Vis-NIR Spectroscopy for Determining Physical and Chemical Soil Properties: An Application to an Area of Southern Italy
DOI:
https://doi.org/10.15377/2409-9813.2014.01.01.3Keywords:
Soil properties, Vis-NIR reflectance spectroscopy, PLSR, Southern Italy.Abstract
The development of rapid, accurate, cost effective methods to determine soil physical and chemical properties is important for sustainable land management. In the last two to three decades, the interest in using visible and near infrared (Vis-NIR) spectroscopy as an alternative method for determining soil properties has increased. To obtain reliable predictions of soil properties, multivariate calibration techniques such as Partial Least Squares Regression (PLSR) are commonly used to correlate the spectra with the chemical, physical and mineralogical properties of soils.
The objective of the paper was to assess the potential of Vis-NIR spectroscopy coupled with PLSR to determine soil chemical and physical properties such as organic carbon (SOC), sand, silt, clay, and calcium carbonate (CaCO3) contents in a sample site of southern Italy.
Spectral curves showed that the soils could be spectrally separable on the basis of chemical and physical properties. PLSR calibration models were derived for each of the soil properties and were validated with an independent data set. The optimum number of factors to be retained in the calibration models was determined by leave-one-out cross-validation. The accuracy of the calibration and validation models for the different soil properties was evaluated with the coefficient of determination (R2) and the root mean squared error (RMSE). The results showed that predictions were satisfactory for all soil properties analyzed with high values of R2 > 80.
A combination of Vis-NIR spectroscopy and multivariate statistical techniques, therefore, can be used as a rapid, low cost and quantitative means of characterizing the soils of southern Italy.Downloads
References
Dwivedi RS. Spatio-temporal characterization of soil degradation. Tropical Ecology 2002; 43: 75-90.
Batjes NH. Total carbon and nitrogen in the soils of the world. Eur J Soil Sci 1996; 47: 151-163. http://dx.doi.org/10.1111/j.1365-2389.1996.tb01386.x DOI: https://doi.org/10.1111/j.1365-2389.1996.tb01386.x
Lal R. Soil carbon sequestration impacts on global climate change and food security. Science 2004; 304: 1623-1627. http://dx.doi.org/10.1126/science.1097396 DOI: https://doi.org/10.1126/science.1097396
Demattê JAM, Sousa AA, Alves MC, Nanni,MR, Fiorio PR, Campos RC. Determining soil water status and other soil characteristics by spectral proximal sensing. Geoderma 2006; 135: 179-195. http://dx.doi.org/10.1016/j.geoderma.2005.12.002 DOI: https://doi.org/10.1016/j.geoderma.2005.12.002
Ben-Dor E, Irons JR, Epema GF. Soil reflectance. Remote Sensing for the Earth Sciences, Manual of Remote Sensing. John Wiley & Sons, Ltd. 1999; 3: 111-188.
Chang CW, Laird DA, Mausbach MJ, Hurburgh Jr CR. Nearinfrared reflectance spectroscopy - principal components regression analysis of soil properties. Soil Sci. Soc. Am. J. 2001; 65: 480-490. http://dx.doi.org/10.2136/sssaj2001.652480x DOI: https://doi.org/10.2136/sssaj2001.652480x
Udelhoven T, Emmerling C, Jarmer, T. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: a feasibility study. Plant and Soil 2003; 251: 319-329. http://dx.doi.org/10.1023/A:1023008322682 DOI: https://doi.org/10.1023/A:1023008322682
Brown DJ, Shepherd KD, Walsh MG, Dewayne Mays M, Reinsch TG. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 2006; 132: 73-290. http://dx.doi.org/10.1016/j.geoderma.2005.04.025 DOI: https://doi.org/10.1016/j.geoderma.2005.04.025
Leone AP, Viscarra-Rossel RA, Amenta P, Buondonno A. Prediction of Soil Properties with PLSR and vis-NIR Spectroscopy: Application to Mediterranean Soils from Southern Italy. Current Analytical Chemistry 2012; 8(2): 283- 299. http://dx.doi.org/10.2174/157341112800392571 DOI: https://doi.org/10.2174/157341112800392571
Conforti M, Buttafuoco G, Leone A, Aucelli PPC, Robustelli G, Scarciglia F. Studying the relationship between waterinduced soil erosion and soil organic matter using Vis-NIR spectroscopy and geomorphological analysis: a case study in a southern Italy area. Catena 2013; 110: 44-58. http://dx.doi.org/10.1016/j.catena.2013.06.013 DOI: https://doi.org/10.1016/j.catena.2013.06.013
Reeves JB, McCarty G.W, Reeves VB. Mid-Infrared diffuse reflectance spectroscopy for the quantitative analysis of agricultural soils. J Agric Food Chem 2001; 49: 766-772. http://dx.doi.org/10.1021/jf0011283 DOI: https://doi.org/10.1021/jf0011283
Shepherd KD, Walsh MG. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Am. J. 2002; 66: 988-998. http://dx.doi.org/10.2136/sssaj2002.9880 DOI: https://doi.org/10.2136/sssaj2002.9880
McBratney AB, Minasny B, Viscarra Rossel RA. Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis. Geoderma 2006; 136: 272-278. http://dx.doi.org/10.1016/j.geoderma.2006.03.051 DOI: https://doi.org/10.1016/j.geoderma.2006.03.051
Viscarra Rossel RA, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 2006; 131: 59-75. http://dx.doi.org/10.1016/j.geoderma.2005.03.007 DOI: https://doi.org/10.1016/j.geoderma.2005.03.007
Stenberg B Viscarra Rossel RA, Mouazen AM, Wetterlind J. Visible and Near Infrared Spectroscopy in Soil Science. Advances in Agronomy 2010; 107: 163-215. http://dx.doi.org/10.1016/S0065-2113(10)07005-7 DOI: https://doi.org/10.1016/S0065-2113(10)07005-7
Ben-Dor E. Quantitative remote sensing of soil properties. Advances in Agronomy 2002; 75: 173-243. http://dx.doi.org/10.1016/S0065-2113(02)75005-0 DOI: https://doi.org/10.1016/S0065-2113(02)75005-0
Ge Y, Thomasson JA, Morgan CL, Searcy SW. VNIR diffuse reflectance spectroscopy for agricultural soil property determination based on regression-kriging. Trans. ASABE 2007; 50(3): 1081-1092. http://dx.doi.org/10.13031/2013.23122 DOI: https://doi.org/10.13031/2013.23122
Ehsani MR, Upadhyaya SK, Slaughter D, Shafii S, Pelletier M. A NIR technique for rapid determination of soil mineral nitrogen. Precis. Agric. 1999; 1: 217-234. http://dx.doi.org/10.1023/A:1009916108990 DOI: https://doi.org/10.1023/A:1009916108990
Leone AP, Sommer S. Multivariate analysis of laboratory spectra for the assessment of soil development and soil degradation in the Southern Apennines (Italy). Remote Sens. Environ. 2000; 72: 346-359. http://dx.doi.org/10.1016/S0034-4257(99)00110-8 DOI: https://doi.org/10.1016/S0034-4257(99)00110-8
Nanni MR, Demattê JAM. Spectral reflectance methodology in comparison to traditional soil analysis. Soil Sci. Soc. Am. J. 2006; 70: 393-407. http://dx.doi.org/10.2136/sssaj2003.0285 DOI: https://doi.org/10.2136/sssaj2003.0285
Aïchi H, Fouad Y, Walter C, Viscarra Rossel RA, Zohra Lili Chabaane, Mustapha Sanaa. 2009. Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosyst Eng 2009; 104: 442-446. http://dx.doi.org/10.1016/j.biosystemseng.2009.08.002 DOI: https://doi.org/10.1016/j.biosystemseng.2009.08.002
Conforti M, Buttafuoco G, Leone AP, Aucelli PPC, Robustelli G, Scarciglia F. Soil erosion assessment using proximal spectral reflectance in VIS-NIR-SWIR region in sample area of Calabria region (Southern Italy). Rend. Onl. Soc. Geol. It. 2012; 21 (Part 2): 1202-1204.
Conforti M, Froio R, Matteucci G, Caloiero T, Buttafuoco G. Potentiality of laboratory visible and near infrared spectroscopy for determining clay content in forest soil: a case study from high forest beech (Fagus sylvatica) in Calabria (southern Italy). EQA 2013; 11: 49-64.
Viscarra Rossel RA, McBratney AB. Diffuse reflectance spectroscopy as a tool for digital soil mapping. Chapter 12. In “Digital Soil Mapping with Limited Data” (AE Hartemink, AB McBratney, L Mendonça-Santos, Eds) Elsevier Science, Amsterdam 2008; 165-172. http://dx.doi.org/10.1007/978-1-4020-8592-5_13 DOI: https://doi.org/10.1007/978-1-4020-8592-5_13
Martens H, Naes T. Multivariate Calibration. John Wiley & Sons, Chichester, United Kingdom, UK 1989.
Viscarra Rossel RA, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 2010; 158: 46-54. http://dx.doi.org/10.1016/j.geoderma.2009.12.025 DOI: https://doi.org/10.1016/j.geoderma.2009.12.025
Yang H, Griffiths PR, Tate JD. Comparison of partial least squares regression and multi-layer neural networks for quantification of nonlinear systems and application to gas phase Fourier transform infrared spectra. Anal Chim Acta 2003; 489: 125-136. http://dx.doi.org/10.1016/S0003-2670(03)00726-8 DOI: https://doi.org/10.1016/S0003-2670(03)00726-8
Farifteh J, Van Der Meer F, Atzberger C, Carranza EJM Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN). Remote Sens Environ 2007; 110: 59-78. http://dx.doi.org/10.1016/j.rse.2007.02.005 DOI: https://doi.org/10.1016/j.rse.2007.02.005
McDowell ML, Bruland GL, Deenik JL, Grunwald S, Knox NM. Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy. Geoderma 2012; 189-190: 312-320. http://dx.doi.org/10.1016/j.geoderma.2012.06.009 DOI: https://doi.org/10.1016/j.geoderma.2012.06.009
Leone AP, Calabrò G, Coppola E. Maffei C, Menenti M. Tosca M, Vella M, Buondonno A. Prediction of soil properties with VIS-NIR-SWIR reflectance spectroscopy and artificial neural networks. A case study. Advances in GeoEcology 2008; 39: 689- 702.
Curcio D, Ciraolo G, D’Asaro F, Minacapilli M. Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences 2013; 19: 494 - 503. http://dx.doi.org/10.1016/j.proenv.2013.06.056 DOI: https://doi.org/10.1016/j.proenv.2013.06.056
Conforti M, Aucelli PPC, Robustelli G, Scarciglia F. Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo Stream catchment (Northern Calabria, Italy). Nat. Hazards 2011; 56: 881-898. http://dx.doi.org/10.1007/s11069-010-9598-2 DOI: https://doi.org/10.1007/s11069-010-9598-2
Lanzafame G, Zuffa G. Geologia e petrografia del foglio Bisignano (Bacino del Crati, Calabria). Geol. Romana 1976; 15: 223-270.
Conforti M. Studio geomorfopedologico dei processi erosivi nel bacino del T. Turbolo (Calabria settentrionale) con il contributo della spettrometria della riflettenza. (PhD Thesis) University of Calabria, Italy 2009; pp.310.
ARSSA. Carta dei suoli della regione Calabria — scala 1: 250000. Monografia divulgativa. ARSSA - Agenzia Regionale per lo Sviluppo e per i Servizi in Agricoltura, Servizio Agropedologia. Rubbettino 2003; pp.387.
USDA-NRCS Soil Survey Staff - United States Department of Agriculture: Keys to Soil Taxonomy, 11th ed., U.S.D.A., Natural Resources Conservation Service: NY, 2010.
Buttafuoco G, Conforti M, Aucelli PPC, Robustelli G, Scarciglia F. Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation. Environ Earth Sci 2012; 66: 1111-1125. http://dx.doi.org/10.1007/s12665-011-1317-0 DOI: https://doi.org/10.1007/s12665-011-1317-0
Lucà F, Conforti M, Robustelli G. Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy. Geomorphology 2011; 134: 297-308. http://dx.doi.org/10.1016/j.geomorph.2011.07.006 DOI: https://doi.org/10.1016/j.geomorph.2011.07.006
Conforti M, Pascale S, Robustelli G, Sdao F. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 2014; 113: 236- 250. http://dx.doi.org/10.1016/j.catena.2013.08.006 DOI: https://doi.org/10.1016/j.catena.2013.08.006
Pagliai M. Metodi di Analisi Fisica del Suolo (Physical methods of soil analysis). Italian Ministry of Agriculture, Franco Angeli, Milan 1997 (In Italian).
Violante P. Metodi di Analisi Chimica del Suolo (Chemical methods of soil analysis). Milan: Italian Ministry of Agriculture, Franco Angeli 2000 (in Italian).
Viscarra Rossel RA. ParLeS: software for chemometrics analysis of spectroscopic data. Chemom Intell Lab Syst 2008; 90: 72-83. http://dx.doi.org/10.1016/j.chemolab.2007.06.006 DOI: https://doi.org/10.1016/j.chemolab.2007.06.006
Geladi P, MacDougall D, Martens H. Scatter correction for near-infrared reflectance spectra of meat. Appl. Spectrosc. 1985; 39, 491-500. http://dx.doi.org/10.1366/0003702854248656 DOI: https://doi.org/10.1366/0003702854248656
Næs T, Isaksson T, Fearn T, Davies T. A User-Friendly Guide to Multivariate Calibration and Classification. Reprinted with corrections. NIR Publications, Chichester 2004.
Demattê JAM, Terra FS. Spectral Pedology: a new perspective on evaluation of soils along pedogenetic alterations. Geoderma 2014; 217-218: 190-200. http://dx.doi.org/10.1016/j.geoderma.2013.11.012 DOI: https://doi.org/10.1016/j.geoderma.2013.11.012
Clark RN, King TVV, Klejwa M, Swayze GA. High spectral resolution reflectance spectroscopy of minerals. J Geophys Res 1990; 95: 12653-12680. http://dx.doi.org/10.1029/JB095iB08p12653 DOI: https://doi.org/10.1029/JB095iB08p12653
Hunt G.R, Ashley RP. Spectra of altered rocks in the visible and near infrared. Econ Geol 1979; 74: 1613-1629. http://dx.doi.org/10.2113/gsecongeo.74.7.1613 DOI: https://doi.org/10.2113/gsecongeo.74.7.1613
Stoner ER, Baumgardner MF. Characteristic variations in reflectance of surface soils. Soil Sci. Soc. Am. J. 1981; 4: 1161-1165. http://dx.doi.org/10.2136/sssaj1981.03615995004500060031 x DOI: https://doi.org/10.2136/sssaj1981.03615995004500060031x
Latz K, Wesimiller RA, Van Scoyoc GE, Baumgarnder MF. Characteristic variation in spectral reflectance of selected eroded Alfisols. Soil Sci. Soc. Am. J. 1984; 48: 1130-1134. http://dx.doi.org/10.2136/sssaj1984.03615995004800050035 x DOI: https://doi.org/10.2136/sssaj1984.03615995004800050035x
White K, Walden J, Drake N, Eckardt F, Settle J. Mapping the iron oxide content of dune sands, Namib Sand Sea, Namibia, using Landsat Thematic Mapper Data. Remote Sens Environ 1997; 62: 30-39. http://dx.doi.org/10.1016/S0034-4257(97)00068-0 DOI: https://doi.org/10.1016/S0034-4257(97)00068-0
Oliveira JF, Brossard M, Vendrame PRS, Mayi S, Corazza EJ, Marchão RL, Guimarães M. Soil discrimination using diffuse reflectance Vis-NIR spectroscopy in a local toposequence. C. R. Geoscience 2013; 345: 446-453. http://dx.doi.org/10.1016/j.crte.2013.12.001 DOI: https://doi.org/10.1016/j.crte.2013.12.001
Hill J. Spectral properties of soils and the use of optical remote sensing systems for soil erosion mapping. In Chemistry of Aquatic Systems: Local and Global Perspectives (G. Bidoglio and W. Stumm, Eds.), ECSC, EEC, EAEC, Brussels and Luxembourg 1994; pp. 497-526. DOI: https://doi.org/10.1007/978-94-017-1024-4_18
Palacios-Orueta A, Ustin SL. Remote Sensing of Soil Properties in the Santa Monica Mountains I. Spectral Analysis. Remote Sens Environ 1998; 65: 170-183. http://dx.doi.org/10.1016/S0034-4257(98)00024-8 DOI: https://doi.org/10.1016/S0034-4257(98)00024-8
Schwanghart W, Jarmer T. Linking spatial patterns of soil organic carbon to topography — a case study from southeastern Spain. Geomorphology 2011; 126: 252-263. http://dx.doi.org/10.1016/j.geomorph.2010.11.008 DOI: https://doi.org/10.1016/j.geomorph.2010.11.008
Hunt GR, Salisbury JW. Visible and near infrared spectra of minerals and rocks. II. Carbonates. Mod Geol 1971; 2: 23-30.
Volkan Bilgili A, van Es HM, Akbas F, Durak A, Hively WD. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semiarid area of Turkey. J Arid Environ 2010; 74: 229-238. http://dx.doi.org/10.1016/j.jaridenv.2009.08.011 DOI: https://doi.org/10.1016/j.jaridenv.2009.08.011
Nocita M, Kooistra L, Bachmann M, Müller A, Powell M, Weel S. Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa. Geoderma 2011: 167-168: 295-302. http://dx.doi.org/10.1016/j.geoderma.2011.09.018 DOI: https://doi.org/10.1016/j.geoderma.2011.09.018
Vendrame PRS, Marchao RL, Brunet D, Becquer T. The potential of NIR spectroscopy to predict soil texture and mineralogy in Cerrado Latosols. Eur. J. Soil Sci. 2012; 63: 743-753. http://dx.doi.org/10.1111/j.1365-2389.2012.01483.x DOI: https://doi.org/10.1111/j.1365-2389.2012.01483.x
Downloads
Published
Issue
Section
License
Copyright (c) 2014 Global Journal of Agricultural Innovation, Research & Development
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All the published articles are licensed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
