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

Abstract

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

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“…(1) at canopy level, reflectance results from various factors, such as vegetation chemical properties, leaf morphology, canopy structure, and tree sizes [60][61][62]; and (2) at soil level, it is a differentiating characteristic for many classes and is an essential part of the definitions for both surface and subsurface diagnostics. Main factors influencing the reflectance for bare soils are roughness and texture, organic matter content, and moisture conditions [63,64].…”

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“…Geostationary satellites are able to extract information on the daily variations in crop canopy reflectance based on high-temporal-resolution imagery. Lin et al [15] proposed the synthetic angle normalization model (SANM), which uses vegetation canopy reflectance as its input. The SANM makes use of the advantages of GSS imaging and is able to quantitatively compare spatiotemporal remote sensing data.…”