Aerosol　optical　depth（AOD）is　one　of　the　most　important　parameters　for　aerosols.　Existing　AOD　products　derived　from　remote　sensing　data　are　seriously　affected　by　clouds，snow，and　many　other　factors.　It　is　thus　of　great　significance　to　generate　AOD　with　large　spatial　coverage.　This　paper　proposes　a　spatio-temporal　continuous　AOD　reconstructed　method（i.e.，IDW-CatBoost）based　upon　Inverse　Distance　Weight　interpolation（IDW）and　CatBoost　models.　It　fuses　the　MAIAC　AOD　of　MODIS，the　AHI　AOD　of　Himawari-8，and　meteorological　and　elevation　data.　The　IDW-CatBoost　model　was　applied　to　the　AOD　reconstruction　of　Beijing-Tianjin-Hebei（BTH）and　Taiwan　Island，and　compared　with　IDW　and　CatBoost　methods.　We　validated　reconstruction　results　using　the　ground-based　monitoring　AERONET　AOD，where　352　samples　were　used　for　BTH　and　641　for　Taiwan　Island.　Results　showed　that　the　AOD　obtained　by　IDW　had　star-dotted　features　in　spatial　distribution，while　CatBoost　and　IDW-CatBoost　AODs　exhibited　texture　features　of　continuous　spatial　distribution.　The　AOD　results　of　IDW　were　close　to　those　of　the　IDW-CatBoost　method　in　the　BTH　region　when　verifying　by　ground　monitoring　AERONET　AOD　data.　Compared　with　IDW　and　CatBoost　methods，the　AOD　results　of　the　IDW-CatBoost　in　Taiwan　Island　were　improved　by　10%　and　5%，respectively，for　the　R2　measure.　Compared　with　single-sensor　AHI　L2，L3，and　MAIAC　AODs，the　accuracy　of　the　IDW-CatBoost　AOD　fused　with　multi-sensor　data　was　significantly　improved，leading　to　an　R2　improvement　of　15%，35%　and　12%，and　a　RMSE　decrease　of　25%，38%　and　22%，respectively，in　the　BTH　region.　Moreover，in　Taiwan　Island，the　R2　was　improved　by　14%，76%　and　76%，and　the　RMSE　decreased　by　6%，24%　and　24%，respectively.　We　conclude　that　the　IDW-CatBoost　method　fused　with　multi-sensor　data　is　suitable　to　reconstruct　accurate　AOD　products　for　large　areas.　©　2023　Science　Press.　All　rights　reserved.