Feasibility of Estimating Chemical Forms of Iron in Acacia victoriae Forest Soils Using Visible and Near-Infrared (Vis-NIR) Spectroscopy

Articles in Press

Document Type : Research Paper

Authors

Department of Soil Science, College of Agriculture, Shiraz University, Shiraz

Abstract

Iron is an essential plant nutrient whose availability and environmental role depend on the distribution of its chemical forms in soil, making accurate measurement important. Conventional laboratory methods are difficult, costly, and time-consuming, so new approaches are needed. Visible and near-infrared (Vis-NIR) spectroscopy is rapid, non-destructive, and cost-effective. This study aimed to estimate soil iron forms using Vis-NIR reflectance data with partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) to develop spectral transfer functions. After measuring iron forms and spectral reflectance of 130 soil samples, PLSR was used to predict iron forms and identify effective spectral bands. Selected bands were then entered into SMLR to develop simple spectral functions. Models were calibrated with training data and validated with test data using R², RMSE, and mean error. Validation R² values for PLSR models were: carbonate-bound iron (0.37), exchangeable iron (0.63), organic-bound iron (0.53), amorphous iron oxides (0.67), crystalline iron oxides (0.68), manganese oxide-bound iron (0.59), residual iron (0.51), and total iron (0.54). For SMLR-based functions, values were 0.41, 0.55, 0.44, 0.41, 0.65, 0.53, 0.48, and 0.48, respectively. Although PLSR outperformed SMLR, the difference was not substantial for some properties. Thus, Vis-NIR spectroscopy combined with regression models, especially PLSR, is an effective, low-cost, non-destructive method for estimating soil iron forms. The developed spectral functions should be validated before use in non-calcareous soils.

Keywords

Main Subjects

Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 08 June 2026

Files

Share

How to cite

Statistics