Satellite　top-of-atmosphere　(TOA)　reflectance　has　been　validated　as　an　effective　index　for　estimating　PM2.5　concentrations　due　to　its　high　spatial　coverage　and　relatively　high　spatial　resolution　(i.e.,　1　km).　For　this　paper,　we　developed　an　emsembled　random　forest　(RF)　model　incorporating　satellite　top-of-atmosphere　(TOA)　reflectance　with　four　categories　of　supplemental　parameters　to　derive　the　PM2.5　concentrations　in　the　region　of　the　Yangtze　River　Delta-Fujian　(i.e.,　YRD-FJ)　located　in　east　China.　The　landscape　pattern　indices　at　two　levels　(i.e.,　type　level　and　overall　level)　retrieved　from　3-year　land　classification　imageries　(i.e.,　2016,　2018,　and　2020)　were　used　to　discuss　the　correlation　between　county-based　PM2.5　values　and　landscape　pattern.　We　achieved　a　cross　validation　R-2　of　0.91　(RMSE　=　9.06　mu　g/m(3)),　0.89　(RMSE　=　10.19　mu　g/m(3)),　and　0.90　(RMSE　=　8.02　mu　g/m(3))　between　the　estimated　and　observed　PM2.5　concentrations　in　2016,　2018,　and　2020,　respectively.　The　PM2.5　distribution　retrieved　from　the　RF　model　showed　a　trend　of　a　year-on-year　decrease　with　the　pattern　of　＂Jiangsu　＞　Shanghai　＞　Zhejiang　＞　Fujian＂　in　the　YRD-FJ　region.　Our　results　also　revealed　that　the　landscape　pattern　of　farmland,　water　bodies,　and　construction　land　exhibited　a　highly　positive　relationship　with　the　county-based　average　PM2.5　values,　as　the　r　coefficients　reached　0.74　while　the　forest　land　was　negatively　correlated　with　the　county-based　PM2.5　(r　=　0.84).　There　was　also　a　significant　correlation　between　the　county-based　PM2.5　and　shrubs　(r　=　0.53),　grass　land　(r　=　0.76),　and　bare　land　(r　=　0.60)　in　the　YRD-FJ　region,　respectively.　Three　landscape　pattern　indices　at　an　overall　level　were　positively　correlated　with　county-based　PM2.5　concentrations　(r　=　0.80),　indicating　that　the　large　landscape　fragmentation,　edge　density,　and　landscape　diversity　would　raise　the　PM2.5　pollution　in　the　study　region.