e.g. quartzites); (3) silification and silcretes; and (4) quartz veins. Use in combination with quartz index, which is often correlated with the Silica index.

1. Band ratio: B4/B3 Blue is low abundance, Red is high abundance (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 (3) 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.

1. Band ratio: (B6+B9/(B7+B8) Blue is low content, Red is high content (potentially includes: calcite, dolomite, magnesite, chlorite, epidote, amphibole, talc, serpentine) Useful for mapping: (1) "hydrated" ferromagnesian rocks rich in OH-bearing tri-octahedral silicates like actinolite, serpentine, chlorite and talc; (2) carbonate-rich rocks, including shelf (palaeo-reef) and valley carbonates(calcretes, dolocretes and magnecretes); and (3) lithology-overprinting hydrothermal alteration, e.g. "propyllitic alteration" comprising chlorite, amphibole and carbonate. The nature (composition) of the silicate or carbonate mineral can be further assessed using the MgOH composition product.

1. Band ratio: B5/B7 Blue is well ordered kaolinite, Al-rich muscovite/illite, paragonite, pyrophyllite Red is Al-poor (Si-rich) muscovite (phengite) useful for mapping: (1) exposed saprolite/saprock is often white mica or Al-smectite (warmer colours) whereas transported materials are often kaolin-rich (cooler colours); (2) clays developed over carbonates, especially Al-smectite (montmorillonite, beidellite) will produce middle to warmers colours. (2) stratigraphic mapping based on different clay-types; and (3) lithology-overprinting hydrothermal alteration, e.g. Si-rich and K-rich phengitic mica (warmer colours). Combine with Ferrous iron in MgOH and FeOH content products to look for evidence of overlapping/juxtaposed potassic metasomatism in ferromagnesian parents rocks (e.g. Archaean greenstone associated Au mineralisation) +/- associated distal propyllitic alteration (e.g. chlorite, amphibole).

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)

1. Band ratio: B11/(B10+B12) Blue is low quartz content Red is high quartz content Geoscience Applications: Use in combination with Silica index to more accurately map "crystalline" quartz rather than poorly ordered silica (e.g. opal), feldspars and compacted clays.

1. Band ratio: B7/B8 Blue-cyan is magnesite-dolomite, amphibole, chlorite Red is calcite, epidote, amphibole useful for mapping: (1) exposed parent material persisting through "cover"; (2) "dolomitization" alteration in carbonates - combine with Ferrous iron in MgOH product to help separate dolomite versus ankerite; (3) lithology-cutting hydrothermal (e.g. propyllitic) alteration - combine with FeOH content product and ferrous iron in Mg-OH to isolate chlorite from actinolite versus talc versus epidote; and (4) layering within mafic/ultramafic intrusives. useful for mapping: (1) exposed parent material persisting through "cover"; (2) "dolomitization" alteration in carbonates - combine with Ferrous iron in MgOH product to help separate dolomite versus ankerite; (3) lithology-cutting hydrothermal (e.g. propyllitic) alteration - combine with FeOH content product and ferrous iron in Mg-OH to isolate chlorite from actinolite versus talc versus epidote; and (4) layering within mafic/ultramafic intrusives. useful for mapping: (1) exposed parent material persisting through "cover"; (2) "dolomitization" alteration in carbonates - combine with Ferrous iron in MgOH product to help separate dolomite versus ankerite; (3) lithology-cutting hydrothermal (e.g. propyllitic) alteration - combine with FeOH content product and ferrous iron in Mg-OH to isolate chlorite from actinolite versus talc versus epidote; and (4) layering within mafic/ultramafic intrusives.

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.

1. Band ratio: (B5+B7)/B6 Blue is low abundance, Red is high abundance potentially includes: phengite, muscovite, paragonite, lepidolite, illite, brammalite, montmorillonite, beidellite, kaolinite, dickite Useful for mapping: (1) exposed saprolite/saprock (2) clay-rich stratigraphic horizons; (3) lithology-overprinting hydrothermal phyllic (e.g. white mica) alteration; and (4) clay-rich diluents in ore systems (e.g. clay in iron ore). Also combine with AlOH composition to help map: (1) exposed in situ parent material persisting through "cover" which can be expressed as: (a) more abundant AlOH content + (b) long-wavelength (warmer colour) AlOH composition (e.g. muscovite/phengite).

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 132317 GA Landsat 8 OLI/TIRS Analysis Ready Data Collection 3</b> The PQ25 product facilitates interpretation and processing of Surface Reflectance (SR-N/NT), Fractional Cover 25 (FC25) and all derivative products. PQ25 is an assessment of each image pixel to determine if it is an unobscured, unsaturated observation of the Earth's surface and also whether the pixel is represented in each spectral band. The PQ product allows users to produce masks which can be used to exclude pixels which don't meet their quality criteria from analysis . The capacity to automatically exclude such pixels is essential for emerging multi-temporal analysis techniques that make use of every quality assured pixel within a time series of observations. Users can choose to process only land pixels, or only sea pixels depending on their analytical requirements, leading to enhanced computationally efficient.