###### Abstract  
The aim of this study was to narrow the research gap of ambiguity in which machine learning algorithms should be selected for evaluation in digital soil organic carbon (SOC) mapping. This was performed by providing a comprehensive assessment of prediction accuracy for 15 frequently used machine learning algorithms in digital SOC mapping based on studies indexed in the Web of Science Core Collection (WoSCC), providing a basis for algorithm selection in future studies. Two study areas, including mainland France and the Czech Republic, were used in the study based on 2514 and 400 soil samples from the LUCAS 2018 dataset. Random Forest was first ranked for France (mainland) and then ranked for the Czech Republic regarding prediction accuracy; the coefficients of determination were 0.411 and 0.249, respectively, which was in accordance with its dominant appearance in previous studies indexed in the WoSCC. Additionally, the K-Nearest Neighbors and Gradient Boosting Machine regression algorithms indicated, relative to their frequency in studies indexed in the WoSCC, that they are underrated and should be more frequently considered in future digital SOC studies. Future studies should consider study areas not strictly related to human-made administrative borders, as well as more interpretable machine learning and ensemble machine learning approaches. random forest; web of science core collection topic search; LUCAS dataset; environmental covariates; digital soil mapping; remote sensing +
Footnote †: journal: Applied sciences  
0000-0001-9990-3399-3  
(SOC) mapping at a national scale due to its comprehensive and systematic approach to soil sampling across diverse landscapes. LUCAS provides standardized soil data, including SOC content, collected at predefined locations that can enhance the representativeness of soil information [9]. Furthermore, the extensive coverage and methodological rigor of LUCAS facilitate the development of predictive models that can extrapolate SOC distribution across wider areas, ultimately contributing to better-informed agricultural practices, carbon stock assessments, and environmental policies at the national level. Knowing which machine learning algorithms to use for digital soil mapping is critical as different algorithms possess unique strengths and limitations that can significantly influence the accuracy and reliability of soil property predictions, including SOC content [10]. Machine learning techniques, such as Random Forests, support vector machines, and neural networks, differ in their ability to handle various data types, manage high dimensionality, and model the complex non-linear relationships that are inherent in soil data. Selecting the appropriate algorithm can enhance the model's performance by optimizing predictive accuracy and minimizing overfitting, which is particularly important in heterogeneous landscapes [11]. Thus, the informed selection of machine learning algorithms not only improves the robustness of SOC predictions, but also fosters informed decision-making for sustainable land use and effective climate action strategies. The integration of machine learning algorithms in digital soil mapping represents a state-of-the-art approach, surpassing conventional geostatistical methods in several aspects [12]. Unlike traditional geostatistical techniques, which rely on spatial autocorrelation and Gaussian processes, machine learning algorithms can effectively handle high-dimensional and heterogeneous datasets, capturing complex non-linear relationships between soil properties and environmental factors [13]. This enables more accurate predictions of SOC content, particularly in areas with diverse landscapes and soil types. Furthermore, machine learning algorithms can automatically select relevant predictors, reducing the need for manual feature selection and minimizing the risk of overfitting.