Cross-regional transfer learning to predict acid sulfate soils in Finland using Random Forest

Proper use of land for various purposes requires digital soil mapping. Nowadays, the use of machine learning techniques in digital soil mapping has been a major breakthrough. The resulting maps are accurate, objective and easily reproducible.  Furthermore, the process is less expensive than traditional methods. A supervised machine learning technique needs soil samples and environmental covariates for the creation of a map. The lack of soil samples in some regions is a major issue in digital soil mapping. In the case of acid sulfate (AS) soils, the absence of maps can be a high risk for the environment. This is due to AS soils can lead to environmental damage when they are oxidized during the drainage of the land. Therefore, in the unavailability of maps, AS soils may be accidentally drained by external activities related to agriculture, forestry or urban activities. A possible solution for mapping areas with few soil samples is to use soil samples from other regions. In a previous work, we showed that a machine learning model is able to correctly classify soil samples from a region where it had not been trained if the composition of the soils of the region where it has been trained is the same [1]. In this study, we have analyzed whether a machine learning technique is capable of predicting the AS soils of a region when the model has been trained in a region with a very different soil composition. Four different regions located in the coastal areas of Finland have been considered. The machine learning method used is Random Forest, which has shown very high predicted abilities for the classification and prediction of AS soils [2-5]. The results show that the model is able to correctly predict AS soils when the model is trained with soil samples from other regions in most cases. This is a significant advancement in the field because it permits the first recognition of regions with a limited number of soil samples.

[1] V. Estévez et al.  2024. “A First Approximation for Acid Sulfate Soil Mapping in Areas with Few Soil Samples”. Environ. Sci. Proc. 2024, 29, 4. https://doi.org/ 10.3390/ECRS2023-15831

[2] V. Estévez et al. 2022.  “Machine learning techniques for acid sulfate soil mapping in southeastern Finland”. Geoderma 406 (2022) 115446.

[3] V. Estévez et al. 2023. “Improving prediction accuracy for acid sulfate soil mapping by means of variable selection”. Front. Environ. Sci. 11:1213069 (2023).

[4] V. Estévez et al. 2024.  “Acid sulfate soil mapping in western Finland: How to work with imbalanced datasets and machine learning”. Geoderma 447 (2024) 116916.

[5] V. Estévez et al. 2025. “Mapping of acid sulfate soil types in Laihianjoki River catchment:  A multiclass classification” .  European Journal of Soil Science 76, no. 5: e70204.