A paper co-authored by Professor Kentaro Takagi of Hokkaido University’s Field Science Center for Northern Biosphere has been published in Remote Sensing of Environment.
- Yuhei Yamamoto, Kazuhito Ichii, Wei Yang, Yui Shikahura, Youngryel Ryu, Minseok Kang, Shohei Murayama, Su-Jin Kim, Yuta Takao, Masahito Ueyama, Tomoko Kawaguchi Akitu, Hiroki Iwata, Hojin Lee, Junghwa Chun, Atsushi Higuchi, Takashi Hirano, AReum Kim, Hyun Seok Kim, Kenzo Kitamura, Yuji Kominami, Kazuho Matsumoto, Jun Suzuki, Kentaro Takagi, Yoshiyuki Takahashi, Satoru Takahashi, Hideki Takenaka, Shingo Taniguchi, Yukio Yasuda. Modeling diurnal gross primary production in East Asia using Himawari-8/9 geostationary satellite data. Remote Sensing of Environment, Date: 1 October 2025, 114866, Volume 328
DOI: https://doi.org/10.1016/j.rse.2025.114866
Abstract
Gross primary production (GPP) is a key indicator of plant growth and ecosystem health, and accurately capturing its diurnal variation is crucial for understanding vegetation responses to extreme heat and drought. However, the applicability of satellite-based semi-empirical models to diurnal GPP estimation remains limited. This study refined diurnal GPP estimation in humid temperate climates by leveraging Himawari-8/9 geostationary satellite data to incorporate direct/diffuse radiation and the nonlinear GPP response to diurnal variations in absorbed photosynthetically active radiation (APAR). The eddy covariance-light use efficiency (EC-LUE) model was employed by adopting three approaches: the direct/diffuse (DD) setting to consider the direct/diffuse components of APAR, DD with nonlinear relationship (DD-NL) setting to additionally consider the nonlinear GPP-APAR relationship, and the baseline setting. The model was calibrated and validated using the eddy-covariance tower observations from 18 sites across Japan and South Korea. The DD-NL setting improved accuracy by correcting the baseline’s overestimation of GPP under high APAR and underestimation under low APAR. Particularly for forest sites, the DD-NL setting reduced midday overestimations by 12–30 % on clear-sky days and morning/afternoon underestimations by 25–40 % on cloudy days. In the baseline setting, low-APAR biases progressively accumulated across daily to annual timescales, whereas the DD-NL setting reduced them to −0.10 g C m−2 day−1 and -51 g C m−2 year−1 (−2.7 % of the site’s average GPP). The DD setting had minimal impact in densely vegetated sites. Our findings show that the DD-NL setting in LUE models enhances geostationary satellite-based GPP estimates across diurnal to annual timescales, supporting ecosystem monitoring during extreme events and long-term carbon assessments.
Keywords: Photosynthesis; Gross primary production (GPP); Light use efficiency (LUE) model; Diurnal variations; Geostationary satellite data; Himawari 8/9; Advanced Himawari imager (AHI)
© 2025 The Authors. Licensed under CC BY 4.0.
Yamamoto, Y. et al., Remote Sensing of Environment, 328, 114866 (2025). https://doi.org/10.1016/j.rse.2025.114866