River　turbidity　is　an　important　index　to　evaluate　the　quality　of　water　environment,　which　is　of　great　significance　to　environmental　monitoring.　Most　of　the　existing　monitoring　methods　rely　on　turbidity　labels　measured　by　river　water　quality　monitoring　stations.　However,　due　to　the　limitation　of　spatial　distribution　of　water　quality　monitoring　stations,　those　turbidity　data　outside　the　monitoring　range　are　difficult　to　obtain　and　the　correlation　of　turbidity　data　between　monitoring　stations　is　poor.　Using　such　data　directly　might　weaken　the　generalization　ability　that　the　model　has.　In　this　paper,　we　propose　a　new　river　turbidity　monitoring　model　based　on　semi-supervised　transfer　learning　(RTMM-SSTL)　to　solve　these　problems.　First,　in　the　pre-training　stage,　we　innovatively　propose　a　semi-supervised　training　method　combined　with　big　data　with　pseudo　labels　for　river　turbidity　monitoring　to　solve　the　problem　of　weakened　model　generalization　ability.　Then,　the　model　is　fine-tuned　using　GEE　hyper　spectral　data　with　ground　truth　to　further　improve　the　monitoring　capability　of　the　model.　Experiments　conducted　on　the　river　turbidity　monitoring　task　demonstrate　that　the　proposed　model　is　superior　to　the　advanced　learning　models,　and　further　proves　that　our　semi-supervised　transfer　learning　method　performs　better　than　the　state-of-the-art　supervision　models.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.