The limited availability of soil information has been recognized as a main limiting factor in digital soil mapping (DSM) studies. It is therefore important to optimize the joint use of the three sources of soil data that can be used as inputs of DSM models, namely spatial sets of measured sites, soil maps and soil sensing products. In this paper, we propose to combine these three inputs, through a cokriging with a categorical external drift (CKCED). This new interpolation technique was applied for mapping seven soil properties over a 24.6 km2 area located in the vineyard plain of Languedoc (Southern France), using an hyperspectral imagery product as example of a soil sensing data. Cross-validation results of CKCED were compared with those of five spatial and non-spatial techniques using one of these inputs or a combination of two of them. The results obtained in the La Peyne Catchment showed i) the utility of soil map and hyperspectral imagery products as auxiliary data for improving soil property predictions ii) the greater added-value of the latter against the former in most situations and iii) the feasibility and the interest of CKCED in a limited number of soil properties and data configurations. Testing CKCED in case study with soil maps of better quality and soil sensing techniques covering more area and depths should be necessary to better evaluate the benefits of this new technique.