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This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 130853 GA Landsat 5 TM Analysis Ready Data Collection 3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

This report describes the results of an extended national field spectroscopy campaign designed to validate the Landsat 8 and Sentinel 2 Analysis Ready Data (ARD) surface reflectance (SR) products generated by Digital Earth Australia. Field spectral data from 55 overpass coincident field campaigns have been processed to match the ARD surface reflectances. The results suggest the Landsat 8 SR is validated to within 10%, the Sentinel 2A SR is validated to within 6.5% and Sentinel 2B is validated to within 6.8% . Overall combined Sentinel 2A and 2B are validated within 6.6% and the SR for all three ARD products are validated to within 7.7%.

<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This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 132310 GA Landsat 7 ETM+ Analysis Ready Data Collection 3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.

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This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 132317 GA Landsat 8 OLI/TIRS Analysis Ready Data Collection 3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

<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)

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 146091 Geoscience Australia Landsat Water Observation Statistics Collection 3</b> WOfS-STATS (WO_STATS_2.1.5) is a set of statistical summaries of the water observations contained in WOfS (WO_2.1.5). The layers available are: the count of clear observations;the count of wet observations;the percentage of wet observations over time. This product is Water Observations from Space - Statistics (WO-STATS), a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. WO-STATS consists of the following datasets: Clear Count: how many times an area could be clearly seen (ie. not affected by clouds, shadows or other satellite observation problems), Wet Count: how many times water was detected inobservations that were clear, Water Summary: what percentage of clear observations were detected as wet (ie. the ration of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the WO-Fil-STATS product, which provide a noise-reduced view of the water summary. WO-STATS is available in multiple forms, depending on the length of time over which the statistics are calculated. At present the following are available: WO-STATS: statistics calculated from the full depth of time series (1986 to present) WO-STATS-ANNUAL: statistics calculated from each calendar year (1986 to present) WO-STATS-NOV-MAR: statistics calculated yearly from November to March (1986 to present) WO-STATS-APR-OCT: statistics calculated yearly from April to October (1986 to present)

A `weighted geometric median' approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 Operational Land Image (OLI) observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. <b>Value: </b>Has broad application in mapping surface geochemistry and mineralogy of exposed soil and bedrock. Has applications in geological mapping and natural resource management including mapping of soil characteristics. <b>Scope: </b>Two enhanced bare earth products have been generated reflecting different Landsat satellites and acquisition periods. The first only uses Landsat 8 observations from 2013 to 2018. The second incorporates the full 30+ year archive combining Landsat 5, 7, and 8 from 1986 to 2018.

Many atmospheric correction schemes of radiance-based optical satellite data require the selection of normalized solar spectral irradiance models at the top of atmosphere (TOA). However, there is no scientific consensus in literature as to which available model is most suitable. This article examines five commonly used models applied to Landsat 8 Operational Land Imager (OLI) TOA radiance and reflectance products to assess the accuracy and stability between models used to derive surface reflectance products. It is assumed that the calibration of the United States Geological Survey (USGS) Landsat 8 OLI TOA reflectance and radiance products are accurate to currently claimed levels. The results show that the retrieved surface reflectance can exhibit significant variations when different solar irradiance models are used, especially in the OLI coastal blue band at 443 nm. From the five solar irradiance models, the Kurucz 2005 model showed the least bias compared with OLI TOA reflectance product and least variance in surface reflectance. Furthermore, improvement was obtained by adjusting the total solar irradiance (TSI) normalization, and additional validation was provided using observed in situ water leaving reflectance data. The results from this article are particularly relevant to aquatic applications and to satellite sensors that provide TOA radiance such as previous Landsat and other current and historical missions. <b>Citation:</b> F. Li, D. L. B. Jupp, S. Sagar and T. Schroeder, "The Impact of Choice of Solar Spectral Irradiance Model on Atmospheric Correction of Landsat 8 OLI Satellite Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 4094-4104, May 2021, doi: 10.1109/TGRS.2020.3011006.

An estimate of the spectra of the barest state (i.e., least vegetation) observed from imagery of the Australian continent collected by the Landsat 5, 7, and 8 satellites over a period of more than 30 years (1983 – 2018). The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. This product complements the Landsat-8 Barest Earth which is based on the same algorithm but just uses Landsat8 satellite imagery from 2013-2108. Landsat-8’s OLI sensor provides improved signal-to-noise radiometric (SNR) performance quantised over a 12-bit dynamic range compared to the 8-bit dynamic range of Landsat-5 and Landsat-7 data. However the Landsat 30+ Barest Earth has a greater capacity to find the barest ground due to the greater temporal depth. Reference: Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest - Dale Roberts, John Wilford and Omar Ghattas Ghattas (2019). Nature Communications, DOI: 10.1038/s41467-019-13276-1. https://www.nature.com/articles/s41467-019-13276-1

<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.