An international collaborative research team from universities in Japan, South Korea, and Germany, led by Assistant Professor Yuhei Yamamoto and Professor Kazuhito Ichii from the Center for Environmental Remote Sensing at Chiba University, announced the development of a method to estimate photosynthesis in vegetation across East Asia at 30-minute intervals using observation data from the meteorological satellites Himawari-8 and Himawari-9. This enables more accurate detection of differences in light use efficiency between clear and cloudy conditions, as well as the suppression of photosynthetic activity observed during intense light, high temperature, and dry conditions at midday. The results are expected to contribute to countermeasures during extreme weather events. The findings were published in Remote Sensing of Environment on June 16.
Image Credit: Yuhei Yamamoto, Chiba University, Japan
Until now, observations of terrestrial areas including vegetation photosynthetic activity have primarily used polar-orbiting satellites with low revisit frequency over the same location, with estimation periods ranging from several days to one month. Additionally, conventional light use efficiency models for estimating photosynthesis from satellite observation data did not account for the diverse meteorological environmental variations that occur within a single day.
Therefore, the research team investigated a new method to estimate photosynthesis in East Asia at 30-minute intervals by utilizing high-frequency observation data from the geostationary satellites Himawari-8 and Himawari-9.
Aiming to improve accuracy from conventional models, the team incorporated light absorption efficiency into the estimation model. The light absorption efficiency was determined based on the following: forest structure (including tree species, tree height, and density) and solar elevation angle, in addition to direct radiation (light arriving directly from the sun) and diffuse solar radiation obtained from Himawari observation data. Forests tend to utilize diffuse radiation, which arrives after being scattered by particles in the atmosphere, more efficiently for photosynthesis than direct radiation.
The model also incorporated the fact that the photosynthetic response to light intensity is not constant but exhibits complex, nonlinear behavior.
To accurately capture the decrease in photosynthetic rate during heat waves, the team evaluated whether air temperature calculated by numerical weather models (models that simulate temporal and spatial changes using supercomputers) or vegetation surface temperature captured by Himawari was more suitable.
As a result, they found that when observation data was applied as-is to conventional models, estimation errors that occurred under low-light conditions such as morning/evening and cloudy conditions accumulated, causing a negative bias.
The new method enables analysis that connects diurnal scales where extreme weather impacts are likely to appear to annual scales where climate change impacts appear. By using vegetation surface temperature data estimated from Himawari, it became possible to detect with higher sensitivity the "midday depression" phenomenon where photosynthetic activity decreases daytime during heat waves.
This enables high-frequency monitoring of the impacts of extreme weather on forests and crops, with expected contributions to early damage detection and risk management in agriculture and forestry.
Yamamoto stated: "To make Himawari, which has been used for meteorological observation, useful for land surface observation as well, we have been working on developing algorithms to estimate land surface temperature and reflectance. This time we took it a step further and demonstrated that it can also be used for estimating photosynthesis. We will continue to pursue methods to more accurately capture the impacts of climate change on terrestrial ecosystems."
Journal Information
Publication: Remote Sensing of Environment
Title: Modeling diurnal gross primary production in East Asia using Himawari-8/9 geostationary satellite data
DOI: 10.1016/j.rse.2025.114866
This article has been translated by JST with permission from The Science News Ltd. (https://sci-news.co.jp/). Unauthorized reproduction of the article and photographs is prohibited.