1. 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. Comerford Graduate student: Gustavo M. Vasques

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

3. NASA Temp.

4. + Environmental landscape factors Indicators of locally operating ecosystem processes C, N and P mineralizable pools Soil Carbon Sequestration 1 Carbon (C) pools: Total Recalcitrant Labile Anthropogenic and natural forcing functions Transfer of total, recalcitrant and labile C imprints into landscapes elucidates on mechanisms that induce C storage/ change Assess the actual pools and potential C sequestration potential

5. Soil Mapping 1 Accurate Rapid Cost-effective Large soil regions Relevant soil properties

6. StatisticsGIScience Soil & Environmental Sciences Cartography Quantitative methods Geo- statistics Digital Soil Mapping 1 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.

7. 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 Sensing 2 Each soil has specific reflectance signature Modern soil surveying

8. Spectroradiometer 2 QualitySpec® Pro (Analytical Spectral Devices Inc., Boulder, CO)

9. TC: 7,132 mg kg -1 (avg.) Pine plantation Typic Aquods TC: 268,995 mg kg -1 Wetland Typic Argiaquolls 120-180 cm 30-60 cm 60-120 cm Visible/Near-infrared Diffuse Reflectance Spectroscopy 2 Total carbon (TC): 169 mg kg -1 Upland forest Typic Quartzipsamments

10. Soil Study – Santa Fe River Watershed, Florida 3 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

11. 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

12. Methodology 3 Laboratory soil data VNIR spectral data Pre-treatment: Log-normalization using base-10 logarithm Testing of 30 different preprocessing transformations Identify 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 Model dataset ~ 30% of data used to test accuracy of model predictions Validation dataset predictions R 2, RMSE

13. Total Carbon (TC) 3 Sampling across land use- soil order trajectories

14. Layer 1 (0-30 cm) Layer 2 (30-60 cm) Layer 3 (60-120 cm) Layer 4 (120-180 cm) Observations (n)143 141135 Mean14,8728,1053,9291,659 Std. Error of Mean1,8282,1271,111182 Median10,5293,7051,8081,087 Mode2,670932384169 Std. Deviation21,86725,43413,1982,117 Skewness6.3618.5037.9985.021 Kurtosis47.1081.2864.4334.19 Range199,318268,062112,72518,749 Minimum2,670932384169 Maximum201,988268,995113,10918,917 Total Carbon (TC) [mg/kg] 3

15. 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 Scanning 3

16. Results: Prediction Performance - logTC [mg/kg] 4 Calibration Validation R 2 RMSER 2 RMSE SMLR0.910.1490.850.176 PCR0.830.2120.830.189 PLSR0.860.1900.860.176 RT0.980.1490.760.226 CT0.970.0870.860.170 [30 pre-processing methods were tested]

17. PCR Validation Results: Prediction Performance - logTC [mg/kg] 4 SMLR [pre-processing: Savitzky-Golay 1st-derivative using a 1st-order polynomial with search window 9 (SGF-1-9) [pre-processing: standard normal variate transformation (SNV)] Laboratory measurements VNIR Predictions

18. PLSRRT 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)]

19. CT Validation Results: Prediction Performance - logTC [mg/kg]4 [pre-processing: Norris gap derivative with a search window of 7 (NGD-7)]

20. (mg/kg) TOCHCRCDOC07DOC02 Observations141 Minimum 2,670371,150214221 Maximum 201,98829,399181,7389,0008,995 Median 10,5292,8927,382644664 Mean 14,8283,70711,122799809 Std. Deviation 21,9933,29219,194827818 Total Organic Carbon and Carbon Fractions (0-30 cm) 5 TOC: Total organic carbon HC: Hydrolysable carbon (after digestion with 6N HCl) - Thermo Electron FlashEA Elemental Analyzer RC: Recalcitrant carbon was calculated as the difference between TOC and HC DOC: Dissolved organic carbon Shimadzu TOC Analyzer after hot water extraction, then filtered into 2 classes: <0.7 µm (DOC07) and <0.2 µm (DOC02).

21. SOC and fractions Best model CalibrationValidation Rc2Rc2 RMSE C Rv2Rv2 RMSE V TOCLOG-PLSR0.930.0820.860.078 HCSAV-PLSR0.490.2180.400.285 RCSAV-PLSR0.900.1090.820.108 DOC07SAV-PLSR0.890.1000.840.086 DOC02SNV-PLSR0.810.1100.690.100 Statistics – Total Organic Carbon and Carbon Fractions 5 LOG: Log (1/Reflectance) SAV: Savitzky-Golay smoothing, and averaging SNV: Standard normal variate transformation

22. VNIRS vs. Conventional Lab Analysis 6 CharacteristicsVNIRSConventional Ease of sample preparation ++++ Ease of analysis++ Speed++++ Labor+++++ Equipments cost++ Use of supplies++++ Cost per sample++++ Accuracy+++

23. VNIRS & Landscape Scale Modeling 7 Organic matter (OM) lab measurements 0-30 cm OM predictions using VNIR spectral data method: committee trees (boosting) 0-30 cm Calibr.Validation R 2 0.940.83 RMSE0.490.77

24. VNIRS & Landscape Scale Modeling7 Semivariograms of OM measurements and VNIRS predictions show very similar spatial autocorrelation structure OM lab measurements OM derived from VNIRS

25. VNIRS & Landscape Scale Modeling7 OM map derived using lab measurements OM map derived from VNIR spectra

26. ErrorSandSiltClay Mean-0.38-0.500.40 Min-14.20-13.24-3.12 Max17.217.303.05 RMSE4.883.691.02 Lamsal S., PhD thesis Soil Texture Geospatial Modeling - SFRW 7 Soil 0-30 cm Method: Spatial stochastic simulation

27. 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 VNIRS

28. Conclusions8 NASA Soil mapping & expertise Soil and remote sensors incl. VNIRS Soils – environmental factors (GIS; soil-landscape analysis) Soil science - global context