NEWS (see all)

我々のグループでは、本研究室で研究をしてみたい（大学院生、研究生など）、データ解析など研究のお手伝いをしてみたい（学部生～大学院生他）などの方々を広く募集しています。学振PDなどの受入も大歓迎です。興味があれば、連絡（メール）下さい。

2020/07/09: Just Accepted ! AsiaFlux Model Review paper for AsiaFlux special issue in Journal of Agricultural Meteorology.
Ito and Ichii (2020) Terrestrial ecosystem model studies and their contribution to AsiaFlux.

2020/06/09: 山本さん＠特任助教が筆頭のCEReSひまわりデータの位置精度の検証に関する論文の記事（CEReSニュースリリース）を出しました。ご笑覧！？いただけたらと思います。

2020/04/24: A Himawari-8 geolocation accuracy assessment paper was published by Remote Sensing (in Geostationary Satellite Special Issue). Great work to support CEReS Himawari-8 datasets! Yamamoto, Ichii, Higuchi, Takenaka (2020) Geolocation Accuracy Assessment of Himawari-8/AHI Imagery for Application to Terrestrial Monitoring. Remote Sensing. 12(9), 1372; https://doi.org/10.3390/rs12091372.

2020/04/01: 研究室の状況を知って頂くことを目的に、毎月1日（前後）に日本語ニュースを"MONTHLYNEWS(日本語)"に掲載することにしました。必ずや継続致しますので、楽しみにご覧いただければと思います。上部メニューのNEWSからご覧いただけます。

MISSION

Mission of our group is "monitoring and modeling of earth system toward better prediction of our future earth."​ Our approach is to integrate and synthesize of available data and models to understand earth system in more comprehensive ways. Our main target is 'terrestrial biosphere' from regional to global scales. We use network of ground observation (e.g. FLUXNET, AsiaFlux), remote sensing data, numerical models (terrestrial ecosystem models), and atmospheric observations.

RESEARCH

Environment Monitoring and Modeling Group

Understandings of Terrestrial CO2 Budget Changes at Global Scales by Synthesis

Using bottom-up approach (e.g ecosystem models) and top-down approaches (e.g. atmospheric inversions), we are working on quantification of terrestrial CO2 budget at various scales (e.g. global, southeast Asia, Asia). These works can become a contribution to global projects such as Future Earth. [Kondo et al. 2018 Nature Comm.; Kondo et al. 2018 GRL; Ichii et al. 2013].

Data-driven estimation of terrestrial CO2 fluxes
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 Using FLUXNET/AsiaFlux data, satellite remote sensing data and machine learning technique, we estimate spatial and temporal variation of terrestrial CO2-Energy-H2O fluxes at regional to global scales [Ichii et al. 2017; Kondo et al. 2015; Ueyama et al. 2013; 2014; Saigusa et al. 2010]. 

Development of a Terrestrial CO2 Flux Product for GCOM-C SGLI Sensor

Using a diagnostic terrestrial biosphere model, AsiaFlux/FLUXNET observation network data, our group is developing a terrestrial CO2 flux (GPP, NPP, NEP) product for GCOM-C SGLI sensor (JAXA's satellite). Our product will be an official research products and released to public.

Satellite-based Monitoring of Vegetation Changes from Regional to Continental Scale

Using long-term data of satellite remote sensing, we analyzed and detected vegetation and land cover changes from regional to global scales [e.g. Sekizawa et al. 2015; Ichii et al. 2013; Ichii et al. 2002; Kawabata et al. 2001].
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Data-Model Integration toward refinement of terrestrial ecosystem models

By integrating satellite data into terrestrial ecosystem model, we estimated unknown ecosystem parameters and successfully improved models. [Kondo et al. 2017; Ichii et al. 2007, 2009]​. 

Top-down and Bottom-up Synthesis toward Better Understandings of Global Carbon Cycle

We are working on synthesis of multiple independent estimations of terrestrial CO2 fluxes. Estimations include top-down approach (atmospheric inversion model) and bottom-up approach (process-based model, data-driven model, satellite products). [Kondo et al. 2015; Ichii et al. 2017].

Earth System Modeling

We demonstrated that future projection of CO2 budget and climate might change if terrestrial carbon cycle model was improved using FLUXNET data. [Suzuki and Ichii, 2010]. We are also working on evaluating ESMs using available RS observation datasets.​

Model-data intercomparison in Asia etc.

We conducted model-data intercomparison project (Asia-MIP, CarboEastAsia-MIP, and Japan-MIP) to test current model performance using multiple models and observation data [Ichii et al. 2013; Ichii et al. 2010].

Keywords:
Terrestrial Biosphere, Remote Sensing, Model, AsiaFlux,Model-data Integration, Synthesis, Carbon Cycle, Water Cycle, ​Machine Learning