Estimating texture and organic carbon of an Oxisol by near infrared spectroscopy

Felipe Fernández-Martínez, Jesús Hernán Camacho-Tamayo, Yolanda Rubiano-Sanabria


Laboratory analyses are a fundamental basis for monitoring soil behavior. These analyses are usually tedious and
expensive depending on the methodology used, which may limit data acquisition. The aim of this research was to evaluate the potential
of Near Infrared (NIR) diffuse refl ectance spectroscopy for the estimation of texture and Soil Organic Carbon (SOC) of an Oxisol. A
total of 313 samples were collected at fi xed depths of 0.0-0.10, 0.10-0.20, 0.20-0.30, 0.30-0.40 and 0.40-0.50 m in 70 points distributed
in 248 ha, from which SOC and the fractions of sand, silt and clay were determined. The spectral signatures were obtained from a
NIRFlex sensor, and the modeling was done applying partial least squares regression. A highly representative model was obtained for
the SOC estimation, with a coeffi cient of determination (R2) of 0.97, Root Mean Square Error (RMSE) of 1.10 g kg-1 and Residual
Prediction Deviation (RPD) of 5.63. For the textural fractions, estimation models of lesser performance were obtained, with R2 values
of 0.62; 0.44 and 0.62, RMSE values of 1.10%, 2.92% and 3.08%, and RPD values of 1.82, 1.61 and 1.81 for sand, silt and clay,
respectively. By means of geostatistical interpolation surfaces, the behavior of the measured and spectrally estimated variables was
compared. NIR spectroscopy proved to be a viable alternative for the precise estimation of SOC, while for the textural fractions it is
convenient to explore the improvement of the estimates.


NIR spectroscopy. Pedometrics. Spectral modelling. Geostatistics. Chemometrics.

Texto completo:

PDF (English)


AHMADI, A. et al. Soil properties prediction for precision agriculture using visible and near-infrared spectroscopy: A systematic review and meta-analysis. Agronomy, v. 11, n. 3, p. 1-14, 2021.

ANGELOPOULOU, T. et al. Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sensing, v. 11, n. 6, p. 1–18, 2019.

CAMACHO-TAMAYO, J. H. et al. Evaluación de textura del suelo con espectroscopía de infrarrojo cercano en un oxisol de Colombia. Colombia Forestal, v. 20, n. 1, p. 5–18, 2017.

CAMACHO-TAMAYO, J. H.; RUBIANO, Y. S.; HURTADO, M. DEL P. Near-infrared (NIR) diffuse reflectance spectroscopy for the prediction of carbon and nitrogen in an Oxisol. Agronomía Colombiana, v. 32, n. 1, p. 86–94, 2014.

CAMBARDELLA, C. A. et al. Field-Scale Variability of Soil Properties in Central Iowa Soils. Soil Science Society of America Journal, v. 58, n. 5, p. 1501–1511, 1994.

CARNIELETTO, A. et al. 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, v. 314, p. 262–274, 2018.

CURCIO, D. et al. Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences, v. 19, p. 494–503, 2013.

DAY, P. R. Particle fractionation and particle size analysis. In BLACK, C. A. Method of soil analysis, Part I. 1. ed. Soil Science Society of America, 1965. cap. 43, p. 545–567.

DIGGLE, P.; RIBEIRO, P. Model-based Geostatistics. 1. ed. New York: Springer, 2007. 228 p.

HOBLEY, E.; PRATER, I. Estimating soil texture from vis–NIR spectra. European Journal of Soil Science, v. 70, n. 1, p. 83–95, 2019.

JACONI, A.; DON, A.; FREIBAUER, A. Prediction of soil organic carbon at the country scale: stratification strategies for near-infrared data. European Journal of Soil Science, v. 68, n. 6, p. 1–11, 2017.

JOVIĆ, B. et al. Empirical equation for preliminary assessment of soil texture. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, v. 206, p. 506–511, 2019.

KENNARD, R. W.; STONE, L. A. Computer Aided Design of Experiments. Technometrics, v. 11, n. 1, p. 137–148, 1969.

LAAMRANI, A. et al. Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada. Remote Sensing, v. 11, n. 11, p. 1–15, 2019.

LASHYA, P. et al. Visible-Near-Infrared Spectroscopy Prediction of Soil Characteristics as Affected by Soil-Water Content. Soil Science Society of America Journal, v. 82, n. 6, p. 1333–1346, 2018.

LIU, S. et al. Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment. Geoderma, v. 348, p. 37–44, 2019.

MCBRATNEY, A. B.; VISCARRA ROSSEL, R. A. Diffuse Reflectance Spectroscopy as a Tool for Digital Soil Mapping. In HARTEMINK, A. E.; MCBRATNEY, A.; MENDONÇA SANTOS, M. L. Digital Soil Mapping with Limited Data. 1. ed. Springer, 2008. cap. 13, p. 165–172.

NAWAR, S.; MOUAZEN, A. M. On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning. Soil & Tillage Research, v. 190, p. 120–127, 2019.

NOCITA, M.; et al. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biology and Biochemistry, v.68, p. 337–347, 2014.

PINHEIRO, É. F. M. et al. Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon. Remote Sensing, v. 9, n. 4, p. 1–22, 2017.

POGGIO, L.; GIMONA, A. 3D mapping of soil texture in Scotland. Geoderma Regional, v. 9, p. 5–16, 2017.

POPPIEL, R. R. et al. Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties. Revista Brasilera de Ciencia Do Solo, v. 42, p. 1–26, 2018.

RAMIREZ-LOPEZ, L.; REINA-SÁNCHEZ, A.; CAMACHO-TAMAYO, J. H. Variabilidad espacial de atributos físicos de un Typic Haplustox de los Llanos Orientales de Colombia. Engenharia Agrícola, v. 28, n. 1, p. 55–63, 2008.

SUMMERS, D. et al. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators, v. 11, p. 123–131, 2011.

TEKIN, Y.; TUMSAVAS, Z.; MOUAZEN, A. M. Effect of Moisture Content on Prediction of Organic Carbon and pH Using Visible and Near-Infrared Spectroscopy. Soil Science Society of America Journal, v. 76, n. 1, p. 188–199, 2012.

TUMSAVAS, Z. et al. Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy. Biosystems Engineering, v. 177, p. 90–100, 2018.

VENDRAME, P. R. S. et al. The potential of NIR spectroscopy to predict soil texture and mineralogy in Cerrado Latosols. European Journal of Soil Science, v. 63, n. 5, p. 743–753, 2012.

VISCARRA ROSSEL, R. A. et al. A global spectral library to characterize the world’s soil. Earth-Science Reviews, v. 155, p. 198–230, 2016.

VISCARRA ROSSEL, R. A.; MCGLYNN, R. N.; MCBRATNEY, A. B. Determining the composition of mineral-organic mixes using UV – vis – NIR diffuse reflectance spectroscopy. Geoderma, v. 137, p. 70–82, 2006.

VISCARRA ROSSEL, R. A.; WEBSTER, R. Predicting soil properties from the Australian soil visible – near infrared spectroscopic database. European Journal of Soil Science, v. 63, p. 848–860, 2012.

Revista Ciência Agronômica ISSN 1806-6690 (online) 0045-6888 (impresso), Site:, e-mail: - Fone: (85) 3366.9702 - Expediente: 2ª a 6ª feira - de 7 às 17h.