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VNIR: Potential for Additional Data
Collection Beyond Rapid Carbon
Larry T. West
National Leader Soil Survey Research and LaboratoryNational Soil Survey Center
Lincoln, NE
Electromagnetic Spectrum
Visible / Near Infrared: 350 – 2,500 nm
Mid Infrared: 2,500 – 25,000 nm
Far Infrared (thermal): 25,000 – 106 nm
VNIR MIR
Spectroscopy
► Measure of the interaction between matter and radiation
► Color of object depends on wavelengths of light that are reflected S
un
IncomingRadiation
Soil
Albedo = reflected / incoming
Infrared Spectroscopy
Atomic Bond EnergyVibrationBendingRotation
Energy of atomic bonds absorbs IR radiation
Greater abundance of specific bonds = higher concentration
IR Spectroscopy► Established methodology for evaluating chemical bonds in
various materials including clay minerals Si-O; Al-O; H-O; C=O; C-OH; Fe-O; etc.
► Laboratory measurement
Amount of IR radiation transmitted through thin film or solid suspension of material in non-absorbent media
In clay mineralogy, analysis of mineral structure; not quantification
Visible and Near InfraRed Diffuse Reflectance Spectroscopy
► Spectra collected is diffuse (unfocused) cloud of reflected radiation
► Overtones (secondary radiation) instead of primary Broader, less well defined peaks
Cannot assign specific peaks to specific bonds
Absorption
SpecularReflectance Diffuse
Reflectance
TransmissionDiffuse
Transmission (Forward Scatter)
Transmitted Primary versus Diffuse Radiation
A Btg 2Btg
Wavelength (nm)Re
flect
ance
Diffuse Reflectance IR Spectroscopy
Incoming
Radiation
Reflecte
d
Soil
Incomin
g
Radiatio
n
Reference Material – Ideal Reflectivity
ReflectedVisible and IR Source
At each wavelength, the detector reports how much light is reflected by the soil compared with the
reference material
Detector
• Spectrometry is a combination of spectroscopy and statistical methods to identify and quantify chemical species
• Essentially the same as developing standard curve for any analytical instrument• Analyze a large number (>100) of known samples
that have a range of values for component of interest, e.g. clay
• Build statistical models that relate spectra to quantity of component – hyper multiple regression
% clay = f(spectrum)
VNIR for Quantifying Soil Properties
Calcium Carbonate Equivalent, %, actual vs. predicted
Evaluate Precision of Model
• Relationship will not be perfect• Precision of VNIR predictions is less than laboratory measurements
Mea
sure
d Cl
ay (%
)
Estimated Clay (%)
Calibration
• Predictive models are best when samples represent a restricted range• Interference from other properties
Global vs. Stratified Models
• Texture, classification, parent material, MLRA, etc. • Size of known sample set could be a problem• Stratify by spectral characteristics?
How to Stratify for U.S.
Life AfterRaCA
The same spectrum can be used to predict multiple properties.Scan
Unknown Soil
Total Carbon CEC Clay pH Carbonates
P R E D I C T I O N S
One Spectrum – Many Properties
Key is development of acceptable predictive models
SSL will have most extensive spectral library in world
Successful Predictions► Carbon; total and fractions
► Particle size distribution
► Chemical properties Extractable Cations
CEC
Extractable acidity
Extractable Al
Selected trace elements
pH
► Quartz, kaolinite, smectite
► Water content
► COLE
► Other CaCO3
Gypsum
Available P
► Most relationships developed from samples in limited area; plot to MLRA
equivalent
Missouri
IllinoisNovelty
Centralia
MLRA 113 – The Central Claypan Regions
Clay Content
Estimated Clay (%)
Mea
sure
d Cl
ay (%
)
Mea
sure
d Cl
ay (%
)
Estimated Clay (%)
Calibration Test Data
Organic Carbon
Estimated OC (%)
Mea
sure
d O
C (%
)
Estimated OC (%)
Mea
sure
d O
C (%
)Calibration Test Data
Cation Exchange (NH4OAc)
Estimated CEC (meq 100g-1)
Mea
sure
d CE
C (m
eq 1
00g-1
)
Estimated CEC (meq 100g-1)
Mea
sure
d CE
C (m
eq 1
00g-1
)
Calibration Test Data
Exchangeable Calcium
Estimated Ca (meq 100g-1)
Mea
sure
d Ca
(meq
100
g-1)
Estimated Ca (meq 100g-1)
Mea
sure
d Ca
(meq
100
g-1)
Calibration Test Data
pH
R2 = 0.74PLSR R2 = 0..66RMSE = 0.4RPD = 1.6
EC1:1
R2 = 0.65PLSR R2 = 0.36RMSE = 64.9RPD = 1.2
Typical Soil Organic Matter Calibration Performance
► Organic matter/organic C % OM, % OC Total C (LECO) %C HUMUS
Humic acid fractions Humic and Fulvic Fulvic acid fractions Lignin content Cellulose content
r2
0.81-0.97
0.93-0.96
0.94
0.95
0.91
0.63
0.77-0.83
0.81
Performance
good – exc.
v.good - exc.
v.good
v.good
v.good
poor
good
good
Martin and Malley, PDK Projects, Inc. unpublished results
Clay
Pre
dic
ted
cla
y,
%
Measured clay, %
r2 = 0.90RMSE = 5%
0 2 0 4 0 6 00
2 0
4 0
6 0
Texas Data
1:1 line
Gypsum
0 0.04 0.08 0.12 0.16 0.2M easured CO LE, cm cm -1
0
0.04
0.08
0.12
0.16
0.2
Pre
dic
ted
CO
LE, c
m c
m-1
1:1 liney=0.585x+0.022
Pedotransfer function*
0 0.04 0.08 0.12 0.16 0.2M easured CO LE, cm cm -1
0
0.04
0.08
0.12
0.16
0.2
Pre
dic
ted
CO
LE, c
m c
m-1
1 :1 liney=0.564+0.017
VNIR Spectroscopy
RMSD= 0.028r2= 0.61RPD= 1.6
RMSD= 0.029r2= 0.57RPD= 1.5
Coefficient of Linear Extensibility
* clay content
Large-scale VNIR Soil Calibrations
►Brown et al., 2006►4,184 samples from all 50 states plus Americas, Africa, Europe & Asia
Brown, D.J., Shepherd, K.D., Walsh, M.G., Mays, M.D., Reinsch, T.G. (2006) Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma, v.132, n.3-4, p. 273-290.
Reflectance Spectra of Clay Minerals
Shifting Al-OH absorbtion peak,2200-2380nm.
Water Absorption Peak, 1900nm
Goetz, A. F. H., Chabrillat, S., Lu, Z. 2001. Field Reflectance Spectrometry for Detection of Swelling Clays at Construction Sites. Field Analytical Chemistry and Technology. 5(3):143-155, 2001.
Phosphorus
► Nutrient often associated with water quality issues Major topic within NRCS Is soil overloaded with P?
► VNIR has been reported to adequately quantify P in soils Results from small area Measurement of accessory properties?
► Small quantities in soils even when soil is overloaded
► Variety of absorbents► May be better able to quantify P adsorption
capacity Fe and Al oxides and oxyhydroxides major P adsorber Relatively abundant
What Properties Should be Evaluated with VNIR
► IR radiation interacts with chemical bonds Expect best results from abundant components that have unique bonds
► Clay, sand – Si-0, Al-O, Al-OH► Organic C – C-OH, C=O, etc.► CaCO3 – Ca-CO3
► Gypsum – Ca-SO4
► Clay minerals – indentify? Quantify – probably not► CEC – cations adsorbed on clay and organic matter (type and amount of
clay and organic matter)► Extractable Ca – adsorption on clay and organic matter
Weaker relationship than other properties Limited area; similar Ca saturation?; type and amount of clay? ESP?
► pH, EC – weak models No chemical bonds directly related to properties Relation to other components?
► P, trace elements, etc. – models applicable for limited region or soils? Accessory properties
VNIR after Rapid Carbon - Why?
Large demand for Soil Property Data
Estimated or measured values?
What is the mean, variance, confidence limits?
More Samples and Measurements
► Equipment► Time► Money
Time may be greatest limitation
Are VNIR data a reasonable alternative?Data are less robust than conventional measurements
Benefits of VNIR for Soil Analysis• Low per-sample cost• Little or no sample preparation• Rapid measurement
• Possible to perform the analysis in the field?• Ability to collect data for multiple locations
• Statistical validity for data• Is it really fine or fine-loamy?
• Ability to collect data a fine depth increments• Property distribution with depth not restricted to genetic horizons
• Single spectrum to predict multiple soil properties
• Critical part is valid predictive models
• Supplement to, not a replacement for laboratory measurement by conventional methods• Less precise
Use of VNIR in Field?
► Equipment is field compatible
► Water is strong absorber of IR radiation Variable water content = variable absorption
► Non-homogenous material Air-dry and crushed = homogenous
Field state = hetrogenous► Mottles
► Coatings
► Redox features
► Research underway to correct for water content (mathematically) and to evaluate effects of non-uniform material
Water Absorption Peak
VNIR and NRCS SSL
►5-6,000 samples analyzed each year►VNIR spectra being collected for each
sample Moist and dry
►Largest spectral library in the world Ability to stratify samples to improve
precision of predictions Library will be available to the public
VNIR and the NCSS
► Is precision good enough? Depends on the question
► Analysis of a single representative pedon Not a good technique
► Analysis of multiple sites of same soil to estimate mean and data confidence May be good enough for many properties
► VNIR not to replace standard analytical methods Good to increase replicates
VNIR Summary
► Viable method for evaluation of soil properties
► Data are spectra Property values depend on calibration model
► Not a replacement for standard methods Lower precision
► Rapid data collection allows greater replication Representative site pre-screening
Large “N” for statistical analysis and confidence limits
Close interval (depth and distance) data collection
► Does the property fit the analytical theory?
► Additional methods and predictive models will be developed in the future
► Applications will depend on soil scientists in the field
Questions?
Comments?