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: (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.

B6/B5 (potential includes: pyrophyllite, alunite, well-ordered kaolinite) Blue is low content, Red is high content Useful for mapping: (1) different clay-type stratigraphic horizons; (2) lithology-overprinting hydrothermal alteration, e.g. high sulphidation, "advanced argillic" alteration comprising pyrophyllite, alunite, kaolinite/dickite; and (3) well-ordered kaolinite (warmer colours) versus poorly-ordered kaolinite (cooler colours) which can be used for mapping in situ versus transported materials, respectively.

1. Band ratio: B5/B4 Blue is low ferrous iron content in carbonate and MgOH minerals like talc and tremolite. Red is high ferrous iron content in carbonate and MgOH minerals like chlorite and actinolite. Useful for mapping: (1) un-oxidised "parent rocks" - i.e. mapping exposed parent rock materials (warm colours) in transported cover; (2) talc/tremolite (Mg-rich - cool colours) versus actinolite (Fe-rich - warm colours); (3) ferrous-bearing carbonates (warm colours) potentially associated with metasomatic "alteration"; (4) calcite/dolomite which are ferrous iron-poor (cool colours); and (5) epidote, which is ferrous iron poor (cool colours) - in combination with FeOH content product (high).

<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.

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: B1/B4 Blue is low abundance, Red is high abundance (potentially includes carbon black (e.g. ash), magnetite, Mn oxides, and sulphides in unoxidised envornments Useful for mapping: (1) magnetite-bearing rocks (e.g. BIF); (2) maghemite gravels; (3) manganese oxides; (4) graphitic shales. Note 1: (1) and (4) above can be evidence for "reduced" rocks when interpreting REDOX gradients. Combine with AlOH group Content (high values) and Composition (high values) products, to find evidence for any invading "oxidised" hydrothermal fluids which may have interacted with reduced rocks evident in the Opaques index product.

Band ratio: B3/B2 Blue is low content Red is high content Use this image to help interpret the amount of "obscuring/complicating" green vegetation cover.

<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.

<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.