The effect of modeller’s choices and measurement errors on the performance of MIR soil spectroscopy models

The growing demand for high-resolution soil data has stimulated the development of visible, near and mid-infrared (MIR) spectroscopy. This study assessed four modeller’s choices that can influence spectral model performance: 1) model type, 2) application of a calibration transfer function, 3) size of the subset selected from a soil spectral library for model calibration, and 4) pre-processing method. The effect of measurement errors in reference wet and dry chemistry data, and MIR spectra on model performance were evaluated through a Monte Carlo approach. Models were calibrated to predict cation-exchange capacity (CEC), pHH2O, total carbon (C) and total nitrogen (N) in the Netherlands. Accurate quantification of measurement errors is crucial, as large errors substantially reduced model performance. Measurement errors in MIR spectra had a smaller impact than those in wet and dry chemistry data, as overall measurement error was smaller. The effect of modeller’s choices varied per soil property. PLSR outperformed RF for the prediction of CEC and pHH2O, while the opposite was observed for total C and N. Lower RF performance was linked to higher sensitivity to correlations between wavenumbers. Calibration transfer did not consistently improve model performance, indicating case-specific performance. Larger spectral subsets improved predictions for CEC, pHH2O, and total N, but not total C. Pre-processing strongly affected results, with the 1st order derivative yielding the best overall performance.The optimal combination of modeller’s choices varied by soil property and/or spectral library, highlighting the importance of treating them as hyperparameters for optimizing spectral models.