rs-local data-mines information from spectral libraries to improve local calibrations
Article
Article Title | rs-local data-mines information from spectral libraries to improve local calibrations |
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ERA Journal ID | 41617 |
Article Category | Article |
Authors | Lobsey, C. R. (Author), Viscarra Rossel, R. A. (Author), Roudier, P. (Author) and Hedley, C. B. (Author) |
Journal Title | European Journal of Soil Science |
Journal Citation | 68 (6), pp. 840-852 |
Number of Pages | 13 |
Year | 2017 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 1351-0754 |
1365-2389 | |
Digital Object Identifier (DOI) | https://doi.org/10.1111/ejss.12490 |
Web Address (URL) | http://onlinelibrary.wiley.com/doi/10.1111/ejss.12490/epdf |
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. |
Keywords | accuracy assessment; algorithm; assessment method; calibration; data mining; laboratory method; organic carbon; soil carbon; soil property; spatial resolution; spectral analysis; spectroscopy |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
410699. Soil sciences not elsewhere classified | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | National Centre for Engineering in Agriculture |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
Manaaki Whenua – Landcare Research, New Zealand | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q479z/rs-local-data-mines-information-from-spectral-libraries-to-improve-local-calibrations
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