日本地球惑星科学連合2023年大会

講演情報

[E] オンラインポスター発表

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] 静止軌道衛星による陸面観測

2023年5月25日(木) 09:00 〜 10:30 オンラインポスターZoom会場 (3) (オンラインポスター)

コンビーナ:山本 雄平(千葉大学 環境リモートセンシング研究センター)、Tomoaki Miura(Univ Hawaii)、市井 和仁(千葉大学)

現地ポスター発表開催日時 (2023/5/24 17:15-18:45)

09:00 〜 10:30

[ACG36-P02] Estimation of diurnal variation in terrestrial gross primary productivity across semi-arid ecosystems using Himawari-8

*住井 章吾1市井 和仁1山本 雄平1 (1.千葉大学)

キーワード:総一次生産、ひまわり8号

Gross Primary Production (GPP) is the total amount of carbon dioxide absorbed by vegetation from the atmosphere through photosynthesis. Detailed estimation of GPP is important for understanding not only the terrestrial carbon cycle but also the environmental response of forests and agricultural land. Geostationary satellites can estimate surface biophysical status with high temporal resolution (e.g. 10 minutes). Therefore, geostationary satellite data can aid in estimating GPP on time scales of less than one day. However, the reproducibility of GPP under hot and dry conditions in arid regions remains a challenge. In this study, we attempted to estimate GPP in the Australian region covered by semi-arid vegetation using two GPP estimation models, the MODIS-GPP model and the EC-LUE model, with inputs of solar radiation from Himawari-8 and temperature and humidity data from a numerical climate model. The GPP estimation model was improved by evaluating its accuracy and optimizing the model parameters using GPP data observed at three OzFlux sites. The original model showed deviations from the observed values, but after optimization of the model parameters, the RMSE was reduced by 22% to 42%, confirming an improvement in accuracy. The EC-LUE model was able to better capture the characteristics of the site. However, it was difficult to adequately reproduce temperature and water stress measurements in semi-arid regions. This study indicates that the diurnal variation of GPP in semi-arid areas in the Australian region can be generally estimated. However, higher temporal resolution of meteorological data and improvement of the stress term in the model are needed for more precise estimation of environmental stress.