日本地球惑星科学連合2023年大会

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[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-CG35] グローバル炭素循環の観測と解析

2023年5月25日(木) 09:00 〜 10:15 104 (幕張メッセ国際会議場)

コンビーナ:市井 和仁(千葉大学)、Patra Prabir(Research Institute for Global Change, JAMSTEC)、伊藤 昭彦(国立環境研究所)、座長:Patra Prabir(Research Institute for Global Change, JAMSTEC)

10:00 〜 10:15

[ACG35-05] Analyzing the Effect of Different MODIS Product Versions Towards Long-term Terrestrial Carbon Cycle Monitoring in Asia

*Daniel Joseph Henri1Kazuhito Ichii1 (1.Chiba University)

キーワード:MODIS, terrestrial carbon cycle, remote sensing, data-driven model

NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor-equipped satellites have been gathering data since the year 2000. Over this time, NASA has produced versions of this data, each of which apply different processing and calibration techniques to more accurately represent the data which the MODIS sensors transmit. There is also the possibility of MODIS sensor degradation which, over time, could lead to changes in the sensor readings, and consequently to the post-processing outputs of MODIS data. Because MODIS data are commonly used in environmental sciences, the changes in the data from version to version, as well as from potential sensor degradation, can have implications for our understanding of the terrestrial carbon cycle. This study will compare different product versions of MODIS data in two ways; by comparing raw values and long-term trends of MODIS land products, and by modeled terrestrial carbon fluxes based on data-driven estimation using MODIS data and data from eddy covariance towers in Asia with a support vector regression machine learning model. This method was previously used in Ichii et al. (2017) before the two most recent MODIS versions (6.0 and 6.1) became available. This study will build on this previous research by attempting to take into account the two newest MODIS data versions, and roughly 5 new years of data. This will allow a better understanding of the changes in accuracy over versions and time of MODIS data in Asia.