9:00 AM - 10:30 AM
[ACG36-P01] How does hyper-temporal observation of terrestrial surface by geostationary satellites improve understanding of terrestrial ecosystems?
Keywords:vegetation , monitoring, carbon cycle
The new generation of geostationary meteorological satellites have multiple observation wavelength bands in the visible and near-infrared regions, enabling ultra-frequent land observations at a frequency of once every 10 minutes. It is expected that the understanding of terrestrial ecosystems (vegetation) will be advanced by using this new ultra-high frequency observation data. In this presentation, I would like to review our research and introduce our group's efforts to answer the question posed in the title.
First, ultra-high-frequency observations every 10 minutes greatly increase the opportunity to obtain cloud-free images, and thus greatly increase the amount of data available on the ground surface. This allows us to see changes in vegetation phenology at a much finer scale than before. This research has also been shown by Miura et al. (2019) and others to have intervals of 3-4 days, compared to 10 days for conventional polar-orbiting satellites. Furthermore, there are studies that show that in areas with traditionally high cloud cover, such as tropical rainforests, where it has been difficult to track seasonal changes in vegetation activity with conventional Earth observation satellites, seasonal changes can be clearly observed with high-frequency observations by geostationary satellites.
Furthermore, it is possible to detect diurnal variations in surface geophysical parameters and obtain new information from them by making observations once every 10 minutes. For example, ground surface temperature is a good indicator of the moisture status of vegetation, and it is becoming possible to detect drought stress and high temperature stress in vegetation by tracking its diurnal changes. If this becomes possible, applications such as early detection of environmental stress in crops may become possible.
The Center for Environmental Remote Sensing Research, Chiba University, plans to construct and publish various geophysical data sets of the earth's surface using geostationary satellites, with high expectations for these studies. Some of the data, such as surface temperature, have already been released to the community, and we hope that these data sets will help promote a better understanding of terrestrial ecosystems.
First, ultra-high-frequency observations every 10 minutes greatly increase the opportunity to obtain cloud-free images, and thus greatly increase the amount of data available on the ground surface. This allows us to see changes in vegetation phenology at a much finer scale than before. This research has also been shown by Miura et al. (2019) and others to have intervals of 3-4 days, compared to 10 days for conventional polar-orbiting satellites. Furthermore, there are studies that show that in areas with traditionally high cloud cover, such as tropical rainforests, where it has been difficult to track seasonal changes in vegetation activity with conventional Earth observation satellites, seasonal changes can be clearly observed with high-frequency observations by geostationary satellites.
Furthermore, it is possible to detect diurnal variations in surface geophysical parameters and obtain new information from them by making observations once every 10 minutes. For example, ground surface temperature is a good indicator of the moisture status of vegetation, and it is becoming possible to detect drought stress and high temperature stress in vegetation by tracking its diurnal changes. If this becomes possible, applications such as early detection of environmental stress in crops may become possible.
The Center for Environmental Remote Sensing Research, Chiba University, plans to construct and publish various geophysical data sets of the earth's surface using geostationary satellites, with high expectations for these studies. Some of the data, such as surface temperature, have already been released to the community, and we hope that these data sets will help promote a better understanding of terrestrial ecosystems.