The spatial scaling effect of canopy FAPAR retrieved by remote sensing

Climate and land-atmosphere models rely on accurate land-surface parameters, such as the Fraction of Absorbed Photo synthetically Active Radiation (FAPAR). It is known that FAPAR values retrieved from remote sensing images suffer from scaling effects. Scaling transformation aims to derive accurate FAPAR values at a specific scale from values at other scales. In this paper, scaling effect mechanism and the scale transformation algorithm are derived using Taylor series expansion method based on FAPAR-P model, after the model was simplified. The scaling algorithm was validated in Heihe River Basin. The multiscale FAPAR are inversed from of 5 m, 50 m and 100 m hyperspectral reflectance data. The scale transformation formula was used and the results agreed well with the actual values.

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