1. Band ratio: B2/B1 Blue-cyan is goethite rich, Green is hematite-goethite, Red-yellow is hematite-rich (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.

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.

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: 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.

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%. <b>Citation:</b> Wilford, J. and Roberts, D., 2020. Enhanced barest earth Landsat imagery for soil and lithological modelling. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

1. 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).

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 132310 GA Landsat 7 ETM+ Analysis Ready Data Collection 3</b> Surface Reflectance (SR) is a suite of Earth Observation (EO) products from GA. The SR product suite provides standardised optical surface reflectance datasets using robust physical models to correct for variations in image radiance values due to atmospheric properties, and sun and sensor geometry. The resulting stack of surface reflectance grids are consistent over space and time which is instrumental in identifying and quantifying environmental change. SR is based on radiance data from the Landsat TM/ETM+ and OLI sensors.

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).

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.