[A-CG36] Terrestrial monitoring using new-generation geostationary satellites

[ACG36-06] Observations and Modeling of the Hotspot Effect in Vegetation Canopy Reflectance using Geostationary Meteorological Satellite Data

*Wei Yang¹, ZHI QIAO¹ (1.Chiba University)

Keywords:Remote sensing, Himawari-8, hotspot, vegetation structure information, BRDF

The hotspot effect refers to a special case of the bidirectional reflectance distribution function (BRDF) when backscattering reflectance rapidly increases when the solar and viewing directions coincide. It is related to shadow hiding within and between vegetation canopies, therefore, the hotspot directional signatures can be used to remotely estimate canopy structure information for improving leaf biochemistry modeling, such as directional area scattering function (DASF), and spectral canopy scattering coefficients (CSC). The hotspot effect in vegetation canopy reflectance has been conventionally observed by field experiments or aerial remote sensing, which are limited in spatial and temporal coverages. As for satellite remote sensing, the Earth Polychromatic Imaging Camera (EPIC) launched to a sun-Earth Lagrange point orbit can observe the hotspot at a daily resolution, however, its spatial resolution is approximately 10 km. With the hyper-temporal (~10 minute) and improved radiometric resolutions, the third-generation geostationary meteorological satellites (e.g., Himawari-8) provide unprecedented opportunities to observe the hotpot effect at a moderate spatial resolution (~1 km). In this study, we first extracted the hotspot directional signatures for different vegetation types using the Himawari-8 Advanced Himawari Imager (AHI) surface reflectance data, which was derived from a 6SV-based atmospheric correction algorithm. Correspondingly, a reflectance dataset composed of thousands of hotspot records was constructed to quantitatively evaluate the BRDF models. Three semi-empirical kernel-driven BRDF models (with and without hotspot factor) were adopted. The evaluation results identified the most robust and flexible model to capture the hotspot signatures even with some missing data due to cloud contaminations. The findings of this study are helpful for enhancing the applications of satellite-based hotspot signatures in canopy reflectance.