The rapid development of the Earth Observation (EO) technology offers substantial data for remote sensing monitoring of water environment, however, the research and applications are often impeded by inconsistent products from multi-resolution remote sensing dataset. A point-spread-function (PSF) based spatial resolution transformation approach was proposed, to mitigate scale errors from multi-platform sensors of varied spatial resolution. Using the high spatial resolution data of GF-1 (16 m) as reference, medium to low spatial resolution data were simulated, including Landsat-like and Terra/Aqua MODIS-like images. Comparisons between the PSF based scale transformation method and classical method revealed significant correlation, which also prove the efficiency of the proposed method. The scale errors of the remote sensing reflectance (RRS) average method are lower than the suspended practical matter (SPM) average method. Higher than 5% scale errors were produced by spatial scale transformation in high dynamic turbid waters, while for calm ocean waters, the error was less than 0.5%. Therefore, it is crucial for selection of proper scale transformation method, to achieve consistent remote sensing products from multi-source data.
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