A Synthetic Angle Normalization Model of Vegetation Canopy Reflectance for Geostationary Satellite Remote Sensing Data

High-frequency imaging characteristics allow a geostationary satellite (GSS) to capture the diurnal variation in vegetation canopy reflectance spectra, which is of very important practical significance for monitoring via remote sensing (RS). However, observation angle and solar high-frequency GSS RS data usually differ, differences bidirectional from spectra are significant, makes it necessary normalize angles data. The BRDF (Bidirectional Reflectance Distribution Function) prototype library effective normalization its spatiotemporal applicability error propagation currently unclear. To resolve this problem, we herein propose synthetic model (SANM) reflectance; exploits characteristics, whereby each pixel has fixed angle. established references topographic correction method canopies based on path-length correction, zenith normalization, Minnaert model. It also considers variations by setting time window. Experiments were carried out eight Geostationary Ocean Color Imager (GOCI) images obtained 22 April 2015 validate performance proposed SANM. results show that SANM significantly improves phase-to-phase correlation GOCI band morning window retains instability noon provides preliminary solution normalizing quantitative comparison possible.