Landsat　Collection　2　Level-2　Surface　Temperature　(LC2L2ST)　was　formally　released　in　December　2020　by　the　U.　S.　Geological　Survey　(USGS).　However,　there　are　few　reports　on　this　new　land　surface　temperature　(LST)　product.　As　this　product　will　be　the　only　LST　data　provided　by　the　USGS　starting　in　2022,　it　is　necessary　to　evaluate　the　product　timely.　Among　various　satellite　LST　products,　the　quality　of　the　MODIS　LST　product　is　well　recognized,　and　widely　used.　Therefore,　this　paper,　for　the　first　time,　performed　a　cross-comparison　between　the　new　Landsat　LST　product　and　the　MODIS　LST　product　to　examine　the　quality　of　the　new　product.　Different　regions　in　China　(Fuzhou,　Taihu,　Yinchuan　and　Dunhuang)　were　selected　as　the　test　areas,　and　20　pairs　of　LC2L2ST　and　MODIS　LST　synchronous　images　were　used　for　the　comparison.　The　images　cover　different　land　types,　such　as　vegetation,　water,　town　and　deserts　across　different　seasons.　A　total　of　560　homogeneous　regions　of　interest　(ROI)　were　selected　from　the　images　of　the　test　areas.　The　regression　analysis　was　carried　out　to　examine　the　fit　of　the　ROIs　and　the　quantitative　relationship　between　the　two　LST　products.　The　conversion　model　between　them　was　also　developed.　The　results　showed　that　the　new　LC2L2ST　product　is　highly　correlated　with　the　MODIS　LST　product.　Each　of　the　four　test　areas　can　achieve　a　coefficient　of　determination　(R-2)　greater　than　0.　98.　Integrating　the　560　samples　from　the　four　test　areas　also　obtain　an　R-2　close　to　0.　98.　Nevertheless,　differences　between　the　two　products　have　also　been　founded.　The　LC2L2ST　is　0.　90　degrees　C　averagely　higher　than　the　MODIS　LST　(RMSE　=　2.　29　degrees　C).　However,　LC2L2ST　can　be　slightly　lower　than　MODIS　LST　in　late　fall　and　winter　seasons　but　significantly　higher　than　extremely　hot　summer　seasons　with　a　bias　close　to　7　degrees　C.　The　analysis　revealed　that　the　differences　were　related　to　spatial　resolution,　sensor　viewing　angles,　land　cover　types　and　seasons.　In　general,　the　new　LC2L2ST　product　strongly　correlates　with　the　MODIS　LST,　but　significant　differences　were　also　observed　in　the　summer　months.　Therefore,　the　new　Landsat　LST　product　must　be　further　tested　with　in-situ　measured　LST　data.　Due　to　the　differences　in　this　paper,　the　two　LST　data　products　need　to　be　converted　when　they　must　be　collaboratively　used.　This　study　developed　the　conversion　equation　between　the　two　LSTs　based　on　the　560　ROIs.　The　verification　found　that　the　differences　between　the　two　data　after　conversion　were　greatly　reduced.　It　is　conducive　to　the　cooperative　use　of　the　two　LST　data　and　providing　continuous　remote　sensing　data　for　long-term　LST　monitoring.