As　global　urbanization　accelerates　and　ecological　challenges　intensify,　effective　monitoring　and　assessing　ecological　conditions　have　become　critical　for　sustainable　development.　Remote　sensing　technologies　play　an　increasingly　crucial　role　in　this　context.　The　Sustainable　Development　Goals　Science　Satellite　1　(SDGSAT-1),　a　next-generation　remote　sensing　satellite,　provides　10-m　spatial　resolution　and　multispectral　imaging　capabilities,　offering　new　opportunities　for　ecological　monitoring.　This　study　explores　the　ecological　potential　of　SDGSAT-1　data,　focusing　on　the　comprehensive　assessment　of　urban　heat　islands　(UHI),　urban　vegetation　coverage,　and　regional　ecological　conditions.　This　is　achieved　through　a　detailed　comparison　with　the　widely-used　Landsat-8/9　data.　The　study　develops　several　methodologies　for　cloud　detection,　atmospheric　correction,　and　land　dryness　retrieval.　Validation　shows　that　the　cloud　removal　effect　achieved　by　the　proposed　SDGSAT　Cloud　Mask　(SCM)　algorithm　is　comparable　to,　or　slightly　better　than,　those　of　the　CFMask　algorithm　for　Landsat-9　and　the　machine　learning-based　S2cloudless　algorithm　for　Sentinel-2A,　with　F1　scores　greater　than　0.92.　The　results　show　that　the　monitoring　of　regional　ecological　conditions　by　SDGSAT-1　is　very　similar　to　that　of　Landsat-8/9,　with　differences　generally　under　5　%.　Because　SDGSAT-1＇s　multispectral　and　thermal　infrared　imagery　has　higher　spatial　resolution　than　Landsat-8/9,　it　can　detect　5.6　%　more　vegetation　area　and　2.6　times　larger　high-temperature　areas　within　urban　environments　than　Landsat　data.　SDGSAT-1＇s　finer　resolution　enables　more　detailed　ecological　assessments,　supporting　urban　sustainability　applications.　However,　due　to　the　lack　of　shortwave　infrared　bands　in　the　SDGSAT-1　imagery,　it　is　less　effective　than　Landsat-8/9　in　interpreting　land　surface　dryness　and　moisture　content.