The　coarse　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　aerosol　optical　depth　(AOD)　product　(spatial　resolution:　3　km)　retrieved　by　the　dark-target　algorithm　always　generates　the　missing　values　when　being　adopted　to　estimate　the　ground-level　PM2.5　concentrations.　In　this　study,　we　developed　a　two-stage　random　forest　using　MODIS　3-km　AOD　to　obtain　the　PM2.5　concentrations　with　full　coverage　in　a　contiguous　coastal　developed　region,　i.e.,　Yangtze　River　delta-Fujian-Pearl　River　delta　(YRD-FJ-PRD)　region　of　China.　A　first-stage　random　forest-integrated　six　meteorological　fields　was　employed　to　predict　the　missing　values　of　AOD　product,　and　the　combined　AOD　(i.e.,　random　forest-derived　AOD　and　MODIS　3-km　AOD)　incorporated　with　other　ancillary　variables　were　developed　for　predicting　PM2.5　concentrations　within　a　second-stage　random　forest　model.　The　results　showed　that　the　first-stage　random　forest　could　explain　94%　of　the　AOD　variability　over　YRD-FJ-PRD　region,　and　we　achieved　a　site-based　cross　validation　(CV)　R-2　of　0.87　and　a　time-based　CV　R-2　of　0.85.　The　full-coverage　PM2.5　concentrations　illustrated　a　spatial　pattern　with　annual-mean　PM2.5　of　46,　40,　and　35　mu　g　m(-3)　in　YRD,　PRD,　and　FJ,　respectively,　sharing　the　same　trend　with　previous　studies.　Our　results　indicated　that　the　proposed　two-stage　random　forest　model　could　be　effectively　used　for　PM2.5　estimation　in　different　areas.