iForest - Biogeosciences and Forestry


Visible and near infrared spectroscopy for predicting texture in forest soil: an application in southern Italy

Massimo Conforti, Raffaele Froio, Giorgio Matteucci, Gabriele Buttafuoco   

iForest - Biogeosciences and Forestry, Volume 8, Issue 3, Pages 339-347 (2015)
doi: https://doi.org/10.3832/ifor1221-007
Published: Sep 09, 2014 - Copyright © 2015 SISEF

Research Articles

Texture is a primary variable affecting the total amount of carbon stock in the soil. The standard methods for determining soil texture, however, are still conducted manually and are largely time-consuming. Reflectance spectroscopy in the visible, near infrared (Vis-NIR, 350-2500 nm) spectral region could be an alternative to standard laboratory methods. The aim of this paper was to develop calibration models based on laboratory Vis-NIR spectroscopy and PLSR analysis to estimate the texture (sand: 2-0.05 mm; silt: 0.05-0.002 mm; clay: <0.002 mm) in a forest area of southern Italy. An additional objective was to produce continuous maps of sand, silt and clay through a geostatistical approach. Soil samples were collected at 235 locations in the study area, and then dried, sieved at 2 mm and analyzed in laboratory for soil texture and Vis-NIR spectroscopic measurements. Spectra showed that soil samples could be spectrally separable on the basis of classes of texture. To establish the relationships between spectral reflectance and soil texture (sand, silt and clay) partial least squared regression (PLSR) analysis was applied to 175 soil samples, while the remaining 60 samples were used to validate the models. The optimum number of factors to be retained in the calibration models was determined by leave-one-out cross-validation. Results of cross validation of calibration models indicated that the models fitted quite well and the values of R2 ranged between a minimum value of 0.74% for silt and a maximum value of 0.84 for sand content. Results for validation were satisfactory for sand content (R2=0.81) and clay content (R2=0.80) and less satisfactory for silt content (R2=0.70). Geostatistics coupled with Vis-NIR reflectance spectroscopy allowed us to produce continuous maps of sand, silt and clay, which are of critical importance for understanding and managing forest soils.


Forest Soils, Soil Texture, Vis-NIR Spectroscopy, Geostatistics, Southern Italy

Authors’ address

Massimo Conforti
Raffaele Froio
Giorgio Matteucci
Gabriele Buttafuoco
Institute for Agricultural and Forest Systems in the Mediterranean (ISAFOM), National Research Council of Italy, v. Cavour 4/6, I-87036 Rende (CS, Italy)

Corresponding author

Gabriele Buttafuoco


Conforti M, Froio R, Matteucci G, Buttafuoco G (2015). Visible and near infrared spectroscopy for predicting texture in forest soil: an application in southern Italy. iForest 8: 339-347. - doi: 10.3832/ifor1221-007

Academic Editor

Davide Ascoli

Paper history

Received: Dec 29, 2013
Accepted: May 29, 2014

First online: Sep 09, 2014
Publication Date: Jun 01, 2015
Publication Time: 3.43 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 51847
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 43853
Abstract Page Views: 2224
PDF Downloads: 4256
Citation/Reference Downloads: 89
XML Downloads: 1425

Web Metrics
Days since publication: 3511
Overall contacts: 51847
Avg. contacts per week: 103.37

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

Total number of cites (since 2015): 33
Average cites per year: 3.67


Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

ARSSA (2003)
Carta dei suoli della regione Calabria, scala 1: 250.000 [Soil map of the Calabria region, scale 1:250.000.]. Monografia divulgativa, ARSSA - Agenzia Regionale per lo Sviluppo e per i Servizi in Agricoltura, Servizio Agropedologia, Rubbettino, CS, Italy, pp. 387. [in Italian]
Aïchi H, Fouad Y, Walter C, Viscarra Rossel R, Lili Chabaane Z, Sanaa M (2009)
Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosystems Engineering 104 (3): 442-446.
CrossRef | Gscholar
Ben-Dor E (2002)
Quantitative remote sensing of soil properties. Advances Agronomy 75: 173-243.
CrossRef | Gscholar
Ben-Dor E, Irons JR, Epema GF (1999)
Soil reflectance. In: “Remote Sensing for the Earth Sciences” (Rencz AN ed). Manual of Remote Sensing, vol. 3, Wiley & Sons, New York, USA, pp. 111-188.
Borsi S, Hieke Merlin O, Lorenzoni S, Paglionico A, Zanettin Lorenzoni E (1976)
Stilo unit and “dioritic-kinzigitic” unit in Le Serre (Calabria, Italy). Geological, petrological, geochronological characters. Bollettino della Società Geologica Italiana 95: 219-244.
Bricklemyer RS, Brown DJ (2010)
On-the-go VisNIR: potential and limitations for mapping soil clay and organic carbon. Computers and Electronics in Agriculture 70 (1): 209-216.
CrossRef | Gscholar
Buttafuoco G, Conforti M, Aucelli PPC, Robustelli G, Scarciglia F (2012)
Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation. Environmental Earth Sciences 66: 1111-1125.
CrossRef | Gscholar
Calcaterra D, Parise M (2010)
Weathering in the crystalline rocks of Calabria, Italy, and relationships to landslides. In: “Weathering as predisposing factor to slope movements” (Calcaterra D, Parise M eds). Geological Society of London, Engineering Geology Series, Special Publication 23, pp. 105-130.
Online | Gscholar
Calcaterra D, Parise M, Dattola L (1996)
Caratteristiche dell’alterazione e franosità di rocce granitoidi nel bacino del torrente Alaco (Massiccio della Serre, Calabria) [Characteristics of the weathering and landsliding of granitic rocks in the Alaco torrent basin (Serre Massif, Calabria)]. Bollettino della Società Geologica Italiana 115: 3-28. [in Italian]
Chang CW, Laird DA, Mausbach MJ, Hurburgh CRj (2001)
Near-infrared reflectance spectroscopy - principal components regression analysis of soil properties. Soil Science Society of America Journal 65 (2): 480-490.
CrossRef | Gscholar
Chilès JP, Delfiner P (2012)
Geostatistics: modelling spatial uncertainty. Wiley, New York, USA, pp. 695.
Clark RN, King TVV, Klejwa M, Swayze GA (1990)
High spectral resolution reflectance spectroscopy of minerals. Journal of Geophysical Research 95: 653-680.
CrossRef | Gscholar
Conforti M, Buttafuoco G, Leone AP, Aucelli PPC, Robustelli G, Scarciglia F (2012)
Soil erosion assessment using proximal spectral reflectance in VIS-NIR-SWIR region in sample area of Calabria region (Southern Italy). Rendiconti Online Società Geologica Italiana 21 (Part 2): 1202-1204.
Conforti M, Buttafuoco G, Leone AP, Aucelli PPC, Robustelli G, Scarciglia F (2013a)
Studying the relationship between water-induced soil erosion and soil organic matter using Vis-NIR spectroscopy and geomorphological analysis: a case study in a southern Italy area. Catena 110: 44-58.
CrossRef | Gscholar
Conforti M, Froio R, Matteucci G, Caloiero T, Buttafuoco G (2013b)
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 - International Journal of Environmental Quality 11: 49-64.
Online | Gscholar
Curcio D, Ciraolo G, D’Asaro F, Minacapilli M (2013)
Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences 19: 494-503.
CrossRef | Gscholar
Curran PJ (1994)
Imaging spectrometry. Progress in Physical Geography 18: 247-266.
CrossRef | Gscholar
Demattê JAM, Terra FS (2014)
Spectral pedology: a new perspective on evaluation of soils along pedogenetic alterations. Geoderma 217-218: 190-200.
CrossRef | Gscholar
Efron B, Tibshirani R (1993)
An introduction to the bootstrap. Monographs on statistics and applied probability, vol. 57, Chapman and Hall, London, UK, pp. 436.
Ehsani MR, Upadhyaya SK, Slaughter D, Shafii S, Pelletier M (1999)
A NIR technique for rapid determination of soil mineral nitrogen. Precision Agriculture 1: 217-234.
CrossRef | Gscholar
Farifteh J, Van Der Meer F, Atzberger C, Carranza EJM (2007)
Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN). Remote Sensing of Environment 110: 59-78.
CrossRef | Gscholar
Ge Y, Thomasson JA, Morgan CL, Searcy SW (2007)
VNIR diffuse reflectance spectroscopy for agricultural soil property determination based on regression-kriging. American Society of Agricultural and Biological Engineers 50: 1081-1092.
Online | Gscholar
Geladi P, Kowalski BR (1986)
Partial least-squares regression: a tutorial. Analytica Chimica Acta 185: 1-17.
CrossRef | Gscholar
Goovaerts P (1997)
Geostatistics for natural resources evaluation. Oxford University Press, New York, NY, USA, pp. 483.
Online | Gscholar
Hill J (1994)
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” (Bidoglio G, Stumm W eds). ECSC, EEC, EAEC, Brussels and Luxembourg, pp. 497-526.
CrossRef | Gscholar
Kemper T, Sommer S (2002)
Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environmental Science and Technology 3: 2742-2747.
CrossRef | Gscholar
Knadel M, Stenberg B, Deng F, Thomsen A, Greve M (2013)
Comparing predictive abilities of three visible-near infrared spectrophotometers for soil organic carbon and clay determination. Journal of Near Infrared Spectroscopy 21: 67-80.
CrossRef | Gscholar
Köppen W (1936)
Das geographische System der Klimate [The current system of climates]. In: “Handbuch der Klimatologie. Band 5” (Köppen W, Geiger R, Teil C eds). Gebrüder Bornträger, Berlin, Germany, pp 1-46. [in German]
Lal R (2005)
Forest soils and carbon sequestration. Forest Ecology and Management 220: 242-258.
CrossRef | Gscholar
Lamsal S, Mishra U (2010)
Mapping soil textural fractions across a large watershed in north-east Florida. Journal of Environmental Management 91:1686-1694.
CrossRef | Gscholar
Leone AP, Sommer S (2000)
Multivariate analysis of laboratory spectra for the assessment of soil development and soil degradation in the Southern Apennines (Italy). Remote Sensing of Environment 72: 346-359.
CrossRef | Gscholar
Martens H, Naes T (1989)
Multivariate calibration. John Wiley & Sons, Chichester, UK, pp. 438.
Online | Gscholar
Matheron G (1971)
The theory of regionalised variables and its applications. Les Cahiers du Centre de Morphologie Mathématique n. 5, Fontainebleau, France, pp. 271.
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.
CrossRef | Gscholar
Mouazen AM, Karoui R, De Baerdemaeker J, Ramon H (2005)
Classification of soil texture classes by using soil visual near infrared spectroscopy and factorial discriminant analysis techniques. Journal of Near Infrared Spectroscopy 13: 231-240.
CrossRef | Gscholar
Nanni MR, Demattê JAM (2006)
Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society of America Journal 70: 393-407
CrossRef | Gscholar
Næs T, Isaksson T, Fearn T, Davies T (2004)
A user-friendly guide to multivariate calibration and classification. Reprinted with corrections. NIR Publications, Chichester, UK, pp. 344.
Osman KT (2013)
Soils. Principles, properties and management. Springer Science+Business Media, Dordrecht, The Neteherlands, pp. 271.
CrossRef | Gscholar
Palacios-Orueta A, Ustin SL (1998)
Remote sensing of soil properties in the Santa Monica mountains I. Spectral analysis. Remote Sensing of Environment 65: 170-183.
CrossRef | Gscholar
Patruno A, Cavazza L, Castrignanò A (1997)
Granulometria [Granulometry]. In: “Metodi di analisi fisica del suolo. III.1”, (Pagliai M ed). Franco Angeli, Rome, Italy, pp. 1-26. [in Italian]
Schwanghart W, Jarmer T (2011)
Linking spatial patterns of soil organic carbon to topography - A case study from south-eastern Spain. Geomorphology 126: 252-263.
CrossRef | Gscholar
Shepherd KD, Walsh MG (2002)
Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal 66: 988-998.
CrossRef | Gscholar
Silver WL, Neff J, McGroddy M, Veldkamp E, Keller M, Cosme R (2000)
Effects of soil texture on belowground carbon and nutrient storage in a lowland Amazonian forest ecosystem. Ecosystems 3: 193-209.
CrossRef | Gscholar
Sorriso-Valvo M (1993)
The geomorphology of Calabria. A sketch. Geografia Fisica e Dinamica Quaternaria 16: 75- 80.
Stenberg B, Viscarra Rossel RA, Mouazen AM, Wetterlind J (2010)
Visible and near infrared spectroscopy in soil science. Advances in Agronomy 107: 163-215.
CrossRef | Gscholar
Stoner ER, Baumgardner MF (1981)
Characteristic variations in reflectance of surface soils. Soil Science Society of America Journal 45: 1161-1165.
CrossRef | Gscholar
Sørensen LK, Dalsgaard S (2005)
Determination of clay and other soil properties by near infrared spectroscopy. Soil Science Society of America Journal 69:159-167.
CrossRef | Gscholar
Telles ECC, Camargo PB, Martinelli LA, Trumbore SE, Costa ES, Santos J, Higuchi N, Oliveira RCJ (2003)
Influence of soil texture on carbon dynamics and storage potential in tropical forest soils of Amazonia. Global Biogeochemical Cycles 17 (2): 1040.
CrossRef | Gscholar
Udelhoven T, Emmerling C, Jarmer T (2003)
Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: a feasibility study. Plant and Soil 251: 319-329.
CrossRef | Gscholar
USDA (2010)
Keys to soil taxonomy (11th edn). Soil Survey Staff, USDA Natural Resources Conservation Service, Washington, DC, USA, pp. 344.
Online | Gscholar
Vendrame PRS, Marchao RL, Brunet D, Becquer T (2012)
The potential of NIR spectroscopy to predict soil texture and mineralogy in Cerrado Latosols. European Journal of Soil Science 63 (5): 743-753.
CrossRef | Gscholar
Viscarra Rossel RA, McBratney AB (1998)
Laboratory evaluation of a proximal sensing technique for simultaneous measurement of soil clay and water content. Geoderma 85: 19-39.
CrossRef | Gscholar
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.
CrossRef | Gscholar
Viscarra Rossel RA (2008)
ParLeS: software for chemometrics analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems 90: 72-83.
CrossRef | Gscholar
Viscarra Rossel RA, McBratney AB (2008)
Diffuse reflectance spectroscopy as a tool for digital soil mapping. In: “Digital Soil Mapping with Limited Data” (Hartemink AE, McBratney AB, Mendonça-Santos L eds). Elsevier Science, Amsterdam, The Netherlands, pp. 165-172.
Viscarra Rossel RA, Behrens T (2010)
Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158: 46-54.
CrossRef | Gscholar
Volkan Bilgili A, van Es HM, Akbas F, Durak A, Hively WD (2010)
Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semiarid area of Turkey. Journal of Arid Environments 74: 229-238.
CrossRef | Gscholar
Wackernagel H (2003)
Multivariate geostatistics: an introduction with applications. Springer, Berlin, Germany, pp. 387.
Online | Gscholar
Waiser TH, Morgan CLS, Brown DJ, Hallmark CT (2007)
In situ characterization of soil clay content with visible near-infrared diffuse reflectance spectroscopy. Soil Science Society of America Journal 71: 389-396.
CrossRef | Gscholar
Webster R, Oliver MA (2007)
Geostatistics for environmental scientists (2nd edn). Wiley, Chichester, UK, pp. 330.
Online | Gscholar
Wetterlind J, Stenberg B, Söderström M (2008)
The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. Precision Agriculture 9: 57-69.
CrossRef | Gscholar
White K,Walden J, Drake N, Eckardt F, Settle J (1997)
Mapping the iron oxide content of dune sands, Namib Sand Sea, Namibia, using Landsat Thematic Mapper data. Remote Sensing of Environment 62: 30-39.
CrossRef | Gscholar
Yang H, Griffiths PR, Tate JD (2003)
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. Analytica Chimica Acta 489: 125-136.
CrossRef | Gscholar

This website uses cookies to ensure you get the best experience on our website. More info