Data　assimilation　is　an　efficient　way　to　improve　the　simulation/prediction　accuracy　in　many　fields　of　geosciences　especially　in　meteorological　and　hydrological　applications.　This　study　takes　unscented　particle　filter　(UPF)　as　an　example　to　test　its　performance　at　different　two　probability　distribution,　Gaussian　and　Uniform　distributions　with　two　different　assimilation　frequencies　experiments　(1)　assimilating　hourly　in　situ　soil　surface　temperature,　(2)　assimilating　the　original　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　Land　Surface　Temperature　(LST)　once　per　day.　The　numerical　experiment　results　show　that　the　filter　performs　better　when　increasing　the　assimilation　frequency.　In　addition,　UPF　is　efficient　for　improving　the　soil　variables　(e.g.,　soil　temperature)　simulation/prediction　accuracy,　though　it　is　not　sensitive　to　the　probability　distribution　for　observation　error　in　soil　temperature　assimilation.　©　2018　The　Authors.