3.1. Fusion of Landsat ETM+ NDVI with time series MODIS NDVIdata
STARFM is developed for blending Landsat and MODIS surfacereflectance by fusing high-frequency temporal information fromMODIS and high spatial resolution information from Landsat data.STARFM predicts pixel values based upon a spatially weighteddifference computed between the Landsat and the MODIS dataacquired at T1, and the Landsat T1-scene and one or more MODIS scenes of prediction day (T2), respectively . Amoving window technique is used to minimize the effect of pixeloutliers thereby predicting changes of the center pixel using the spatially and spectrally weighted mean difference of pixels withinthe window area . The STARFM was also extendedfor blending NDVI data of different spatial and temporal resolutionsto produce high temporal and spatial resolution NDVI dataset, andsatisfactory results were achieved (Meng et al., 2011). Therefore,Landsat ETM+ NDVI data acquired on 18 August, 2005 which was
3.1. Fusion of Landsat ETM+ NDVI with time series MODIS NDVIdata
STARFM is developed for blending Landsat and MODIS surfacereflectance by fusing high-frequency temporal information fromMODIS and high spatial resolution information from Landsat data.STARFM predicts pixel values based upon a spatially weighteddifference computed between the Landsat and the MODIS dataacquired at T1, and the Landsat T1-scene and one or more MODIS scenes of prediction day (T2), respectively . Amoving window technique is used to minimize the effect of pixeloutliers thereby predicting changes of the center pixel using the spatially and spectrally weighted mean difference of pixels withinthe window area . The STARFM was also extendedfor blending NDVI data of different spatial and temporal resolutionsto produce high temporal and spatial resolution NDVI dataset, andsatisfactory results were achieved (Meng et al., 2011). Therefore,Landsat ETM+ NDVI data acquired on 18 August, 2005 which was
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