【Objective】Before the digital soil mapping technology emerged, the soil category linkage method (SCLM),linking means or median values of properties of the soils of the same soil category with their corresponding polygons in the soil map, or linking soil properties with polygons based on pedological expertise (including type of the soil and its location), was the major method used in mapping of soil organic carbon density (SOCD). Even nowadays, it is still of quite high practical value, because it is quite hard to build up a DSM model for relationships of external environmental covariates with SOCD in deep soil layers and/or on large scale, e.g. Provincial, continental and global. To understand in-depth relative efficiency of the two linking methods, it is necessary to perform some comparative studies. In terms of the DSM technology, most of the comparative studies have come to the conclusions that sophisticated machine learning models are superior to simple ones and that mixed models (regression Kriging) are of high superiority in most cases. However, there are a few papers reported some contradictory results. All the conclusions suggest that SOCD mapping quality could not be explained merely by method and also affected by the effectiveness and accuracy of the parameters used in the method. To elaborate in-depth the contradictory conclusions and to analyze the essence of the problems, in this paper a comparison was performed of SCLM with weighted least squares regression(WLS), geographically weighted regression(GWR), random forest (RF),ordinary kriging (OK) and regression kriging (RK) in SOCD mapping, and establishment of relationships between abilities of the methods to explain SOCD variability and effects of their mapping was discussed. 【Method】A tract of land, 26600 km2 in area, in Nanyang of Henan Province, was selected as a study area, of which soil categories, elevation, slope, aspect, and normalized difference vegetation index (NDVI), and wetness (TCW) of tasseled cap transformation (TC) were worked out as parameters of the SOCD prediction model, based on a 1:50 000 soil map, Digital Elevatation Model, 30m in resolution and a 1990 thematic mapper(TM) image. A total of 439 soil profiles were cited as training dataset for SOCD mapping using SCLM, WLS, GWR, RF, OK and RK, separately, and another 49 soil profiles were used to verify accuracy of the maps. 【Result】Results show that soil genus and soil organic matter content of the topsoil layer is the most important and the second most important parameter, explaining jointly 57.5% of the SOCD variance, while terrain and remote sensing parameters jointly explain just less than 2%, and hence are very limited in contribution to SOCD mapping. However, SCLM makes use only of variables in soil category, like soil group, soil subgroup, soil genus, etc., while regression methods, like WLS and GWR, can only use numerical variables, like SOM and TCW, so none of these can achieve satisfactory prediction result. RF is based on both variables in soil category and numerical variables (SOM and TCW) and hence much better in SOCD prediction. The use of RK in prediction may end up in the following two situations. 1) The residues of WLS regression, GWR regression, and soil OM-and-TCW-based RF vary spatially and structurally to a varying extent, then regression kriging (RK) could improve the SOCD predictions of these models. 2) The residues of the predictions using SCLM, SOM+soil genus-based RF, SOM+soil genus+TCW based RF and all-variables-based RF vary spatially and randomly, for which the use of RK is meaningless. The cross-verified accuracy of WLS, GWL and soil OM-and-TCW-based RF increased in turn, however their RK ability predicting test data are reversed. And all prediction ability of RKs do not reach as high as the SOM+soil genus-based RF.【Conclusion】All the findings demonstrate that the ability of method to explain SOCD variability is the causa essentiae deciding the effect of SOCD mapping, and RK is not necessarily the fittest model because of the interactions in explanation ability between covariates and the spatial correlation.