<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 146261 DEA Geometric Median and Median Absolute Deviation (Landsat)</b> This product provides ‘second order’ statistical techniques that follow from the geometric median, which is useful for environmental characterisation and change detection. The Median Absolute Deviation (MAD) is a generalisation of the classic one-dimensional statistic for multidimensional applications, and is a measure of variance in a dataset through comparison to the median. It is similar in concept to the way that the standard deviation in statistics can be used to understand variance compared to the mean.

<b>This record was retired 02/03/2023 with approval from M. Wilson as it has been superseded by <a href="https://dx.doi.org/10.26186/146552">eCat 146552 </a>& <a href="https://dx.doi.org/10.26186/146551">eCat 146551</a></b> Surface Reflectance product has been corrected to account for variations caused by atmospheric properties, sun position and sensor view angle at time of image capture. These corrections have been applied to all satellite imagery in the Sentinel-2 archive. This is undertaken to allow comparison of imagery acquired at different times, in different seasons and in different geographic locations. These products also indicate where the imagery has been affected by cloud or cloud shadow, contains missing data or has been affected in other ways. The Surface Reflectance products are useful as a fundamental starting point for any further analysis, and underpinall other optical derivedDigital Earth Australiaproducts.

<b>This record was retired 02/03/2023 with approval from M. Wilson as it has been superseded by <a href="https://dx.doi.org/10.26186/146552">eCat 146552 </a>& <a href="https://dx.doi.org/10.26186/146551">eCat 146551</a></b> The Surface Reflectance product has been corrected to account for variations caused by atmospheric properties, sun position and sensor view angle at time of image capture. These corrections have been applied to all satellite imagery in the Sentinel-2 archive. This is undertaken to allow comparison of imagery acquired at different times,in different seasons and in different geographic locations. These products also indicate where the imagery has been affected by cloud or cloud shadow, contains missing data or has been affected in other ways. The Surface Reflectance products are useful as a fundamental starting point for any further analysis, and underpinall other optical derivedDigital Earth Australiaproducts.

<div>The United States Geological Survey's (USGS) Landsat satellite program has been capturing images of the Australian continent for more than 30 years. This data is highly useful for land and coastal mapping studies.</div><div><br></div><div>In particular, the light reflected from the Earth’s surface (surface reflectance) is important for monitoring environmental resources – such as agricultural production and mining activities – over time.</div><div><br></div><div>We make accurate comparisons of imagery acquired at different times, seasons and geographic locations. However, inconsistencies can arise due to variations in atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. These are reduced or removed to ensure the data is consistent and can be compared over time.</div><div><br></div><div>The Geoscience Australia Landsat 9 OLI TIRS Analysis Ready Data Collection 3 contains three sub-products that provide corrections or attribution information:</div><div>- DEA Surface Reflectance NBAR* (Landsat 9)</div><div>- DEA Surface Reflectance NBART** (Landsat 9)</div><div>- DEA Surface Reflectance OA*** (Landsat 9)</div><div><br></div><div>Note: DEA produces NBAR as part of the Landsat ARD, this is available in the National Computing Infrastructure environment only and is not available in the DEA cloud environments.</div><div><br></div><div>The resolution is a 30 m grid based on the USGS Landsat Collection 2 archive, or 15 m for the panchromatic band. This data forms part of the DEA Collection 3 archive. </div><div><br></div><div>* Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR)</div><div>** Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance with terrain illumination correction (NBART)</div><div>*** Observation Attributes (OA)</div>

<b>This record has been superseded by eCat 148920 DEA Waterbodies v3.0 (Landsat) with approval from N.Mueller on 01/02/2024 This record was retired 15/09/2022 with approval from S.Oliver as it has been superseded by eCat 146197 DEA Waterbodies (Landsat) </b> <p>Up to date information about the extent and location of surface water provides all Australians with a common understanding of this valuable and increasingly scarce resource. <p>Digital Earth Australia Waterbodies shows the wet surface area of waterbodies as estimated from satellites. It does not show depth, volume, purpose of the waterbody, nor the source of the water. <p>Digital Earth Australia Waterbodies uses Geoscience Australia’s archive of over 30 years of Landsat satellite imagery to identify where almost 300,000 waterbodies are in the Australian landscape and tells us the wet surface area within those waterbodies. <p>It supports users to understand and manage water across Australia. For example, users can gain insights into the severity and spatial distribution of drought, or identify potential water sources for aerial firefighting during bushfires. <p>The tool uses a water classification for every available Landsat satellite image and maps the locations of waterbodies across Australia. It provides a timeseries of wet surface area for waterbodies that are present more than 10% of the time and are larger than 3125m2 (5 Landsat pixels). <p>The tool indicates changes in the wet surface area of waterbodies. This can be used to identify when waterbodies are increasing or decreasing in wet surface area.

<b>BACKGROUND</b> <p> <p>The United States Geological Survey's (USGS) Landsat satellite program has been capturing images of the Australian continent for more than 30 years. This data is highly useful for land and coastal mapping studies. <p>In particular, the light reflected from the Earth’s surface (surface reflectance) is important for monitoring environmental resources – such as agricultural production and mining activities – over time. <p>We need to make accurate comparisons of imagery acquired at different times, seasons and geographic locations. However, inconsistencies can arise due to variations in atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. These need to be reduced or removed to ensure the data is consistent and can be compared over time. <p>&nbsp</p> <b>WHAT THIS PRODUCT OFFERS</b> <p> <p>GA Landsat 7 ETM+ Analysis Ready Data Collection 3 takes Landsat 7 Enhanced Thematic Mapper (ETM+) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. <p> <p>The ETM+ instrument is a fixed ‘whisk broom’, eight-band, multispectral scanning radiometer capable of providing high-resolution imaging information of the Earth’s surface. It is an enhanced version of the Thematic Mapper (TM) sensor. <p> <p>This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. <p> <p>It contains three sub-products that provide corrections or attribution information: <p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1) GA Landsat 7 ETM+ NBAR Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2) GA Landsat 7 ETM+ NBART Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3) GA Landsat 7 ETM+ OA Collection 3 <p> <p>The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

<b>BACKGROUND</b> <p> <p>The United States Geological Survey's (USGS) Landsat satellite program has been capturing images of the Australian continent for more than 30 years. This data is highly useful for land and coastal mapping studies. <p>In particular, the light reflected from the Earth’s surface (surface reflectance) is important for monitoring environmental resources – such as agricultural production and mining activities – over time. <p>We need to make accurate comparisons of imagery acquired at different times, seasons and geographic locations. However, inconsistencies can arise due to variations in atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. These need to be reduced or removed to ensure the data is consistent and can be compared over time. <p>&nbsp</p> <b>WHAT THIS PRODUCT OFFERS</b> <p> <p>GA Landsat 5 TM Analysis Ready Data Collection 3 takes Landsat 5 Thematic Mapper (TM) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. <p> <p>The TM instrument is an advanced, multispectral scanning, Earth resources sensor which is designed to categorise the Earth's surface. It is particularly useful for agricultural applications and identification of land use. <p> <p>This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. <p> <p>It contains three sub-products that provide corrections or attribution information: <p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1) GA Landsat 5 TM NBAR Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2) GA Landsat 5 TM NBART Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3) GA Landsat 5 TM OA Collection 3 <p> <p>The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

<div>Tasseled Cap percentiles provide an annual summary of how the environment has varied through a year. The Tasseled Cap percentiles provide the upper, lower and middle conditions as described by the 90th, 10th and 50th percentiles respectively, of greenness, wetness and brightness across the landscape.</div><div><br></div><div>These percentiles are intended for use as inputs into classification algorithms to identify such environmental features as wetlands and groundwater dependent ecosystems, and characterise salt flats, clay pans, salt lakes and coastal land forms.</div><div><br></div>

<b>BACKGROUND</b> <p> <p>The United States Geological Survey's (USGS) Landsat satellite program has been capturing images of the Australian continent for more than 30 years. This data is highly useful for land and coastal mapping studies. <p>In particular, the light reflected from the Earth’s surface (surface reflectance) is important for monitoring environmental resources – such as agricultural production and mining activities – over time. <p>We need to make accurate comparisons of imagery acquired at different times, seasons and geographic locations. However, inconsistencies can arise due to variations in atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. These need to be reduced or removed to ensure the data is consistent and can be compared over time. <p>&nbsp</p> <b>WHAT THIS PRODUCT OFFERS</b> <p> <p>GA Landsat 8 OLI/TIRS Analysis Ready Data Collection 3 takes Landsat 8 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. <p> <p>The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 8. <p> <p>This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. <p> <p>It contains three sub-products that provide corrections or attribution information: <p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1) GA Landsat 8 OLI/TIRS NBAR Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2) GA Landsat 8 OLI/TIRS NBART Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3) GA Landsat 8 OLI/TIRS OA Collection 3 <p> <p>The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

<div>The DEA Geometric Median and Median Absolute Deviation products use statistical analyses to provide information on variance in the landscape over a given year. They provide insight into the “average” conditions observed over Australia in a given year, as well as the amount of variability experienced around that average. These products are useful for monitoring change detection, such as from cropping, urban expansion or burnt area mapping.&nbsp;</div><div><br></div><div>Satellite imagery allows us to observe the Earth with significant accuracy and detail. However, missing data — such as gaps caused by cloud cover — can make it difficult to create a complete image. In order to produce a single, complete view of a certain area, satellite data must be consolidated by stacking measurements from different points in time to create a composite image.&nbsp;</div><div><br></div><div>The Digital Earth Australia GeoMAD (Geometric Median and Median Absolute Deviation) data product is a cloud-free composite of satellite data compiled annually over each calendar year.&nbsp;</div><div><br></div><div>Large-scale image composites are increasingly important for a variety of applications such as land cover mapping, change detection, and the generation of high-quality data to parameterise and validate bio-physical and geophysical models. A number of compositing methodologies are being used in remote sensing in general, however, challenges still exist. These challenges include mitigating against boundary artifacts due to mosaicking scenes from different epochs ensuring spatial regularity across the mosaic image and maintaining the spectral relationship between bands.&nbsp;</div><div><br></div><div>The creation of good composite images is especially important due to the opening of the United States Geological Survey’s Landsat archive. The greater availability of satellite imagery has resulted in demand to provide large regional mosaics that are representative of conditions over specific time periods while also being free of clouds and other unwanted visual noise. One approach is to ‘stitch together’ multiple selected high-quality images. Another is to create mosaics in which pixels from a time series of observations are combined (using an algorithm). This ‘pixel composite’ approach to mosaic generation provides more consistent results than with stitching high-quality images due to the improved colour balance created by combining one-by-one pixel-representative images. Another strength of pixel-based composites is their ability to be automated, hence enabling their use in large data collections and time series datasets.&nbsp;</div><div><br></div><div>The DEA GeoMAD product can be used for seeing how an area of land usually looks rather than only viewing it at a single point in time. Hence you can assess the land cover and land use on a general basis rather than at a specific date. It can also be used to assess how much an area changes over time. You will notice areas like bare rock that are very stable versus those like cropping areas that change dramatically.&nbsp;</div><div><br></div><div>The DEA GeoMAD product combines the Geometric Median and the Median Absolute Deviation algorithms in a single package. The Geometric Median output provides information on the general conditions of the landscape for a given year. Meanwhile the Median Absolute Deviation output provides information on how the landscape is changing in the same year.&nbsp;</div><div><br></div>