Occlusion　modeling　is　critical　for　light　field　depth　estimation,　since　occlusion　destroys　the　photo-consistency　assumption,　which　most　depth　estimation　methods　hold.　Previous　works　always　detect　the　occlusion　points　on　the　basis　of　Canny　detector,　which　can　leave　some　occlusion　points　out.　Occlusion　handling,　especially　for　multi-occluder　occlusion,　is　still　challenging.　In　this　paper,　we　propose　a　novel　occlusion-aware　depth　estimation　method,　which　can　better　solve　the　occlusion　problem.　We　design　two　novel　consistency　costs　based　on　the　photo-consistency　for　depth　estimation.　According　to　the　consistency　costs,　we　analyze　the　influence　of　the　occlusion　and　propose　an　occlusion　detection　technique　based　on　depth　consistency,　which　can　detect　the　occlusion　points　more　accurately.　For　the　occlusion　point,　we　adopt　a　new　data　cost　to　select　the　un-occluded　views,　which　are　used　to　determine　the　depth.　Experimental　results　demonstrate　that　the　proposed　method　is　superior　to　the　other　compared　algorithms,　especially　in　multi-occluder　occlusions.