Japan Geoscience Union Meeting 2025

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-CG Complex & General

[A-CG44] Terrestrial monitoring using geostationary satellites

Wed. May 28, 2025 10:45 AM - 12:15 PM Exhibition Hall Special Setting (5) (Exhibition Hall 7&8, Makuhari Messe)

convener:Yuhei Yamamoto(Center for Environmental Remote Sensing, Chiba University), Tomoaki Miura(Univ Hawaii), Kazuhito Ichii(Chiba University), Chairperson:Yuhei Yamamoto(Center for Environmental Remote Sensing, Chiba University)

10:45 AM - 11:00 AM

[ACG44-05] Advancing Geostationary Satellite Data Integration: Spectral Band Adjustment Using Hyperspectral Observations and Radiative Transfer Modeling

*Taiga Sasagawa1, Kazuhito Ichii1, Yuhei Yamamoto1, Wei Yang1, Masayuki Matsuoka2, Hiroki Yoshioka3, Weile Wang4, Hirofumi Hashimoto4, Kenlo Nishida Nasahara5 (1.Chiba University, 2.Mie University, 3.Aichi Prefectural University, 4.NASA Ames Research Center, 5.University of Tsukuba)

Keywords:Geostationary Satellites, Data Fusion, Hyperspectral Data

Third-generation geostationary satellites, such as Japanese Himawari-8 and 9, the U.S. GOES series, Korean GK-2A, European MTG1, and Chinese Feng Yun-4, provide sub-hourly satellite observations that have significantly advanced Earth system monitoring. These hyper-temporal datasets are widely applied in ecosystem studies, particularly for vegetation monitoring, phenology analysis, gross primary production (GPP) estimation, and leaf area index (LAI) assessments using visible and near-infrared observations. However, due to the inherent orbital constraints of geostationary satellites, their coverage is regionally limited, posing challenges for global-scale data integration. To address this limitation, spectral band adjustments between different geostationary satellite sensors are essential. This study demonstrates a spectral band adjustment method that integrates 3D radiative transfer modeling with hyperspectral remote sensing data derived from both in situ and satellite observations. Our analysis revealed non-linear relationships between certain spectral bands across third-generation geostationary satellites. However, by leveraging vegetation indices, we successfully derived conversion equations to achieve a linear transformation, enabling seamless integration of hyper-temporal datasets across different satellite platforms. This approach enhances the consistency and comparability of geostationary satellite data, supporting global-scale Earth system monitoring.