River　turbidity,　serving　as　an　important　evaluation　index　for　monitoring　water　contamination　and　guiding　pollution　control,　is　mainly　measured　based　on　the　data　gathered　from　contacting　turbidity　sensors　or　contactless　space　satellites.　Nevertheless,　the　prevalence　of　those　abovementioned　two　measurements　is　strongly　limited　due　to　the　disadvantages　of　low-density　spatial　distribution　of　sensor　data　and　extremely　high　price　of　satellite　data.　To　solve　such　difficulty,　depending　on　the　Google　earth　engine　(GEE)　that　freely　supplies　hyperspectral　remote　sensing　data,　in　this　article,　we　propose　a　novel　river　turbidity　measurement　model　based　on　random　forest　ensemble.　First,　by　fully　taking　advantage　of　each　spectral　information　and　their　tuned　spectral　information,　a　newly　proposed　full　combination　subspace　is　deployed　to　generate　all　the　possible　base　random　forests.　Second,　we　introduce　a　novel　error-minimization-based　pruning　algorithm　to　circularly　delete　poor　base　random　forests　in　accordance　with　the　dynamic　threshold.　Finally,　a　weighted　average　method　solved　by　regularized　linear　regression　is　used　to　aggregate　the　entire　remainder　base　random　forests　that　are　preserved　after　pruning,　thereby　yielding　the　final　measurement　result　of　river　turbidity.　Experiments　corroborate　the　superiority　of　our　proposed　model　over　state-of-the-art　competitors　and　its　simplified　counterparts.