VNIR: Potential for Additional Data Collection Beyond Rapid Carbon Larry T. West National Leader Soil Survey Research and Laboratory National 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 – 10 6 nm VNIR MIR

Spectroscopy ► Measure of the interaction between matter and radiation ► Color of object depends on wavelengths of light that are reflected SunSunSunSun IncomingRadiation Soil Albedo = reflected / incoming

Infrared Spectroscopy Atomic Bond Energy VibrationBendingRotation 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 Specular Reflectance Diffuse Reflectance Transmission Diffuse Transmission (Forward Scatter)

Transmitted Primary versus Diffuse Radiation A Btg2Btg Wavelength (nm) Reflectance

Diffuse Reflectance IR Spectroscopy Incoming Radiation Reflected Soil Incoming Radiation Reference Material – Ideal Reflectivity Reflected Visible 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 speciesSpectrometry is a combination of spectroscopy and statistical methods to identify and quantify chemical species Essentially the same as developing standard curve for any analytical instrumentEssentially 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 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 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 Relationship will not be perfect Precision of VNIR predictions is less than laboratory measurements Precision of VNIR predictions is less than laboratory measurements Measured Clay (%) Estimated Clay (%) Calibration

Predictive models are best when samples represent a restricted rangePredictive models are best when samples represent a restricted range Interference from other properties Interference from other properties Global vs. Stratified Models

Texture, classification, parent material, MLRA, etc.Texture, classification, parent material, MLRA, etc. Size of known sample set could be a problemSize of known sample set could be a problem Stratify by spectral characteristics?Stratify by spectral characteristics? How to Stratify for U.S.

Life After RaCA

The same spectrum can be used to predict multiple properties. Scan Unknown Soil Total CarbonCECClaypHCarbonates 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 Illinois Novelty Centralia MLRA 113 – The Central Claypan Regions

Clay Content Estimated Clay (%) Measured Clay (%) Estimated Clay (%) Calibration Test Data

Organic Carbon Estimated OC (%) Measured OC (%) Estimated OC (%) Measured OC (%) CalibrationTest Data

Cation Exchange (NH 4 OAc) Estimated CEC (meq 100g -1 ) Measured CEC (meq 100g -1 ) Estimated CEC (meq 100g -1 ) Measured CEC (meq 100g -1 ) CalibrationTest Data

Exchangeable Calcium Estimated Ca (meq 100g -1 ) Measured Ca (meq 100g -1 ) Estimated Ca (meq 100g -1 ) Measured Ca (meq 100g -1 ) CalibrationTest Data

pH R 2 = 0.74 PLSR R 2 = RMSE = 0.4 RPD = 1.6

EC 1:1 R 2 = 0.65 PLSR R 2 = 0.36 RMSE = 64.9 RPD = 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 r Performance good – exc. v.good - exc. v.good poor good Martin and Malley, PDK Projects, Inc. unpublished results

Clay Predicted clay, % Measured clay, % r 2 = 0.90 RMSE = 5% Texas Data 1:1 line Gypsum

Pedotransfer function*VNIR Spectroscopy RMSD= r 2 = 0.61 RPD= 1.6 RMSD= r 2 = 0.57 RPD= 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

Reflectance Spectra of Clay Minerals Shifting Al-OH absorbtion peak, nm. Water Absorption Peak, 1900nm Goetz, A. F. H., Chabrillat, S., Lu, Z Field Reflectance Spectrometry for Detection of Swelling Clays at Construction Sites. Field Analytical Chemistry and Technology. 5(3): , 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. ► CaCO 3 – Ca-CO 3 ► Gypsum – Ca-SO 4 ► 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 Benefits of VNIR for Soil Analysis Low per-sample cost Low per-sample cost Little or no sample preparation Little or no sample preparation Rapid measurement Rapid measurement Possible to perform the analysis in the field? Possible to perform the analysis in the field? Ability to collect data for multiple locations Ability to collect data for multiple locations Statistical validity for data Statistical validity for data Is it really fine or fine-loamy? Is it really fine or fine-loamy? Ability to collect data a fine depth increments Ability to collect data a fine depth increments Property distribution with depth not restricted to genetic horizons Property distribution with depth not restricted to genetic horizons Single spectrum to predict multiple soil properties Single spectrum to predict multiple soil properties Critical part is valid predictive models Critical part is valid predictive models Supplement to, not a replacement for laboratory measurement by conventional methods Supplement to, not a replacement for laboratory measurement by conventional methods Less precise 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?