aceProductivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs
This study evaluates an innovative field-scale targeted sampling strategy within a regional hybrid spatial prediction model that combines machine learning and geostatistics. The framework is designed so that newly collected field observations are incorporated only through the local residual kriging step, while the regional Random Forest trend model remains unchanged, allowing field-scale predictions to be refined without full model refitting. The proposed sampling approach integrates a Normalized Difference Vege…
Integrating an NDVI-based Productivity Index with regional prediction uncertainty to select four field samples reduced mean field-scale SOC prediction error from 0.24% to 0.18% RMSE, achieving accuracy comparable to using all available field observations.
Evaluation was limited to 28 test agricultural fields with 7 to 19 in-field observations per field, constraining generalizability to other regions, soil types, and management systems.
Evidence
- Peer-reviewedEnvironmental Monitoring and Assessment2026-08-17
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Truvace Impact Record TRV-2026-0823, v1: “Productivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs.” Truvace, 2026-08-18. /record/TRV-2026-0823 (accessed at citation time). sha256 62bc434ff1e11bb2…
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