Soil　is　a　complicated　historical　natural　continuum　that　presents　gradual　changes　in　its　properties　and　geographic　area.　Conventional　soil　survey　and　cartography　methods　on　a　macroscopic　scale　based　on　grids　with　a　coarse　resolution　are　inadequate　for　the　rapid　development　of　precision　agriculture.　The　demand　for　soil　mapping　content　and　accuracy　has　increased　as　more　convenient　methods　of　acquiring　multi-source　geo-spatial　data　have　been　developed,　and　such　data　are　commonly　employed　to　extract　basic　mapping　units　and　environmental　variables　in　related　algorithms.　We　employ　geo-objects　as　basic　units　of　soil　property　mapping,　which　are　extracted　from　high-resolution　remote　sensing　images　using　a　convolutional　neural　network　based　learning　algorithm.　Multi-source　geo-spatial　data　are　transferred　into　each　geo-object　as　environmental　variables,　and　the　relationships　between　soil　properties　and　environmental　variables　are　mined　using　powerful　tree-based　machine　learning　algorithms,　including　regressions　with　random　forests　and　XGBoost.　A　data　set　that　includes　soil　sample　points　and　multi-source　geo-spatial　data　is　used　to　evaluate　the　effectiveness　of　the　proposed　method.　The　experimental　results　demonstrate　that　the　method　allows　for　better　soil　organic　matter　mapping　than　state-of-the-art　interpolation-based　and　linear-regression-based　methods.　The　proposed　procedure　has　potential　to　be　a　general　method　for　mapping　other　soil　properties.　Its　advantages　are　embodied　in　the　modeling　of　relatively　miscellaneous　data　with　implicitly　associated　non-linear　relationships　between　soil　properties　and　environmental　variables.　The　spatial　scale　and　accuracy　of　the　finer　maps　capture　more　detailed　characteristics　of　the　soil　properties　and　are　applicable　to　the　micro-domain　fields　required　for　refined　soil　mapping　with　small　variations.