Analysis of Soil Organic
Carbon in Soil Samples using
an ASD NIR Spectrometer
By: Michaela Kastanek, Applications Coordinator and
George Greenwood, Senior Market Manager - Remote Sensing
ASD Inc., a PANalytical company
Boulder, Colorado, 80301, USA
October 2013
Introduction
Growing concerns regarding the impact of global carbon
emissions have led to rigorous studies to find effective
methods and processes to sequester atmospheric carbon
back into a terrestrial phase of the carbon cycle. A number of
innovative mechanical sequestration solutions have been
developed including geologic sub surface and deep ocean
storage, while research into passive natural systems have
led to a better understanding of the potential for certain soils
to act as carbon sinks. Soil Organic Carbon (SOC)
sequestration, in which carbon dioxide present in the
atmosphere is transferred into the soil through crop residues
and other organic solids is not only an important source of
carbon in the soil but also provides an off-set from fossil-fuel
combustion and other carbon emitting activities (OSU, SOC
Factsheet).
Overview
Introduction
Experimental
Model Development
Results Statistics Overview
Percent Soil Organic Carbon
Conclusion
Understanding the mechanics of SOC is important for determining soil health, productivity, and
developing land management strategies, as well as carbon dioxide fluxes in the atmosphere.
Accurately measuring current soil organic carbon (SOC) concentrations, understanding the
contributing factors, and being able to assess a given soil’s potential to sequester additional
carbon (Iowa EPSCoR) are at the core of understanding these dynamics. SOC sequestration
potential is defined as the maximum possible storage of Total Below-Ground Carbon Allocation
(TBCA) under a specific soil-climate-land management regime. Traditional lab-based methods
for determining TBCA are costly, time-consuming and tend to destroy the sample in which it is
measured (Iowa EPSCoR).
Near-infrared (NIR) reflectance spectroscopy provides an efficient cost-effective alternative to
traditional lab-based SOC analysis. With NIR reflectance analysis, rapid non-destructive
measurements can be taken in the field or in a controlled laboratory environment. Quantitative
calibration models can be developed for rapid characterization of soil nutrients and other
physical properties. Coupling this technology with hyperspectral imagery and improved spatial
statistical methodologies breaks the bottleneck of sample collection and lab analysis and
facilitates large-area soil characterization assessments.
The goal of this evaluation was to create a calibration model for percent SOC using near-infrared
spectra of soil samples collected on the ASD LabSpec 2500 by Kenneth Wacha at the University
of Iowa.
Figure 1, Ph.D. candidate and research associate Kenneth Wacha performs NIR measurements with the help
of a NASA undergraduate assistant
Experimental
Spectra were collected using the LabSpec 2500 near-infrared spectrometer and Muglight sample
accessory. The soil samples used to create the SOC calibration model were dried ground
materials collected from Iowa County, Iowa and sieved through 2mm sieve. Samples were
packed into the small sample holder and scanned using the Muglight sample accessory. The
LabSpec 2500 system has a wavelength range of 350-2500 nm and collects spectra at the rate
of 0.1 seconds per spectral scan. A 100 spectrum average was used which is approximately 10
seconds of scan collection per sample. Spectra were supplied to ASD for creation of the
multivariate model.
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Figure 2, ASD LabSpec 2500 and Muglight Sample Accessory
Primary reference data for SOC was obtained using a C/N analyzer with an absolute error of +/0.3% by weight. The minute detection limits and low error associated with this method make it
ideal for producing chemometric models.
Model Development
GRAMS IQ version 9.1 multivariate chemometrics program from Thermo Fisher Scientific,
Woodbridge, NJ, was used to create the soil model. Spectral data files were imported into
GRAMS format as absorbance, Log (1/R) spectra. A PLS1 model was developed for percent
SOC. The samples had already been divided into calibration and test sets by Kenneth Wacha at
the University of Iowa. The samples reserved for use in the independent test set were not used
in the creation of the calibration model. The test samples were assigned by rank ordering the
data from low to high then placing every fifth sample into the independent test set.
Results Statistics Overview
The statistics shown in Table 1 describe the calibration model performance. N is the number of
samples remaining in the calibration over the number of samples that were initially imported in
the calibration set. Typically less than 10% of the samples should be removed as outliers. If
more samples than that are removed the model may not validate well using an independent test
set.
Factors are the number of descriptive vectors that were used in the model. Typically no more
than one factor should be used for every 10-20 samples in the model. If more factors are used
this may also cause the model to perform poorly.
The RSQ is the correlation coefficient indicating how well the actual sample data points
correlated with the predicted data points. An RSQ of 1.0 indicates a perfect correlation whereas
an RSQ value closer to 0 indicates no relationship between the data. RSQ values were
calculated for both the calibration set and independent test set.
The Standard Error of Cross-Validation (SECV) is a measurement estimating the amount of
normal error expected in new unknown samples when using the calibration model. The SECV is
used in comparison with the Standard Error of Prediction (SEP) which calculates the amount of
error of predicted samples in the independent test set. There are no absolute guidelines to
follow for the SECV or SEP. However, if the model has been properly fitted, then the calibration
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set RSQ and test set RSQ should be approximately equal, as should be the SECV and SEP
values.
The final metric used to gauge the usefulness of the model to make quantitative prediction is
called the Ratio of Performance Deviation (RPD). A value of less than 1.75 generally indicates
that a model will not be useful for quantitation. Values between 1.75 and 4.0 can indicate that a
model may be useful for quantitation, and values greater than 4.0 indicate a model is very likely
to be useful for making quantitative predictions.
Total Carbon
N
245/250
Calibration Set
Factors
RSQ
9
0.85
SECV
0.104
N
63
Independent Test Set
RSQ
SEP
0.77
0.093
RPD
2.85
Table 1, Model Statistics Summary
Percent Soil Organic Carbon
A total of 250 spectra were available for creation of the percent SOC model. A total of 5 outliers
were removed during the modeling process for a total of 245 samples in the final calibration set.
This was an acceptable number of samples to remove during the modeling process. 9 factors
were used during the creation of the model, which is an appropriate number of factors
considering the large number of samples used in the model. Savitzky-Golay first derivative with
41 point smoothing was applied in GRAMS IQ as a pretreatment of the spectra, and the
wavelength range was restricted to 3 regions, specifically 450-700, 1300-1500 and 1846-2500.
The model had an RSQ of 0.85 and an SECV of 0.104. The independent test set had an RSQ of
0.77 and an SEP of 0.093. The test set validated well compared to the calibration statistics. The
independent test set performance demonstrates that this model would also be expected to
perform similarly on samples represented by the model. The RPD value for the model was 2.85
indicating that the model may be useful for making quantitative predictions.
Figure 3, Soil Organic Carbon Model and Test Set prediction
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Conclusion
A model for percent SOC was developed using the ASD LabSpec 2500 with the Muglight sample
accessory on specific soil types from Iowa County, Iowa. The model was validated using
samples that were withheld as independent test samples. Calibration set and test set statistics
indicate that the model validated well and could be expected to perform similarly on samples of a
similar type and would be useful for quantitative determinations of percent SOC. The model that
was developed using NIR reflectance spectroscopy in combination with samples having
quantified SOC values demonstrates the potential for real-time measurement of SOC.
Contact ASD Inc., a PANalytical company at (303) 444-6522 or
[email protected] for further information regarding analysis of soil
organic carbon in soil samples.
About the LabSpec 4 Line of Lab Analyzers
The Labspec® line of laboratory instrumentation offers research-grade instrument performance in a
rugged package suitable for transport to the sample location. LabSpec analytical instrumentation
performs rapid, non-destructive materials analysis for qualitative and quantitative applications.
About ASD Inc., a PANalytical Company
Established in 1990 in Boulder, Colo., ASD Inc., a PANalytical company, is the global leader in highperformance analytical instrumentation solutions, solving some of the most challenging real-world
materials measurement problems. ASD spectrometers — unparalleled in providing laboratory-grade
results in the field or on-site — are the instruments of choice for remote sensing, environmental sciences,
agricultural and mining industry applications, where results drive paradigm-changing insights, efficiency
and profit. ASD’s collaborative culture and world-class customer service put the best, fastest and most
accurate spectroscopic instruments to work for industry and science in more than 70 countries around the
world. For more information, please visit www.asdi.com.
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