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寺本 宗正*; Liang, N.*; 高木 健太郎*; 近藤 俊明*; 近藤 俊明*; 小嵐 淳; 安藤 麻里子; 高木 正博*; 石田 祐宣*; 楢本 正明*; et al.
no journal, ,
Soil respiration (Rs) consists of root respiration and heterotrophic respiration (Rh, decomposition of soil organic carbon by soil microbiota). Rh contributes to more than the half of Rs. Because Rh exponentially increases with temperature, it is concerned that increased Rh under future warmer environment might be further accelerate global warming (positive feedback). Therefore, long-term response of Rh to global warming is one of the most important elements for precise prediction for future climate change. Soil warming experiment in field is one of the effective methods to examine the long-term response of Rh against global warming. However, such long-term monitoring data under warmed environment is totally limited, especially in Asian monsoon region. To examine long-term response of Rh in Asian monsoon forest soil, we installed the same multi-channel automated chamber measurement system in typical forests in Asian monsoon region, and we conducted several years of soil warming experiments. In this presentation, we show the long-term response of Rh against artificial soil warming and control factors for the seasonal and inter-annual variation of the warming effect on Rh in those several forest ecosystems.
市井 和仁*; 山貫 緋称*; Liang, N.*; 寺本 宗正*; 高橋 善幸*; Zeng, J.*; 高木 健太郎*; 平野 高司*; 石田 祐宣*; 高木 正博*; et al.
no journal, ,
陸域生態系のCO
等のフラックスの推定には、近年はAsiaFluxやFLUXNETなど観測ネットワーク網や衛星リモートセンシングデータの充実により、観測データに基づく推定(データ駆動型(data-driven)の推定)が可能になってきた。一方、「土壌呼吸」に関しては、様々な課題を抱えており、広域推定は十分には実現されていない。国立環境研究所らのグループでは統一された観測手法・データ処理手法によるアジア域のチャンバー連続観測ネットワークを構築しており課題を解決できる可能性がある。そこで、我々は、衛星データと機械学習を用いることで土壌呼吸の広域推定を試みている。まずは、日本を対象にした8観測サイトのデータを用いた解析を進めている。本発表では、(1)AsiaFluxやFLUXNETデータベースと衛星観測データを利用して機械学習法を適用することによるCOフラックス(総一次生産量,生態系CO交換量)推定手法の紹介と、(2)土壌呼吸ネットワークと衛星観測データと機械学習を用いた土壌呼吸の広域推定と既存のデータセットとの比較解析について紹介し、今後の課題についても議論したい。
山貫 緋称*; 市井 和仁*; Liang, N.*; 寺本 宗正*; 高橋 善幸*; Zeng, J.*; 高木 健太郎*; 平野 高司*; 石田 祐宣*; 高木 正博*; et al.
no journal, ,
In this study, we updated our data-driven estimation of soil respiration (SR) across Japan with observation data (eight sites across Japan), remote sensing data (MODIS land products), and random forest regression. Our estimation shows a reasonable performance with R
=0.87 for remote sensing only model and R = 0.91 for remote sensing and in-situ combined model. Based on the established model, we also produced upscaled estimations of SR across Japan with 1km spatial resolution from 2000 to 2020. Intercomparison of our estimation with other available datasets was also conducted to understand advantages of our estimation. Our results show spatially more explicit variations compared with other global products. In addition, our advantage is to capture temporal variations (e.g. 8 days). We also confirmed that previous estimations do not reproduce our observation network datasets, indicating consistent observation approach is important to upscale soil respiration.