日本地球惑星科学連合2016年大会

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セッション記号 A (大気水圏科学) » A-CG 大気水圏科学複合領域・一般

[A-CG10] Earth and Planetary satellite observation project Part II

2016年5月24日(火) 10:45 〜 12:15 303 (3F)

コンビーナ:*沖 理子(宇宙航空研究開発機構)、早坂 忠裕(東北大学大学院理学研究科)、佐藤 薫(東京大学　大学院理学系研究科　地球惑星科学専攻)、佐藤 正樹(東京大学大気海洋研究所)、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)、奈佐原 顕郎(筑波大学生命環境系)、中島 孝(東海大学情報理工学部情報科学科)、沖 大幹(東京大学生産技術研究所)、松永 恒雄(国立環境研究所環境計測研究センター)、高薮 縁(東京大学 大気海洋研究所)、村上 浩(宇宙航空研究開発機構地球観測研究センター)、岡本 創(九州大学)、Gail Skofronick Jackson(NASA Goddard Space Flight Center)、Paul Chang(NOAA College Park)、Crisp David(Jet Propulsion Laboratory, California Institute of Technology)、座長:Crisp David(Jet Propulsion Laboratory)、横田 達也(独立行政法人国立環境研究所)

11:15 〜 11:30

[ACG10-21] Satellite Retrieval of Overstory and Understory Leaf Area Index in High Northern Forests

*楊 偉1、小林 秀樹2、奈佐原 顕郎3 (1.千葉大学　環境リモートセンシング研究センター、2.海洋研究開発機構、3.筑波大学)

キーワード：Satellite Remote Sensing, Leaf Area Index, Northern Forests

Leaf area index (LAI), defined as one-half the total green leaf are per unit of horizontal ground surface area, is a crucial input parameter for global carbon cycle modeling. Since carbon fixed through net primary productivity has different residence times for different components, the LAI for overstory and understory vegetation in forest ecosystems need to be treated differently in carbon cycle modeling. Currently, satellite remote sensing is the only feasible technique to measure the LAI at a continental and/or global scale over a long periods of multiple years. However, there are no existing satellite products that provide simultaneous estimation of overstory and understory leaf area index (LAIo and LAIu) at present. Consequently, we proposed an integrating look-up table (LUT) method to remotely estimate the LAIo and LAIu for boreal forests, where are encountering rapider temperature change than other areas. In the newly proposed method, the understory normalized vegetation difference index (NDVIu) is first retrieved from multiple satellite observations by using a semi-empirical method. Then the LAIu is estimated from the retrieved NDVIu through searching a LUT generated by radiative transfer simulation for understory vegetation. In order to estimate the LAIo, a new land-cover map of forest types, which classifies the boreal forests as low, medium and high types, is generated by using a wall-to-wall canopy height product to replace the conventional global land-cover maps. The LUTs containing angles, LAIu, LAIo, and corresponding reflectance at red and near-infrared bands are generated for each forest type by running a radiative transfer model. Specifically, the forest landscape parameters are determined by an empirical forest structure model. Moreover, the retrieved NDVIu is used as an ancillary information to constrain the relationship between LAIo and canopy reflectance. The validation results showed acceptable accuracy based on our filed measurement at interior Alaska, America.