rs-local data-mines information from spectral libraries to improve local calibrations

Lobsey, C. R. and Viscarra Rossel, R. A. and Roudier, P. and Hedley, C. B. (2017) rs-local data-mines information from spectral libraries to improve local calibrations. European Journal of Soil Science, 68 (6). pp. 840-852. ISSN 1351-0754

Abstract

Diffuse reflectance spectroscopy in the visible–near infrared (vis–NIR) and mid infrared (mid-IR) can be used to estimate soil properties, such as organic carbon (C) content. Compared with conventional laboratory methods, it enables practical and inexpensive measurements at finer spatial and temporal resolutions, which are needed to improve the assessment and management of soil and the environment. Measurements of soil properties with spectra require empirical calibration and soil spectral libraries (SSL) have been developed for this purpose at the regional, continental and global scales. Calibrations derived with these SSLs, however, are often shown to predict poorly at local sites. Here we present a new method, rs-local, that uses a small representative set of site-specific (or ‘local’) data and re-sampling techniques to select a subset of data from a large vis-NIR SSL to improve calibrations at the site. We demonstrate the implementation of rs-local by estimating soil organic C in two fields with different soil types, one in Australia and one in New Zealand. We found that with as few as 12 to 20 site-specific samples and the SSL, training datasets derived with rs-local could accurately predict soil organic C concentrations. Predictions with the rs-local data were comparable to, or better than those made with site-specific calibrations with up to 300 samples. Our method outperformed other published ‘local’ spectroscopic techniques that we tested. Thus, rs-local can effectively improve both the accuracy and financial viability of soil spectroscopy.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Access to published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Institute for Agriculture and the Environment
Date Deposited: 29 Jan 2018 03:59
Last Modified: 29 Jan 2018 03:59
Uncontrolled Keywords: accuracy assessment; algorithm; assessment method; calibration; data mining; laboratory method; organic carbon; soil carbon; soil property; spatial resolution; spectral analysis; spectroscopy
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
05 Environmental Sciences > 0503 Soil Sciences > 050399 Soil Sciences not elsewhere classified
Socio-Economic Objective: D Environment > 96 Environment > 9614 Soils > 961499 Soils not elsewhere classified
Identification Number or DOI: 10.1111/ejss.12490
URI: http://eprints.usq.edu.au/id/eprint/33336

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