iLEAPS-Japan 研究集会 2022
⼤気−陸⾯プロセスの研究の進展：観測とモデルによる統合的理解

要旨集*

開催⽇ ：2022 年 12 ⽉ 1 ⽇(⽊)・2 ⽇(⾦)
場所

：名古屋⼤学研究所共同館Ⅱ・409 室 （地球⽔循環共⽤室）
および、オンライン会場

主催：名古屋⼤学宇宙地球環境研究所・⽇本学術会議 iLEAPS ⼩委員会※

(兼)名古屋⼤学宇宙地球環境研究所
令和 4 年度共同利⽤・共同研究（研究集会）
報告書

開催趣旨

iLEAPS（Integrated Land Ecosystem‒ Atmosphere Process Study：統合
陸域⽣態系―⼤気プロセス研究計画）は、⼤気−陸域境界で⽣じる物理的・化学的・⽣物学的な諸過程についての理解の促進を⽬的とした国際研究計画です。iLEAPS は、持続可能な⼈間社会の構築を⽬的とした国際的な研究プラットフォームである Future Earth の、GRPs (Global Research
Projects) の⼀つとして位置づけられています。我が国においては、⽇本学
術会議 環境学委員会・地球惑星科学委員会合同 FE・WCRP 合同分科会の下部組織として iLEAPS ⼩委員会が設けられています。⼤気−陸域プロセスに関わる研究としては、これまで野外観測、広域モニタリングデータ解
析、数値モデル開発が⾏われ、個葉の環境応答からグローバルな変動まで様々な時間・空間スケールにおける研究が⾏われてきました。iLEAPS は、
このような多岐にわたる⼿段から得られた様々な知⾒の統合的理解を⽬指しています。本研究集会では、⼤気−陸域プロセスに関⼼のある研究者にお集まりいただき、各⾃の最新の研究成果を報告し、その統合的理解に向けた情報共有と議論を⾏います。特に、観測とモデルによる統合的理解について議論し、⼤気−陸域プロセスをベースとした気候変動予測研究の推進において、今後何が必要であるかについて議論します。

⽂責：佐藤永（海洋研究開発 機構・⽇本学術会議 iLEAPS ⼩委員会委員⻑）iLEAPS-Japan2022 研究集会

プログラム

12 ⽉ 1 ⽇（⽊）
座⻑：伊勢武史（京都⼤学）
13:00〜13:05 主催者挨拶と主旨説明（佐藤永・iLEAPS-Japan ⼩委員会 委員⻑）
13:05〜13:10 ロジ説明（檜⼭哲哉・iLEAPS-Japan ⼩委員会 前委員⻑）
13:10〜13:35 菊池 千尋（⼤阪府⼤）ほか 2 名
Increases in CH4 uptake associated with rising atmospheric CH4 concentrations at
a temperate forest soil
13:35〜14:00 ⾼橋 けんし（京都⼤）ほか 7 名Methane emission from the stem surfaces of Alnus japonica
14:00〜14:25 ⾼村 直也（東⼤）ほか５名
El Niño-Southern Oscillation forcing on carbon and water cycling in a Bornean
tropical rainforest
14:25〜14:50 篠塚 賢⼀（岐⾩⼤）ほか 6 名
Nitrate input to the river from atmospheric deposition observed in Yakushima Island,
the world natural heritage site
14:50〜15:15 ⽥代 悠⼈（名古屋⼤）ほか 3 名
Possibility of permafrost degradation for the source of the great increase indissolved iron concentration in the Amur River basin during 1995‒ 1997
15:15〜15:30 休憩
座⻑：佐藤永（海洋研究開発機構）
15:30〜15:55 ペク ムンソン（千葉⼤）ほか 3 名
Generating a new land cover product across Siberia by fusing global land coverdatasets
15:55〜16:20 ⼭本 雄平（千葉⼤）ほか 5 名
Terrestrial vegetation monitoring using Himawari-8 hyper-temporal land surface
temperature data
16:20〜16:45 Hantao Li（北海道⼤）ほか 18 名
Mapping Forest Canopy Height over Japan Using Multisource Remote Sensing Data
16:45〜17:10 Kazuhito Ichii（千葉⼤）ほか 9 名
Toward hyper-temporal terrestrial monitoring with new generation geostationary
satellites
17:10〜17:35 近藤 雅征（名古屋⼤）Key uncertainties in the uncertain world of biogeochemistry
17:35〜17:45 第⼀⽇⽬・総括
18:30〜20:30 懇親会（レストラン花の⽊）12 ⽉ 2 ⽇（⾦）
座⻑：近藤 雅征（名古屋⼤学）
09:00〜09:25 中岫茉希（北海道⼤）ほか 2 名
Projected climate change impacts on wheat production by process-based model
MATCRO-Wheat
09:25〜09:50 Astrid Yusara（北海道⼤）ほか 9 名
Development of the process-based soybean growth model (MATCRO-Soybean)
09:50〜10:15 佐藤 永（海洋研究開発機構）
Predicting dominant terrestrial biomes at a global scale: Assessments of machinelearning algorithms, climate variables indexing, and extreme climate
10:15〜10:25 休憩
10:25〜10:50 福富 慶樹（名古屋⼤）ほか１名
Characteristics of Synoptic-Scale Waves in the Northern Eurasian Storm Trackduring Summer

10:50〜11:15 伊藤 昭彦（国⽴環境研）Online 発表
Ammonia volatilization simulated by global biogeochemical models
11:15〜11:30 第⼆⽇⽬・総括、閉会の挨拶
11:40〜12:30 第 25 期・第 3 回 iLEAPS ⼩委員会（⼩委員会メンバーのみ参加・⾮公開）※発表は、プレゼンテーション 15 分+質疑応答 10 分
オンサイト会場のご案内
名古屋⼤学東⼭キャンパス 宇宙地球環境研究所 研究所共同館 II・409 号室(地球⽔循環共⽤室)
https://www.nagoya-u.ac.jp/upload_images/campus_map_jp.pdf ←この地図の右端 F3⑧です
オンライン（Zoom）会場のご案内
2022 年 12 ⽉ 1 ⽇（⽊） 13:00〜17:45
https://us02web.zoom.us/j/83773797036?pwd=N2lDMmNQd3NNb28yUExtTG5KOFBwZz09
2022 年 12 ⽉ 2 ⽇（⾦） 09:00〜11:55
https://us02web.zoom.us/j/83853765819?pwd=YnVpT0RCQUs5SVhLVG5JSjgvLzdPQT09
パスワードは配信しません。待機室を設けて、実⾏委員会で⼊室管理いたします。
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Increases in CH4 uptake associated with rising atmospheric CH4
concentrations at a temperate forest soil
Chihiro Kikuchi1*, Masahito Ueyama2, Satoru Takanashi3
1

Osaka Prefecture University, 2Osaka Metropolitan University
3
Forestry and Forest Products Research Institute*
Corresponding author (e-mail: scc03017@st.osakafu-u.ac.jp)

Objectives: CH4 is a potent greenhouse gas that contributes to global warming, and their atmosphericconcentration has risen continuously. Although methanotrophs uptake CH4 in aerobic soils, no field study
has quantified how CH4 uptake increases with rising atmospheric CH4 concentrations. In this study, wedeveloped a measurement system for artificially rising atmospheric CH4 concentrations based on an
automated closed chamber system. We quantified the response of CH4 uptake at a temperate forest soil torising atmospheric CH4 concentration in near future.
Materials and methods: The experiment was carried out at the Yamashiro Forest Meteorology Researchsite in Kyoto. The soil type was immature soil. We measured CH4 and CO2 fluxes at four locations in theforest using the automated closed chambers. A system that automatically injected a high concentration ofCH4 gas (CH4: 9739 ppm) every 6 hours was connected to the chamber. Owing to the injection, CH4concentration in the chamber increased approximately 2.3 ppm and 2.5 ppm with different injection
volumes. These concentrations represent future CH4 concentration at 2050 expected by the SSP5-85
scenario. The CH4 flux was calculated from the linear slope for 45 seconds, 165 seconds after the start ofhigh CH4 gas injection. The difference between the flux at the time of the injection and the average of thefluxes during the 30 minutes before and after the time of the injection was used to quantify the effect of theincreased concentration.
Results and discussion: CH4 uptake increased 1.1- 9.8
times owing to rising CH4 concentrations (Fig. 1).
Magnitudes of the increased CH4 uptake were greater
under the 2-second injections than the 1-second
injections, representing that higher CH4 uptakes wereobserved under higher CH4 concentrations. Owing to asystem modification at the end of July, the increased
CH4 concentrations in the chamber were changed:
approximately 2100 ppb at 1-second injection and 2200
ppb at 2-second injection until the end of July and to
approximately 2300 ppb and 2500 ppb hereafter.
Therefore, the increased CH4 uptake since August was
higher than in July, and the difference in uptake between
1-second and 2-second injections was clearer.
CH4 uptake linearly increased with rising CH4
concentration (Fig 2; R2 = 0.69). The range of thesensitivity to CH4 concentrations was 0.0012 to 0.006
nmol m-2 s-1 ppb-1for the four chambers. This suggeststhat methanotrophs in the soil were stimulated with rising
CH4 concentrations.
Future works: In this experiment, we could not rule out
a potential artifact by diffusion into the soil owing to nonsteady conditions of CH4 concentration profile within thesoil. The injection produced a sudden increase in CH4
concentration, which drove diffusion without sink bymethanotrophs. The artifact will be evaluated by solving
a non-steady diffusion model. Subtracting the artifact
would clarify the sensitivity of biological uptake to rising
CH4 concentrations.

Fig 1. CH4 fluxes measured by a chamber.
The black line represents fluxes without
injecting high CH4 gas, the blue circle
represents the flux when injecting the gas for
2 seconds, and the red circle represents theflux when injecting the gas for 1 second.

Fig 2. Relationship between increase in CH4
uptake and rising CH4 concentrations. The
increase in CH4 uptake is quantified as thedifference between the mean flux before andafter the injection of the high CH4
concentration and the flux during injecting
high CH4 gas.

Methane emission from the stem surfaces of Alnus japonica
Kenshi Takahashi1*, Ayaka Sakabe2,3* Wakana A. Azuma4*, Masayuki Itoh5, Tomoya Imai1,
Yasuki Matsumura1, Makiko Tateishi3, Yoshiko Kosugi3
Research Institute for Sustainable Humanosphere, Kyoto University
2
The Hakubi Center, Kyoto University
3
Graduate School of Agriculture, Kyoto University
4
Graduate School of Agricultural Science, Kobe University
5
School of Human Science and Environment, University of Hyogo*
Corresponding authors (e-mail: takahashi.kenshi.2v@kyoto-u.ac.jp, sakabe.ayaka.4a@kyoto-u.ac.jp,
wakana@port.kobe-u.ac.jp)
1

Atmospheric methane (CH4) is an important greenhouse gas; therefore, the identification of its source and its
quantification are crucial issues in addressing climate change. In recent years, emission of CH4 from the stems ofliving and dead trees has drawn considerable attention as a potentially important new source of atmospheric CH4(Carmichael et al., 2014; Barba et al., 2019a; Covey and Megonigal, 2019). Findings of experimental studies haveincreasingly suggested that tree-mediated processes can contribute significantly as a pathway of CH4 emission inwetland ecosystems (e.g., Terazawa et al., 2007, Pangala et al, 2017). However, the understanding of themechanisms by which CH4 is emitted from the stems of living trees remains ambiguous. For example, it is unclear
whether trees function as passive “pipes” through which CH4 is transported diffusively from the rhizosphere to theatmosphere, whether they serve as pipes in which xylem flow transports CH4 in the dissolved state, or whether
living trees produce CH4 in the heartwood.
The aim of the present study was to elucidate the mechanisms of CH4 transport inside wetland trees. Ourgroup has been investigating the process by which CH4 is emitted from the stems of Alnus japonica (Thunb.) Steud.
trees growing in a riparian wetland with a monsoon climate. In this talk, we report CH4 emission rates and theirseasonal variations based on year-round measurements using a closed-chamber method coupled with near-infraredlaser spectrometry for in situ CH4 detection (Sakabe et al., 2021). The rates of CH4 emission from the stems of A.
japonica trees exhibited maxima in summer and minima in winter, and this pattern was closely related to themethanogenic activity in the soil, as demonstrated by path analysis. Furthermore, hourly based measurements
conducted throughout the year enabled us to examine the diurnal properties of CH4 emission rates in detail. We
found CH4 emissions during the leafy season exhibited a diurnally changing component superimposed upon anunderlying continuum in which the diurnal variation was in phase with sap flux. We discuss the possible processes
giving rise to diurnal variation in the rates of CH4 emission from the stem surfaces of A. japonica, especially interms of the relation between the CH4 emission rates and sap flux (Takahashi et al., 2022).
In addition, in order to explore the mechanistic insights into stem CH4 emissions in the present study, fineroot sampled of A. japonica were collected and observed using optical and cryo–scanning electron microscopy(cryo-SEM). Previous optical microscopy studies have indicated that the pathway for gas exchange between theroots and aboveground environment is composed of the aerenchyma, a type of tissue comprising a relatively highproportion of spaces or lacunae (Takahashi et al., 2014). In our study, cryo-SEM was also used to elucidate the
distributions of water in the observed tissue at a given point in time (Azuma et al., 2016). Using these tools to
examine the root tissues of A. japonica could help elucidate the mechanisms by which CH4 is transported in fineroots, either in the gaseous form or dissolved in sap flow. Here, we report the identification of intercellular spacesthat could function as conduits for transporting CH4 molecules in the gaseous state.
References
Azuma, W. et al (2016) Trees 30, 141-152. doi: 10.1007/s00468-015-1283-3
Barba, J. et al. (2019) New Phytologist 222, 18-28. doi: 10.1111/nph.15582
Carmichael, M.J. et al. (2014) Biogeochemistry 119, 1-24. doi:10.1007/s10533-014-9974-1
Covey, K.R. and Megonigal, J.P. (2019) New Phytologist 222, 35-51. doi/10.1111/nph.15624
Pangala S.R. et al. (2017) Nature 552, 230-234, doi: 10.1038/nature24639
Sakabe, A. et al. (2021) J. Geophys. Res.: Biogeosci. 126, e2021JG006326. doi: 10.1029/2021JG006326
Takahashi, H. et al. (2014) Aerenchyma Formation in Plants. In: van Dongen, J., Licausi, F. (eds) LowOxygen Stress in Plants. Plant Cell Monographs, vol 21. Springer, Vienna. doi: 10.1007/978-3-7091-1254-0_13
Takahashi, K. et al. (2022) New Phytologist, 235, 1757-1766. doi: 10.1111/nph.18283
Terazawa, K. et al. (2007) Soil Biol. Biochem. 39, 2689-2692. doi: 10.1016/j.soilbio.2007.05.013

El Niño-Southern Oscillation forcing on carbon and water cycling in a
Bornean tropical rainforest
Naoya Takamura1, Yoshiaki Hata1, Kazuho Matsumoto2, Tomonori Kume3, Masahito
Ueyama4, and Tomo’omi Kumagai1,5,6*
1

Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657,
Japan
2
Faculty of Agriculture, University of the Ryukyus, Nishihara-cho, Okinawa 903-0213, Japan3
Shiiba Research Forest, Kyushu University, Shiiba-son, Miyazaki 883-0402, Japan
4
Graduate School of Agriculture, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
5
Institute for Space-Earth Environmental Research, Nagoya University, Chikusa-ku, Nagoya 464-8601 Japan
6
Water Resources Research Center, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, USA*
Corresponding author (e-mail: tomoomikumagai@gmail.com)
Inter- and intra-annual variations in the tropical forest canopy CO2 and H2O exchanges with the atmospherethrough photosynthesis, respiration, and evapotranspiration (ET), might be affected by ordinary seasonal rhythm
and sporadic events of tropical climate, influencing total carbon and water cycling in global and regional scales.
We assessed the effects of El Niño-Southern Oscillation (ENSO) on net ecosystem CO2 exchange (NEE) and ET
using a 10-year-long eddy-covariance (EC) flux observation, which includes some El Niño (EN) and La Niña
(LN) events, and the inverse EC flux model analysis to infer ecophysiological drivers, e.g., canopy stomatal
conductance (Gc) and maximum carboxylation rate (Vcmax25_BL), in a Bornean tropical rainforest. Analyzing thespectral properties of NEE and ET revealed their strong seasonality, although the study site has the least seasonalvariations in precipitation and air temperature worldwide. Also, this implied inter-annual variations in NEE andET, which ENSO might have induced. The frequency characteristics of the meteorological and ecophysiologicaldrivers correlated to those of NEE and ET. We found that while mean ET over EN, LN, and normal condition(NC) were equivalent, the mean NEE decreased in the order LN > NC > EN. Here, we could explain how Gc,
Vcmax25_BL, and atmospheric humidity altered the ET and the NEE on each ENSO condition. Further investigationof the mechanisms of the exchanges suggested that solar radiation and Vcmax25_BL was the primary controlling
factor for ET and NEE, respectively, on LN and NC, but Gc became more significant for both on EN. Further
investigation will include Thai tropical evergreen and deciduous forests to find more far-reaching environmental
control on Southeast Asian tropical forests canopy exchanges

Nitrate input to the river from atmospheric deposition observed inYakushima Island, the world natural heritage site
Ken’ichi Shinozuka1, Osamu Nagafuchi2*, Koyomi Nakazawa3, Urumu Tsunogai4
Fumiko Nakagawa4, Kenshi Tetsuka5, Natsumi Tetsuka5
1River Basin Research Center, Gifu University
2

3

Fukuoka Institute of Technology

Department of Environmental and Civil Engineering, Toyama Prefectural University
4

Graduate School of Environmental Studies, Nagoya University
5*

Yaku-shima Yakutane-goyo Research Group

Corresponding author (e-mail: yakushiama2006@gmail.com)

Introduction: Once nitrate (NO3-) emitted to the atmosphere, it may transport everywhere and depositedin terrestrial ecosystem. Several studies have been reported that the increased atmospheric NO3- deposition
affects the forests and oceans. Yakushima Is. is a mountainous island and located on leeward side of East
Asia Continent and is susceptible to the effects of nitrogen oxides from the atmosphere long-range transport.
More than 30 rivers flowing down the Yakushima Is. and making it a suitable research site for studying the
dynamics of NO3- deposition from the atmosphere. The measurement of Δ17O NO3 enables us to reveal theorigin of NO3- between atmospheres and biological metabolism production. Our object of present research
was to determine the proportion of atmospherically produced and biologically reproduced NO3- in stream
water flowing in Yakushima Is.
Materials and methods: The stream water samples were collected from 21 rivers in Yakushima in
December 2018. For the detailed survey, Kawara No.2 stream water samples which is located western areaof Yakushima Is. were collected every 100 m elevation from the summit of 1,300 m a.s.l. to the river mouthof 0 m a.s.l. in December 2018, January 2019 and September 2019. These samples were analyzed for
dissolved major ion and stable isotope ratios in NO3-.
Results and discussions：From the results of the k-mean method using the data set of major ion, Yakushima
Is. Rivers were classified into three groups: western forested areas, other rivers and groundwater. The NO3in each class were 20.8, 13.9 and 10.6 µmol L-1, respectively. The river water in the western forested area
had higher Δ17ONO3 values than other areas and more NO3- of atmospheric origin.
NO3- concentration observed in Kawara No.2 stream was 55.0 µmol L-1 at 1,000 m a.s.l. elevation andatmospheric origin NO3- concentration was also high in winter. Both Cl- and SO42- ions were high at 1,000
m a.s.l., where is prone to fog, suggesting that intense atmospheric deposition is occurring to the island area.
With the steep topography of the river, as seen in Kawara No.2 stream, the supply of atmospheric NO3- tocoastal areas were found to be high.

Possibility of permafrost degradation for the source of the great increase indissolved iron concentration in the Amur River basin during 1995–1997
Yuto Tashiro1*, Tetsuya Hiyama1, Hironari Kanamori3, Masayuki Kondo1
1

Institute for Space–Earth Environmental Research, Nagoya University*
Yuto Tashiro (e-mail: tassy40y@gmail.com)

From 1995 to 1997, a great increase in dissolved iron (dFe) concentration was observed in the Amur Riverand its tributaries. As a reason for this increase, permafrost degradation due to high air temperature in the 1990s
was hypothesized. However, the discharge mechanisms of dFe were hardly understood. In order to attack thisphenomenon, we studied the interannual variations in annual air temperature (Ta) and net precipitation(precipitation minus evaporation: P － E) in late summer (July, August, and September) from 1960 to 2000 in
the Amur River Basin, and then examined the relationship with long-term variation in dFe concentration for the
40 years. Relatively high Ta was found from 1988 to 1990, which was unprecedented continuous warm years.
More interestingly, cross correlation analysis showed a significant correlation with a 7-years lag between annual
dFe concentration and Ta (r = 0.43–0.55, p<0.01). This 7-years lag correlation was also found between the latesummer dFe concentrations and Ta (r = 0.54–0.69, p<0.01). It is also worth noting that correlation between dFe
concentration and Ta in the Russian Far East, which is partly underlain permafrost, showed the strongest
correlation rather than those in the other subbasins of the Amur River (i.e. Mongolia and China), indicating that
increase in dFe concentration in the Amur River is closely associated with warm Ta of 7 years ago in the Russian
Far East. Moreover, we found that net precipitation in the Amur River basin increased from 1977 and kept a highvalue for the next 20 years, which was significantly related to positive PDO (Pacific Decadal Oscillation) from1977 to 1997. Based on these findings, we provide the following hypothesis for the reason why dFe
concentration in the Amur River greatly increased during 1995–1997. (1) Since 1977, wetter soil conditions
continued due to increased net precipitation accompanied by positive PDO, which could be favor for microbial
dFe generation under an anaerobic condition. (2) Warm years from 1988 to 1990 progressed permafrost
degradation, leading to intensive dFe generation in deeper part of the active layer. (3) Generated dFe tookapproximately 7 years to reach rivers due to low permeability and then caused the great increase in riverine dFe
concentration during 1995–1997. Although it is necessary to make this hypothesis more certain, our results
emphasize the importance to investigate the climate factors for better understanding the changing arctic riverbiogeochemistry.

Generating a new land cover product across Siberia
by fusing global land cover datasets
Munseon Beak1*, Beichen Zhang1, Yuhei Yamamoto 1, Kazuhito Ichii1
1Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba
263-8522, Japan*

Corresponding author (e-mail: b.munseon100@gmail.com)

Siberia is one of the regions where temperature increases have been most pronounced in recent years due toclimate change. Thus, monitoring of the surface environment is strongly needed. Although land cover is one of
the most fundamental variables, large uncertainties still remain. For example, intercomparisons of available land
cover products revealed significant discrepancies among them, probably because of sparse validation datasetscompared with other regions. Thus, a reliable land cover product for Siberia is required. In this study, we createda new land cover product for Siberia based on the fusions of widely used global land cover products such as
Moderate Resolution Imaging Spectroradiometer (MODIS) product, Climate Change Initiative Land Cover(CCI-LC) data, and Copernicus Global Land Service (CGLS)) data. To validate land cover data, we used amulti-source validation dataset (Liu et al., 2019). First, we evaluated available land cover datasets using
validation datasets to check accuracy of each land cover product. We identified large variabilities in the accuracyof land cover datasets. Second, we applied three approaches, mode and two fuzzy approaches (e.g. Jung et al.
2006) to integrate existing land cover products. The resulting land cover products were further evaluated using
the validation dataset. Finally, the new fusion product of land cover in Siberia is obtained from the best resultsof the 3 methods. The resulting datasets based on the prototype model using three land cover data show that: (a)the land cover product made by the fuzzy 1 method has the highest overall accuracy (71.85%) in the Siberia
region, while ones for the mode method is 70.98% and the fuzzy 2 method is 71.49%; (b) the evaluation of theconsistency indicates that the forest, cropland, and snow/ice are satisfactory results, although the classificationof sparse vegetation and wetland may not be defined enough due to ambiguous boundaries. The new fusion
product obtained in this study provides a targeted reference for terrestrial environmental studies in Siberia, andprovides data support for exploring changes in vegetation and carbon cycle.

Terrestrial vegetation monitoring using Himawari-8 hyper-temporal land
surface temperature data
Yuhei Yamamoto1*, Kazuhito Ichii1, Youngryel Ryu2, Minseok Kang3, Shohei Murayama4,
and Su-Jin Kim5
1

Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba, 2638522, Japan.
2
Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
3
National Center for AgroMeteorology, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
4National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba West 16-1,
Onogawa, Tsukuba, Ibaraki, 305-8569, Japan.
5
National Institute of Forest Science, 57, Hoegi-ro, Dongdaemun-gu, Seoul 02455, Republic of Korea.
*
Corresponding author (e-mail: yamamoto_y@chiba-u.jp)

Land surface temperature (LST) is a key parameter of the surface energy, water, and carbon budgets at
various scales. The geostationary meteorological satellite Himawari-8 has improved temporal, spatial, andspectral resolutions, enabling high-frequency (10 min) LST estimation in Asia and Oceania regions. We havedeveloped different retrieval algorithms from Himawari-8 data (two nonlinear split-window algorithms and anonlinear three-band (NTB) algorithm) and validated it using in-situ LST at AsiaFlux and OzFlux sites, andintercomparison with the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station LST. The
validation results showed reasonable accuracy (~2.0 K in vegetated surfaces) for all algorithms, with the NTB
algorithm having the highest accuracy and the highest robustness to input error. In particular, the NTB algorithm
had ~1.0 °C higher accuracy than the other algorithms in regions with extremely high temperature, humidity, andviewing angle (e.g., Australia, Southeast Asia, and northern China). In this presentation, we will introduce thedeveloped Himawari-8 LST product and its applications. High-frequency LST data is expected to have uniqueapplications such as detection of vegetation drying (change in thermal inertia) and estimation of diurnal changes
in gross primary production and evapotranspiration.

Mapping Forest Canopy Height over Japan Using Multisource Remote
Sensing Data
Li Hantao1*, Kato Tomomichi2,3, Hayashi Masato4
1

Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Hokkaido, Japan
Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Hokkaido, Japan3
Global Center for Food, Land and Water Resources, Research Faculty of Agriculture, Hokkaido University,
Sapporo 060-8589, Hokkaido, Japan
4
Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), Tsukuba 305-8505, Ibaraki,
Japan
2

*

Corresponding author (e-mail: lihantao@env.agr.hokudai.ac.jp)

Forest is one of the most significant factors in the global carbon budget, storing a grant number ofterrestrial carbon in their biomass (Dong, Kaufmann et al. 2003). Accurate forest estimation is crucial tounderstand the mitigation of the global carbon budget, anthropogenic disturbance to forest degradation anddeforestation, and contemporary global land use change. Improved mapping of forest structure provides criticalinformation for such questions(Drake, Dubayah et al. 2002).Especially, the forest canopy height is considered as
the most commonly used attribute to measure forest structure(Hunter and Hunter Jr 1999), as it is always used asa predictor for calculation of relative forest criterions such as aboveground biomass (AGB), carbon storage (CS),
forest productivity and biodiversity(Lefsky, Cohen et al. 2002, Simard, Pinto et al. 2011, Zhang, Nielsen et al.
2016) Fine resolution mapping of forest canopy height map is crucial to understand terrestrial ecological system.
Global Ecosystem Dynamics Investigation (GEDI) mission provides high accurate spaceborne LiDAR data
globally to estimate forest structure attributes, while still leaves a high uncertainty on the accuracy disturbancefrom forest types and topographic factors. To tackle this research gap and obtain high accuracy modeling datafor country wide mapping, we collected airborne LiDAR data over Japan with 1273.80 km2 to calibrate GEDI
footprints in different forest types including deciduous broadleaf forest, deciduous needleleaf forest, evergreen
broadleaf forest and evergreen need leaf forest. The modified GEDI derived forest canopy height is thencomputed by weighted function to smooth the bias of data and used to be as modeling data for the probermapping framework. In our study, we mapped the forest canopy height in 25-m spatial resolution using
multisource remote sensing data and GEDI L2A data with high accuracy over Japan. The accuracy assessmentby airborne LiDAR with root mean scored error (RMSE) of 5.95 m and validated by GEDI data ranges from3.72 to 5.75 m stratified by 10 Japan’s ecozones. Our result explored the mean average forest canopy height inJapan holds 17.07 m ± 4.50 m, and the forest canopy heigh can be affected by several ecological factors. The
proposed machine learning based approach for mapping forest canopy height achieve higher accuracy comparedwith ground reference data with root mean squared error (RMSE) of 5.95 m. By obtaining and using extensive
airborne LiDAR over Japan, we explored that the different types of forest show individually relationship with
GEDI data. This intersected GEDI footprints are also compared with slope data and beam sensitivity to evaluate
the response between topographic and forest density to data accuracy. A novel framework is conducted to selectthe most accurate subset of GEDI data for utilizing as training data. We used the modified and accurate GEDI
data as training data to map forest canopy height over Japan in 25-m spatial resolution. The validated data ranges
from 3.72 to 5.75 m depends on ecozones showing a difference in areas. Our country wide forest canopy height
products then compared with the ecological data and explored the forest response to ecological factors. The
method may be further improved by upgrading spatial resolution and obtaining high quality satellite images. A
more extensive airborne LiDAR dataset over Japan is expected to obtain in the near future. The increasing
number of ground reference data may provide a more valid analysis for GEDI data in several directions.

Toward hyper-temporal terrestrial monitoring withnew generation geostationary satellites
Kazuhito Ichii1*, Yuhei Yamamoto1, Beichen Zhang1, Wei Li1, Wei Yang1, Atsushi Higuchi1,
Hiroki Yoshioka2, Masayuki Matsuoka3, Kenta Obata2, Hirokazu Yamamoto4
1

Center for Environmental Remote Sensing (CEReS), Chiba University, Japan
2
Aichi Prefectural University, Japan
3
Mie University, Japan
3
The National Institute of Advanced Industrial Science and Technology (AIST), Japan*
Corresponding author (e-mail: ichii@chiba-u.jp)
The new generation of geostationary satellites have suitable wavelength ranges for vegetation monitoring,
including the visible and near-infrared regions, and can make observations at high frequencies, such as once
every 10 minutes. Therefore, these are expected to play an important role in terrestrial vegetation monitoring.
Himawari-8 is the first new-generation geostationary satellite and observes the Asia-Oceania region every 10
minutes. These data sets will enable us to capture diurnal changes in vegetation and to make observations withhigher temporal resolution than ever before. Furthermore, the data sets will be useful for monitoring tropicalrainforests, which have been extremely difficult to observe by satellite because of their frequent cloud cover.
We initiated a new project, JSPS core-to-core program GEOLAND-NET to establish an international
collaboration network. We will establish a unified ultra-high frequency land surface data set every 10 minutes
across Asia, Oceania, and the Americas, centered on the state-of-the-art Himawari-8 meteorological satellite,
which Japan was the first country in the world to begin operating. In addition to satellite observations, we willconduct joint research covering in-situ observation networks and modeling communities to contribute to theestimation of the greenhouse gas budget of terrestrial ecosystems in Asia, Oceania, Europe, and the United States,
and to the future projection and countermeasures against climate change and weather disasters. This project will
foster young researchers and their networks that will lead terrestrial environmental monitoring toward carbon
neutrality. In this presentation, we introduce the current status of geostationary satellite-based land observationand our new project and its goals.

Key uncertainties in the uncertain world of biogeochemistry
Masayuki Kondo1*
1

Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Furocho, Chikusa Ward,
Nagoya, Aichi 464-8601 Japan*
Corresponding author: Masayuki Kondo (e-mail: redmk92@gmail.com)

Owing to the expanding network of atmospheric and field observations, the increasing availability ofsatellite remote sensing data, and the sophistication of process modeling and data assimilation, biogeochemical
studies have rapidly flourished in the past 30 years. Especially between the late 1990s and early 2000s, key
findings were made in today's biogeochemical studies, such as the increasing carbon uptake along with thegreening in the northern hemisphere, the increasing carbon emissions by land-use and land-cover changes(LULCC), and the periodic outbreak of massive tropical fire emissions. These findings are considered theestablished facts today, and various follow-up studies have been conducted over the last two decades.
Along with scientific findings in the past decades, new global and regional estimates of vegetation state andassociated carbon fluxes have been developed and become available to the science community. However, thisdevelopment has introduced another level of difficulty in biogeochemistry, as such inconsistent spatiotemporalvariability in multiple data. For instance, more than eight independent global products of Leaf Area Index (LAI)
are available, but with different global states on greening or browning of vegetation. Similar issues have been
identified in LULCC flux, and fire emissions as multiple data on those variables became available.
This presentation is aimed to have a comprehensive discussion about the plausibility of the past findings onvegetation greening, carbon emissions by LULCC and tropical fire, and how the science community should deal
with variability or inconsistency among the currently available datasets. Considering the cases of LAI, LULCC
flux, and fire emissions, extra caution should be taken when interpreting a global or regional state of LAI andcarbon fluxes using a particular individual product.

Projected climate change impacts on wheat production by process-basedmodel MATCRO-Wheat
Maki Nakaguki¹, Tomomichi Kato², Yuji Masutomi³
1

Graduate School of Global Food Resources, Hokkaido University
2
Research Faculty of Agriculture, Hokkaido University
3
National Institute for Environmental Studies*
Maki Nakaguki (e-mail: mn_hand_7962@eis.hokudai.ac.jp)

Climate change has a significant impact on crop yield and food security at the global scale. High temperatureby global warming has negative effects on wheat yield(Gourdji et al., 2013). Temperature rise causes inhabitationof vernalization and damages to grain filling, thus leading to yield reduction(Lobell et al., 2011). On the other
hand, high carbon dioxide concentration activates photosynthesis and improves nitrogen efficiency. Elevated CO
₂ increased the grain filling rate and final kernel weight, compared to under ambient CO₂(Li et al., 2001). Underthe risk that rapid population growth could cause food shortages in near future, therefore, many crop models havebeen developed to assess future impacts of climate change. For example, the decision Support System for
Agrotechnology Transfer (DSSAT) model(Jones et al., 2003) and Agricultural Production Systems
Simulator(APSIM) model (Keating et al., 2003) are widely used to simulate effects of climate on crop
growth(Asseng et al., 2013). However, such models simplify the physiological processes that affect crop growth
and are therefore uncertain for examining the effects of climate change.
This study aims to assess climate change impacts on wheat production in the future and geographically identify
key climate variables, by using a crop model, MATCRO-Wheat, recently developed based on MATCRO-Ricemodel(Masutomi et al., 2016). In MATCRO-Wheat, the detailed biochemical processes of photosynthesis (e.g.
Farquhar et al., 1980; Ball et al.,1987) are incorporated so that climate change impacts on wheat production canbe understood through the biochemical processes.
MATCRO-Wheat was run at global scale with the spatial resolution of 0.5 degree x 0.5 degree and with thesimulation period from 2015 to 2100 to associate this with global temperature and CO₂ changes from preindustrial
levels. We examined the relative yield change between 1980-2010(‘the baseline’) and 2071-2100(‘the future’).
Climate data and CO₂ data are derived from ISIMIP(The Inter-Sectoral Impact Model Intercomparison Project).
Socioeconomic assumptions (represented by the Shared Socioeconomic Pathway,SSP) are used to cause theuncertainty for the yield growth outlook. In the presentation, the preliminary results on key climate variables will
be presented.
Asseng, S., Ewert, F., Rosenzweig, C et al.: Uncertainty in simulating wheat yields under climate change, Nat.
Clim. Chang., 3, 827–832, https://doi.org/10.1038/NCLIMATE1916, 2013.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: Progress in Photosynthesis Research, Prog. Photosynth. Res.,https://doi.org/10.1007/978-94-017-0519-6, 1987.
Farquhar, G.D., von Caemmerer, S., Berry, J.A et al.: A Biochemical Model of Photosynthetic CO 2
Assimilation in Leaves of C 3 Species, Planta 149, 78-90 ,1980
Gourdji, S. M., Sibley, A. M., and Lobell, D. B. et al.: Global crop exposure to critical high temperatures in the
reproductive period: Historical trends and future projections, Environ. Res. Lett., 8,
https://doi.org/10.1088/1748-9326/8/2/024041, 2013.
Jones, J. W., Hoogenboom, G., Porter, C. H et al.: The DSSAT cropping system model, Europ. J. Agronomy 18
235-265, 2003
Keating, B. A., Carberry, P. S., Hammer, G. L et al.: An overview of APSIM, a model designed for farming
systems simulation, Europ. J. Agronomy 18 267-288, 2003
Li, A., Hou, Y., and Trent, A et al.: Effects of elevated atmospheric CO 2 and drought stress on individual grain
filling rates and durations of the main stem in spring wheat, Agricultural and Forest Meteorology, 289–301 pp.,
2001.
Lobell, D. B., Schlenker, W., and Costa-Roberts, J et al.: Climate trends and global crop production since 1980,
Science (80-. )., 333, 616–620, https://doi.org/10.1126/science.1204531, 2011.
Masutomi, Y., Ono, K., Mano, M et al.: A land surface model combined with a crop growth model for paddy
rice (MATCRO-Rice v. 1)–Part 1: Model description. Geoscientific Model Development, 9(11), 4133-4154.

Development of the process-based soybean growth model (MATCROSoybean)
Astrid Yusara1*, Tomomichi Kato2, Yuji Masutomi3, Elizabeth A. Ainsworth4, Rafael Battisti5,
Yushan Wu6, Etsushi Kumagai7, Satoshi Nakano8
1

Graduate School of Agriculture, Hokkaido University, Sapporo, Hokkaido, Japan
Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido, Japan
3
National Institute of Environmental Studies, Japan
4
USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL
5
Escola de Agronomia, Universidade Federal de Goiás, Brazil
6
College of Agronomy, Sichuan Agricultural University, China
7
Division of Agro-Environment Research Agricultural Meteorology Group, Tohoku Agricultural ResearchCenter, NARO, Japan
8
Division of Climate Change, Institute for Agro-Environmental Sciences, NARO, Tsukuba, Ibaraki, Japan*
Corresponding author (e-mail: astridyusara@env.agr.hokudai.ac.jp)
2

Soybean is one of the world’s most important agricultural commodities that provides resources of protein
and oil. Most of the soybean products are used for animal feed, and about lower than 25% are for industry anddirect human consumption. It is expected that soybean consumption will grow higher in the future. However,climate variability and environmental degradation will lead to agricultural yield loss in some vulnerable areas.
Global projections and impact assessments of crop production are needed to prepare for national/regional
disasters from the food loss hazard, which will help to minimize the damage to agricultural investments in varied
conditions. Hence, an accurate estimation of global soybean production is necessary for terms of providing gooddata for food supply chains.
Current crop simulation models have been widely used to understand the effect of environmental factors oncrop growth and development. This study applied a process-based crop growth model MATCRO (Masutomi etal., 2016) with the present climate data to globally simulate soybean production (MATCRO-Soybean).
MATCRO was initially developed for rice crops and allows the crop growth model to simulate different cropparameters that can be easily changed including different specific leaf nitrogen content for soybean as anitrogen-fixing crop. Soybean and Rice are C3 plants that have a similar photosynthetic process.
This study simulates specific leaf nitrogen on soybean that affects the photosynthesis process to estimate
the soybean yields and calibrated parameters with field-scale data with different varieties in Brazil (2013-2014),
Japan (2013-2017), China (2014-2016), and the United States (2002-2007). The varieties used were Pioneer
93B15, Ryuhou, Jiuyuehang, BRS 284, Nandou12, and Texuan13 which were classified into different relative
maturity groups (3, 4, 5, 6.5, 6.5, and 7 respectively). A variety is classified into a specific relative maturity
group based on the length of time from planting to maturity that is determined by photoperiod and temperature ina particular geographic location. Different maturity groups and environmental conditions used in this study are
expected to represent model capability in capturing varied phenological conditions.
The objectives of this study are to 1) develop a soybean growth version of MATCRO, 2) validate
observational soybean yields, aboveground biomass, and LAI measured at four countries, and 3) represent global
soybean production. The developed model is a flexible tool that is coupled with climate models and has the input
of climate factors, nitrogen fertilizer, crop calendar, soil texture, and carbon dioxide concentration. The
development of this process-based crop growth model for soybean will support further analysis of soybean
products and the climate change effect in the future. Hence, it is expected that further analysis regarding theeffects of environmental changes (e.g. excessed water, drought, and nitrogen dynamics) on soybean yields can berecognized to provide a comprehensive study of the global soybean yield model.

Predicting dominant terrestrial biomes at a global scale:
Assessments of machine learning algorithms, climate variables indexing,
and extreme climate
Hisashi SATO*
Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC)*
Corresponding author (e-mail: hsatoscb@gmail.com)

A biome is a major regional ecological community characterized by distinctive life forms and principal plants.
Biome distribution is specifically useful for estimating land potential and raising public awareness about landchange. At a global scale, biome distribution is largely determined by climatic conditions, while biome
distribution is feedback to climate via biophysical and biochemical ways. Therefore, biome distribution is also
important for climate projection.
Modeling biome distribution is a classic biogeographical subject, and many methods have been proposed so far(reviewed by Sato & Ise 2022). In this research area, climate data, such as monthly mean temperature andmonthly precipitation, was typically summarized as small numbers of climate indices, such as annual
precipitation and coldest month mean temperature, and constructed models driven by these indices.
However, with the availability of machine learning algorithms such as decision trees and random forests inrecent years, the restrictions on the amount of data used in model building have been relaxed, and it is nolonger essential to summarize environmental data into climatic indices. For example, Hengl et al. (2018) used a
total of 160 types of environmental variables, such as soil and topography, as well as non-indexed climatic
values, such as monthly precipitations and monthly averaged temperatures, in addition to the climate index, tobuild an empirical model of biome distribution using machine learning algorithms. Besides, increasing thenumber of variables in models entails costs such as a decrease in model adaptability and an increase in the
demands for computational power, so it would also be important to employ only a small number of variables
that are selected in a parsimonious way.
On the other hand, from the perspective of plant physiology and ecology, the intensity of incidents such as
droughts and low temperatures that occur once every ten years (hereinafter referred to as the extreme climate)is a major factor limiting the biome boundaries. Indeed, Beigaite et al. (2022) reported that employing extreme
climate indices in the model in addition to the typical (average) climate indices increases the accuracy of thedecision tree model.
Here, I compared four machine learning algorithms for accuracy to model relationships between the global
biome distributions and climate characteristics. I also assessed the influence of converting monthly
precipitation and average air temperature (24 variables) into 16 climatic indices on model accuracy. Finally,
the influence of the inclusion of extreme climate indices on modeling accuracy was also assessed.
References
Beigaite, R. et al. (2022) Global Change Biology 28(11): 3557-3579
Hengl, T. et al. (2018) Peerj 6
Sato, H. and Ise, T. (2022) Geoscientific Model Development 15(7): 3121-3132

Characteristics of Synoptic-Scale Waves in the Northern Eurasian Storm
Track during Summer
Yoshiki Fukutomi1*, Tetsuya Hiyama1
1

Institute for Space-Earth Environmental Research ,Nagoya University*

Y. Fukutomi (e-mail: fukutomi@hyarc.nagoya-u.ac.jp)

Synoptic-scale baroclinic waves have been regarded as a primary rain-bearing system which accompanies
development of extratropical cyclones and fronts in the high-latitude continental regions. This study
examines the dominant structure and characteristics of synoptic-scale (2−8-day periods) waves overnorthern Eurasia during the 40 summer seasons (JJA 1979−2018). Main focus is on the dynamical
characterization of the synoptic-scale waves that are responsible for the day-to-day weather variability in
Siberia. The synoptic-scale wave patterns are isolated by using an extended emphatical orthogonal function(EEOF) analysis and a composite based on global atmospheric reanalysis (JRA-55) and gridded gaugesatellite precipitation product (MSWEPv2.8). The resulting wave patterns are classified into two types fromtwo pairs of EEOF modes of the synoptic-scale variability in the 300-hPa geopotential height anomalies.
These two different wave types are defined as polar frontal (PF) type and Arctic frontal (AF) type,respectively. The PF (AF) type is represented by the first (second) EEOF pair. The EEOF-based composite
analysis distinguishes between these two types of synoptic-scale waves that have different propagation
pathways and background conditions. The PF-type waves initiate in the North Atlantic sector to the west of
the British Isles. They propagate eastward across Siberia into the North Pacific, and they produce rain
mainly over the climatological Eurasian polar frontal zone along the latitudes of 50˚−65˚N. They exhibit astrong zonal wave train pattern with zonal wavenumber 7−8 scale and eastward phase speed at about 6 m/s.
The AF-type wave train arcs along the climatological Arctic frontal zone. The AF-type waves originate in
the vicinity of the Scandinavian Peninsula, and propagate eastward at about 7 m/s with zonal wavenumber
4−5 scale. Their passage brings rain in the latitudes of 60˚−80˚N around the climatological Arctic frontal
zone. The development of the synoptic-scale waves is shown to be reflected by unique backgroundconditions over Northern Eurasia. Lower-tropospheric baroclinicity in the south of Siberia and central Asia
favors baroclinic growth of the PF-type waves. The AF-type waves are trapped in the well-organizedbaroclinic zone along the north coast of the Eurasian continent. The baroclinic zone is coupled with a bandof strong positive gradient of potential vorticity, suggesting that this band acts as a waveguide for the AFtype waves.

Ammonia volatilization simulated by global biogeochemical models
Akihiko Ito 1,2,3,4*
2

1
National Institute for Environmental Studies
Japan Agency for Marine-Earth Science and Technology
3
Nagoya University
3
Tohoku University*
Corresponding author (e-mail: itoh@nies.go.jp)

Ammonia (NH3) is a form of reactive nitrogen and plays an important role in the biogeochemical nitrogen
cycle. Volatilization from terrestrial ecosystems is major source of NH3 to the atmosphere, especially fromcroplands receiving a plenty of chemical fertilizer and manure. However, our quantification of NH3 volatilizationis still uncertain, because less attention has been paid to the process in comparison with greenhouse gases. In thisstudy, I examined the spatial and temporal variations in the NH3 volatilization simulated by global terrestrial
biogeochemical models.
First, I examined the simulation results by a process-based model, VISIT (Vegetation Integrative SImulator
for Trace gases [1]). In the model, NH3 volatilization rate is estimated as a function of soil temperature, watercontent, pH, and ammonium content. Global annual NH3 volatilization in 2010–2019 was estimated as 86.3 Tg
NH3-N yr–1; this is about triple of that in 1901, 28.2 Tg NH3-N yr–1. The increase of NH3 volatilization wasalmost in parallel with the increase of fertilizer input (R2 = 0.989). Strong emissions occurred in central tonorthern China, India, western Europe, and central North America, where were occupied by croplands (Fig. 1).
In the Nitrogen Model Intercomparison Project (NMIP [2]), at least five more models estimate NH3volatilization: DLEM, ISAM, LPX-Bern, ORCHIDEE, and LM3VN. In the presentation, I would like to show
several preliminary results of comparison between the models, in terms of annual flux, seasonal change, spatial
distribution, and emission factors.

Fig.1 Distribution of annual NH3 volatilization (kg NH3 m–2 s–1) simulated by VISIT during 2010–2019.
References[1] Ito, A., K. Nishina, K. Ishijima, S. Hashimoto, and M. Inatomi (2018), Emissions of nitrous oxide (N2O)
from soil surfaces and their historical changes in East Asia: a model-based assessment, Progress in Earth andPlanetary Science, 5, 55. https:/doi.org/10.1186/s40645-018-0215-4[2] Tian, H., et al. (2018), The global N2O Model Intercomparison Project, Bulletin of the American
Meteorological Society, 99, 1231–1251. https:/doi.org/10.1175/BAMS-D-17-0212.1