Scaling Transform Method for Remotely Sensed FAPAR Based on FAPAR-P Model

Climate and land-atmosphere models rely on accurate land-surface parameters, such as the fraction of absorbed photosynthetically 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 letter, the scaling-effect mechanism and the scale-transformation algorithm are derived using a Taylor series expansion method based on the FAPAR model based on P after simplification. The scaling algorithm was validated in the Heihe River Basin. The multiscale FAPAR values are inverted from 5-, 50-, and 100-m hyperspectral reflectance data. The scale-transformation formula was used, and the results agreed well with actual values.