Spectral　mixture　analysis　is　an　efficient　approach　to　spectral　decomposition　of　hyperspectral　remotely　sensed　imagery,　using　land　cover　proportions　which　can　be　estimated　from　pixel　values　through　model　inversion.　In　this　paper,　a　kernel　least　square　regression　algorithm　has　been　developed　for　nonlinear　approximation　of　subpixel　proportions.　This　procedure　includes　two　steps.　The　first　step　is　to　select　the　feature　vectors　by　defining　a　global　criterion　to　characterize　the　image　data　structure　in　the　feature　space　and　the　second　step　is　the　projection　of　pixels　onto　the　feature　vectors　and　the　application　of　classical　linear　regressive　algorithm.　Experiments　using　simulated　data,　synthetic　data　and　Enhanced　Thematic　Mapper　(ETM)+　data　have　been　carried　out,　and　the　results　demonstrate　that　the　proposed　method　can　improve　proportion　estimation.　By　using　the　simulated　and　synthetic　data,　over　85%　of　the　total　pixels　in　the　image　are　found　to　lie　between　the　10%　difference　lines,　and　the　root　mean　square　error　(RMSE)　is　less　than　0.09.　Using　the　real　data,　the　proposed　method　can　also　perform　satisfactorily　with　an　average　RMSE　of　about　0.12.　This　algorithm　was　also　compared　with　other　widely　used　kernel　based　algorithms,　i.e.　support　vector　regression　and　radial　basis　function　neutral　network　and　the　results　show　that　the　proposed　algorithm　outperforms　other　algorithms　about　5%　in　subpixel　proportion　estimation.