We　used　deep　learning　networks　to　establish　a　relationship　model　among　MODIS　daily　surface　reflectance　product　(MOD09GA)　and　Arctic　melt　ponds　fraction　(MPF),　ice　fraction　(IF),　and　open　water　fraction　(OWF).　We　applied　this　model　to　MODIS　8-day　surface　reflectance　(MOD09A1)　to　derive　Arctic　8-day　MPF　and　SIF　(SIF　as　the　sum　of　IF　and　MPF).　The　results　demonstrate　that　our　model　improved　MPF　estimation　accuracy　to　an　RMSE　of　3.7%,　compared　with　previous　models.　The　characteristics　of　MPF　spatiotemporal　changes　seen　in　early　summer　(May-July)　indicate　that　MPF　increased　first　from　May-June,　reaching　its　peak　around　early　July,　and　then　decreased.　In　addition,　early　summer　MPF　was　significantly　negatively　correlated　with　sea　ice　extent　(SIE)　in　September.　We　also　found　that　early　summer　MPF　caused　sea　ice　in　the　Beaufort　Sea,　the　Chukchi　Sea,　and　the　East　Siberian　Sea　to　move　to　warm　water.　Moreover,　the　movement　of　sea　ice　from　the　marginal　sea　to　the　center　of　the　Arctic　was　shown　to　be　conducive　to　the　reduction　of　SIE　in　September.　Early　summer　MPF　was　also　related　to　Arctic　oscillation　(AO)　during　June　to　July,　and　significantly　positively　related　to　air　temperature　in　the　East　Siberian　and　Chukchi　Seas　in　September.　As　a　consequence,　these　areas　produced　more　open　water　and　absorbed　more　heat,　reducing　the　extent　of　sea　ice　in　September,　while　increasing　their　air　temperatures.　The　results　also　show　that　early　summer　MPF　has　a　high　negative　correlation　with　air　temperature　in　northern　China,　and　MPF　can　be　used　to　predict　air　temperature　in　northern　China.　These　new　findings　should　be　investigated　in　future　studies　with　additional　data　collection　and　field　observations.