Data

Enhanced barest earth Landsat imagery for soil and lithological modelling: Dataset

Geoscience Australia
Wilford, J. ; Roberts, D.
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://pid.geoscience.gov.au/dataset/ga/144231&rft.title=Enhanced barest earth Landsat imagery for soil and lithological modelling: Dataset&rft.identifier=https://pid.geoscience.gov.au/dataset/ga/144231&rft.description=Please Note: The data related to this Abstract can be obtained by contacting Manager Client Services 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%.Maintenance and Update Frequency: asNeededStatement: The landsat image enhancements describe in the paper are based on the barest earth dataset explained in Roberts D., Wilford J. & Ghattas O., 2019. Exposed soil and mineral map of the Australian continent revealing the land at its barest. Nature Communications 10:5297.&rft.creator=Wilford, J. &rft.creator=Roberts, D. &rft.date=2019&rft.coverage=westlimit=111.4225; southlimit=-44.2137; eastlimit=155.5436; northlimit=-8.9285&rft.coverage=westlimit=111.4225; southlimit=-44.2137; eastlimit=155.5436; northlimit=-8.9285&rft_rights=&rft_rights=Creative Commons Attribution 4.0 International Licence&rft_rights=CC-BY&rft_rights=4.0&rft_rights=http://creativecommons.org/licenses/&rft_rights=Australian Government Security ClassificationSystem&rft_rights=https://www.protectivesecurity.gov.au/Pages/default.aspx&rft_rights=WWW:LINK-1.0-http--link&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_subject=geoscientificInformation&rft_subject=EARTH SCIENCES&rft_subject=remote sensing&rft_subject=EFTF&rft_subject=Satellite imagery&rft_subject=Soils&rft_subject=Rock&rft_subject=Mineralogy&rft_subject=Landsat TM&rft_subject=Exploring for the Future&rft_subject=Surface&rft_subject=Published_External&rft.type=dataset&rft.language=English Access the data

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CC-BY

Creative Commons Attribution 4.0 International Licence
http://creativecommons.org/licenses/by/4.0

Creative Commons Attribution 4.0 International Licence

CC-BY

4.0

http://creativecommons.org/licenses/

Australian Government Security ClassificationSystem

https://www.protectivesecurity.gov.au/Pages/default.aspx

WWW:LINK-1.0-http--link

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Open

Contact Information



Brief description

Please Note: The data related to this Abstract can be obtained by contacting Manager Client Services 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%.

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Maintenance and Update Frequency: asNeeded
Statement: The landsat image enhancements describe in the paper are based on the barest earth dataset explained in Roberts D., Wilford J. & Ghattas O., 2019. Exposed soil and mineral map of the Australian continent revealing the land at its barest. Nature Communications 10:5297.

Notes

Purpose
Dataset

Data time period: 1980-01-01

This dataset is part of a larger collection

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155.5436,-8.9285 155.5436,-44.2137 111.4225,-44.2137 111.4225,-8.9285 155.5436,-8.9285

133.48305,-26.5711

text: westlimit=111.4225; southlimit=-44.2137; eastlimit=155.5436; northlimit=-8.9285

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Other Information
Data Description (pdf) [125 KB]

uri : https://d28rz98at9flks.cloudfront.net/144231/144231_00_0.pdf

Link to related Abstract

uri : https://pid.geoscience.gov.au/dataset/ga/134472

Identifiers