National Cooperative Soil Survey Conference 2007 Madison, WI National Cooperative Soil Survey Conference 2007 Madison, WI

Soil Spectroscopy for Rapid and Cost-Effective Soil Mapping Across Larger Landscapes

Sabine Grunwald

NRCS-CESU (Carolyn G. Olson)“Linking Experimental and Soil Spectral Sensing for Prediction of Soil Carbon Pools and Carbon Sequestration at Landscape Scale”Investigators: Grunwald S., J.O. Sickman and N.B. ComerfordGraduate student: Gustavo M. Vasques

NASA

Soils store ~ 3 times more C than biosphere (vegetation)~ 2 times more C than atmosphere~ 1.5 time more C than surface ocean

NASA

Temp.

+

Santa Fe River Watershed

Laboratory analyses:Total, stable and labile carbon pools

Visible near-infrared diffuse reflectance spectroscopy

Regress soil analytical and soil spectral data

(chemometrics)

GIS-based quantitative soil-landscape modeling(environmental correlation)

Maps: land use,geology,topography,etc.

Predicted carbon pools derived fromsoil-landscape modeling and analytical data

Predicted carbon pools derived from soil-landscape modeling and soil spectral data

]~,[ˆ txS ichemTC

]~,[ˆ txS ichemstable

]~,[ˆ txS ichemlabile

]~,[ˆ txS isllabile

]~,[ˆ txS islstable

]~,[ˆ txS islTC

Surface Geology Coverage in the Santa Fe Watershed

Data sources:Geology: Florida DEPWatershed boundary: SRWMD

¯0 7.5 15 22.5 303.75Kilometers

Depth of Layer 1 in the Soils of the Santa Fe Watershed

SourceSoil data: NRCS, SSURGO datasetWatershed Boundary: SRWMD

¯0 5 10 15 202.5Kilometers

Surface Geology Coverage in the Santa Fe Watershed

Data sources:Geology: Florida DEPWatershed boundary: SRWMD

¯0 7.5 15 22.5 303.75Kilometers

Surface Geology Coverage in the Santa Fe Watershed

Data sources:Geology: Florida DEPWatershed boundary: SRWMD

¯0 7.5 15 22.5 303.75Kilometers

Depth of Layer 1 in the Soils of the Santa Fe Watershed

SourceSoil data: NRCS, SSURGO datasetWatershed Boundary: SRWMD

¯0 5 10 15 202.5Kilometers

Environmental landscape factors

Indicators of locally operating

ecosystem processes

C, N and Pmineralizablepools

Soil Carbon Sequestration1

Carbon (C) pools:• Total• Recalcitrant• Labile

Santa Fe River Watershed

Laboratory analyses:Total, stable and labile carbon pools

Visible near-infrared diffuse reflectance spectroscopy

Regress soil analytical and soil spectral data

(chemometrics)

GIS-based quantitative soil-landscape modeling(environmental correlation)

Maps: land use,geology,topography,etc.

Predicted carbon pools derived fromsoil-landscape modeling and analytical data

Predicted carbon pools derived from soil-landscape modeling and soil spectral data

]~,[ˆ txS ichemTC

]~,[ˆ txS ichemstable

]~,[ˆ txS ichemlabile

]~,[ˆ txS isllabile

]~,[ˆ txS islstable

]~,[ˆ txS islTC

Anthropogenic and natural forcing functions

Transfer of total, recalcitrant andlabile C imprints into landscapeselucidates onmechanisms that induce C storage/change

Assess the actualpools and potentialC sequestrationpotential

Soil Mapping 1

• Accurate • Rapid • Cost-effective

• Large soil regions• Relevant soil properties

StatisticsGIScience

Soil &Environmental

Sciences

Carto

graphy Quantitative

methods

Geo-statistics

Digital Soil Mapping1

• GIS - multi-scale data integration• Complex geospatial methods

Environmental datasets:• Field and analytical data• Soil sensing • Remote sensing

Soil-landscape models:• Functional (stochastic; deterministic)• Mechanistic (simulation)

Grunwald S. (ed) 2006. Environmental Soil-Landscape Modeling–Geographic Information Technologies & Pedometrics. CRC Press, New York.

Visible/near-infrared spectroscopy (VNIRS) is a fast, cheap and accurate alternative for the investigation of soil properties, and is now recognized as a powerful analytical tool in soil science

Soil Sensing2

Each soilhas specificreflectance signature

Modern soil surveyingModern soil surveying

Spectroradiometer2

QualitySpec® Pro (Analytical Spectral Devices Inc., Boulder, CO)

Site #38 layer 2

Site #412 layer 4

Site #923 layer 2

Reflectance

TC: 7,132 mg kg-1 (avg.)Pine plantationTypic Aquods

TC: 268,995 mg kg-1

WetlandTypic Argiaquolls

120-180 cm

30-60 cm

60-120 cm

Visible/Near-infrared Diffuse Reflectance Spectroscopy2

Total carbon (TC): 169 mg kg-1

Upland forestTypic Quartzipsamments

Soil Study – Santa Fe River Watershed, Florida3

Objectives:

• Investigate the usefulness of VNIRS for rapid and

accurate assessment of soil carbon

• Understand the linkages between labile, recalcitrant

(stable) and total organic carbon

• Assess the usefulness of VNIRS to map larger soil-

landscapes

0 10 20 30

Kilometers±

Land use categoryPine plantationUpland forestWetlandWaterImproved pastureCropUrbanOther

0 10 20 30

Kilometers±Elevation (m)

90.90 1.49

Data sources maps:DEM: National Elevation Model (US Geological Service)Land use: Florida Fish and Wildlife Conservation Commission (2003)Geology: FL Dept. of Environmental Protection Soil Orders: Soil Survey Geographic Database (SSURGO) Natural Resources Conservation Service

Santa Fe River Watershed, Florida 3

Methodology3

Laboratory soil data

VNIR spectral dataPre-treatment:Log-normalization using base-10 logarithmTesting of 30 different preprocessing transformations

Identify relationshipsIdentify relationships

Complete dataset

Methods: Stepwise Multiple Linear Regression (SMLR) Principal Components Regression (PCR) Partial Least-Squares Regression (PLSR) Regression Tree (RT) Committee Trees (CT) (bagging)

~70%of data

Modeldataset

~ 30% of dataused to test accuracyof model predictions

Validationdataset

predictions R2, RMSE

Total Carbon (TC)3

Samplingacrossland use-soil ordertrajectories

Layer 1(0-30 cm)

Layer 2(30-60 cm)

Layer 3(60-120 cm)

Layer 4(120-180 cm)

Observations (n) 143 143 141 135

Mean 14,872 8,105 3,929 1,659

Std. Error of Mean 1,828 2,127 1,111 182

Median 10,529 3,705 1,808 1,087

Mode 2,670 932 384 169

Std. Deviation 21,867 25,434 13,198 2,117

Skewness 6.361 8.503 7.998 5.021

Kurtosis 47.10 81.28 64.43 34.19

Range 199,318 268,062 112,725 18,749

Minimum 2,670 932 384 169

Maximum 201,988 268,995 113,109 18,917

Total Carbon (TC) [mg/kg]3

Mean

Min.

Max.

+ 1 SD

- 1 SD

Wavelength (nm)

Spectral scans of 554 soil samples collected in the SFRW at 4 different soil depths (0-30, 30-60, 60-120 and 120-180 cm)

VNIR Scanning3

Results: Prediction Performance - logTC [mg/kg]4

Calibration ValidationR2 RMSE R2 RMSE

SMLR 0.91 0.149 0.85 0.176PCR 0.83 0.212 0.83 0.189PLSR 0.86 0.190 0.86 0.176RT 0.98 0.149 0.76 0.226CT 0.97 0.087 0.86 0.170

[30 pre-processing methods were tested]

PCR

Validation Results: Prediction Performance - logTC [mg/kg]4

SMLR

[pre-processing: Savitzky-Golay 1st-derivative using a 1st-order polynomialwith search window 9 (SGF-1-9)

[pre-processing: standard normal variate transformation (SNV)]

Laboratorymeasurements

VNIRPredictions

PLSR RT

Validation Results: Prediction Performance - logTC [mg/kg]4

[pre-processing: Savitzky-Golay 1st-derivative using a 3rd-order polynomial with search window of 9 (SGF-3-9)]

[pre-processing: Norris gap derivative with a search window of 5 (NGD-5)]

CT

Validation Results: Prediction Performance - logTC [mg/kg]4

[pre-processing: Norris gap derivative with a search window of 7 (NGD-7)]

(mg/kg)

TOC HC RC DOC07 DOC02

Observations 141 141 141 141 141

Minimum 2,670 37 1,150 214 221

Maximum 201,988 29,399 181,738 9,000 8,995

Median 10,529 2,892 7,382 644 664

Mean 14,828 3,707 11,122 799 809

Std. Deviation 21,993 3,292 19,194 827 818

Total Organic Carbon and Carbon Fractions (0-30 cm)5

TOC: Total organic carbonHC: Hydrolysable carbon (after digestion with 6N HCl) - Thermo Electron FlashEA Elemental AnalyzerRC: Recalcitrant carbon was calculated as the difference between TOC and HCDOC: Dissolved organic carbon Shimadzu TOC Analyzer after hot water extraction, then filtered into 2 classes: <0.7 µm (DOC07) and <0.2 µm (DOC02).

SOC and fractions

Best modelCalibration Validation

Rc2 RMSEC Rv

2 RMSEV

TOC LOG-PLSR 0.93 0.082 0.86 0.078

HC SAV-PLSR 0.49 0.218 0.40 0.285

RC SAV-PLSR 0.90 0.109 0.82 0.108

DOC07 SAV-PLSR 0.89 0.100 0.84 0.086

DOC02 SNV-PLSR 0.81 0.110 0.69 0.100

Statistics – Total Organic Carbon and Carbon Fractions5

LOG: Log (1/Reflectance) SAV: Savitzky-Golay smoothing, and averagingSNV: Standard normal variate transformation

VNIRS vs. Conventional Lab Analysis6

Characteristics VNIRS Conventional

Ease of sample preparation

+++ +

Ease of analysis + +

Speed +++ +

Labor +++ ++

Equipments cost + +

Use of supplies +++ +

Cost per sample +++ +

Accuracy +++ +++

VNIRS & Landscape Scale Modeling7

Organic matter (OM)lab measurements

0-30 cm 0-30 cm

OM predictions using VNIRspectral datamethod: committee trees (boosting)

0-30 cm 0-30 cm

Calibr. ValidationR2 0.94 0.83RMSE 0.49 0.77

VNIRS & Landscape Scale Modeling7

Semivariograms of OM measurements and VNIRS predictions show very similar spatial autocorrelation structure

OM lab measurements OM derived from VNIRS

VNIRS & Landscape Scale Modeling7

OM map derived using lab measurements

OM map derived from VNIR spectra

Error Sand Silt Clay

Mean -0.38 -0.50 0.40

Min -14.20 -13.24 -3.12

Max 17.21 7.30 3.05

RMSE 4.88 3.69 1.02

Lamsal S., PhD thesis

Soil Texture Geospatial Modeling - SFRW7

Soil 0-30 cmMethod: Spatial stochastic simulation

National and Global VNIRS Applications8

Locations / soil-landscape settings:• Africa (Shepherd and Walsh, 2002)• Australia (Dalal and Henry, 1980)• Australia (Viscarra Rossel et al., 2006)• Brazil (Masserschmidt et al., 1999)• Netherlands (Koistra et al., 2003)• USA, Maryland (Reeves III et al., 2001)• Global (USA & Africa) (Brown et al., 2006) …. many more

Soil properties:• Carbon; organic matter• Texture • Nutrients (N, P, Mg, Ca, …)• Metals (Fe, Al,…..)• CEC• BD …. many more

VNIRSVNIRS

Conclusions8

NASA

Soil mapping &expertise

Soil and remotesensorsincl. VNIRS

Soils – environmentalfactors(GIS; soil-landscape analysis)

Soil science- global context