An　observation　operator　is　a　bridge　linking　the　system　state　vector　and　observations　in　a　data　assimilation　system.　Despite　its　importance,　the　degree　to　which　an　observation　operator　influences　the　performance　of　data　assimilation　methods　is　still　poorly　understood.　This　study　aimed　to　analyze　the　influences　of　linear　and　nonlinear　observation　operators　on　the　sequential　data　assimilation　through　soil　temperature　simulation　using　the　unscented　particle　filter　(UPF)　and　the　common　land　model.　The　linear　observation　operator　between　unprocessed　simulations　and　observations　was　first　established.　To　improve　the　correlation　between　simulations　and　observations,　both　were　processed　based　on　a　series　of　equations.　This　processing　essentially　resulted　in　a　nonlinear　observation　operator.　The　linear　and　nonlinear　observation　operators　were　then　used　along　with　the　UPF　in　three　assimilation　experiments:　an　hourly　in　situ　soil　surface　temperature　assimilation,　a　daily　in　situ　soil　surface　temperature　assimilation,　and　a　moderate　resolution　imaging　spectroradiometer　(MODIS)　land　surface　temperature　(LST)　assimilation.　The　results　show　that　the　filter　improved　the　soil　temperature　simulation　significantly　with　the　linear　and　nonlinear　observation　operators.　The　nonlinear　observation　operator　improved　the　UPF＇s　performance　more　significantly　for　the　hourly　and　daily　in　situ　observation　assimilations　than　the　linear　observation　operator　did,　while　the　situation　was　opposite　for　the　MODIS　LST　assimilation.　Because　of　the　high　assimilation　frequency　and　data　quality,　the　simulation　accuracy　was　significantly　improved　in　all　soil　layers　for　hourly　in　situ　soil　surface　temperature　assimilation,　while　the　significant　improvements　of　the　simulation　accuracy　were　limited　to　the　lower　soil　layers　for　the　assimilation　experiments　with　low　assimilation　frequency　or　low　data　quality.　©　2018　Hohai　University