基于不同地表曲面模型预测土壤有机碳含量

Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of first-and second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30％ and 9.68％.The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.

Mapping soil organic carbon using local terrain attributes: A comparison of different polynomial models

Local terrain attributes, which are derived directly from the digital elevation model, have been widely applied in digital soil mapping. This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China, by combining prediction methods with local terrain attributes derived from different polynomial models. The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice, rather than how morphometric variables and their geomorphologic interpretations are understood and calculated. In this study, 2 neighborhood shapes (square and circular) and 6 representative algorithms (Evans-Young, Horn, Zevenbergen-Thorne, Shary, Shi, and Florinsky algorithms) were applied. In general, 35 combinations of first- and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e., kriging with an external drift and geographically weighted regression). The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography. Among the different combinations of first- and second-order derivatives used, there was a best combination with a more accurate estimate. For different prediction methods, the relative improvement in the two zones varied between 0.30% and 9.68%. The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors. Therefore, it is concluded that the performance of predictive methods, which incorporated auxiliary variables, could be improved by attempting different terrain analysis algorithms.