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.

1. Band ratio: (B5+B7)/B6 Blue is low abundance, Red is high abundance potentially includes: phengite, muscovite, paragonite, lepidolite, illite, brammalite, montmorillonite, beidellite, kaolinite, dickite Useful for mapping: (1) exposed saprolite/saprock (2) clay-rich stratigraphic horizons; (3) lithology-overprinting hydrothermal phyllic (e.g. white mica) alteration; and (4) clay-rich diluents in ore systems (e.g. clay in iron ore). Also combine with AlOH composition to help map: (1) exposed in situ parent material persisting through "cover" which can be expressed as: (a) more abundant AlOH content + (b) long-wavelength (warmer colour) AlOH composition (e.g. muscovite/phengite).

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 Earth Observations from space created by Geoscience Australia. This collection specifically is focused on derived or value-added products. Example products include: Fractional Cover (FC), Australian Geographic Reference Image (AGRI), and InterTidal Extents Model (ITEM) etc.

This collection contains Earth Observations from space created by Geoscience Australia. This collection specifically is focused on data and derived data from the European Commission's Copernicus Programme. Example products include: Sentinel-1-CSAR-SLC, Sentinel-2-MSI-L1C, Sentinel-3-OLCI etc.

<div>The recent federal funding of the <em>National Space Mission for Observation</em> is in no small part a recognition of the capability of the Australian EO community and central to this is the ability to mount effective national-scale field validation programs.</div><div><br></div><div>After many delays, Landsat 9 was launched on the 27th September 2021. Before being handed to the USGS for operational use, NASA had oversight of configuring and testing the new platform and navigating it into its final operational orbit.&nbsp;For a brief few days and a handful of overpasses globally, Landsat 9 was scheduled to fly ‘under’ its predecessor Landsat 8. &nbsp;This provided the global EO community a ‘once in a mission lifetime’ opportunity to collect field validation data from both sensors.</div><div><br></div><div>At short notice the USGS were advised on the timing and location of these orbital overpasses. &nbsp;For Australia, this meant that between the 11th and 17th&nbsp;of November we would see a single overpass with 100% sensor overlap and three others that featured only 10% overlap. Geoscience Australia (who have a longstanding partnership with the USGS on satellite Earth observation) put out a call to the Australian EO community for collaborators.</div><div><br></div><div>Despite this compressed timeline, COVID travel restrictions and widespread La Niña induced rain and flooding, teams from CSIRO, Queensland DES, Environment NSW, University of WA, Frontier SI and GA were able to capture high value ground and water validation data in each of the overpasses.</div><div><br></div><div>Going forward, the Australian EO community need to maintain and build on these skills and capabilities such that the community can meet the future demands of not only our existing international EO collaborations but the imminent arrival of Australian orbiting EO sensors. Abstract presented at Advancing Earth Observation Forum 2022 (https://www.eoa.org.au/event-calendar/2021/12/1/advancing-earth-observation-aeo-2021-22-forum)

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.

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.

<div>This document steps educators and students through some of the uses of the satellite data on the Digital Earth Australia (DEA) Portal, with a particular focus on changes to landscapes and coasts over time. Instructions and questions are provided so educators and students can explore the data sets as they work their way through the document. The document also gives a brief background on how satellites operate and how they capture imagery.</div><div><br></div>

<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.