日本地球惑星科学連合2024年大会

講演情報

[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] 衛星による地球環境観測

2024年5月27日(月) 09:00 〜 10:15 105 (幕張メッセ国際会議場)

コンビーナ:沖 理子(宇宙航空研究開発機構)、本多 嘉明(千葉大学環境リモートセンシング研究センター)、松永 恒雄(国立環境研究所地球環境研究センター/衛星観測センター)、高橋 暢宏(名古屋大学 宇宙地球環境研究所)、座長:松永 恒雄(国立環境研究所地球環境研究センター/衛星観測センター)、本多 嘉明(千葉大学環境リモートセンシング研究センター)

09:00 〜 09:15

[ACG36-01] Challenges toward hyper-temporal monitoring of terrestrial vegetation using 3rd generation geostationary satellites, Himawari-8/9

*市井 和仁1山本 雄平1李 偉1、張 北辰1長谷 美咲1柴山 怜雄1住井 章吾1 (1.千葉大学)

キーワード:静止衛星、陸域植生、炭素循環、干ばつモニタリング

Third-generation geostationary meteorological satellites, such as Himawari 8/9, are capable of making observations as frequently as once every 10 minutes, and are characterized by having multiple observation wavelength bands in the visible and near-infrared regions compared to conventional geostationary satellites. In addition, they have improved spatial resolution and observation frequency, including the thermal infrared band. The visible and near-infrared wavelength bands are similar to those of sensors onboard polar-orbiting satellites (MODIS, SGLI, etc.), which are widely used for land surface observations. It is possible to observe land surfaces at a higher frequency than conventional polar-orbiting satellites. Therefore, they are expected to be applied to monitoring changes in the terrestrial environment, such as vegetation and land cover. In this presentation, we will introduce our efforts to monitor the land surface and vegetation using data from Himawari and other countries' geostationary meteorological satellites. On the other hand, in order to apply geostationary meteorological satellite data to land area monitoring, it is necessary to calculate and verify surface reflectance. In this presentation, I will introduce the following efforts and results on the advancement of land surface monitoring by geostationary meteorological satellites: (1) the plan and progress of data products in our group, (2) increasing cloud-free data by improving the frequency of observations to more accurately determine the vegetation phenology, (3) detecting vegetation phenology in tropical rainforest areas that have been difficult to observe by satellite due to cloud cover, (4) rapid identification of water stress conditions of vegetation using 10-minute changes in surface temperature, (5) early detection of water stress conditions of vegetation through observations at 10-minute intervals, and (6) establishing an international observation and collaboration network.