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.

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.

This collection contains processing environments for use by external users of the Australian Geoscience Data Cube (AGDC).

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 raw and ancillary information used to generate Geoscience Australia data products.

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).

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 145498 Geoscience Australia Landsat Fractional Cover Collection 3</b> The Fractional Cover (FC) algorithm was developed by the Joint Remote Sensing Research Program and is described in described in Scarth et al. (2010). It has been implemented by Geoscience Australia for every observation from Landsat Thematic Mapper (Landsat 5), Enhanced Thematic Mapper (Landsat 7) and Operational Land Imager (Landsat 8) acquired since 1987. It is calculated from surface reflectance (SR-N_25_2.0.0). FC_25 provides a 25m scale fractional cover representation of the proportions of green or photosynthetic vegetation, non-photosynthetic vegetation, and bare surface cover across the Australian continent. The fractions are retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model. For further information please see the articles below describing the method implemented which are free to read: - Scarth, P, Roder, A and Schmidt, M 2010, 'Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis', Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference, Schmidt, M, Denham, R and Scarth, P 2010, 'Fractional ground cover monitoring of pastures and agricultural areas in Queensland', Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference A summary of the algorithm developed by the Joint Remote Sensing Centre is also available from the AusCover website: http://data.auscover.org.au/xwiki/bin/view/Product+pages/Landsat+Fractional+Cover Fractional cover data can be used to identify large scale patterns and trends and inform evidence based decision making and policy on topics including wind and water erosion risk, soil carbon dynamics, land management practices and rangeland condition. This information could enable policy agencies, natural and agricultural land resource managers, and scientists to monitor land conditions over large areas over long time frames.

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).