vnir: potential for additional data collection beyond rapid carbon larry t. west national leader...
TRANSCRIPT
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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
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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
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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
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Infrared Spectroscopy
Atomic Bond EnergyVibrationBendingRotation
Energy of atomic bonds absorbs IR radiation
Greater abundance of specific bonds = higher concentration
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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
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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)
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Transmitted Primary versus Diffuse Radiation
A Btg 2Btg
Wavelength (nm)Re
flect
ance
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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
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• 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
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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
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• Predictive models are best when samples represent a restricted range• Interference from other properties
Global vs. Stratified Models
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• 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.
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Life AfterRaCA
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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
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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
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Missouri
IllinoisNovelty
Centralia
MLRA 113 – The Central Claypan Regions
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Clay Content
Estimated Clay (%)
Mea
sure
d Cl
ay (%
)
Mea
sure
d Cl
ay (%
)
Estimated Clay (%)
Calibration Test Data
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Organic Carbon
Estimated OC (%)
Mea
sure
d O
C (%
)
Estimated OC (%)
Mea
sure
d O
C (%
)Calibration Test Data
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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
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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
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pH
R2 = 0.74PLSR R2 = 0..66RMSE = 0.4RPD = 1.6
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EC1:1
R2 = 0.65PLSR R2 = 0.36RMSE = 64.9RPD = 1.2
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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
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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
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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
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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.
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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.
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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
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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
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VNIR after Rapid Carbon - Why?
Large demand for Soil Property Data
Estimated or measured values?
What is the mean, variance, confidence limits?
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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
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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
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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
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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
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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
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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
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Questions?
Comments?