Soil　moisture　is　an　important　variable　in　the　fields　of　hydrology,　meteorology,　and　agriculture,　and　has　been　used　for　numerous　applications　and　forecasts.　Accurate　soil　moisture　predictions　on　both　a　large　scale　and　local　scale　for　different　soil　depths　are　needed.　In　this　study,　a　soil　moisture　assimilation　and　prediction　based　on　the　Ensemble　Kalman　Filter　(EnKF)　and　Simple　Biosphere　Model　(SiB2)　have　been　performed　in　Meilin　watershed,　eastern　China,　to　evaluate　the　initial　state　values　with　different　assimilation　frequencies　and　precipitation　influences　on　soil　moisture　predictions.　The　assimilated　results　at　the　end　of　the　assimilation　period　with　different　assimilation　frequencies　were　set　to　be　the　initial　values　for　the　prediction　period.　The　measured　precipitation,　randomly　generated　precipitation,　and　zero　precipitation　were　used　to　force　the　land　surface　model　in　the　prediction　period.　Ten　cases　were　considered　based　on　the　initial　value　and　precipitation.　The　results　indicate　that,　for　the　summer　prediction　period　with　the　deeper　water　table　depth,　the　assimilation　results　with　different　assimilation　frequencies　influence　soil　moisture　predictions　significantly.　The　higher　assimilation　frequency　gives　better　soil　moisture　predictions　for　a　long　lead-time.　The　soil　moisture　predictions　are　affected　by　precipitation　within　the　prediction　period.　For　a　short　lead-time,　the　soil　moisture　predictions　are　better　for　the　case　with　precipitation,　but　for　a　long　lead-time,　they　are　better　without　precipitation.　For　the　winter　prediction　period　with　a　lower　water　table　depth,　there　are　better　soil　moisture　predictions　for　the　whole　prediction　period.　Unlike　the　summer　prediction　period,　the　soil　moisture　predictions　of　winter　prediction　period　are　not　significantly　influenced　by　precipitation.　Overall,　it　is　shown　that　soil　moisture　assimilations　improve　its　predictions.　©　2019,　The　Chinese　Meteorological　Society　and　Springer-Verlag　Berlin　Heidelberg.