| Abstract | In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt. |
| Text | 422469 2019 10.3390/w11081712 Scopus 2 s2.0 85070980610 ISI Web of Science WOS 000484561500185 reflectance spectroscopy soil texture organic carbon field capacity permanent wilting point Capability of diffuse reflectance spectroscopy to predict soil water retention and related soil properties in an irrigated lowland district of southern Italy Leone A.P.; Leone G.; Leone N.; Galeone C.; Grilli E.; Orefice N.; Ancona V. Institute for Mediterranean Agriculture and Forest System, National Research Council ISAFoM CNR , via Patacca, 85, Ercolano NA , 80056, Institute for Mediterranean Agriculture and Forest System, National Research Council ISAFoM CNR , via Patacca, 85, 80056 Ercolano NA , Italy; antonio.leone@isafom.cnr.it, , Italy; Department of Science and Technology DST , University of Sannio, via dei Mulini, Benevento, 82100, Department of Science and Technology DST , University of Sannio, via dei Mulini, 82100 Benevento, Italy, , Italy; Water Research Institute, National Research Council IRSA CNR , V.le F. De Blasio, 5, Bari, 70132, Water Research Institute, National Research Council IRSA CNR , V.le F. De Blasio, 5, 70132 Bari, Italy, , , Italy; Water Research Institute, National Research Council IRSA CNR , V.le F. De Blasio, 5, Bari, 70132, Water Research Institute, National Research Council IRSA CNR , V.le F. De Blasio, 5, 70132 Bari, Italy, , , Italy; Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Campania University Luigi Vanvitelli, via Vivaldi 43, Caserta, 81100, Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Campania University Luigi Vanvitelli, via Vivaldi 43, 81100 Caserta, Italy, , Italy In this study, we examined the potential of vis NIR reflectance spectroscopy, coupled with partial least squares regression PLSR analysis, for the evaluation and prediction of soil water retention at field capacity FC and permanent wilting point PWP and related basic soil properties organic carbon OC , sand, silt, and clay contents in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis NIR reflectance spectra were measured in the laboratory, using a high resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two thirds of samples for calibration and one third for validation. Spectroscopic data were pre processed multiplicative scatter correction MSC , standard normal variance SNV , wavelet detrending WD , first and second derivative transformation, and filtering prior to multivariate calibration. The results revealed very good models 2.0 < RPD < 2.5 for the prediction of FC, PWP and sand, and excellent RPD > 2.5 models for the prediction of clay and OC, whereas a poor RPD < 1.4 prediction model was obtained for silt. 11 Published version https //www.mdpi.com/2073 4441/11/8/1712 Capability of Diffuse Reflectance...... water_11_01712_v2.pdf Articolo in rivista Molecular Diversity Preservation International 2073 4441 Water Basel Water Basel Water Basel Water. Basel cirogaleone GALEONE CIRO natalialeone LEONE NATALIA valeria.ancona ANCONA VALERIA antoniopasquale.leone LEONE ANTONIO PASQUALE nadia.orefice OREFICE NADIA |
