The　red-edge　bands　and　their　derived　vegetation　indices　play　a　crucial　role　in　monitoring　vegetation　health.　The　Gaofen-6　(GF-6)　and　Sentinel-2A　satellites　are　equipped　with　two　and　three　red-edge　bands,　respectively,　thus　making　them　invaluable　for　monitoring　forest　health.　To　compare　the　performance　of　these　two　satellites＇　red-edge　bands　in　monitoring　forest　health,　this　study　selected　forests　in　Liuyang　City,　Hunan　Province　and　Tonggu　County,　Jiangxi　Province　and　Hanzhong　City,　Shaanxi　Province　in　China　as　study　areas　and　used　three　commonly　used　red-edge　indices　and　the　Random　Forest　(RF)　algorithm　for　the　comparison.　The　three　selected　red-edge　indices　were　the　Normalized　Difference　Red-Edge　Index　1　(NDRE1),　the　Missouri　emergency　resource　information　system　Terrestrial　Chlorophyll　Index　(MTCI),　and　the　Inverted　Red-Edge　Chlorophyll　Index　(IRECI).　Through　training　of　sample　regions,　this　study　determined　the　spectral　differences　among　three　forest　health　levels　and　established　classification　criteria　for　these　levels.　The　results　showed　that　GF-6　imagery　provided　higher　accuracy　in　distinguishing　forest　health　levels　than　Sentinel-2A,　with　an　average　accuracy　of　90.22%　versus　76.55%.　This　difference　is　attributed　to　variations　in　the　wavelengths　used　to　construct　the　red-edge　indices　between　GF-6　and　Sentinel-2A.　In　the　RF　algorithm,　this　study　employed　three　distinct　band　combinations　for　classification:　all　bands　including　red-edge　bands,　excluding　red-edge　bands,　and　only　red-edge　bands.　The　results　indicated　that　GF-6　outperformed　Sentinel-2A　when　using　the　first　and　second　band　combinations,　yet　slightly　underperforming　with　the　third.　This　outcome　was　closely　associated　with　the　importance　of　each　band＇s　contribution　to　classification　accuracy　reveled　by　the　Gini　importance　score,　their　sensitivity　in　detecting　forest　health　conditions,　and　the　total　number　of　bands　employed　in　the　classification　process.　Overall,　the　NDRE1　derived　from　GF-6　achieved　the　highest　average　accuracy　(90.22%).　This　study　provides　a　scientific　basis　for　selecting　appropriate　remote　sensing　data　and　techniques　for　forest　health　monitoring,　which　is　of　significant　importance　for　the　future　ecological　protection　of　forests.