The　ecological　quality　of　a　region　is　significantly　influenced　by　its　geographical　conditions,　which　can　yield　different　effects　on　ecosystems.　Nevertheless,　the　lack　of　adequate　technology　has　impeded　quantitative　investigations　into　these　differences.　Consequently,　there　is　an　increasing　demand　for　effective　techniques　to　quantitatively　measure　differences　in　ecological　quality　resulting　from　variations　in　geographical　conditions.　This　study　applied　the　novel　Remote　Sensing-based　Ecological　Index　(RSEI)　concurrently　to　two　distinct　provincial-level　regions　in　China,　Fujian　and　Ningxia,　to　quantitatively　detect　their　ecological　differences.　These　two　regions　possess　contrasting　geographical　conditions,　with　Fujian　having　high　forest　coverage　and　abundant　rainfall,　while　Ningxia　features　low　forest　coverage　and　extensive　loess　plateau　and　desert　terrain.　By　linking　geographical　factors　with　their　corresponding　ecological　responses,　we　conducted　a　comprehensive　analysis　to　determine　whether　the　contrasting　geographical　conditions　between　the　two　regions　had　caused　significant　disparities　in　their　ecological　status.　The　results　indicate　that　the　contrasting　geographical　conditions　have　indeed　led　to　marked　ecological　differences,　with　Fujian　exhibiting　excellent　ecological　status,　while　Ningxia　lags　behind　due　to　unfavorable　geographical　conditions.　In　terms　of　RSEI　scores,　Fujian　consistently　achieved　higher　RSEI　values　(＞0.8)　in　the　study　years,　reaching　an　excellent　ecological　level,　whereas　Ningxia　recorded　scores　lower　than　0.45　during　the　comparable　years,　corresponding　to　a　poor　to　moderate　ecological　level.　Regarding　the　impact　of　geographical　factors　on　ecological　conditions,　the　positive　contributions　of　greenness　and　wetness　indicators　to　the　ecology　in　Fujian　were　significantly　greater　than　those　in　Ningxia　(58%　vs.　39%),　whereas　the　contributions　of　negative　indicators,　dryness　and　hotness,　were　notably　higher　in　Ningxia　compared　to　Fujian　(|-61|%　vs.　|-42|%).　The　successful　concurrent　application　of　RSEI　to　these　two　geographically　distant　regions　also　demonstrates　the　robustness　of　the　RSEI　technique.