Google　Earth　Engine　(GEE)　is　a　cloud-based　platform　that　provides　powerful　capabilities　for　remote　sensing　image　compositing,　processing,　and　analysis.　Among　GEE＇s　diverse　applications,　temporal　aggregation　of　multiple　images　is　one　of　its　widely　used　techniques.　Nevertheless,　a　systematic　exploration　of　the　advantages　and　disadvantages　of　its　five　primary　image　compositing　methods　-　minimum　(Min),　maximum　(Max),　Median,　Mean,　and　Mode　-　has　not　been　conducted.　It　remains　unclear　whether　the　commonly　used　Median　method　is　genuinely　universal.　Additionally,　it　is　usually　unknown　which　input　images　and　their　seasonal　origins　predominantly　contribute　to　the　final　composite　image.　Therefore,　this　study　systematically　explores　the　performance　of　the　five　compositing　methods　across　four　regions　in　China　and　Uganda,　with　different　geographic　locations　and　climatic　conditions,　by　examining　their　strengths,　weaknesses,　and　applicable　scenarios.　A　novel　quantitative　metric,　contribution　percentage　(CP),　is　developed　to　identify　which　input　images　and　bands　in　a　time　series　primarily　contribute　to　the　final　composite　image.　The　results　show　that　the　commonly　used　Median　metric　is　not　always　the　optimal　choice.　The　Min　and　Mode　methods　perform　better　than　the　Median　method　in　haze　and　cloudy　regions,　particularly　in　haze　removal　and　vegetation　monitoring.　The　Max　method　also　exhibits　superior　performance　in　compositing　thermal　infrared　and　near-infrared　images　compared　to　the　Median.　Based　on　this　study,　the　applicable　scenarios　for　the　five　compositing　methods　have　been　clarified.　Furthermore,　the　proposed　CP　metric　effectively　reveals　the　input　images　and　bands　that　contribute　principally　to　the　final　composite.　Understanding　these　insights　is　crucial　for　users　to　choose　appropriate　GEE　compositing　methods　and　study　plant　phenology　and　surface　thermal　environments,　thereby　providing　a　foundation　for　the　scientific　application　of　GEE　compositing　methods.