The　quality　of　Landsat　8　sensor　data　has　been　improved　after　a　series　of　corrections　and　data　reprocessing　since　its　successful　launch　on　February　11,　2013.　These　include　the　correction　of　all　calibration　parameters,　the　improvement　of　the　radiance　conversion　coefficients　for　the　all　Operational　Land　Imager　(OLI)　sensor　bands,　the　refinement　of　the　OLI　detector　linearization,　the　radiometric　offset　correction　for　the　two　Thermal　Infrared　Sensor　(TIRS)　bands,　the　slight　improvement　to　the　geolocation　of　the　TIRS　data,　and　the　reprocessing　of　all　Landsat　8　data　held　in　the　USGS　archives.　In　addition,　several　algorithms　specially　developed　for　Landsat　8　data　have　also　been　proposed　over　the　pass　year.　This　paper　aims　to　assess　the　accuracy　of　the　retrieved　the　reflectance　of　the　OLI　sensor　and　the　land　surface　temperature　(LST)　of　the　TIRS　sensor　of　the　new　satellite　with　those　of　the　well-calibrated　Landsat　7　ETM+　sensor　and　the　ground-measured　LST.　This　study　retrieved　the　top　of　the　atmosphere　(TOA)　reflectance　of　the　OLI　multispectral　bands　and　the　LST　of　the　TIRS　thermal　bands　with　the　most　recent　calibration　parameters,　algorithms　and　USGS-processed　Landsat　8　image　data.　In　addition,　the　Chavez＇s　COST　model　has　been　introduced　to　correct　the　atmospheric　effects　on　the　OLI　multispectral　bands.　To　examine　the　performance　of　the　calibration　parameters　and　the　developed　algorithms,　the　retrieved　TOA　reflectance　of　each　multispectral　band　and　the　computed　LST　of　the　thermal　infrared　bands　have　been　compared　with　that　of　the　corresponding　band　of　the　synchronized,　well-calibrated　Landsat　7　data　and　the　in　situ　LST,　respectively.　The　results　show　that　the　current　Landsat　8　calibration　parameters　of　multispectral　bands　can　achieve　high　accuracy　for　the　retrieval　of　TOA　reflectance.　The　proposed　COST-based　atmospheric　correction　algorithm　can　also　have　a　nearly　identical　performance　when　compared　with　the　Landsat　7　COST　model＇s　result.　Nevertheless,　two　recently-proposed　split　window　algorithms　for　computing　the　LST　from　Landsat　8　thermal　infrared　bands　did　not　perform　well,　as　they　offered　a　large　difference　between　the　algorithm-modeled　LST　and　the　ground-measured　LST.　Given　the　scaling　parameters　of　the　TIRS　thermal　infrared　band　11　is　still　unstable,　as　announced　by　the　Landsat　8　project　team,　it　is　recommended　that　at　this　stage　users　might　use　the　single　channel　(SC)　algorithm　of　Jimenez-Munoz　and　Sobrino　to　retrieve　the　LST　from　Landsat　8　thermal　band　10　(　like　working　on　Landsat　TM/ETM　+　band　6)　rather　than　attempt　a　split-window　algorithm　using　both　TIRS　bands　10　and　11.　However,　care　should　be　taken　in　the　selection　of　correct　atmosphere　parameters　for　the　SC-based　LST　computing,　especially　when　a　very　high　atmospheric　water　vapor　condition　occurs.