日本地球惑星科学連合2023年大会

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[E] 口頭発表

セッション記号 A (大気水圏科学) » A-CG 大気海洋・環境科学複合領域・一般

[A-CG36] 静止軌道衛星による陸面観測

2023年5月24日(水) 13:45 〜 15:15 104 (幕張メッセ国際会議場)

コンビーナ:山本 雄平(千葉大学 環境リモートセンシング研究センター)、Tomoaki Miura(Univ Hawaii)、市井 和仁(千葉大学)、Chairperson:Tomoaki Miura(Univ Hawaii)

15:00 〜 15:15

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

*楊 偉1喬 治1 (1.千葉大学)

キーワード:リモートセンシング、ひまわり8号、ホットスポット、植生構造情報、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.