An Open Framework for Downscaling Soil Carbon and Clay Maps Using Sensor Data: Five Case Studies Across Diverse European Landscapes

Sustainable soil management is recognised as a pivotal solution for addressing current and future global challenges, but existing global and national soil property maps often lack the fine-scale resolution required for local or intra-field assessments. Here, we aimed to develop an open access framework to downscale soil property maps using remote and proximal sensor data and test it for predicting soil organic carbon (SOC) and clay across different regions of Europe. To facilitate the dissemination of this framework, we developed the R package “soilscaler”, which contains integrated functions for producing downscaled soil maps. This approach uses coarse resolution maps as a baseline, incorporating sensor data and soil observations to train a model explaining local variation of soil properties. We tested the framework in Denmark, Northern Ireland, Lithuania, The Netherlands, and Turkey. For comparison, we also created high-resolution maps using a conventional digital soil mapping (DSM) approach for each field independently. We found that the downscaling performance depends on the quality of the coarse-resolution soil maps, the spatial variability of soil properties within a given field, and the range of inter-field variations in each country. Although the downscaling process showed lower performance than the conventional DSM approach, the results indicate that the downscaled maps better represent local variability than existing national and global soil maps. Additionally, we found that remote sensing sensors generally better represent the spatial distribution of SOC, while proximal soil sensors better capture clay contents. Future studies should focus on gathering more sensor data and correlating it with soil properties to improve predictions based solely on sensor data.