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SOIL | Volume 5, issue 2
SOIL, 5, 275–288, 2019
https://doi.org/10.5194/soil-5-275-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
SOIL, 5, 275–288, 2019
https://doi.org/10.5194/soil-5-275-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Original research article 25 Sep 2019

Original research article | 25 Sep 2019

Error propagation in spectrometric functions of soil organic carbon

Monja Ellinger et al.
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Cited articles  
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Altermann, M., Rinklebe, J., Merbach, I., Körschens, M., Langer, U., and Hofmann, B.: Chernozem – Soil of the Year 2005, J. Plant Nutr. Soil Sc., 168, 725–740, https://doi.org/10.1002/jpln.200521814, 2005. 
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Beleites, C., Baumgartner, R., Bowman, C., Somorjai, R., Steiner, G., Salzer, R., and Sowa, M. G.: Variance reduction in estimating classification error using sparse datasets, Chemometr. Intell. Lab., 79, 91–100, https://doi.org/10.1016/j.chemolab.2005.04.008, 2005. 
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Vis–NIR spectrometry is often applied to capture soil organic carbon (SOC). This study addresses the impact of the involved data and modelling aspects on SOC precision with a focus on the propagation of input data uncertainties. It emphasizes the necessity of transparent documentation of the measurement protocol and the model building and validation procedure. Particularly, when Vis–NIR spectrometry is used for soil monitoring, the aspect of uncertainty propagation becomes essential.
Vis–NIR spectrometry is often applied to capture soil organic carbon (SOC). This study addresses...
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