An　adaptive　kernel　matching　pursuit　(AKMP)　algorithm　to　estimate　mixture　pixel　proportion　of　remotely　sensed　image　has　been　proposed.　The　AKMP　algorithm　applies　greedy　sparse　approximation　algorithm　to　the　feature　space　induced　by　a　nonlinear　kernel　function,　and　can　therefore　be　able　to　capture　nonlinear　effects　of　image　and　performed　better　than　conventional　linear　approaches.　Moreover,　it　has　the　capability　of　adaptive　selection　of　the　kernel　parameter　before　starting　the,　greedy　approximating　procedure　to　avoid　complex　procedures　of　kernel　function　parameter　selection.　Experiments　with　ETM+　associated　with　IKONOS　image　have　been　carried　out,　and　the　result　demonstrates　that　the　proposed　method　can　provide　accurate　proportion　estimation.　Comparisons　with　support　vector　regression　(SVR)　and　linear　mixture　model　(LMM)　have　also　been　done,　and　the　experiments　show　that　the　proposed　method　outperform　SVR　and　LMM　in　terms　of　RMSE.