## 3 Results and Discussion
This observation agrees with a previous digital soil mapping study of total carbon in the majority of Europe, in which QRF was the second-most-accurate algorithm behind deep learning [60]. Predicted SOC maps according to the most accurate machine learning methods are displayed in Figure 3. The scatterplots of the final predictions are displayed in Figure 4 for France (mainland) and Figure 5 for the Czech Republic. Figure 4: The scatterplots of predicted and observed SOC values based on the \(15\) evaluated machine learning regression algorithms in France. Figure 5: The scatterplots of predicted and observed SOC values based on the \(15\) evaluated machine learning regression algorithms in the Czech Republic. NRMSE, as an absolute accuracy metric, offered significant interpretability advantages in evaluating model performance, particularly in comparative studies across different datasets or scales [61]. By normalizing the RMSE against the range or mean of the observed data, NRMSE provided a dimensionless measure that enhances the contextual understanding of prediction accuracy [62]. This is particularly useful when dealing with heterogeneous data that can vary widely in magnitude, as it allows for a more meaningful comparison of model efficacy. Despite these benefits, NRMSE remains underrepresented in the literature compared to more commonly used metrics like R\({}^{2}\) or traditional RMSE. This oversight hinders the development of a more nuanced understanding of model performance across disciplines, as NRMSE's ability to convey relative error in a standardized format can facilitate better decision-making in model selection and refinement. Moreover, it does not have specific computational requirements, as the vast majority of previous studies calculated RMSE during the accuracy assessment, also leading to the simple calculation of NRMSE. Overall, the relative difference between the most and least accurate evaluated machine learning methods was 9.1% for France (mainland) and 14.8% for the Czech Republic. The covariates which produced the highest feature importance for the most accurate machine learning model for France (mainland), ranked by importance, were the digital elevation model, land cover classes, the MODIS surface reflectance at 483-493 nm (blue) band, and two CHELSA bioclimatic variables, including the mean daily air temperatures of the coldest quarter and the mean monthly precipitation amount of the wettest quarter. For the Czech Republic, the five most important covariates according to most accurate model were the bare soil index and green coverage index from Sentinel-2 data, the Sentinel-2 (near-infrared) band at 842 nm central wavelength, the Sentinel-1 annual median raster with VH polarization, and the MODIS surface reflectance at 545-565 nm (green) band. Table 4 provides a ranking of machine learning algorithms based on their performance in terms of accuracy and their ranking in the WoSCC. The algorithms are ranked using RMSE as the metric for accuracy assessment results. The RF algorithm ranks the highest in terms of accuracy assessment, with a rank of 1 for both France and the Czech Republic, and a rank difference of 0, indicating a consistent performance and a justified place as the most frequently used machine learning algorithm for digital SOC mapping in the literature. CUB ranked second in accuracy assessment but had a significantly lower WoSCC ranking in both countries, resulting in a rank difference of \(-9\) and \(-10\), indicating the most overrated algorithm in terms of prediction accuracy based on the results from this study. The MLR and SVM algorithms had similar accuracy assessment ranks but lower WoSCC rankings, resulting in negative rank differences. However, the accuracy assessment results from this study indicated that GBM and KNN were underrated in terms of prediction accuracy, suggesting that these should be considered more often in future studies.