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Total soil carbon assessment: linking field, lab, and landscape through VNIR modelling

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Abstract

Context

Point based measurements provide only a limited overview of landscape variation in measured properties. Upscaling of measurements from point to landscape comes with challenges particularly considering error propagation.

Objectives

We investigated the impact of using proximal derived measurements of soil total carbon taken at point locations on upscaling to landscape levels.

Methods

1087 soil samples across Florida, USA were collected, laboratory (LAB) analysed for total carbon (TC), and then measured using visible-/near-infrared (VNIR) spectroscopy. Proximal TC values were generated through chemometric modelling using random forest (RF) and partial least squares (PLS) regression. These three datasets were then upscaled to the State of Florida, USA using ordinary kriging and compared.

Results

R2 (RPD) values for the PLS and RF chemometric models were 88% (2.96) and 91% (3.23), respectively. All 3 spatial models had an accuracy of 54% on an independent validation dataset, with greater than 70% accuracy if predicted values were considered within the interpolation variance range. When comparing spatial interpolations derived from the proximally measured samples, only 18% of the PLS versus 51% of the RF fell within a range of 0.05 logTC (g kg−1) of the LAB measured interpolations.

Conclusions

Using proximal sampling and modelling provides comparable output to laboratory measured soil TC measurements at point level, but when upscaled to landscape level the selection of proximal modelling method will impact the spatial interpolations derived. The error propagation within sequential modelling must be considered particularly when one wishes to use sequential modelling to analyse change in environmental properties.

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References

  • Bellon-Maurel V, Fernandez-Ahumada E, Palagos B, Roger J-M, McBratney A (2010) Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. Trends Anal Chem 29:1073–1081

    Article  CAS  Google Scholar 

  • Bellon-Maurel V, McBratney AB (2011) Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils e Critical review and research perspectives. Soil Biol Biochem 43:1–13

    Article  Google Scholar 

  • Ben-Dor E, Chabrillat S, Demattê JAM, Taylor GR, Hill J, Whiting ML, Sommer S (2009) Using imaging spectroscopy to study soil properties. Remote Sens Environ 113:S38–S55

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Cambule AH, Rossiter DG, Stoorvogel JJ, Smaling EMA (2012) Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique. Geoderma 183–184:41–48

    Article  Google Scholar 

  • Cohen MJ, Prenger JP, DeBusk WF (2005) Visible-near infrared reflectance spectroscopy for rapid, nondestructive assessment of wetland soil quality. J Environ Qual 34:1422–1434

    Article  CAS  Google Scholar 

  • Craine JM, Gelderman TM (2011) Soil moisture controls on temperature sensitivity of soil organic carbon decomposition for a mesic grassland. Soil Biol Biochem 43:455–457

    Article  CAS  Google Scholar 

  • Dotto AC, Dalmolin RSD, ten Caten A, Grunwald S (2018) A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma 314:262–274

    Article  CAS  Google Scholar 

  • Florida Fish and Wildlife Conservation Commission (FWC) (2003) Digital vegetation and land cover data set for Florida derived from 2003 Landsat ETM + imagery. Tallahassee, FL

  • Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17

    Article  CAS  Google Scholar 

  • Grunwald S, Thompson Ja, Boettinger JL (2011) Digital soil mapping and modeling at continental scales: finding solutions for global issues. Soil Sci Soc Am J 75:1201

    Article  Google Scholar 

  • Grunwald S, Vasques GM, Rivero RG (2015) Fusion of soil and remote sensing data to model soil properties. Adv Agron 131:1–109

    Article  Google Scholar 

  • Knox NM, Grunwald S, McDowell ML, Bruland GL, Myers DB, Harris WG (2015) Modelling soil carbon fractions with visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy. Geoderma 239–240:229–239

    Article  Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2:18–22

    Google Scholar 

  • McBratney AB, Odeh IOA, Bishop TFA, Dunbar MS, Shatar TM (2000) An overview of pedometric techniques for use in soil survey. Geoderma 97:293–327

    Article  Google Scholar 

  • McDowell ML, Bruland GL, Deenik JL, Grunwald S, Knox NM (2012) Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy. Geoderma 189–190:312–320

    Article  Google Scholar 

  • Mevik B-H, Cederkvist HR (2004) Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR). J Chemom 18:422–429

    Article  CAS  Google Scholar 

  • Mevik B-H, Wehrens R (2007) The pls package: principal component and partial least squares regression in R. J Stat Softw 18:1–24

    Article  Google Scholar 

  • National Climate Data Centre (NCDC) (2008) Monthly Surface Data

  • Natural Resources Conservation Service (NRCS) (2009) Soil Survey Geographic Database (SSURGO)

  • Palacios-Orueta A, Ustin S (1998) Remote sensing of soil properties in the Santa Monica Mountains I. Spectral Analysis. Remote Sens Environ 65:170–183

    Article  Google Scholar 

  • Peng Y, Xiong X, Adhikari K, Knadel M, Grunwald S, Greve MH (2015) Modeling soil organic carbon at regional scale by combining multi-spectral images with laboratory spectra. PLoS ONE 10:e0142295

    Article  Google Scholar 

  • Rasel SMM, Groen TA, Hussin YA, Diti IJ (2017) Proxies for soil organic carbon derived from remote sensing. Int J Appl Earth Obs Geoinf 59:157–166

    Article  Google Scholar 

  • Sarkhot DV, Grunwald S, Ge Y, Morgan CLS (2011) Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy. Geoderma 164:22–32

    Article  CAS  Google Scholar 

  • Scharlemann J, Tanner EV, Hiederer R, Kapos V (2014) Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag 5:81–91

    Article  CAS  Google Scholar 

  • Seni G, Elder JF (2010) Ensemble methods in data mining: improving accuracy through combining predictions, synthesis. Morgan & Claypool Publishers, San Rafael

    Google Scholar 

  • Shepherd KD, Walsh MG (2002) Development of reflectance spectral libraries for characterization of soil properties. Soil Sci Soc Am J 66:988–998

    Article  CAS  Google Scholar 

  • Stockmann U, Padarian J, McBratney A, Minasny B, de Brogniez D, Montanarella L, Hong SY, Rawlins BG, Field DJ (2015) Global soil organic carbon assessment. Glob Food Sec 6:9–16

    Article  Google Scholar 

  • Subke J-A, Heinemeyer A, Reichstein M (2009) Experimental design: scaling up in time and space and its statistical considerations. In: Kutsch WL, Bahn M, Heinemeyer A (eds) Soil carbon dynamics: an integrated methodology, 1st edn. Cambridge University Press, Cambridge, pp 34–48

    Google Scholar 

  • Team RDC (2010) R: a language and environment for statistical computing

  • Vasques GM, Grunwald S, Harris WG (2010) Spectroscopic models of soil organic carbon in Florida, USA. J Environ Qual 39:923

    Article  CAS  Google Scholar 

  • Vasques GM, Grunwald S, Myers DB (2012) Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida, USA. Landsc Ecol 27:355–367

    Article  Google Scholar 

  • Vasques GM, Grunwald S, Sickman JO (2008) Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146:14–25

    Article  CAS  Google Scholar 

  • Viscarra Rossel RA, Behrens T, Ben-Dor E, Brown DJ, Demattê JAM, Shepherd KD, Shi Z, Stenberg B, Stevens A, Adamchuk V, Aïchi H, Barthès BG, Bartholomeus HM, Bayer AD, Bernoux M, Böttcher K, Brodský L, Du CW, Chappell A, Fouad Y, Genot V, Gomez C, Grunwald S, Gubler A, Guerrero C, Hedley CB, Knadel M, Morrás HJM, Nocita M, Ramirez-Lopez L, Roudier P, Campos EMR, Sanborn P, Sellitto VM, Sudduth KA, Rawlins BG, Walter C, Winowiecki LA, Hong SY, Ji W (2016) A global spectral library to characterize the world’s soil. Earth Sci Rev 155:198–230

    Article  Google Scholar 

  • Viscarra Rossel RA, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO (2006) Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131:59–75

    Article  CAS  Google Scholar 

  • Wehrens R (2011) Chemometrics with R, Use R!. Springer, Heidelberg

    Book  Google Scholar 

  • Wehrens R, Mevik B-H (2007) PLS: Partial least squares regression (PLSR) and principal component regression (PCR)

  • Williams PC (1987) Variables affecting near-infrared reflectance spectroscopic analysis. In: Williams P, Norris K (eds) Near-infrared technology in the agricultural and food industries. American Association of Cereal Chemists, St. Paul, pp 143–167

    Google Scholar 

  • Xiong X, Grunwald S, Myers DB, Kim J, Harris WG, Bliznyuk N (2015) Assessing uncertainty in soil organic carbon modeling across a highly heterogeneous landscape. Geoderma 251:105–116

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by USDA-CSREES-NRI Grant Award 2007-35107-18368 ‘Rapid Assessment and Trajectory Modeling of Changes in Soil Carbon across a Southeastern Landscape’ (National Institute of Food and Agriculture (NIFA) – Agriculture and Food Research Initiative (AFRI)). This project is a Core Project of the North American Carbon Program. The authors would like to thank Aja Stoppe, Samiah Moustafa, Lisa Stanley, Adriana Comerford, Anne Quidez, Xiong Xiong, C. Wade Ross, and D. Brenton Myers for their hard work in field soil sampling and lab analyses. We also like to thank Dr. W.G. Harris and Dr. N.B. Comerford for their valuable contributions in the NIFA-AFRI project. Jongsung Kim and T. Osborne are given special acknowledgment for the soil sample collection in wetlands in South Florida.

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Authors and Affiliations

  1. Department of Geo-Spatial Science and Technology, Namibia University of Science and Technology, Pvt Bag 13388, Windhoek, Namibia

    Nichola Maria Knox

  2. Soil and Water Sciences Department, University of Florida, 2181 McCarty Hall, PO Box 110290, Gainesville, FL, 32611, USA

    Nichola Maria Knox & Sabine Grunwald

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  1. Nichola Maria Knox
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  2. Sabine Grunwald
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Correspondence to Nichola Maria Knox.

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Knox, N.M., Grunwald, S. Total soil carbon assessment: linking field, lab, and landscape through VNIR modelling. Landscape Ecol 33, 2137–2152 (2018). https://doi.org/10.1007/s10980-018-0729-6

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