Prediction And Mapping Of Soil Clay And Sand Contents Using Visible And Near-Infrared Spectroscopy
| Autor: | Yücel Tekin, Zeynal Tumsavas, Yahya Ulusoy, Abdul Mounem Mouazen |
|---|---|
| Přispěvatelé: | Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Toprak Bilimi ve Bitki Besleme Bölümü., Tümsavaş, Zeynal, Tekin, Yücel, Ulusoy, Yahya, AAG-6056-2021 |
| Rok vydání: | 2019 |
| Předmět: |
Infrared devices
Partial least squares regressions (PLSR) Leave-one-out cross validations Mean squared error Soil test Reflectance spectroscopy Agricultural engineering Color Soil Science Soil science Qaulity Least squares approximations Agriculture multidisciplinary Visible and near-infrared spectroscopy Cross-validation Sand Nir spectroscopy Partial least squares regression Linear regression Calibration Texture Water content Organic-carbon Regression coefficient Visible and near infrared Moisture-content Vis-nir spectroscopy Mean square error Textures Agriculture Predictive analytics Spectrum analysis Soil Color Near-Infrared Spectroscopy Hyperspectral Mapping Root-mean-square error of predictions Pls regression analysis Control and Systems Engineering Soil water Clay Soils Environmental science Prediction performance Laboratories Regression analysis Agronomy and Crop Science Near infrared spectroscopy Forecasting Food Science |
| Popis: | The 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. ICT-AGRI (The European Commission's ERA-NET scheme under the 7th Framework Programme) Department for Environment, Food & Rural Affairs (DEFRA) FWO |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|