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Organic carbon (OC) plays a pivotal role in earth surface systems. However, current three-dimensional (3D) mapping studies usually focus on a soil depth of 1 m rather than the depth to the bedrock. A top-down method using piecewise depth functions was proposed to fit the OC vertical decline patterns in a subtropical catchment in southern China. The vertical variation in OC was greatly affected by the heterogeneous topsoil due to natural processes and anthropogenic disturbances. Thus, topsoil OC maps were produced and utilized as covariates to indicate the OC decline rates and to benefit the 3D OC simulation. A distribution map of the underground critical zone thickness (UCZT) was applied as a lower boundary for the 3D simulation. Six widely used mapping techniques were performed to predict the spatial distribution of topsoil OC and depth function parameters. The overall cross-validation results showed a root mean squared error (RMSE) of 1.7 g kg–1 and a ratio of performance to deviation (RPD) of 1.82. Given limited boreholes, validation showed that the depth function performed better in the lower part (>1 m) than in the upper part (<1 m). The proposed framework could holistically employ sufficient topsoil samples and limited boreholes and provides a promising solution to simulate the 3D information of subsurface systems. We anticipate that continuous 3D OC maps can be integrated with other critical zone information to elucidate the complex interactions of various processes and to support potential service management decisions. [ABSTRACT FROM AUTHOR]

Spatial soil quality information is needed for the agricultural development in Hong Kong. This study produced digital maps of more than 20 soil quality indicators of a study area in Hong Kong, using digital soil mapping techniques. These maps were then employed to evaluate the soil quality of this area via the application of scoring functions and integrated quality index (IQI) methods. The accuracy and uncertainty of the evaluated spatial soil quality information were assessed based on a probability sample and sequential Gaussian simulation (SGS), respectively. The sources of uncertainty were analyzed using stochastic sensitivity analysis. The results showed that mapping accuracy varied dramatically among soil indicators, with soil quality index (SQI) in the area ranging from 0.43 to 0.87. The obtained spatial soil quality information appeared to be moderately accurate with high uncertainty, which suggests that it cannot be fully relied on. The soil quality could have been overestimated with a probability of more than 0.95 for nearly half of the study area while being underestimated for 0.2% of the study area. Hence, this study shows that it is vitally important to derive uncertainty for soil quality information evaluated based on digital soil maps. Generally, heavily weighted soil quality indicators in the soil quality evaluation model contributed the most uncertainty, such as available phosphorus (A-P), total phosphorus (TP), and bulk density in this study. [ABSTRACT FROM AUTHOR]