Comparison of global and zonal modeling strategies - A case study of soil organic matter and C:N ratio mapping in Altay, Xinjiang, China

Abstract

Digital soil mapping (DSM) based on remote sensing is the dominant method for soil monitoring. Currently, the global modeling strategy (GMS) is used in most soil mapping studies. In the GMS, it is assumed that the relationship between soil and the landscape is the same throughout a region. However, the soil–landscape relationship varies in different geographic zones, such as among different land cover types. In the zonal modeling strategy (ZMS), a region is divided into multiple geographic zones on the basis of zoning rules, and each geographic zone is modeled individually, to fully capture the soil–landscape relationships within different zones. This study was conducted in Altay, Xinjiang, China. The soil organic matter (SOM) content and C:N ratio were mapped on the basis of the GMS and the ZMS to compare the performance differences between the two strategies. The ZMS mapping results exhibited better spatial heterogeneity across different land cover types. Moreover, the ZMS mapping results displayed lower uncertainty and were closer to the observed values than were the GMS results, which included more outliers. Overall, we recommend the ZMS. The accuracy validation results indicated that the accuracy of the ZMS is not necessarily higher than that of the GMS in some zones, but the overall accuracy is similar. Combining similar zones for modeling can improve the accuracy of the ZMS, surpassing that of the GMS. Moreover, the importance of synthetic aperture radar (SAR) data was analyzed. The results revealed that SAR data are highly important for mapping the SOM of bare land and cropland and the C:N ratio of bare land and forest. SAR data may provide soil nutrient information indirectly from moisture levels; therefore, we believe that SAR data have great potential for soil nutrient mapping.