Productivity-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 Vegetation Index (NDVI)-based Productivity Index, a field-specific index derived from long-term satellite-based NDVI time series, with regional model-derived prediction uncertainty. The approach was evaluated on 28 test agricultural fields containing 7 to 19 in-field soil organic carbon (SOC) observations, which allowed assessment across diverse sampling configurations and levels of within-field variability. The results showed that the regional model alone provided a reasonable baseline prediction (mean root mean square error (RMSE) = 0.24% SOC) but was unable to adequately represent local spatial heterogeneity. Incorporating all available field observations produced the highest accuracy (mean RMSE = 0.17% SOC), while the proposed targeted sampling strategy achieved comparable performance (mean RMSE = 0.18% SOC) using only four strategically selected samples. In contrast, the results also showed that seemingly representative random sampling can lead to poor predictions when informative locations are missed, in some cases performing even worse than the regional model alone. In addition to conventional accuracy metrics, minimum detectable change (MDC) was used to evaluate the capability of the framework to detect meaningful SOC changes beyond prediction error, relevant for SOC monitoring and carbon farming. The results demonstrate that integrating regional models with productivity-based targeted sampling can substantially improve field-scale SOC prediction while avoiding the risks of misleading assessments associated with unfavorable sampling configurations.