Combining Vis–NIR spectroscopy and advanced statistical analysis for estimation of soil chemical properties relevant for forest road construction

A thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible–near-infrared (Vis–NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO₃, organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis–NIR spectrometer within a range of 350–2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS–DI–ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS–DI–ANFIS) models, as well as narrow band spectral indices were applied. The obtained results ​​indicate that the PLS–DI–ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R² values and lower RMSE than the other investigated models. The estimation accuracy for CaCO₃, however, was low for all applied methods. The results confirm that Vis–NIR spectroscopy may be applied as a rapid and cost-efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance.