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      Georeferenced soil provenancing with digital signatures

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          Abstract

          The provenance or origin of a soil sample is of interest in soil forensics, archaeology, and biosecurity. In all of these fields, highly specialized and often expensive analysis is usually combined with expert interpretation to estimate sample origin. In this proof of concept study we apply rapid and non-destructive spectral analysis to the question of direct soil provenancing. This approach is based on one of the underlying tenets of soil science – that soil pedogenesis is spatially unique, and thus digital spectral signatures of soil can be related directly, rather than via individual soil properties, to a georeferenced location. We examine three different multivariate regression techniques to predict GPS coordinates in two nested datasets. With a minimum of data processing, we show that in most instances Eastings and Northings can be predicted to within 20% of the range of each within the dataset using the spectral signatures produced via portable x-ray fluorescence. We also generate 50 and 95% confidence intervals of prediction and express these as a range of GPS coordinates. This approach has promise for future application in soil and environmental provenancing.

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          Most cited references 24

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          Using data mining to model and interpret soil diffuse reflectance spectra

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            Spatial autocorrelation in multi-scale land use models

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              Chapter Five—Visible and Near Infrared Spectroscopy in Soil Science

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                Author and article information

                Contributors
                mtighe2@une.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 February 2018
                16 February 2018
                2018
                : 8
                Affiliations
                [1 ]ISNI 0000 0004 1936 7371, GRID grid.1020.3, School of Environmental and Rural Science, , University of New England, ; Armidale, NSW 2351 Australia
                [2 ]138 Toms Gully Road, Black Mountain, NSW 2365 Australia
                [3 ]ISNI 0000 0004 1936 7371, GRID grid.1020.3, Archaeomaterials Science Hub, Archaeology & Palaeoanthropology, , University of New England, ; Armidale, NSW 2351 Australia
                [4 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, Sydney Institute of Agriculture, School of Life and Environmental Sciences, , University of Sydney, ; Camperdown, NSW 2006 Australia
                Article
                21530
                10.1038/s41598-018-21530-7
                5816621
                29453358
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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