Mouazen, Abdul M2022-12-132022-12-132018-06-28Tümsavaş, Z. vd. (2019). ''Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy''. Biosystems Engineering, 177, 90-100.1537-51101537-5129https://doi.org/10.1016/j.biosystemseng.2018.06.008https://www.sciencedirect.com/science/article/pii/S1537511017311480http://hdl.handle.net/11452/29844The aim of this research was to examine the potential of visible and near infrared (Vis-NIR) spectroscopy for the prediction and mapping of sand and clay fractions of soils in one irrigated field having clay texture in Karacabey district of Bursa Province, Turkey. Eighty six soil samples, collected from the study area, were divided into calibration (80%) and validation (20%) sets. A partial least squares regression (PLSR) with leave-one-out cross-validation analysis was carried out using the calibration set, and the resulting model prediction ability was tested using the prediction set. Models developed were used to predict sand and clay content using laboratory spectra and spectra collected on-line from the field. Results showed an "excellent" laboratory prediction performance for both sand (regression coefficient (R-2) = 0.90, root mean square error of prediction (RMSEP) = 2.91% and ratio of prediction deviation (RPD) = 3.25 in cross-validation; R-2 = 0.81, RMSEP = 3.84% and RPD = 2.33 in the prediction set) and clay (R-2 = 0.91, RMSEP = 2.67% and RPD = 3.51 in cross validation; R-2 = 0.85, RMSEP = 3.40% and RPD = 2.66 in the prediction set). On-line predictions were less accurate than the laboratory results, although the online predictions were still very good (RPD = 2.25-2.31). Kappa statistics showed reasonable similarities between measured and predicted maps, particularly for those obtained with laboratory scanning. This study demonstrated that soil sand and clay can be successfully measured and mapped using Vis-NIR spectroscopy under both laboratory and on-line scanning conditions.eninfo:eu-repo/semantics/closedAccessPls regression analysisSandClayVis-nir spectroscopyReflectance spectroscopyMoisture-contentOrganic-carbonTextureQaulityColorCalibrationForecastingInfrared devicesLaboratoriesLeast squares approximationsMappingMean square errorNear infrared spectroscopyRegression analysisSoilsSpectrum analysisTexturesLeave-one-out cross validationsNir spectroscopyPartial least squares regressions (PLSR)Prediction performanceRegression coefficientRoot-mean-square error of predictionsVisible and near infraredVisible and near-infrared spectroscopyPredictive analyticsAgricultureArticle0004563525000092-s2.0-8504902506190100177Agricultural engineeringAgriculture, multidisciplinarySoil Color; Near-Infrared Spectroscopy; Hyperspectral