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.

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

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)

This collection contains processing environments and code repositories created by Geoscience Australia used to generate National Earth and Marine Observations products.

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

This collection contains satellite imagery or Earth Observations from space created by Geoscience Australia. Among others, the collection includes data from various satellite sensors including Landsat Thematic Mapper and Multi-Spectral Scanner, Terra and Aqua MODIS.

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).

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).

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).

<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.