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Estimation of andic properties from Vis-NIR diffuse reflectance spectroscopy for volcanic soil classification

Di Iorio, Erika, Circelli, Luana, Lorenzetti, Romina, Costantini, Edoardo A.C., Egendorf, Sara Perl, Colombo, Claudio
Catena 2019 v.182 pp. 104109
Andosols, allophane, aluminum, chemometrics, discriminant analysis, dithionite, iron, least squares, models, organic matter, oxalates, pedotransfer functions, prediction, reflectance spectroscopy, silicon, sodium pyrophosphate, soil classification, soil profiles, soil properties, spectrophotometers, volcanic soils
Volcanic soils show peculiar characteristics related to the presence of poorly ordered crystalline minerals. These minerals are highly sensitive to specific spectral bands in the visible and near-infrared (Vis-NIR) regions and could be used to establish important relationships between andic properties and soil classification. In order to overcome the expense of traditional soil laboratory analysis and the limitations of pedotransfer functions, this research was designed to test i) the possibility of using diffuse reflectance spectra (DRS) for a supervised chemometric classification of soils with different degrees of andicity, ii) the possibility of using diffuse reflectance spectra (DRS) to estimate the andic properties needed for soil classification (iron and aluminum forms, allophane, phosphate retention, vitric content etc.), and iii) the different multivariate statistical approaches for the analysis of spectra. Diffuse reflectance spectra were measured between 200 and 2500 nm in the laboratory with a Jasco spectrophotometer equipped with an integrated sphere.Chemometric analysis was carried out by discriminant analysis that correctly divided 86% of the horizon samples in 5 classes, representing different levels of expression of the andic properties. Chemometric calibration was obtained by pre-processing Vis-NIR spectra with the application of partial least-squares regression (PLSR). Andic soil attributes were then predicted using calibrated models through PLSR methods and compared with the findings from the Supported Vector Machine (SVM). SVM generally outperformed PLSR on predicting andic properties by Vis-NIR. The most accurate predictive models were obtained for Al and Fe dithionite extracted, both with PLSR (ratio of performance to deviation, RPD 1.9) and SVM (1.7 < RPD < 1.9). The total amount of i) non-crystalline Al and Fe forms (oxalate extractable, Alo, Feo), ii) Alo + 1/2Feo (1.7 < RPD < 1.8), iii) non-crystalline Si (Sio) (RPD 1.5), and iv) Al associated with organic matter (sodium pyrophosphate extractable, Alp; RPD 1.5), Alo-Alp (RPD 1.5), allowed a first soil Andosol qualification, when obtained through SVM. Therefore, the results of this research highlight that Vis-NIR can be used as a preliminary analysis to estimate diagnostic parameters of Andosols. Results indicate that Vis-NIR is a promising technique for volcanic soil classification according to soil properties (especially Fe and Al forms) and horizons, which can be used in lieu of complex chemical and physical analyses involved in routine soil profile classification. Further studies should confirm Vis-NIR ability to discriminate Andosols at more detailed classification levels.