An　accurate　estimation　of　evapotranspiration　(ET)　from　crops　is　crucial　in　irrigation　management,　crop　yield　assessment,　and　optimal　allocation　of　water　resources,　particularly　in　arid　regions.　This　study　explores　the　estimation　of　seasonal　evapotranspiration　for　crops　using　multisource　remote　sensing　images.　The　proposed　estimation　framework　starts　with　estimating　daily　evapotranspiration　(ETd)　values,　which　are　then　used　to　calculate　ET　estimates　during　the　crop　growing　season　(ETs).　We　incorporated　Landsat　images　into　the　surface　energy　balance　algorithm　over　land　(SEBAL)　model,　and　we　used　the　trapezoidal　and　sinusoidal　methods　to　estimate　the　seasonal　ET.　The　trapezoidal　method　used　multitemporal　ET(d)images,　while　the　sinusoidal　method　employs　time-series　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　images　and　multitemporal　ET(d)images.　Experiments　were　implemented　in　the　agricultural　lands　of　the　Kai-Kong　River　Basin,　Xinjiang,　China.　The　experimental　results　show　that　the　obtained　ET(d)estimates　using　the　SEBAL　model　are　comparable　with　those　from　the　Penman-Monteith　method.　The　ET(s)obtained　using　the　trapezoidal　and　sinusoidal　methods　both　have　a　relatively　high　spatial　resolution　of　30　m.　The　sinusoidal　method　performs　better　than　the　trapezoidal　method　when　using　low　temporal　resolution　Landsat　images.　We　observed　that　the　omission　of　Landsat　images　during　the　middle　stage　of　crop　growth　has　the　greatest　impact　on　the　estimation　results　of　ET(s)using　the　sinusoidal　method.　Based　on　the　results　of　the　study,　we　conclude　that　the　proposed　sinusoidal　method,　with　integrated　multisource　remote　sensing　images,　offers　a　useful　tool　in　estimating　seasonal　evapotranspiration　for　crops　in　arid　regions.