Fusing environmental variables into soil spectroscopy modeling using a novel two-step regression method

Abstract

Soil spectroscopy modelling has been extensively studied as the cost-effective proximal sensing method for soil total and organic carbon predictions. Soil carbon properties were highly predictable due to the existence of active carbon molecular bonds within the Visible/Near-Infrared (VNIR) wavelength region. However, prediction results are highly variable for soil properties without active molecular bonds within the VNIR region, such as soil pH, sum of bases (SB), and cation exchange capacity (CEC). This research is intended to enhance soil organic carbon (SOC), nitrogen (N), pH, SB and CEC prediction accuracies by fusing categorical environmental variables (soil sample depth class, soil order, landform, and parent material) with continuous soil spectral data. We introduce a novel two-step regression method (2Step-R) to properly integrate the mixed type variables utilizing Partial Least Squares Regression (PLSR) and Ridge Regression in the modelling. Results from our analysis showed that the novel 2Step-R method was capable to improve the standard PLSR prediction model performances from fair (with ratio of performance to deviation or RPD between 1 and 1.4) to acceptable (RPD between 1.4 to 2), particularly for N, pH, and SB predictions. Slight model performance improvements were achieved for SOC and CEC predictions, although RPD values were within the acceptable range. In conclusion, the 2Step-R method is promising to enhance soil prediction performances and offers flexibility to include different types of ancillary model covariates suited to mix categorical soil-environmental and continuous spectral data.