Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics

Many national and international initiatives rely on spatially explicit information soil organic carbon (SOC) stock change at multiple scales to support policies aiming land degradation neutrality climate mitigation. In this study, we used regression cokriging with random forest spatial stochastic cosimulation predict the SOC between two years (i.e. 1992 2010) in Hungary aggregation levels point support, 1 × km, 10 km square blocks, Hungarian counties entire Hungary). We also quantified uncertainty associated these predictions order identify delimit areas statistically significant change. Our study highlighted that prediction of totals averages requires a geostatistical approach cannot be solved by machine learning alone, because it does not account for correlation errors. The total topsoil was predicted increase 2010 14.9 Tg, lower upper limits 90% interval equal 11.2 Tg 18.2 respectively. Results showed both uncertainties average were smaller larger supports, while made easier obtain changes. latter is important accounting studies need prove Measurement, Reporting Verification protocols observed changes are only practically but significant.