High-spatial-resolution soil data collected on behalf of the private sector (e.g., farmers) represents a largely untapped resource for EU soil monitoring initiatives. Georeferenced soil samples also raise privacy issues since sampling locations can be linked to individual farm parcels and their respective activities. This work presents a Privacy-Preserving Digital Soil Mapping Framework (PP-DSM) that enables integration of private georeferenced soil datasets while releasing only aggregated spatial outputs, minimising risks of individual farm identification. The framework consists of three components: a secure soil data processing environment that keeps private point data under institutional control, a Quantile Regression Forest (QRF) engine that produces spatially explicit predictions and uncertainty estimates, and spatial aggregation of raster outputs to the EU LUCAS 2 × 2 km monitoring grid, releasing only anonymised polygon-level statistics based on polygons centred on the grid points. This methodology is demonstrated using a case study of a published georeferenced soil dataset of organic carbon (403 topsoil samples) from the Kastoria region, Northern Greece. Aggregated predictions preserved regional soil patterns while eliminating farm-level identifiability. Across six independently validated LUCAS polygons, QRF polygon statistics differed from independent test set means by an average difference of 0.070% SOC, consistent with the expected spatial smoothing. This study suggests that privately held soil datasets can support EU monitoring infrastructures within the existing regulatory environment, contributing to Soil Monitoring Law objectives and Carbon Removals and Carbon Farming initiatives.
Tziachris et al. (Thu,) studied this question.