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23 January 2017 Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy
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Abstract

Assessing the success of soil reclamation programs can be costly and time-consuming due to the cost of traditional soil analytical techniques. One cost-effective tool that has been successfully used to efficiently analyze a range of soil parameters is reflectance spectroscopy. We used reflectance data to analyze natural and reclaimed soils in the field, examining three key soil parameters: soil organic carbon (SOC), total nitrogen (TN), and soil pH. Continuous wavelet transforms combined with machine learning models were used to predict these parameters. Based on the root mean square error (RMSE), R2 value, and the ratio of performance to deviation (RPD), the Cubist model produced the most accurate models for SOC, TN, and pH. The RMSE, R2, and RPD values for SOC were 0.60%, 0.80, and 2.2, respectively. The TN model results were 0.05%, 0.81 and 2.5, and pH model results were 0.44, 0.69 and 1.8. Overall, the optimal model can be used to predict SOC and TN accurately, and improvements in the pH model are needed as pH values less than 6.5 were consistently overpredicted.

Copyright remains with the author(s) or their institution(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. Updated online 9 August 2017: The license for this article has been changed to the CC BY 4.0 license. The PDF and HTML versions of the article have been modified accordingly.
P.T. Sorenson, C. Small, M.C. Tappert, S.A. Quideau, B. Drozdowski, A. Underwood, and A. Janz "Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy," Canadian Journal of Soil Science 97(2), 241-248, (23 January 2017). https://doi.org/10.1139/cjss-2016-0116
Received: 12 September 2016; Accepted: 1 January 2017; Published: 23 January 2017
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