<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 130853 GA Landsat 5 TM Analysis Ready Data Collection 3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.

<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This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 132317 GA Landsat 8 OLI/TIRS Analysis Ready Data Collection 3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.

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).

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.

<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: 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.

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.

<b>Please Note:</b> The data related to this Abstract can be obtained by contacting <a href = "mailto: clientservices@ga.gov.au">Manager Client Services</a> and quoting Catalogue number 144231. The data are arranged by regions, so please download the Data Description document found in the Downloads tab to determine your area of interest. 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%.

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.

1. Band ratio: (B10+B12)/B11 Blue is low gypsum content. Red is high gypsum content. Accuracy: Very Low: Strongly complicated by dry vegetation and often inversely correlated with quartz-rich materials. Affected by discontinuous line-striping. Use in combination with FeOH product which is also sensitive to gypsum. Geoscience Applications: Useful for mapping: (1) evaporative environments (e.g. salt lakes) and associated arid aeolian systems (e.g. dunes); (2) acid waters (e.g. from oxidising sulphides) invading carbonate rich materials including around mine environments; and (3) hydrothermal (e.g. volcanic) systems.