The　ground-based　Global　Navigation　Satellite　System　(GNSS)　water　vapor　tomography　is　increasingly　important　in　GNSS　meteorology.　As　the　multi-GNSS　and　more　ground-based　GNSS　sites　can　be　incorporated　into　the　regional　water　vapor　tomographic　model,　determining　the　tomographic　window　and　sampling　rate　is　crucial　for　the　modelling　of　the　water　vapor　tomography.　These　two　factors　affect　not　only　the　number　of　available　signal　rays　from　the　satellites,　but　also　the　number　of　tomographic　voxels　crossed　by　the　signal　rays.　This　study　uses　Hong　Kong　as　the　research　area　to　explore　the　impact　of　12　schemes　with　different　tomographic　window　and　sampling　rate　on　the　three　water　vapor　tomography　methods,　including　Least　squares,　Kalman　filtering,　and　Multiplicative　Algebraic　Reconstruction　Technique　(MART).　Numerical　results　show　that　the　tomographic　results　with　the　three　methods　get　better　as　the　width　of　the　tomographic　window　decreases　and　the　sampling　rate　increases　in　these　12　schemes,　and　it　is　found　that　the　Least　squares　method　is　most　affected　by　the　two　factors,　followed　by　Kalman　filtering　and　MART　methods.　It　is　recommended　to　set　a　tomographic　window　width　of　10　min　and　a　sampling　rate　of　300　s　in　a　GNSS　water　vapor　tomographic　experiment　with　dense　GNSS　site　like　Hong　Kong.