Comparing vis–NIRS, LIBS, and Combined vis–NIRS‐LIBS for Intact Soil Core Soil Carbon Measurement
Abstract
Core Ideas First rigorous integration of VisNIR and LIBS spectroscopic techniques for soil profile C measurement. Field‐moist intact soil cores scanned with LIBS and VisNIR simulating in situ measurements. Combined VisNIR‐LIBS wavelengths selected by MRCE were split nearly equally between sensors, suggesting molecular and elemental information was successfully integrated for predicting TC, IC, and SOC. Stoichiometric relationships in LIBS elemental emissions are more strongly correlated to IC concentration than were carbonate absorptions in VisNIR. Lack of variation and consistent, definable chemical composition and stoichiometry likely contributed to poor LIBS and combined VisNIR – LIBS prediction accuracy for SOC. Soil organic carbon (SOC) measurement is critically important to quantify regional and global soil C stocks and better understand soil C biogeochemical processes. Recent studies employing laser‐induced breakdown spectroscopy (LIBS) and visible‐near infrared diffuse reflectance spectroscopy (vis–NIRS) indicate their potential for in situ soil C determination. Visible and near infrared diffuse reflectance spectroscopy and LIBS spectroscopy fundamentally differ and we hypothesize that their integration would provide improved soil C predictions. We report the first rigorous integration of vis–NIRS and LIBS, evaluating the precision of vis–NIRS, LIBS, and combined vis–NIRS‐LIBS spectra for simulated in situ soil profile total C (TC), inorganic C (IC)more »
- Authors:
-
- Washington State Univ. Crop and Soil Science Pullman WA 99164‐6420, Environmental &, Spaital Analytics Monsanto Company St. Louis MO 63167
- Washington State Univ. Crop and Soil Science Pullman WA 99164‐6420
- Western Data Analytics, LLC 85 S. Union Blvd. Lakwood CO 80228
- Los Alamos National Lab. Chemical Diagnostics and Engineering Group P.O.Box 1663 MSJ565 Los Alamos NM 57545
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1582110
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Soil Science Society of America Journal
- Additional Journal Information:
- Journal Name: Soil Science Society of America Journal Journal Volume: 82 Journal Issue: 6; Journal ID: ISSN 0361-5995
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Bricklemyer, Ross S., Brown, David J., Turk, Philip J., and Clegg, Samuel. Comparing vis–NIRS, LIBS, and Combined vis–NIRS‐LIBS for Intact Soil Core Soil Carbon Measurement. United States: N. p., 2018.
Web. doi:10.2136/sssaj2017.09.0332.
Bricklemyer, Ross S., Brown, David J., Turk, Philip J., & Clegg, Samuel. Comparing vis–NIRS, LIBS, and Combined vis–NIRS‐LIBS for Intact Soil Core Soil Carbon Measurement. United States. https://doi.org/10.2136/sssaj2017.09.0332
Bricklemyer, Ross S., Brown, David J., Turk, Philip J., and Clegg, Samuel. Thu .
"Comparing vis–NIRS, LIBS, and Combined vis–NIRS‐LIBS for Intact Soil Core Soil Carbon Measurement". United States. https://doi.org/10.2136/sssaj2017.09.0332.
@article{osti_1582110,
title = {Comparing vis–NIRS, LIBS, and Combined vis–NIRS‐LIBS for Intact Soil Core Soil Carbon Measurement},
author = {Bricklemyer, Ross S. and Brown, David J. and Turk, Philip J. and Clegg, Samuel},
abstractNote = {Core Ideas First rigorous integration of VisNIR and LIBS spectroscopic techniques for soil profile C measurement. Field‐moist intact soil cores scanned with LIBS and VisNIR simulating in situ measurements. Combined VisNIR‐LIBS wavelengths selected by MRCE were split nearly equally between sensors, suggesting molecular and elemental information was successfully integrated for predicting TC, IC, and SOC. Stoichiometric relationships in LIBS elemental emissions are more strongly correlated to IC concentration than were carbonate absorptions in VisNIR. Lack of variation and consistent, definable chemical composition and stoichiometry likely contributed to poor LIBS and combined VisNIR – LIBS prediction accuracy for SOC. Soil organic carbon (SOC) measurement is critically important to quantify regional and global soil C stocks and better understand soil C biogeochemical processes. Recent studies employing laser‐induced breakdown spectroscopy (LIBS) and visible‐near infrared diffuse reflectance spectroscopy (vis–NIRS) indicate their potential for in situ soil C determination. Visible and near infrared diffuse reflectance spectroscopy and LIBS spectroscopy fundamentally differ and we hypothesize that their integration would provide improved soil C predictions. We report the first rigorous integration of vis–NIRS and LIBS, evaluating the precision of vis–NIRS, LIBS, and combined vis–NIRS‐LIBS spectra for simulated in situ soil profile total C (TC), inorganic C (IC) and SOC measurement. Three multivariate variable selection and regression approaches were evaluated for soil C prediction. The highest soil C prediction accuracies were observed using multivariate regression with covariance estimation (MRCE). Inorganic C was best predicted by LIBS, vis–NIRS provided better SOC predictions, and TC was best predicted using combined vis–NIRS‐LIBS data. Combined vis–NIRS‐LIBS did not consistently increase soil C prediction accuracy. Soil organic C was not well predicted, presumably due to challenges associated with scanning surfaces of intact soil cores, variable SOC chemistries, and low SOC variation in the dataset. Considering the challenging conditions under which combined vis–NIRS– LIBS was tested for soil C measurement, data integration and model calibrations had acceptable performance. Further testing under more controlled soil conditions with samples containing greater SOC diversity is necessary to determine the technical potential of combined vis–NIRS/LIBS for soil C determination.},
doi = {10.2136/sssaj2017.09.0332},
journal = {Soil Science Society of America Journal},
number = 6,
volume = 82,
place = {United States},
year = {Thu Oct 18 00:00:00 EDT 2018},
month = {Thu Oct 18 00:00:00 EDT 2018}
}
https://doi.org/10.2136/sssaj2017.09.0332
Web of Science
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