Beach　surface　moisture　(BSM)　is　crucial　to　studying　coastal　aeolian　sand　transport　processes.　However,　traditional　measurement　techniques　fail　to　accurately　monitor　moisture　distribution　with　high　spatiotemporal　resolution.　Remote　sensing　technologies　have　garnered　widespread　attention　for　providing　rapid　and　non-contact　moisture　measurements,　but　a　single　method　has　inherent　limitations.　Passive　remote　sensing　is　challenged　by　complex　beach　illumination　and　sediment　grain　size　variability.　Active　remote　sensing　represented　by　LiDAR　(light　detection　and　ranging)　exhibits　high　sensitivity　to　moisture,　but　requires　cumbersome　intensity　correction　and　may　leave　data　holes　in　high-moisture　areas.　Using　machine　learning,　this　research　proposes　a　BSM　inversion　method　that　fuses　UAV　(unmanned　aerial　vehicle)　orthophoto　brightness　with　intensity　recorded　by　TLSs　(terrestrial　laser　scanners).　First,　a　back　propagation　(BP)　network　rapidly　corrects　original　intensity　with　in　situ　scanning　data.　Second,　beach　sand　grain　size　is　estimated　based　on　the　characteristics　of　the　grain　size　distribution.　Then,　by　applying　nearest　point　matching,　intensity　and　brightness　data　are　fused　at　the　point　cloud　level.　Finally,　a　new　BP　network　coupled　with　the　fusion　data　and　grain　size　information　enables　automatic　brightness　correction　and　BSM　inversion.　A　field　experiment　at　Baicheng　Beach　in　Xiamen,　China,　confirms　that　this　multi-source　data　fusion　strategy　effectively　integrates　key　features　from　diverse　sources,　enhancing　the　BP　network　predictive　performance.　This　method　demonstrates　robust　predictive　accuracy　in　complex　beach　environments,　with　an　RMSE　of　2.63%　across　40　samples,　efficiently　producing　high-resolution　BSM　maps　that　offer　values　in　studying　aeolian　sand　transport　mechanisms.　©　2025　by　the　authors.