Prediction of Soil Properties with PLSR and vis-NIR Spectroscopy: Application to Mediterranean Soils from Southern Italy

摘要

This study demonstrated the use of visible-near infrared (vis-NIR) reflectance spectroscopy and partial least squares regression (PLSR) for the effective analysis of important properties of Mediterranean soils from southern Italy. Understanding soil properties is an essential pre-requisite for sustainable land management. Assessment of these properties has long been gained through conventional laboratory analysis, which is considered costly and time consuming. Therefore, there is a need to develop alternative cheaper and faster techniques for soil analysis. In recent years, special attention has been given to vis-NIR reflectance spectroscopy and chemometrics. In this study we evaluated the potential of vis-NIR spectroscopy and PLSR for prediction of chemical and physical properties [sand, silt and clay, organic carbon (OC), total nitrogen (N), cation exchange capacity (CEC), and calcium carbonate (CaCO_(3))] of soils representative of three Mediterranean agro-ecosystems from the Campania region, southern Italy. We performed the analysis for each agro-ecosystem separately (local predictions) and for the combined ones (regional prediction). PLSR is one of the most popular modelling techniques used in chemometrics and is commonly used for quantitative spectroscopic analysis. We derived PLSR models, which were validated using an independent subset of data that was not used in the modelling. The accuracy of the calibrations and validations for the different soil properties were assessed using the root mean squared error (RMSE) and the relative percent deviation (RPD). Our results showed that regional and local predictions are from very good to excellent for OC (RPD of validation velence (2.36)/(3.03)) and clay content (RPD velence (2.31)/(2.95)). For the remaining properties, RPD values ranged from (1.40)/(2.07) (poor/fair-very good), for regional predictions, to (1.10)/(2.33) (poor-very good), for local predictions.