Previous　studies　that　have　used　remote　sensing　data　to　estimate　the　PM2.5　concentrations　mainly　focused　on　the　retrieval　of　aerosol　optical　depth　(AOD)　with　moderate-to-low　spatial　resolution.　However,　the　complex　process　of　retrieving　AOD　from　satellite　Top-of-Atmosphere　(TOA)　reflectance　always　generates　the　missingness　of　AOD　values　due　to　the　limitation　of　AOD　retrieval　algorithms.　This　study　validated　the　possibility　of　using　satellite　TOA　reflectance　for　estimating　PM2.5　concentrations,　rather　than　using　conventional　AOD　products　retrieved　from　remote　sensing　imageries.　Given　that　the　TOA-PM2.5　relationship　cannot　be　accurately　expressed　by　simple　linear　correlation,　we　developed　a　random　forest　model　that　integrated　satellite　TOA　reflectance　from　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　Level　1B　product,　meteorological　fields　and　land-use　variables　to　estimate　the　ground-level　PM2.5　concentrations.　The　highly-polluted　Yangtze　River　Delta　(YRD)　region　of　eastern　China　was　employed　as　our　study　case.　The　results　showed　that　our　model　performed　well　with　a　site-based　and　a　time-based　CV　R2　of　0.92　and　0.88,　respectively.　The　derived　annual　and　seasonal　distributions　of　PM2.5　concentrations　exhibited　high　PM2.5　values　in　northern　YRD　region　(i.e.,　Jiangsu　province)　and　relatively　low　values　in　southern　region　(i.e.,　Zhejiang　province),　which　shared　a　similar　distribution　trend　with　the　observed　PM2.5　concentrations.　This　study　demonstrated　the　outstanding　performance　of　random　forest　model　using　satellite　TOA　reflectance,　and　also　provided　an　effective　method　for　remotely　sensed　PM2.5　estimation　in　regions　where　AOD　retrievals　are　unavailable.　©　2020　Elsevier　Ltd