VNIR: Potential for Additional Data Collection Beyond Rapid Carbon Larry T. West National Leader...

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?