The ANOVA of the molecular indicators measured on ROI indicated significant differences (p<0.05) between OM composition at T0 and T1, whereas differences between green manure and straw were significant only for O/N alkyl and aryl contributions, with higher O/N alkyl and lower aryl contributions for straw compared to green manure (Table 1). No significant differences in POM composition between depths were found. No significant interactions between incubation time, depths and type of added OM were found so 3-way ANOVA models without interactions were applied for all variables presented in Table 1.

Prediction of POM properties from machine learning using hyperspectral images

For predicting C/N, the random forest modelling was optimal with 200 trees, whereas the random ensemble ANN was optimal with 100 ANNs in the ensemble. For predicting alkyl ratio, the random forest modelling was optimal with 400 trees, and the random ensemble ANN was optimal with 200 neural networks in the ensemble. Both modelling approaches yielded accurate predictions, with high to very high coefficients of determination (R² > 0.7). The predictions for C/N were better than for alkyl ratio, and in general, the random ensemble ANN outperformed the RF models, and the median estimates of the ANN were better than the mean estimates (Table S1).
For C/N, model averaging improved the predictions, with the best estimates obtained using a weighted average of the RF and median of the random ensemble ANN (R²=0.90, RMSE 14.3, MAE 10.4). For alkyl ratio predictions, the random ensemble ANN outperformed the RF and model averaging yielded no improvement as assessed by the goodness-of-fit parameters. In predicting the measured values close to the maximum and minimum, the RF models tended to underestimate the higher and overestimate the lower, while the ensemble ANN models did not show this tendency (Figure 1 and Fig. S1).