第 26 回 CEReS
環境リモートセンシングシンポジウム
資料集
Proceedings of the 26th Symposium on
Remote Sensing for Environment

2024 年 2 月 15 日
ハイブリッド形式（千葉大学＆Zoom）
千葉大学環境リモートセンシング研究センター
Center for Environmental Remote Sensing (CEReS)
Chiba University

The 26th CEReS Environmental Remote Sensing Symposium
Hybrid: Keyaki Kaikan, Chiba University & Zoom

ver. 20240214

会期： 2024 年 2 月 15 日（木）形式： ハイブリッド形式
千葉大学西千葉キャンパスけやき会館 & Zoom（※）※Zoom 接続情報はシンポジウム直前に参加者に別途メールでお知らせします。
※Zoom connection information is emailed to participants before the symposium.
口頭発表
・1 件あたりの発表時間は 15 分（発表 10 分＋質疑 4 分＋予備 1 分）です。
・発表はご自身の PC をプロジェクタに接続して行います。PC に HDMI 端子が必要です。
必要に応じて変換コネクタなどをご持参ください。
・現地設置の PC でも発表できるよう、念のため、プレゼンテーションのファイルをご準備ください。
・ご自身の PC の準備が難しい場合は、事前に CEReS 共同利用研究推進委員会にご連絡ください。
CEReS 共同利用研究推進委員会： kyoudo@ceres.cr.chiba-u.ac.jp
・発表前の休憩時間などに、PC とプロジェクタの接続確認を推奨します。
・スライドは日本語でも英語でも構いません。
・発表では powerpoint のレーザーポインター機能の利用を推奨します。ただ、現地のレーザーポイン
ターも使用できます。ただし、オンライン参加者には見えない点、ご留意ください。
ポスター発表・ポスターボードのサイズは、900 mm（横） × 1800 mm（縦） です。
・日本語でも英語でも構いません。
・ポスターは終日の掲示を推奨します。
・ポスターセッションのコアタイムはプログラムをご確認ください。
・ポスターの固定に必要なピン等は CEReS 側で準備いたします。
その他
・進行は現地の状況を優先します。オンラインで不具合が生じた場合はご了承ください。
・学内のセキュリティのため、会場での Wi-Fi の提供はできない見込みです。
・発表後に発表資料をメールで収集し、資料集とさせていただく予定です。その際、資料集として公開に適さない部分は削除していただいて構いません。発表で使った資料を 1 スライド 1 枚の PDF ファイ
ルでご提供ください。ポスターは PDF に変換した形でご提供ください。ご協力のほど、よろしくお願いいたします。
・懇親会参加費（5,000 円/人）はシンポジウム受付にて集金いたします。お釣りのないようにご準備い
ただくようお願いいたします。
The participation fee for the social gathering will be collected at the symposium reception.
Please have exact change (5,000 JPY) ready.

■February 15, 2024 (Thr)please use your laptop PC for your talk.
presentation: 10 min. Q&A: 4 min.

UTC JST
00:30 09:30 Welcome
服部克巳 (CEReS/Chiba-U)
00:35 09:35 Logistics
入江仁士 (CEReS/Chiba-U)

災害・マイクロ波リモートセンシング [Chair: ヨサファット ＆ 柏原久人]
00:40 09:40 Pre- and Co-seismic Ionospheric Disturbances of the 1 January 2024 Noto
Peninsula M7.6 Earthquake*Tiger Jann-Yenq Liu, Katsumi Hattori, Chia-Hung Chen, Chi-Yen Lin, Tie-Chi
Liu, Yun-Cheng Wen, Fu-Yuan Chang, Chia-Wei Chang, Charles Chien-Hung
Lin, and Yuh-Ing Chen
00:55 09:55 [online] Pre-earthquake signatures associated with Turkey on February 6th,
2023, revealed with the multi-parameter ground and space observation*Dimitar Ouzounov, Sedat Inan, Pavel Kalenda, Sergey Pulinets, J.Y. Liu,
Xuhui Shen, Rui Yan, Katsumi Hattori, Jana Rušajová, Menas C. Kafatos, andPatrick Taylor
01:10 10:10 [online] Coastal Land Deformation of Palu City: City Above Two Continental
Plate Subduction Zone
*M. Helmi, A. Hartoko, and Hariadi
01:25 10:25(Break)

陸域・炭素循環 [Chair: 市井和仁 ＆ 山本雄平]
01:40 10:40 3 次元レーザーによる森林内空間の把握
加藤顕, 青柳寛太郎, 若林日菜, 早川裕弌, 蝦名益仁, 堤田成政, 笠井美青
小倉拓郎
01:55 10:55 UAV データ解析による巨大前方後円墳の周濠探査*光本順, ライアン・ジョセフ, 清家章, 山口雄治, 久世宏明, 本郷千春
02:10 11:10 気候モデル数値実験結果による衛星プロダクト導出アルゴリズムの検証―全球バイオマス量変動監視・解析に向けて（その５）―*馬淵和雄, 曽山典子, 梶原康司, 本多嘉明
02:25 11:25 [online] トップダウン・ボトムアップ手法による日本全国での樹木による炭素固定量の原単位の推定*平林聡, 加藤顕, 市井和仁
02:40 11:40 [online] 航空レーザ測量データ（ALB）を活用した河道内樹木の解析*周月霞, 戸田祐嗣
02:55 11:55 [online] GMS/VISSR からの LST 推定のための AVHRR/LST データの整備*森山雅雄
03:10 12:10(Lunch)04:10 13:10
to
05:10 14:10

Poster Core Time

05:15 14:15

Group Photo (@1st floor)

onsite participants only

onsite participants only

CEReS の新任教員の紹介 [Chair: 樋口篤志 ＆ 竹中栄晶]
05:30 14:30 （TBD）*岡﨑淳史 (CEReS/Chiba-U)
05:45 14:45 （TBD）*山本雄平 (CEReS/Chiba-U)
大気・放射伝達 1 [Chair: 金子凌 ＆ 岡﨑淳史]
06:00 15:00 [online] A hybrid cloud detection and cloud phase classification algorithm
using classic threshold-based tests and extra randomized tree model*Huazhe Shang, Husi Letu, Xu Ri, Wei Lesi, Laixiong Wu, Jiangqi Shao,
Takashi M. Nagao, Takashi Y. Nakajima, Jérôme Riedi, Jie He, and Liangfu
Chen
06:15 15:15 [online] A precipitation estimation method by considering clouds
microphysical parameters based on observations from geostationary
satellite
Dabin Ji, *Husi Letu, Huazhe Shang, and Jiancheng Shi
06:30 15:30 3D effects of surface inhomogeneity on radiance of ground- and satellitebased observations*Masahiro Momoi, Oleg Dubovik, Pavel Litvinov, and Christian Matar
06:45 15:45 乱気流を伴うトランスバースバンドの特徴*藤田友香, 渡来靖, 工藤淳(Break)07:00 16:00

大気・放射伝達 2 [Chair: 齋藤尚子 ＆ Belikov Dmitry]
07:15 16:15 中赤外スーパーコンティニューム光源を利用した大気中 CO2 の計測*染川智弘, 眞子直弘, 倉橋慎理, 松田晶平, 余語覚文, 椎名達雄, 久世宏明
07:30 16:30 ゴビ砂漠におけるダスト消散係数－質量変換係数（MECF）の研究*甲斐憲次, 河合慶, 神慶孝, 椎名達雄, E. Davaanyam
07:45 16:45 [online] Classification of air pollution levels in urban areas using satellites
imagery – low-cost GNSS and machine learning techniques*Mokhamad Nur Cahyadi, Failaqul Haq, and Agus Budi Raharjo
08:00 17:00 [online] Validation of satellite GOME-2(A/B/C), OMI, TROPOMI and GEMS
NO2 and HCHO data with MAXDOAS data
*Gaia Pinardi, Hitoshi Irie, Steven Compernolle, Tijl Verhoelst, Isabelle De
Smedt, Bavo Langerock, Jean-Christopher Lambert, and Michel Van
Roozendael
08:15 17:15 次年度の公募について
入江仁士 (CEReS/Chiba-U)
08:20 17:20 閉会の挨拶
市井和仁 (CEReS/Chiba-U)
09:30 18:30
to
11:30 20:30

Social Gathering @ エドモンド（ＪＲ千葉駅南口より徒歩約 7 分）
https://www.hotpepper.jp/strJ003625364/map/
この案内の最後に掲載の地図を参照ください。

※懇親会参加費 5,000 円/人は、シンポジウム受付にて集金します。
お釣りのないようにご準備いただくようお願いいたします。
The participation fee for the social gathering will be collected
at the symposium reception. Please have exact change ready.
(5,000 JPY)

■Postercancel

P-01 Satellite Image based Disaster Damage Detection and Severity Analysis using Deep
Convolutional and Generative Adversarial Neural Networks*Tasnim Ahmed
P-02 深層学習に用いる解像度と評価に用いる解像度の関係*横山洋斗, 高橋徹
P-03 レーダー雨量と衛星データを利用した豪雨検知ディープラーニングモデル*金子凌, 坂内匠
P-04 Sentinel-1 データを使用した機械学習による浸水被害領域抽出精度評価*若林裕之, 五十嵐貴大
P-05 ひまわり八号の海面温度による海洋熱波の高精度検出*肖琦, 楊偉P-06 Monitoring active volcanoes by means geostationary satellite observations*N. Genzano, A. Falconieri, K. Hattori, F. Marchese, N. Pergola, and V. Tramutoli
P-07 ひまわり AHI データを用いた火山溶岩活動の監視：溶岩噴出前の地表面温度の時空間変動*北出明嗣, 金子柊, 吉野千恵, 服部克巳
P-08 ひまわり 8/9 号エアロゾルプロダクトを用いた陸域大気補正アルゴリズムの検討*山本浩万, 入江 仁士
P-09 Utilizing geostationary satellite data for ultra-short-term forecasting of solar
irradiance by a multivariate Long-Short-Term Memory Recurrent Neural Network*Nifat Sultana, and Narumasa Tsutsumida
P-10 衛星リモートセンシングを用いたソーラーパネル検出アルゴリズムの検証・改善*植田晴
P-11 奈良盆地における全天日射量とエアロゾルの関係
門村芙実, 菊池咲也花, 田中伶奈, 横谷愛美李, *久慈誠
P-12 地上光学観測による奈良と仙台のエアロゾルの比較*菊池咲也花, 横谷愛美李, 田中伶奈, 門村芙実, 久慈誠
P-13 IoT ネットワークを用いた局所的環境モニタリングシステムの開発*佐藤祐輝, 小室信喜
P-14 大気汚染常時監視局測定値の準リアルタイム更新・格子点化データ作成*早崎将光, 入江仁士, 樋口篤志
P-15 福岡都市圏における窒素酸化物の時空間不均質性
高島 久洋, *乙部 直人
P-16 上部対流圏の二酸化炭素変動について*江口菜穂, 齋藤尚子
P-17 3 次元森林空間情報と音情報の関係解析*笠原真珠, 加藤顕, 澤田義人

P-18 森林内空間パターンと微気象との関係*青柳寛太郎, 加藤顕, 中島史雄, 瀬戸口浩彰
P-19 地上レーザーを用いた樹木の傾きと土塊の関係*若林日菜
P-20 Estimation of Canopy Clumping Index Using Himawari Satellite Land Surface
Reflectance Data
*喬治, 楊偉
P-21 静止気象衛星ひまわり 8 号画像における位置誤差検出手法の検討*平間達也, 島﨑彦人, 山本雄平, 市井和仁
P-22 気象衛星ひまわりの観測データを利用した GAN による風景画像の生成について*宮﨑貴大
P-23 シミュレーションモデルとリモートセンシングを用いた作物生産量推定法の検討 第 7 報 RGB
画像を用いた水稲収量推定ツールの農家圃場における試用*鈴木遥夏，茨島有吾，田中佑，本郷千春，本間香貴
P-24 衛星画像およびドローン画像を用いて推定した水稲圃場の生育ムラの差異の評価*牧雅康, 本郷千春
P-25 UAV 搭載を目指した分光イメージャーのデータ取得と評価方法について*大前宏和, 三宅俊子, ヨサファット
P-26 マイクロ波円偏波を用いた移動型リモートセンシング方式の研究*瀧澤由美, 深澤敦司, Cahya Edi Santosa, Josaphat Tetuko Sri Sumantyo
P-27 Cooley Tukey アルゴリズムの SIMD 並列化*山崎進
P-28 UAV 搭載 CP-SAR 画像処理システムの ZYBO ZYNQ-7020 への実装
*大塚祐生, 難波 一輝, Josaphat Tetuko Sri Sumantyo
P-29 容量性円形平板アンテナを用いた LF 帯広帯域干渉計の開発と単素子で観測された LF 帯電
波の特徴*太田悠一朗, 三浦健伸, 吉野千恵, 服部克巳, 今住則之P-30 FMCW 方式を用いた HF ドップラー観測システムの開発*中田裕之, 細川敬祐, 藤本晶子, 野崎憲朗, 並木紀子, 坂井純, 冨澤一郎, 有澤豊志
P-31 Web-GIS 型エネルギー・環境評価ツールを用いた ARIE/kLAB Japan モデルの紹介*林希一郎, Stefano Balbi, 長島匠
P-32 火星気象センサ開発に向けたフィールド試験*乙部直人, 椎名達男, 千秋博紀, はしもとじょーじ, 岩山隆寛
cancel

P-33 (TBD)*Roxana Hoque■Poster from CEReS/Chiba-U students and staffs

ポスター番号は決めません。
空いているスペースに掲示してください。

P-xx コロナ禍を含む長期連続観測による日本の大気境界層中の二酸化硫黄濃度変動の要因解明*米谷颯太, 入江仁士
P-xx 衛星画像を用いた湖沼の水生植物繁茂のモニタリングとメカニズムの解明*小田理人、楊偉
P-xx Quantifying Anthropogenic Impacts on Global CO2: A Robust Isotopic ModelingApproach* Uddalak CHAKRABORTY, Naoko SAITOH, Prabir PATRA, Dmitry BELIKOV, Naveen
CHANDRA
P-xx Monitoring of Global Vegetation Phenology Using GCOM-C SGLI Satellite Data
* 張淋寧, 李夢禹, 楊偉
P-xx 全偏波 UAV-SAR 用 X バンド円偏波マイクロストリップアレーアンテナの開発* 柏原久人, Josaphat Tetuko Sri Sumantyo, 泉佑太, 伊藤公一, S. Gao, 難波一輝
P-xx アンサンブルデータ同化を用いた雨量計からの降水場復元* 塩尻大也, 武藤裕花, 岡﨑淳史, 小槻峻司
… more additional posters from CEReS/Chiba-U students and staffs - enjoy!!!

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February 15th, 2024. CHIBA University JapanŽĂƐƚĂů>ĂŶĚĞĨŽƌŵĂƚŝŽŶŽĨWĂůƵ ŝƚǇ͕/ŶĚŽŶĞƐŝĂ͗dŚĞŝƚǇĂďŽǀĞdǁŽŽŶƚŝŶĞŶƚĂůWůĂƚĞ^ƵďĚƵĐƚŝŽŶŽŶĞDƵŚĂŵŵĂĚ,>D/͕ŐƵƐ ,ZdK<K͕ZŝǀĂE͘Z͘E<͕ĂŶĚ,ĂƌŝĂĚŝ
Email : muhammadhelmi69@gmail.com; agushartoko.undip@gmail.com

Faculty of Fisheries and Marine Science

1. Intruduction WĂůƵ Ăƚ ĞůĞďĞƐ /ƐůĂŶĚ͕ /ŶĚŽŶĞƐŝĂ ŝƐ Ă ĐŝƚǇ ĂďŽǀĞ ƚŚĞƐƵďĚƵĐƚŝŽŶ ǌŽŶĞ ŽĨ ƚŚĞ ƵƌĂƐŝĂ ĂŶĚ WĂĐŝĨŝĐ ƚĞĐƚŽŶŝĐ ƉůĂƚĞ͘ dĞĐƚŽŶŝĐ ƉůĂƚĞ ĞĂƌƚŚƋƵĂŬĞ ŚĂƉƉĞŶĞĚ ŝŶ ^ĞƉƚĞŵďĞƌ ϮϴƚŚ ϮϬϭϴ͕ŵĂŐŶŝƚƵĚĞ ϳ͘ϰ ĚĞƉƚŚ ϭϬŬŵ͕ ĂŶĚ ĐĂƵƐĞ ŽĨ ůĂŶĚͲƐůŝĚĞ ĂŶĚůŝƋƵĞĨĂĐƚŝŽŶ ǌŽŶĞ͘ ,ƵŵĂŶ ĚĞĂĚ ǀŝĐƚŝŵ ϭ͕ϮϬϬ͖ ƐĞǀĞƌĞůǇ ǁŽƵŶĚĞĚ ϱϬϬ͖ Ϯϵ ƉĞŽƉůĞ
ůŽƐƚ͖ ϮϬϱϬ ĚĂŵĂŐĞ ŚŽƵƐĞƐ͖ ϳϰϰ ƐƵďƐŝĚĞŶĐĞ ŚŽƵƐĞƐ͘ /ŶƚĞŐƌĂƚŝŽŶ ŽĨ ĂŶĂůǇƐŝƐ ŽĨ ^Z ƐĂƚĞůůŝƚĞ ŝŵĂŐĞƌǇ͕ ŽƉƚŝĐĂů
ƐĂƚĞůůŝƚĞ ĚĂƚĂ ŝŵĂŐĞƌǇ͕ ĂŶĚ D ĚĂƚĂ͕ ŚĂƐ ƚŚĞ ĂďŝůŝƚǇ ƚŽŽďƐĞƌǀĞ ƚŚĞ ƐƵďĚƵĐƚŝŽŶ ǌŽŶĞ ĂŶĚ ƵƌďĂŶ ƉŽƐŝƚŝŽŶƐ ŝŶ WĂůƵŝƚǇ͕ ĐŽŶĚŝƚŝŽŶƐ ďĞĨŽƌĞ ĂŶĚ ĂĨƚĞƌ ůŝƋƵĞĨĂĐƚŝŽŶ͕ ĂŶĚĚĞĨŽƌŵĂƚŝŽŶ ĚƵĞ ƚŽ ƚŚĞ ĞĂƌƚŚƋƵĂŬĞ ŽŶ ^ĞƉƚĞŵďĞƌ ϮϴƚŚ ϮϬϭϴ͘Dynamics of the Tectonics Plate and the Formation of Celebes Island

55

Eurasian Plate

30

Eurasian Plate

Pacific Plate

Pacific Plate

Philiphine
Plate
Philiphine
Plate
Hindia-Aurtralia
PlateHindia-Aurtralia
Plate,Ăůů͕Z͕͘ϮϬϬϭ Celebes Island is thelocation where dynamic
tectonic plates meet eachother among the Eurasianplate, Hindia-Australia,
Philippine and Pacificplates.

Eurasian Plate

1.1. Objective dŽ ƐƚƵĚǇ ƚŚĞ ƉŽƐŝƚŝŽŶ ŽĨ ƚŚĞ ƐƵďĚƵĐƚŝŽŶ ǌŽŶĞ ĂŶĚ ƐƉĂƚŝĂů
ĚŝƐƚƌŝďƵƚŝŽŶ ŽĨ WĂůƵ ŝƚǇ ďĂƐĞĚ ŽŶ ƚŚĞ ŝŶƚĞŐƌĂƚŝŽŶ ŽĨ DĚĂƚĂ ĂŶĚ ^Z /ŶƚĞƌĨĞƌŽŵĞƚƌǇ ĂŶĂůǇƐŝƐ͘ dŽƐƚƵĚǇƚŚĞŝŵƉĂĐƚŽĨƚŚĞĞĂƌƚŚƋƵĂŬĞ;ŽŶ^ĞƉƚĞŵďĞƌϮϴϮϬϭϴͿǁŚŝĐŚĐĂƵƐĞĚůŝƋƵĞĨĂĐƚŝŽŶďĂƐĞĚŽŶĂŶĂůǇƐŝƐŽĨŽƉƚŝĐĂůƐĂƚĞůůŝƚĞŝŵĂŐĞƌǇĂŶĚĚƵĂůƉŽůĂƌŝǌĂƚŝŽŶ^Z͘ dŽƐƚƵĚǇƚŚĞĂƌĞĂŽĨĚĞĨŽƌŵĂƚŝŽŶĚƵĞƚŽĞĂƌƚŚƋƵĂŬĞƐďĂƐĞĚŽŶ^ZŝĨĨĞƌĞŶƚŝĂů/ŶƚĞƌĨĞƌŽŵĞƚƌǇĂŶĂůǇƐŝƐ͕ĂŶĚƵŶĚĞƌƐƚĂŶĚƚŚĞƉŽƚĞŶƚŝĂůĨŽƌĞĂƌƚŚƋƵĂŬĞĂŶĚůŝƋƵĞĨĂĐƚŝŽŶŚĂǌĂƌĚƐŝŶWĂůƵ ŝƚǇŝŶƚŚĞĨƵƚƵƌĞďĂƐĞĚŽŶůŽŶŐͲƚĞƌŵŚŝƐƚŽƌŝĐĂůƐĞƌŝĞƐĚĂƚĂŽĨĞĂƌƚŚƋƵĂŬĞĞǀĞŶƚƐ͘Indonesia

1.2. Study Area
 Palu City, Celebes Island,
Indonesia
 The Capital city of Central
Sulawesi Province,
 Area coverage around
395 m2 at the Alluvial land
 Total population 288,944
people (Statistics Office,Palu City, 2023)

Palu City

2. Methoda. Data D ϯ ƌĐ͘^ĞĐ͘ ϯϬ ŵĞƚĞƌ ;^ZdD D͕ h^'^ ϮϬϮϰͿ
 ,ŝŐŚZĞƐ͘^ĂƚĞůůŝƚĞ/ŵĂŐĞĂƋ͘&Ğď͘ϮϬƚŚ͕ϮϬϭϴĂŶĚKĐƚ͘ϮŶĚ͕ϮϬϭϴ͘ ^ĞŶƚŝŶĞůͲϭ^Z;^ǇŶƚŚĞƚŝĐƉĞƌƚƵƌĞZĂĚĂƌͿĂƋ͘DĂƌĐŚϭϯƚŚ͕ϮϬϭϳĂŶĚKĐƚ͘ϮϮƐĚ͕ϮϬϭϴ dŝŵĞ ^ĞƌŝĞƐ ,ŝƐƚŽƌŝĐĂůĂƌƚŚƋƵĂŬĞ;h^'^͕ϮϬϮϯͿ 'ĞŽůŽŐǇ ĚŝŐŝƚĂůŵĂƉ͕ϭ͗ϭϬϬ͘ϬϬϬ ƐĐĂůĞ;^D͕ϮϬϭϬͿ

b. MethodD ĚĂƚĂ ĂŶĂůǇƐŝƐ͕ ǀŝƐƵĂů /ŶƚĞƌƉƌĞƚĂƚŝŽŶ ĂŶĚ ŽŶ ƐĐƌĞĞŶ
ĚŝŐŝƚĂƚŝŶŐ͕ ŝĨĨĞƌĞŶƚŝĂů /ŶƚĞƌĨĞƌŽŵĞƚƌŝĐ ^Z ĚĂƚĂƉƌŽĐĞƐƐŝŶŐ͘3. Result and Discussionϯ͘ϭ͘dŚĞƉŽƐŝƚŝŽŶŽĨƚŚĞƐƵďĚƵĐƚŝŽŶǌŽŶĞĂŶĚƐƉĂƚŝĂůĚŝƐƚƌŝďƵƚŝŽŶŽĨWĂůƵ ŝƚǇA

B

Palu City

Alluvial Land above
Palu-Koro FaultWĂůƵ ĐŝƚǇŽŶƚŚĞĨĂƵůƚǌŽŶĞŝƐϭϰϬϬŵďĞůŽǁƚŚĞƵƌĂƐŝĂŶŚŝůůƚĞƌƌĂŝŶ;ͿĂŶĚϱϬϬŵďĞůŽǁƚŚĞWĂĐŝĨŝĐŚŝůůƚĞƌƌĂŝŶ;Ϳ͘tŝĚĞŽĨWĂůƵ ĐŝƚǇĂďŽǀĞƚŚĞĨĂƵůƚǌŽŶĞ͗ ϮϲϬƐƋƵĂƌĞͲŬŵ;Ϯ͕ϲϬϰϯŚĂͿ

3. Result and Discussionϯ͘Ϯ͘dŚĞŝŵƉĂĐƚŽĨƚŚĞĞĂƌƚŚƋƵĂŬĞ;ŽŶ^ĞƉƚĞŵďĞƌϮϴϮϬϭϴͿǁŚŝĐŚĐĂƵƐĞĚůŝƋƵĞĨĂĐƚŝŽŶďĂƐĞĚŽŶĂŶĂůǇƐŝƐŽĨŽƉƚŝĐĂůƐĂƚĞůůŝƚĞŝŵĂŐĞƌǇWƌĞͲĞĂƌƚŚƋƵĂŬĞ ĞǀĞŶƚ ƐĂƚĞůůŝƚĞ ŝŵĂŐĞ &Ğď͘ ϮϬƚŚ͕ ϮϬϭϴ ĂŶĚ ƉŽƐƚͲĞĂƌƚŚƋƵĂŬĞ KĐƚ͘ ϮŶĚ͕ϮϬϭϴWƌĞͲĞĂƌƚŚƋƵĂŬĞ

WŽƐƚͲĞĂƌƚŚƋƵĂŬĞ

Areas Affected by Liquefaction due to the Earthquake on 18 Sept 2024WƌĞͲĞĂƌƚŚƋƵĂŬĞ ĞǀĞŶƚ ƐĂƚĞůůŝƚĞ ŝŵĂŐĞ &Ğď͘ ϮϬƚŚ͕ ϮϬϭϴ ĂŶĚ ƉŽƐƚͲĞĂƌƚŚƋƵĂŬĞ KĐƚ͘ ϮŶĚ͕ϮϬϭϴWƌĞͲĞĂƌƚŚƋƵĂŬĞ

WŽƐƚͲĞĂƌƚŚƋƵĂŬĞ

Areas Affected by Liquefaction due to the Earthquake on 18 Sept 2024WƌĞͲĞĂƌƚŚƋƵĂŬĞ ĞǀĞŶƚ ƐĂƚĞůůŝƚĞ ŝŵĂŐĞ &Ğď͘ ϮϬƚŚ͕ ϮϬϭϴ ĂŶĚ ƉŽƐƚͲĞĂƌƚŚƋƵĂŬĞ KĐƚ͘ ϮŶĚ͕ϮϬϭϴWƌĞͲĞĂƌƚŚƋƵĂŬĞ

WŽƐƚͲĞĂƌƚŚƋƵĂŬĞ WĂůƵ ƌŝǀĞƌƐĂŶĚͲďĂƌŚĂƐ ďĞĞŶ ŝŵƉĂĐƚĞĚďǇůŝƋƵĞĨĂĐƚŝŽŶŝƐĂƌŽƵŶĚϱ͕ϲŬŵ

3. Result and Discussionϯ͘ϯ͘dŚĞĂƌĞĂŽĨĚĞĨŽƌŵĂƚŝŽŶĚƵĞƚŽĞĂƌƚŚƋƵĂŬĞƐďĂƐĞĚŽŶ^ZŝĨĨĞƌĞŶƚŝĂů/ŶƚĞƌĨĞƌŽŵĞƚƌǇĂŶĂůǇƐŝƐ

 >ĂŶĚ ĚĞĨŽƌŵĂƚŝŽŶ ƵƐŝŶŐ ĐŽŵƉĂƌŝƐŽŶ ŽĨƚǁŽ ^ĞŶƚŝŶĞůͲϭ ^Z ĚĂƚĂ ŝŶ DĂƌĐŚ ϭϯƚŚ͕ϮϬϭϳ ďĞĨŽƌĞ͕ ĂŶĚ KĐƚŽďĞƌ ϮϮƚŚ͕ ϮϬϭϴ ĂĨƚĞƌƚŚĞ ĞĂƌƚŚƋƵĂŬĞ͘ dŽƚĂů ǁŝĚĞ ĂƌĞĂ ŽĨ ĚĞĨŽƌŵĂƚŝŽŶ ŝƐ ϭϱ͕ϳƐƋƵĂƌĞͲŬŵ͘ ZĂŶŐĞ ŽĨ ůĂŶĚͲĚŝƐƉůĂĐĞŵĞŶƚ ŝƐ Ϭ͘ϲ ŵ ƵƉͲůŝĨƚĞĚ ĂƌĞĂ ĂŶĚ ƵƉ ƚŽ ͲϬ͘ϲ ŵ ƐƵďƐŝĚĞŶĐĞ͘

4. Conclusion dŚĞ WĂůƵ ŝƚǇ ŝƐ ůŽĐĂƚĞĚ ŽŶ ƚŚĞ ĂůůƵǀŝĂů ƉůĂŝŶ͕ ĂďŽǀĞ ƚŚĞWĂůƵͲ<ŽƌŽ ĨĂƵůƚ ǁŚŝĐŚ ŝƐ ǁŚĞƌĞ ƚŚĞ ŵĞĞƚŝŶŐ ƐŝƚĞ ŽĨ ƵƌĂƐŝĂŶͲ WŚŝůŝƉƉŝŶĞ ƚĞĐƚŽŶŝĐ ƉůĂƚĞƐ͘ dŚĞ ŝŵƉĂĐƚ ŽĨ ƚŚĞ ĞĂƌƚŚƋƵĂŬĞ ;ŽŶ ^ĞƉƚĞŵďĞƌ Ϯϴ ϮϬϭϴͿ ǁĂƐƐŝŐŶŝĨŝĐĂŶƚůǇ ŚŝŐŚ ŽŶ ƚŚĞ ůŝƋƵĞĨĂĐƚŝŽŶ͘ dŚĞ ĞĂƌƚŚƋƵĂŬĞƐ ŚĂǀĞ ŝŵƉĂĐƚĞĚ ƐŝŐŶŝĨŝĐĂŶƚ ĚĞĨŽƌŵĂƚŝŽŶ ĂƚWĂůƵ ŝƚǇ͕ ĂŶĚ /ƚ ŝƐ ŚŝŐŚ ƌŝƐŬ ŽŶ ƚŚĞ ƚŚƌĞĂƚ ŽĨ ĞĂƌƚŚƋƵĂŬĞĂŶĚ ůŝƋƵĞĨĂĐƚŝŽŶ ŚĂǌĂƌĚƐ ŝŶ ƚŚĞ ĨƵƚƵƌĞ͘Refrences,ĂƌƚŽŬŽ͕ ͕͘ D͘,Ğůŵŝ͕ D͘ ^ƵŬĂƌŶŽ͕ ĂŶĚ ,ĂƌŝǇĂĚŝ͘ ϮϬϭϲ͘ ĂƌƚŚƋƵĂŬĞĂŶĚ ^ƉĂƚŝĂů dƐƵŶĂŵŝ DŽĚĞůůŝŶŐ ĨŽƌ ƚŚĞ ^ŽƵƚŚ :ĂǀĂ ŽĂƐƚĂů
ƌĞĂ ʹ /ŶĚŽŶĞƐŝĂ͘ &ŝĨƚŚ /ŶƚĞƌŶĂƚŝŽŶĂů ŽŶĨĞƌĞŶĐĞ ŽŶ 'ĞŽͲƚĞĐŚŶŝƋƵĞ͕ ŽŶƐƚƌƵĐƚŝŽŶ DĂƚĞƌŝĂůƐ ĂŶĚ ŶǀŝƌŽŶŵĞŶƚ͕KƐĂŬĂ͕ :ĂƉĂŶ͕ EŽǀ͘ ϭϲͲϭϴ͕ ϮϬϭϱ͕ /^E͗ ϵϳϴͲϰͲϵϵϬϱϵϱϴͲϰͲϱϯϬϱϭ͘ /Ŷƚ :ŽƵƌŶĂů ŽĨ 'ĞŽŵĂƚĞ͘ sŽů͘ϭϭ;ϮϱͿ ͗ ϮϰϱϱͲϮϰϲϬ͘ZĂǌŝ͕ W͕͘ ^ƵŵĂŶƚǇŽ͕ :͘ d͘ ^͕͘ tŝĚŽĚŽ͕ :͕͘ WĞƌŝƐƐŝŶ͕ ͘ ϮϬϮϬ͘ >ĂŶĚĞĨŽƌŵĂƚŝŽŶ DŽĚĞůůŝŶŐ ŽĨ dĂŝǁĂŶ ĂƌƚŚƋƵĂŬĞ ƵƐŝŶŐ
/ŶƚĞƌĨĞƌŽŵĞƚƌǇ dĞĐŚŶŝƋƵĞ͘ :ŽƵƌŶĂů ŽĨ WŚǇƐŝĐƐ͗ ŽŶĨĞƌĞŶĐĞ
^ĞƌŝĞƐ sŽů͘ ϭϰϴϭ͕ EŽ͘ ϭ͕ WĂƉĞƌ͘ ϬϭϮϬϬϵ͘ /KWͲWƵďůŝƐŚŝŶŐ.

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忁н㿼䟨
䀒ࡱ١
‫בئ‬١
㈇㉱١
嵄ٕ١
٘чԚ
㭆ٌԚ
ѥ嫁Ԛ
ԘԚ
ИԚ
ԘԚ
⩾ँ١
࡜ӄ斴୎١
չࡡ١
⾦૨١
‫ؿ‬㉱١
‫ޢ‬ी١
‫ם‬չ慔ٌ١
㛽䟼Ԛ
И‫ס‬Ԛ
ԘԚ
੼Խ䟨

2/15/2024

L
L7UHH(FRध໨ͶΓΖ୺ો‫ݽ‬ఈྖϬ͘Ϭϯϳϴ
Ϭ͘ϯϬϳϰ
Ϭ͘ϮϱϭϮ
Ϭ͘ϯϱϭϮ
Ϭ͘ϰϯϵϰ
Ϯ͘ϭϮϳϭ
Ϭ͘ϮϯϬϲ
Ϭ͘ϲϴϲϯ
Ϭ͘Ϯϰϭϭ
Ϭ͘ϮϬϵϬ
Ϭ͘ϯϰϭϵϬ͘ϴϴϴϵϮ͘ϲϳϳϮ
Ϭ͘ϳϮϯϳ
Ϭ͘ϵϱϳϱ
Ϭ͘ϰϴϮϮ
ϭ͘ϲϲϳϲ
Ϭ͘ϱϮϯϲ
Ϯ͘Ϯϲϰϲ
Ϭ͘ϲϯϰϬ
Ϭ͘ϮϲϱϬ
ϭ͘ϬϮϮϮ
Ϭ͘ϭϬϬϲ
Ϭ͘ϳϵϴϭ

ϯ͕ϯϮϲ
ϱϬ
ϮϬ
ϵϰϯ
ϳ
Ϯϳ͕ϭϲϰ
ϮϵϬ
ϰ͕ϮϴϮ
ϳϱ
ϵ͕ϯϱϰ
ϲϳϰ
ϰϬϱ
ϯϯ
ϭϭ͕Ϭϵϯ
ϮϮ
ϭϯϮϴ
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ϭϬϱ
ϵϲϯ
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ϭϭ͕ϱϳϬ
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10

୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

‫ٶ‬族䎮প֡ؔ୏
(kgC/m2/yr)

2/15/202411୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

02',6YVL
L
L7UHH(FRR౨๼ஏ๏

‫ٶ‬族䎮প֡ؔ୏
(kgC/m2/yr)

MODIS NPP

i-Tree Eco

2/15/202412୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

02',6YVL
L
L7UHH(FRR‫ؖ‬౨ஏ๏

‫ٶ‬族䎮প֡ؔ୏
(kgC/m2/yr)

MODIS NPP
2/15/2024

i-Tree Eco13

02',6YVL
L
L7UHH(FRR஦෨ʀۛ‫ق‬ஏ๏୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

‫ٶ‬族䎮প֡ؔ୏
(kgC/m2/yr)

MODIS NPP

i-Tree Eco

2/15/202414୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

02',6YVL
L
L7UHH(FRR۟यஏ๏

‫ٶ‬族䎮প֡ؔ୏
(kgC/m2/yr)

MODIS NPP
2/15/2024

i-Tree Eco15୺ો‫ݽ‬ఈྖ‫ݬ‬ୱҒ͹‫ྭࣆ༽׈‬ʁ୺ોો‫ݽ‬ఈྖ‫ݬ‬ୱҒ
L7UHH&DQRS\୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

2/15/2024੊ీ୫ۢͲ͹୺ો‫ݽ‬ఈྖ͹‫ݬ‬ୱҒΝઅఈ

2/15/2024

16୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

17γϱϕϨϱήͳౖஏඅ෶൓ఈ

୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

2/15/2024༁ΩॶͲ͹γϱϕϨϱή

2/15/202418୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

19‫ݬ‬ୱҒ
Ғʹध໨අ෶໚੷ʻ୺ો‫ݽ‬ఈྖ୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

2/15/202420୊յ ‫ڧ؂‬ϨϠʖφιϱεϱήεϱϛζΤϞ

濯ࢎ别濝瀥濧瀃濡濯濱濟瀙濲濺tĞď͗ǁǁǁ͘ŝƚƌĞĞƚŽŽůƐ͘ŽƌŐŵĂŝů͗^ĂƚŽƐŚŝ͘,ŝƌĂďĂǇĂƐŚŝΛĚĂǀĞǇ͘ĐŽŵ

2/15/2024

21

GMS/VISSR ͔Βͷ LST ਪఆͷͨΊͷ
AVHRR/LST σʔλͷ੔උ˓௕࡚େֶ ৿ࢁխ༤15 Feb. 2024

Moriyama

AVHRR LSTൃදͷྲྀΕ

1. എ‫ܠ‬: ա‫ڈ‬ͷσʔλ΁ͷ৽‫ن‬ΞϧΰϦζϜͷඞཁੑ
2. ४ղੳ‫ܕ‬ਪఆΞϧΰϦζϜ
3. NOAA/AVHRR ΁ͷҠ২
4. ऩଋ཰
5. ·ͱΊ

1

Moriyama

AVHRR LST

LST as ECV

1. ա‫ڈ‬: ඪ४ϓϩμΫτͱ͍͏֓೦͕ͳ͘ɼӴ੕‫؍‬ଌً౓ͷΈΞʔΧΠϒ͞Ε͍ͯΔӴ੕‫؍‬ଌً౓Թ౓: ηϯαؒͷ૬ҧɼ஍ද໘Թ౓: ҟͳΔηϯαͰൺֱՄೳ

2. ୯όϯυ೤੺֎ଳҬ͔͠༗͞ͳ͍੩ࢭ‫ؾ‬৅Ӵ੕ͷ‫؍‬ଌσʔλ͔Β஍ද໘Թ౓Λਪఆ͢ΔΞϧΰϦζϜΛ։ൃ͢Δ
2

Moriyama

AVHRR LST୯όϯυ೤੺֎σʔλ͔Βͷ஍ද໘Թ౓ਪఆ
I = τ (θ)Is + Ia(θ), Is = εB(Ts) + (1 − ε)• I: Ӵ੕‫؍‬ଌً౓• Ts: ஍ද໘Թ౓
• ε: ஍ද໘ࣹग़཰ (ଳҬຖ)

Fπ• τ (θ): ಁա཰• Ia(θ): ύεϥδΞϯε
• F : ஍දͰͷԼ޲͖์ࣹর౓• τ (θ), Ia(θ), F : େ‫ؾ‬ϓϩϑΝΠϧ͔Β‫ࢉܭ‬Մೳ (େ‫ิؾ‬ਖ਼)• Ts, ε: ະ஌਺ (ଳҬ਺+1) → ࣹग़཰͕ܾ·Ε͹஍ද໘Թ౓͕ܾ·Δ
AVHRR btightness
temperature

ERA5

AVHRR/CFLG, LSTGEO Brightness
temperature

GEO CFLG,
Emissivity
estimation
algorithm

3

Moriyama

AVHRR LST

४ղੳ‫ ܕ‬LST ਪఆΞϧΰϦζϜ
Ts

ೖྗ: ‫؍‬ଌً౓Թ౓ T1, T2,਺஋༧ใϞσϧΛ༻͍ͨେ‫ิؾ‬ਖ਼ τ, Ia, F
ະ஌਺: ε1, ε2, Ts

Solution
Split window
plane

ε2
Solution curvefrom RTE

ε1

F1] + Ia1(θ)} − T1 = 0
π
F2
f2 = B2−1{τ2(θ)[ε2B2(Ts) + (1 − ε2) ] + Ia2(θ)} − T2 = 0π
f3 = C0 + (C1 + r1C2)T1 + C3r1 + (C4 + r2C5)T2 + C6r2 − Ts = 0, (ri = 1 − εi)௥Ճ͢Δࣜ: Split window (ً౓Թ౓ɺ஍ද໘Թ౓/ࣹग़཰ͷ౷‫ܭ‬తͳؔ܎ࣜ)ಁա཰ɺύεϥδΞϯεɺԼ޲͖์ࣹর౓͸؆қ์ࣹ఻ୡίʔυ
f1 = B1−1{τ1(θ)[ε1B1(Ts) + (1 − ε1)⇓ χϡʔτϯ๏J=

f12 + f22 + f32 → min. ͱͳΔ Ts, ε1, ε2 Λղͱ͢Δɻ

J ≤ 1[K]: ऩଋ, 1 < J ≤ 2[K]: ४ऩଋ, J > 2[K]: ൃࢄ → QA ৘ใ
4Moriyama

AVHRR LST

਺஋γϛϡϨʔγϣϯʹΑΔ஍ද໘Թ౓ϓϩμΫτ‫ূݕ‬(SGLI)
5

0.8

4

3

0.6

2

0.4

1

0.2

0

0

0

1

0.6

2

0.4

1

0.2

0
-1

2

0

0

1

5

0.8

4

0.6

2

0.4

1

0.2

0

0

0

1

Relative frequency

1

3

-1

T1tr. > 0.6 Relative frequency
T1tr. <= 0.6 Relative frequency
T1tr. > 0.6 Bias error[K]T1tr. <= 0.6 Bias error[K]T1tr. > 0.6 RMS error[K]T1tr. <= 0.6 RMS error[K]2 x NEdT
LST estimation error [K]LSTestimation error [K]

4

0.6
0.4

1

0.2
0

0

1

5

0.8

4

0.4

1

0.2

0

0

2
Residual [K]Relative frequency

1

2

1

T1tr. > 0.6 Relative frequency
T1tr. <= 0.6 Relative frequency
T1tr. > 0.6 Bias error[K]T1tr. <= 0.6 Bias error[K]T1tr. > 0.6 RMS error[K]T1tr. <= 0.6 RMS error[K]

5 x NEdT

0.6

02
Residual [K]

6

3

-1

0.8

2

-1

LST estimation error [K]LST estimation error [K]

4

1

0

2

T1tr. > 0.6 Relative frequency
T1tr. <= 0.6 Relative frequency
T1tr. > 0.6 Bias error[K]T1tr. <= 0.6 Bias error[K]T1tr. > 0.6 RMS error[K]T1tr. <= 0.6 RMS error[K]5

3 x NEdT

3

Residual [K]62
Residual [K]6

T1tr. > 0.6 Relative frequency
T1tr. <= 0.6 Relative frequency
T1tr. > 0.6 Bias error[K]T1tr. <= 0.6 Bias error[K]T1tr. > 0.6 RMS error[K]T1tr. <= 0.6 RMS error[K]

5

0.8

3

Residual [K]61

Relative frequency-1

1 x NEdT

Relative frequency

1

10 x NEdT
1
0.8

3

0.6

2

0.4

1

0.2

0
-1

0

0

1

2
Residual [K]5Relative frequency

4

T1tr. > 0.6 Relative frequancy
T1tr. <= 0.6 Relative frequancy
T1tr. > 0.6 Bias error[K]T1tr. <= 0.6 Bias error[K]T1tr. > 0.6 RMS error[K]T1tr. <= 0.6 RMS error[K]0 x NEdT

Relative frequency

LSTestimation error [K]6

T1tr. > 0.6 Relative frequency
T1tr. <= 0.6 Relative frequency
T1tr. > 0.6 Bias error[K]T1tr. <= 0.6 Bias error[K]T1tr. > 0.6 RMS error[K]T1tr. <= 0.6 RMS error[K]5

LST estimation error [K]6Proﬁle: ECMWF 2000 Monthly
mean proﬁle (averaged over 10[deg.] latitude interval)
Surface temperature: Air
temperature at the surface+ 0, 5, 10, 15, 20, 25, 30, 35[K]ε̄: 0.95, 0.96, 0.97, 0.98, 0.99, 1
a: -0.5, 0.25, 0, 0.25, 0.5
Observation zenith: 0, 15, 30,
45, 60[deg.]
Observation error: 0.04[K](@300[K]) × 0, 1, 2, 3, 5, 10

Moriyama

AVHRR LSTϑϥοΫελϫʔͰͷ௕೾์ࣹ‫ܭ‬ଌʹΑΔ‫ূݕ‬

RMS ‫ࠩޡ‬: 2.1[K]6Moriyama

AVHRR LSTAVHRR CDR
• னؒͷ AVHRR GAC ཮ҬσʔλશόϯυΛ 0.05 ౓ϝογϡʹ࠶഑ྻͨ͠શ‫ٿ‬σʔληοτɽӢϑϥάɼ‫؍‬ଌ/ଠཅ‫ز‬Կɼ‫؍‬ଌ࣌ࠁ͕෇༩
͞Εɼ൓ࣹଳҬ (Channel 1, 2) ͔ΒɼΤΞϩιϧ‫ީؾ‬஋Λ༻͍ͨେ‫ؾ‬ิਖ਼Λࢪ͠ɼ஍ද໘൓ࣹ཰Λਪఆͨ͠΋ͷɽ• ஍ද໘Թ౓͸‫͞ࢉܭ‬Ε͍ͯͳ͍ɽ

7

Moriyama

AVHRR LST

1981/07/26

8

Moriyama

AVHRR LSTAVHRR ΁ͷҠ২: ܎਺ܾఆ؆қ์ࣹ఻ୡίʔυ܎਺

Proﬁle: ECMWF 2000 Monthly
mean proﬁle (averaged over 10 I = τ (θ)[εB(Ts) + (1 − ε)F/π] + Ia(θ)[deg.] latitude interval)
τ (θ) = exp[−ξ(u)/ cos θ]Surface temperature: Air
ξ(u) = AuB + C
temperature at the surface
2+ 0, 5, 10, 15, 20, 25, 30, 35[K] Ia(θ) = ax + bx, x = [1 − τ (θ)]B[T (0)]2ε̄: 0.95, 0.96, 0.97, 0.98, 0.99, 1 F = αX + βX, X = [1 − τ (0)]B[T (0)]Split window ܎਺
a: -0.5, 0.25, 0, 0.25, 0.5
Observation zenith: 0, 15, 30,
Ts = (C1 + C2r1)T1 + C3r1
45, 60[deg.]+ (C4 + C5r2)T2 + C6r2 + C0
Observation error: 0[K]ε̄ = 12 (ε1 + ε2),

ε1 −ε2a = 2(1−ε̄)r1 = 1 − ε 1 , r 2 = 1 − ε 29Moriyama

AVHRR LST؆қ์ࣹ఻ୡίʔυͷً౓Թ౓ʹΑΔ‫ࠩޡ‬ධՁSensorSGLI
NOAA07NOAA09T1 Bias[K]0.249-0.00854-0.0386

T1 RMS[K]
0.429
0.305
0.294

T2 Bias[K]-0.0694-0.529-0.195T2 RMS[K]
0.325
0.652
0.422

SensorNOAA11
NOAA14
NOAA16

T1 Bias[K]-0.0380
0.00544-0.142

T1 RMS[K]
0.295
0.313
0.322

T2 Bias[K]-0.259-0.314-0.0242

T2 RMS[K]
0.456
0.509
0.453

10

Moriyama

AVHRR LST

਺஋γϛϡϨʔγϣϯʹΑΔ AVHRR/LST ͷ‫ࠩޡ‬ධՁऩଋ͢Ε͹ RMS ‫ ࠩޡ‬2[K] ҎԼͷਫ਼౓
11

Moriyama

AVHRR LSTERA5
• ECMWF ͕ఏ‫͢ڙ‬Δ٬‫؍‬ղੳࡁ਺஋༧ใσʔλ• 1940 ೥ʙ‫( ࡏݱ‬ϦʔυλΠϜࡾϲ݄)• ۭؒղ૾౓ 0.25[deg.], ࣌ؒ෼ղೳ 1[hour]• ࢦఆ‫ؾ‬ѹ໘ɼ஍දͦΕͧΕʹ਺ଟ͘ͷύϥϝʔλ• ຊ‫Ͱڀݚ‬͸ɼ஍ද‫ؾ‬Թͱ૯ਫৠ‫ྔؾ‬Λར༻12Moriyama

AVHRR LST

1981/12/25 NOAA07/LST

13

Moriyama

AVHRR LSTNOAA/07, 09, 11 LST

14Moriyama

AVHRR LSTऩଋੑ1Conversion rate

0.8
0.7
0.6

3e+06

0.5

2e+06

0.4
1e+06

0.3

Number of the clear pixel

0.9

0.2
0
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Year
NOAA07 Conversion rate
NOAA07 # of clear pixel
NOAA09 Conversion rate
NOAA09 # of clear pixel
NOAA11 Conversion rate

NOAA11 # of clear pixel
NOAA14 Conversion rate
NOAA14 # of clear pixel
NOAA16 Conversion rate
NOAA16 # of clear pixel

15

Moriyama

AVHRR LSTऩଋըૉ஋ͱ‫؍‬ଌ࣌ࠁͷτϨϯυ

16

Moriyama

AVHRR LST·ͱΊ

1. SGLI ޲͚஍ද໘Թ౓ਪఆΞϧΰϦζϜΛ NOAA/AVHRR γϦʔζ޲͚ʹҠ২͠ɼ਺஋γϛϡϨʔγϣϯͰɼಉ౳ͷਫ਼౓͕ಘΒΕΔ͜ͱ͕໌Β͔ʹͳͬͨɽ
2. ࠓ‫ޙ‬ɼ͜ͷ஍ද໘Թ౓ɼӢϑϥάΛਅ஋ͱͯ͠ɼಉ࣌‫ʹظ‬Քಇ͍ͯͨ͠੩ࢭ‫ؾ‬৅Ӵ੕ʹ౥ࡌ͞Εͨ୯όϯυ೤੺֎ηϯα͔Βͷ஍ද໘Թ౓ਪఆΞϧΰϦζϜΛߏங͢Δɽ
(୯όϯυ೤੺֎ηϯα͸ɼա‫ڈ‬ͷӴ੕ʹ౥ࡌ͞Ε͍͚ͯͨͩͰͳ͘ɼ
ࠓ‫ޙ‬ɼ௒খ‫ܕ‬Ӵ੕ʹ౥ࡌ͞ΕΔՄೳੑ͕ߴ͍ɽ)17
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自己紹介
I3( RTrg
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  東京大学 大学院 工学系研究科 社会基盤学専攻 @芳村研 理化学研究所 計算科学研究センター @三好チーム
 
ペンシルベニア州立大学 気象学・大気科学部 @F. Zhangʼs Group 弘前大学 理工学研究科 地球環境防災学コース
  千葉大学 IAAR / CEReS/   
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૱ແ঴ভ๹ுʁྫ࿪̓೧ʤ݆͖̓Δ̗݆ʥ͹೦஦঳ͶΓΖ‫ٺٻ‬൘ૻয়‫ڱ‬कགྷ͵‫ڂݜ‬τʖϜ੫ࢯٌಕӶ੗ʰͽΉΚΕʱΝ‫ͪ͢༽׈‬ʞ⿥ ཰໚෼ཀྵྖϕϫξέφ͹֋൅‫ڂݜ‬ஏන໚Իౕɼஏන໚ൕིࣻɼޭ߻੔ྖɼৢ൅ࢆྖ͵ʹ⿥ ৪ਫ਼ʀ౐ࢤ‫΃ڧ؂‬͹Ԣ༽‫ڂݜ‬ͽΉΚΕ̖ߺʀ̗ߺ

৪ਫ਼͹‫ף‬૫Կεήψϩ͹‫ݗ‬ड़౐ࢤߑଆͳԻౕ৏তͳ͹‫ܐؖ‬਼஍‫ޫـ‬Ϡυϩ͹ώ΢Πη඲Ճ໢ॷͶΓΖ೦‫ڧ؂‬͹รಊմੵhttps://www.data.jma.go.jp/mscweb/ja/general/himawari.html

ஏන໚Իౕ͹߶ළౕਬఈ

ஏන໚๎ིࣻ͹ਬఈ
Yamamoto and Ishikawa (2018, JMSJ)ʰ౐ࢤʀ৪ਫ਼͹‫ش‬ՁߑଆͶΓΖ๎ིࣻ΃͹Ӫ‫ڻ‬Νߡྂͪ͢ਬఈघ๑Ν֋൅ʱӤҮ‫ݗ‬ड़ʀஏන໚Իౕਬఈ
Yamamoto et al. (2018, JMSJ)ʰ؈ส͖ͯ߶੠ౕ͵ӤҮ‫ݗ‬ड़घ๑ͳɼ
ඉત‫̑ܙ‬ώϱχ๑͹఑ҌʱΠϩβϨθϞրྒྷʀ‫ৄݗ‬
Yamamoto et al. (2022, ISPRS)ʰஏ৏ϓϧρέη؏଎ϋρφϭʖέͳɼ
ISS؏଎υʖνΝཤ༽ͪ͢‫ৄݗ‬ʱஏන໚Իౕ͹߶ළౕਬఈ
༹ʓ͵؏଎ড়݇Ͷ͕͏ͱਬఈ੠ౕ͹߶͏घ๑Ν֋൅
Yamamoto et al. (2022, ISPRS)౐ࢤߑଆͳԻౕ৏তͳ͹‫ܐؖ‬‫ݒ‬෼ືॄౕ͗ೖ஦͹Իౕ৏তͶ‫ر‬༫ͤΖ͞ͳ͗໎Δ͖Ͷ
Yamamoto and Ishikawa (2020, Urban Clim.)৪ਫ਼͹‫ף‬૫Կεήψϩ͹‫ݗ‬ड़ʰԻౕೖֳࠫʱͲɼ࣬७‫ޫـ‬ԾͶ͕͜Ζ‫ף‬૫Կ͹‫ݗ‬ड़͗Ն೵Ͷ
Yamamoto et al. (2023, Remote Sens. Environ.)ࠕ‫ޛ‬͹‫ڂݜ‬ʁޭ߻੔ྖ͹߶ළౕਬఈ
ೖ಼ηίʖϩͶన༽Ն೵͵ਬఈϠυϩ͹ߡҌॊཔϠυϩΝͨ͹ΉΉన༽ͤΖ͞ͳͺೋ͢͏ʞʰޭ๠࿪ʱͳʰओޭԾ͹ޭ߻੔ްིʱΝߡྂͤΖචགྷ͍͗Ζ߶ࢃγ΢φ Իଵཚཁधྜྷ Ͷ͕͜Ζޭ߻੔ྖ͹෾ਬఈࣰ‫ݩ‬ޭ߻੔ྖ (gC/m2/s)

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৪ਫ਼͹‫ڧ؂‬ηφϪη͹‫ݗ‬ड़‫ݗ‬ड़Ν਼ೖηίʖϩ͖Δɼॢ࣎ηίʖϩ΃ʞ
$OLFH6SULQJVγ΢φ ൔ‫ף‬૫ஏଵ͹ᕴ໨ஏҮҡ͵Ζ‫ޫـ‬ଵΝΉͪ͏Ͳɼ‫ݗ‬ड़΃͹ཤ༽Ն೵੓Ν୵Ζ

fˈͶध‫ץ‬͹‫ݸ‬ֆ͍͗Ζʃ໢ॷͶΓΖ౐ࢤ೦‫΃ڧ؂‬͹Ӫ‫ڻ‬඲Ճೖ࠹߶Իౕ ໢ॷʷฑ೧ౖஏཤ༽෾ྪਦ
ೖ࠹ఁԻౕ ໢ॷʷฑ೧

nॷ೦|΃͹Ӫ‫ڻ‬ͺʹ͖͑ʃ਼஍‫ޫـ‬Ϡυϩ͹ώ΢Πη඲Ճ྘Ү‫ޫـ‬ϠυϩͶΓΖNP͹ྙָద'6ͺʹ͞ΉͲਜ਼͖֮ʃ
গཔ‫ޫـ‬༩଎υʖν G3')NP ͹ਜ਼֮͵඲ՃͶ‫͝ܪ‬Ζࠕ‫ޛ‬͹&(5H6‫ڠ‬ಋཤ༽‫ͪ৿Ͳڂݜ‬ͶՅΚΖτʖϜ఼॑ԥஇϕϫζΥέφ

ʶ཰໚෼ཀྵྖ͹ਬఈʀ‫ڂݜৄݗ‬̏ ߿ӏࡄ֒༩଎ʀ؄ࢻϕϫζΥέφ
ʶ৪ਫ਼ʀ౐ࢤ‫ڧ؂‬͹ਏஇ‫ڂݜ‬̐ ஏ‫؏ځ‬଎Ӷ੗‫ৄݗ‬ϕϫζΥέφ̑ Ի࣪ްՎΪηफࢩ‫ڂݜ‬ϕϫζΥέφ
ʶऑघ͹‫ॄڂݜ‬ճ͵ʹ̒ ઎୼Ϝ΢έϫഀϨϠʖφιϱεϱή̓ ੫ࢯ‫ـ‬েӶ੗؏଎໤Ν‫ͪ͢༽׈‬ஏ‫ڧ؂ځ‬ϠωνϨϱή̔ ஏҮ೸‫ۂ‬ϕϫζΥέφ̕ ը͗ࠅ͹ஏ‫؏ځ‬଎͹͍Ε๏ʀ࣏੊େӶ੗ϝρεϥϱ‫ݗ‬౾ϕϫζΥέφ

ͨ͹ଠʁͽΉΚΕͶΓΖՆ߳ਭྖ͹߶ළౕਬఈ
೦੼֐ʶਭৢ‫ـ‬ώϱχΝ༽͏ͪ‫ؽ‬փָसͶΓΖਬఈք৏Ն߳ਭྖ͹߶ළౕ؏଎
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ೖຌԌ‫༺֐ͳ؝‬͹ਭৢ‫ଁ͗ྖླྀـ‬Յ͢ͱ
͏Ζք৏ʤӆӪɼപ໾һʥ͹Ն߳ਭྖΝ੯͹઩ഭͶ౧ࡎͪ͢*166ʀϜ΢έϫഀ๎ࣻ‫ܯ‬ʀ*36μϱυͲ߶ළౕ؏଎

ᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

ɶហ‫ٳ‬ἋὊἣὊἅὅἘỵἝἷὊἲήเửМဇẲẺ‫ٻ‬ൢɶᵡᵭᵐỉᚘยᰁᕝᬛᘯ㻝㻘㻞䠈┾Ꮚ┤ᘯ㻟䠈಴ᶫៅ⌮㻝䠈ᯇ⏣ᬗᖹ㻝䠈వㄒぬᩥ㻞䠈᳝ྡ㐩㞝㻠䠈ஂୡᏹ᫂㻟䠄䝺䞊䝄䞊⥲◊㻝䠈㜰኱䝺䞊䝄䞊◊㻞㻘㻌༓ⴥ኱㻯㻱㻾㼑㻿㻟 㻘༓ⴥ኱㝔ᕤ㻠䠅ᵲᵿᶀᶊᶃᴾᶍᶄᴾᶁᶍᶌᶒᶃᶌᶒᶑ㻝㻚 ᆅ⌫ ᬮ໬ᑐ⟇䛻ྥ䛡䛯ྲྀ䜚⤌䜏㻞㻚 ୰㉥እ㻿㻯ග※䛻䜘䜛኱Ẽ୰㻯㻻㻞䛾྾཰ィ㻟㻚 䜎䛸䜑
ᵟᶁᶉᶌᶍᶕᶊᶃᶂᶅᶃᶋᶃᶌᶒᶑ
ᮏ◊✲䛿༓ⴥ኱⎔ቃ䝸䝰䞊䝖䝉䞁䝅䞁䜾◊✲䝉䞁䝍䞊ඹྠ฼⏝◊✲䠄:ϮϯͲϭϮ͕
WϮϬϮϯͲϭ䠅䛻䜘䛳䛶㐙⾜䛥䜜䜎䛧䛯䚹䛣䛣䛻ㅰព䜢⾲䛧䜎䛩䚹ᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

‫ྶע‬ภଡ଼҄‫ݣ‬ሊỆӼẬề
2018ᖺ䛻ᐇ᪋䛥䜜䛯ᅜ㐃Ẽೃኚື䛻㛵䛩䜛ᨻᗓ㛫䝟䝛䝹䛷䛿䚸ᆅ⌫ ᬮ໬䜢
1.5Υ䛻ᢚ䛘䜛䛣䛸䛾㔜せᛶ䛜㏙䜉䜙䜜䚸඲ୡ⏺䛾ேⅭⓗ䛺CO2᤼ฟ๐ῶ䜢ᐇ᪋䛩䜛ᚲせᛶ䛜ᘬ䛝⥆䛝ᣦ᦬䠄https://www.unic.or.jp/news_press/info/44283/䜘䜚䠅䠄https://www.env.go.jp/content/900442320.pdf䜘䜚䠅」ᩘ䛾 ᐊຠᯝ䜺䝇䛾ྠ᫬ィ 䛜ᐇ᪋ྍ⬟䛺ィ ᢏ⾡䛾㛤Ⓨ䛷 ᬮ໬ᑐ⟇䛻㈉⊩ᵐᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

ἋὊἣὊἅὅἘỵἝỸἲήเỉԈӓᚘยồМဇ䝇䞊䝟䞊䝁䞁䝔䜱䝙䜴䝮㻔㻿㼡㼜㼑㼞㼏㼛㼚㼠㼕㼚㼡㼡㼙㻕㉸▷䝟䝹䝇䝺䞊䝄䞊䜢㠀⥺ᙧගᏛᮦᩱ䛻ධᑕ䛧䛯㝿䠈⮬ᕫ఩┦ኚㄪ䠈┦஫఩┦ኚㄪ䠈㻠ගἼΰྜ䠈䝷䝬䞁ᩓ஘䛺䛹䛾㠀⥺ᙧගᏛຠᯝ䛻䜘䜚䠈䛭䛾ග䝇䝨䜽䝖䝹䛜㐃⥆ⓗ䛻ᛴ⃭䛻ᗈ䛜䛳䛯ග※䖃䝇䞊䝟䞊䝁䞁䝔䜱䝙䜴䝮ග※㻺㻷㼀㻌㻼㼔㼛㼠㼛㼚㼕㼏㼟♫㻴㻼䜘䜚䠄㼔㼠㼠㼜㼟㻦㻛㻛㼣㼣㼣㻚㼚㼗㼠㼜㼔㼛㼠㼛㼚㼕㼏㼟㻚㼏㼛㼙㻛㼘㼍㼟㼑㼞㼟㻙㼒㼕㼎㼑㼞㼟㻛䠅䝺䞊䝄䞊ග※

㠀⥺ᙧගᏛᮦᩱ

䝢䝁⛊䝬䜲䜽䝻䝏䝑䝥䝺䞊䝄䞊 䝣䜷䝖䝙䝑䜽⤖ᬗ䝣䜯䜲䝞䞊ฟຊ䠄䝟䝹䝇䜶䝛䝹䜼䞊䠅䡚㻢㻌㼃㻔䡚㻝㻜㻜㻌㽀㻶㻫㻕㉥እ㡿ᇦ䛻ཬ䜆㠀ᖖ䛻ᗈᖏᇦ䛺ⓑⰍග※䛷䛒䜚䚸䝺䞊䝄䞊䛾䜘䛖䛻ᣦྥᛶ䚸㍤ᗘ䛜㧗䛔ᵑᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

ἻỶἒὊࣖဇầӧᏡễἋὊἣὊἅὅἘỵἝỸἲήเ䝺䞊䝄䞊ග※

㠀⥺ᙧගᏛᮦᩱ

ฟຊ䠄䝟䝹䝇䜶䝛䝹䜼䞊䠅䝢䝁⛊䝬䜲䜽䝻䝏䝑䝥䝺䞊䝄䞊 䝣䜷䝖䝙䝑䜽⤖ᬗ䝣䜯䜲䝞䞊䝣䜵䝮䝖⛊䝺䞊䝄䞊䡚ϲt;䡚ϭϬϬђ:Ϳᕼ䜺䝇Ϯt䠄ϮϬϬŵ:䠅

䝣䜯䜲䝞䞊䛾䝎䝯䞊䝆㜈್䛷ไ㝈䛥䜜䚸䝟䝹䝇䜶䝛䝹䜼䞊䛿ᑠ䛥䛟䚸䝷䜲䝎䞊ᛂ⏝䛿㞴䛧䛔䝔䞊䝤䝹䝖䝑䝥TW䝣䜵䝮䝖⛊䝺䞊䝄䞊䝅䝇䝔䝮

9 m䛾ᕼ䜺䝇䝉䝹䝷䜲䝎䞊ᛂ⏝䛜ᐜ᫆䛺䚸㧗䝟䝹䝇䜶䝛䝹䜼䞊䛾ⓑⰍග䝺䞊䝄䞊䛜⏕ᡂྍ⬟䟿ᵒᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

᭗ࢍࡇἧỹἲἚᅺἾὊἈὊửМဇẲẺႉᑥήἻỶἒὊ䖃ⓑⰍග䝷䜲䝎䞊䛾ᐇ㦂㓄⨨ᅗ

1000䜶䜰䝻䝌䝹䞉㞼䛺䛹䛾 ᐃᑐ㇟
Intensity (A.U.)

Kr : 1atm
Air

㊥㞳

100

10

1
300

400

500

600

700

800

900

Wavelength (nm)ᮃ㐲㙾䝷䜲䝎䞊᳨ฟჾ㒊ศ䜢ኚ᭦䛩䜛䛰䛡䛷ᵝ䚻䛺ほ 䛜ྍ⬟䝁䝠䞊䝺䞁䝖ⓑⰍග䝺䞊䝄䞊䛾≉ᛶ䠄ᣦྥᛶ䚸೫ග➼䠅䜢ᣢ䛴⣸እ䛛䜙㉥እ䛻ཬ䜆㐃⥆䝇䝨䜽䝖䝹z ከἼ㛗ྠ᫬೫ග䝷䜲䝎䞊z ㉥እᇦ䛷䛾㻯㻻㻞䛾྾཰ィᵓᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

ႉᑥή͞ήἻỶἒὊỆợỦ‫ٻ‬ൢᚇย
㯤◁䛾ほ

800δ

550ɷ

450ɷ

800

2.0

800δ

800

3.0

800δ

㧗ᗘ(km)

2.0
1.0

㞼

1.0

1.0

䜶䜰䝻䝌䝹
0

0

0

0

30 0

30 0
᫬㛫 (ศ)

30 0

0

30

30 0

᫬㛫 (ศ)

㞼䛾⢏ᚄ䞉ᙧ≧䛾᥎ᐃ͞ήᚐෞࡇί᷀ᾌᵱᵍᵮὸ
᷀῍ᵎ
ྶ࢟

᷀ᾉ‫ٻ‬
᩼ྶ࢟

0

30

ᐇ㝿䛾⎔ቃィ

30 0

᫬㛫 (ศ)

ᙉ

1.0

0.5

ᙅ

0

೫ගゎᾘᗘδ

800P

2.0;ᕥ䠖㯤◁䛺䛧䚸ྑ䠖㯤◁䛒䜚䠅ಙྕᙉᗘ (arb.unit.)௵ព䛾Ἴ㛗䛷䛾ከἼ㛗ྠ᫬ィ

30

䜈䛾ᛂ⏝

ከἼ㛗㽢೫ග䛸䛔䛳䛯ᚑ᮶ᡭἲ䜢⤌䜏ྜ䜟䛫䜛䛣䛸䛜ᐜ᫆䛷䛒䜚䚸ከ✀ከᵝ䛺 ᐃᑐ㇟䛻ᑐ䛩䜛ྠ᫬ほ䛜ྍ⬟ᵔᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

ᧈήែࠀЎԈӓЎήίᵢᵭᵟᵱὸඥửႉᑥήἻỶἒὊỆࣖဇ䝁䝠䞊䝺䞁䝖ⓑⰍග䛿㉥እᇦ䜎䛷䝇䝨䜽䝖䝹䜢ᗈ䛢䛶䛔䜛䛯䜑䛻䚸 ᐊຠᯝ䜺䝇䛷䛒䜛KϮ䛾྾཰ィ䛜ྍ⬟䛰䛜䚸㉥እᇦ䛾ⓑⰍගᙉᗘ䛜ᙅ䛟䚸ཷග⣲Ꮚ䜒ឤᗘ䛜ᝏ䛔䞉䞉䞉䖃䝁䝠䞊䝺䞁䝖ⓑⰍග䜢฼⏝䛧䛯DOASᐇ㦂㓄⨨ᅗ䖃DOASἲ䛾䜲䝯䞊䝆
KϮ≉᭷䛾྾཰䝷䜲䞁ĨсϱŵD;ZсϭϬŵͿ,DDd^h ϵϵϭϰ';ϭϭϬϬ䡚ϮϮϬϬŶŵͿdt>ĂƐĞƌ^ǇƐƚĞŵ
;ϭϬϬŵ:͕ϭϬϬĨƐ͕ϭϬ,ǌ͕ϴϬϬŶŵͿ<ƌ ŐĂƐĐĞůů䠄ϵŵ͕Ϭ͘ϱĂƚŵ䠅ΕϭϮŵ^ƉĞĐƚƌŽŵĞƚĞƌ
Εϯϱŵ

KϮⓎ⏕※ⓑⰍග䝇䝨䜽䝖䝹

ග※䛸ཷග⣔䛾
2Ⅼ㛫䛾ᖹᆒ⃰ᗘ䛜 ᐃྍ⬟

ΕϱϬŵ

䐤Ƶ D

䐣䡈 D

䐢ů D
䐡䡈 D

䐠ů D

ϭϬϬϬϬ

ϭ͘ϮϬ

ϭ͘ϬϬ
ϭϬϬϬ

ϭϬϬ

0m

dƌĂŶƐŵŝƚƚĂŶĐĞ

^ŝŐŶĂůŝŶƚĞŶƐŝƚǇ;ĐŽƵŶƚĂƌď͘ƵŶŝƚͿ

䐟ů D

Ϭ͘ϴϬ

Simulation

Ϭ͘ϲϬ

Data

Ϭ͘ϰϬ

ϭϬ
452 m

CO2䛾྾཰䜢᳨ฟ

Ϭ͘ϮϬ

Ϭ͘ϬϬ
ϭ
ϭϵϬϬ
ϭϯϬϬ ϭϰϬϬ ϭϱϬϬ ϭϲϬϬ ϭϳϬϬ ϭϴϬϬ ϭϵϬϬ ϮϬϬϬ ϮϭϬϬtĂǀĞůĞŶŐƚŚ;ŶŵͿ

ϭϵϱϬ

ϮϬϬϬ
tĂǀĞůĞŶŐƚŚ;ŶŵͿ

ϮϬϱϬ

ϮϭϬϬ

ᵕ

ᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

ႉᑥήἻỶἒὊỉܱဇ҄ỆӼẬề䝣䜵䝮䝖⛊dt䝺䞊䝄䞊䛻䜘䜛኱ᆺ䛾ⓑⰍග䝺䞊䝄䞊z㧗౯䛷䛒䜚䚸䝯䞁䝔䝘䞁䝇䛜」㞧
z኱ᆺ䛷ྍᦙᛶ䛜䛺䛔zkm䛻ཬ䜆ほ 䛜ྍ⬟ⓑⰍග䝷䜲䝎䞊䛻䜘䜛ᛂ⏝ྍ⬟ᛶ䛿ᗈ䛔䛜䚸ᐇ㝿䛾ほ 䛻䛿ග※䛜୙ྥ䛝ⓑⰍග䝷䜲䝎䞊䛾ᐇ⏝໬䛻ྥ䛡䛶኱Ẽ୰䛾CO2⃰ᗘ䛾䝘䝜⛊䝟䝹䝇䝺䞊䝄䞊䛸䝣䜷䝖䝙䝑䜽⤖ᬗ䝣䜯䜲䝞䞊䜢⤌䜏ྜ䜟䛫䛯ᑠᆺ䛾ⓑⰍග䝺䞊䝄䞊>h<K^Ͳ^DͲϮϬͲKD
;ϰϱŵt͕фϭŶƐ͕ϮϬŬ,ǌ͕ϰϮϬ䡚ϮϰϬϬŶŵͿᐃzᑠᆺ䛷䛒䜚ྍᦙᛶ䛜䛒䜛z䝍䞊䞁䜻䞊᧯స䛷䛒䜚䚸䝯䞁䝔䝘䞁䝇䜒ᐜ᫆z䝟䝹䝇䜶䝛䝹䜼䞊䛿ప䛔䛯䜑䚸ᩓ஘ಙྕ䛾ྲྀᚓ䛿ཝ䛧䛔

ϭϱĐŵ

ᵖ
ᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

И஖ỉᵱᶓᶎᶃᶐᶁᶍᶌᶒᶇᶌᶓᶓᶋήเỂỉᵢᵭᵟᵱ᬴ܱ
䖃Supercontinuumග※䜢฼⏝䛧䛯DOASἲ䛾ᐇ㦂 䖃䡚ϱϲϴŵ኱Ẽఏᦙ䝇䝨䜽䝖䝹
ϱϬϬϬϬ
H2O

CO2

Ϭ͘ϴ

ϯϬϬϬϬ
ϮϬϬϬϬ

Ϭ͘ϲ

,ϮK䛾྾཰

ϭϬϬϬϬϬ͘ϰKϮ䛾྾཰

dƌĂŶƐŵŝƚƚĂŶĐĞ

/ŶƚĞŶƐŝƚǇ;ĐŽƵŶƚͿ

ϰϬϬϬϬ

ϭ

Ϭ͘Ϯ

Ϭ
Ϭ
㻝㻟㻜㻜 㻝㻠㻜㻜 㻝㻡㻜㻜 㻝㻢㻜㻜 㻝㻣㻜㻜 㻝㻤㻜㻜 㻝㻥㻜㻜 㻞㻜㻜㻜 㻞㻝㻜㻜tĂǀĞůĞŶŐƚŚ;ŶŵͿ䖃DKdZE䛸䛾ẚ㍑䠄CO2⃰ᗘ䠖398 ppm䚸‵ᗘ䠖52%䠅

㻿㼡㼜㼑㼞㼏㼛㼚㼠㼕㼚㼡㼡㼙䜢฼⏝䛧䛯㉥እ྾཰ィ䛻䜘䜚 ᐊຠᯝ䜺䝇䛷䛒䜛㻯㻻㻞⃰ᗘᐃ䛜ྍ⬟䝇䞊䝟䞊䝁䞁䝔䜱䝙䜴䝮ග※䛿䚸ከ✀ከᵝ䛺᭷ᐖ䜺䝇䜈䛾ᛂ⏝䛜ྍ⬟䛷䛿䛒䜛䛜䚸ᩘ㻝㻜㻜䡉䛾኱Ẽఏᦙ㊥㞳䛜ᚲせ䛸䛺䜛䛸ᛂ⏝䛜ไ㝈䞉䞉䞉dƌĂŶƐŵŝƚƚĂŶĐĞ

ϭ

Ϭ͘ϴ

Ϭ͘ϲ

Ϭ͘ϰ
㻝㻥㻡㻜

㻞㻜㻜㻜
tĂǀĞůĞŶŐƚŚ;ŶŵͿ

㻞㻜㻡㻜

ᵗ

ᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

Ԉӓ᣽ầ‫ٻ‬Ẩẟɶហ‫ٳ‬ίᵫᵧᵰὸ᪸؏ỉᵱᵡήเầ̅ảễẟẦᾎ䖃Electro MIR䠄LEUKOS♫䠅
5000

Wavenumber (cm-1)
3000
2500
4000 3500

2000

dƌĂŶƐŵŝƚƚĂŶĐĞ

ϭ

KϮ;ϮĐŵͲϭͿ,ϮK;ϮĐŵͲϭͿϬ͘ϱ,ϮK;Ϭ͘ϭĐŵͲϭͿ
KϮ;Ϭ͘ϭĐŵͲϭͿ

Ϭ
ϮϬϬϬ

ϮϱϬϬ

ϯϬϬϬ
ϯϱϬϬ
ϰϬϬϬ
tĂǀĞůĞŶŐƚŚ;ŶŵͿ

ϰϱϬϬ

ϱϬϬϬ

ᅗ䠊ග㊰㛗300 mm䛾HITRAN䛻䜘䜛H2O䠄57%䠅䚸CO2
䠄398ppm䠅䛾㏱㐣䝇䝨䜽䝖䝹䛾ィ Ἴ㛗ศゎ⬟䛾㐪䛔୰㉥እ䛾㻿㻯ග※䜢฼⏝䛩䜛䛣䛸䛷䚸▷䛔኱Ẽఏᦙ㊥㞳䛷 ᐊຠᯝ䜺䝇䛷䛒䜛㻯㻻㻞⃰ᗘᐃ䛜ྍ⬟䛛䠛
䠄https://www.leukos-laser.com/our-products/electro-mir/䜘䜚䠅ᵏᵎ
ᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

ᵫᵧᵰỉᵱᵡήเửМဇẲẺ‫ٻ‬ൢɶᵡᵭᵐỉᚘย䖃ᐇ㦂㓄⨨ᅗ
ϲϬϬŵŵ

ϭϬϬϬŵŵ

KƉƚŝĐĂůĨŝďĞƌ

ϯϬϬŵŵ

FT-MIR ROCKET䠄ARCoptix䠅ᐃἼ㛗⠊ᅖ䠖㻞䡚㻝㻞㻌ʅŵἼ㛗ศゎ⬟䠖㻞㻌㼏㼙㻙㻝௨ୖ>ŽŶŐͲƉĂƐƐĨŝůƚĞƌ
ŽůůŝŵĂƚŽƌ

ƵŵŝƌƌŽƌ
KƉƚŝĐĂůĨŝďĞƌ

ϮϬϬϬŵŵz 㻝㻚㻢㻡㻌㽀㼙䛾䝻䞁䜾䝟䝇䝣䜱䝹䝍䞊䛷ບ㉳䝺䞊䝄䞊䠄㻝㻚㻡㻌㽀㼙䠅䜢㝖ཤz 䜰䜲䝸䝇ᚋ䛷ฟຊ䛿㻞㻜㻜㻌㼙㼃㻔㻜㻚㻤䡚㻢㻌㽀㼙䛾䝟䝽䞊䝯䞊䝍䛷 ᐃ㻕
5000

Wavenumber (cm-1)
3000
2500
4000 3500

2000

dƌĂŶƐŵŝƚƚĂŶĐĞ

ϭ,ϮK;ϮĐŵͲϭͿ

KϮ;ϮĐŵͲϭͿ୰㉥እ䛾Ἴ㛗㡿ᇦ䛿྾཰䛜኱䛝䛔䛾䛷䚸Ἴ㛗ศゎ⬟䛜㧗䛔䠄㻜㻚㻝㻌㼏㼙㻙㻝䠅ཷග䝅䝇䝔䝮䛷䛿䚸▷䛔㊥㞳䛷྾཰䛾㣬࿴䛜㉳䛣䜛䈈Ϭ͘ϱ,ϮK;Ϭ͘ϭĐŵͲϭͿ
KϮ;Ϭ͘ϭĐŵͲϭͿ

Ϭ
ϮϬϬϬ

ϮϱϬϬ

ϯϬϬϬ
ϯϱϬϬ
ϰϬϬϬ
tĂǀĞůĞŶŐƚŚ;ŶŵͿ

ϰϱϬϬ

ϱϬϬϬ

Ἴ㛗ศゎ⬟䛜ᑡ䛧ᝏ䛔䠄㻞㻌㼏㼙㻙㻝䠅䛾ཷග䝅䝇䝔䝮䛷䛿㣬࿴䛜㉳䛣䜙䛪䚸୰㉥እ䛾㻿㻯ග※䛾䝇䝨䜽䝖䝹 ᐃ䛜ྍ⬟ᵏᵏᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋ

ᵫᵧᵰỉᵱᵡήเỉ‫ٻ‬ൢˡ੿ࢸỉἋἬἁἚἽỉᚸ̖
KϮ

;ďͿ

;ĂͿ

KƉƚŝĐĂůĨŝďĞƌ

ŽƵďůĞĞǆƉŽŶĞŶƚŝĂů,ϮK͕KϮϯϬϬŵŵ

^ŝŶŐůĞĞǆƉŽŶĞŶƚŝĂů

ϯϬϬŵŵ

ϲϬϬŵŵ

ϳϬϬŵŵ

ϭϬϬϬŵŵ

ϭϳϬϬŵŵ

ϮϬϬϬŵŵ

ᅗ䠊኱Ẽఏᦙᚋ䛾୰㉥እ䝇䝨䜽䝖䝹㻿㻯ග⏕ᡂ䛾୰✵䝣䜯䜲䝞䞊䜔䝁䝸䝯䞊䝍ෆ䛾㻯㻻㻞 䛾྾཰䛜ぢ䜙䜜䜛䛯䜑䚸㏱㐣⋡䜢ィ⟬䛩䜛䛯䜑䛾྾཰䛾䛺䛔䝇䝨䜽䝖䝹 ᐃ䛜ᅔ㞴䈈ᅗ䠊ග㊰㛗300 mm䛷つ᱁໬䛧䛯㏱㐣⋡䛸Ἴ㛗4237.29 nm
䛷䛾㏱㐣⋡䛸ග㊰㛗䛾㛵ಀ䠄Ⅼ⥺䛿HITRAN䛾ィ⟬⤖ᯝ䠅ග㊰㛗㻟㻜㻜㻌㼙㼙䛷つ᱁໬䛧䚸㻟㻜㻜㻌㼙㼙䜢ග㊰㛗㻜㻌㼙㼙䛸䛧䛶䝥䝻䝑䝖䛩䜛䛸䚸㻴㻵㼀㻾㻭㻺䛾ィ⟬⤖ᯝ䛸䜋䜌୍⮴ᚓ䜙䜜䛯㏱㐣⋡䛿䚸ᚑ᮶䛾༢୍䛾㻱㼤㼜䜹䞊䝤䛷䛿㏆ఝ䛷䛝䛺䛔䛜䚸㣬࿴ᡂศ䛸㠀㣬࿴ᡂศ䛾㻞䛴䛾㻱㼤㼜䜹䞊䝤䛷㏆ఝ䛜ྍ⬟୰㉥እ㻿㻯ග※䛸Ἴ㛗ศゎ⬟䛾኱䛝䛺ཷග䝅䝇䝔䝮䜢฼⏝䛩䜛䛣䛸䛷䚸኱Ẽ୰䛾㻯㻻㻞⃰ᗘ䛾ィ 䛜ྍ⬟䟿ᵏᵐ
ᵐᵒᵎᵐᵏᵓ ᵲᶆᶃᴾᵐᵔᶒᶆ ᵡᵣᵰᶃᵱ ᵣᶌᶔᶇᶐᶍᶌᶋᶃᶌᶒᵿᶊᴾᵰᶃᶋᶍᶒᶃᴾᵱᶃᶌᶑᶇᶌᶅᴾᵱᶗᶋᶎᶍᶑᶇᶓᶋộểỜ」ᩘ䛾 ᐊຠᯝ䜺䝇䛾ྠ᫬ィ 䛻ྥ䛡䛶䚸୰㉥እ^ග※䜢฼⏝䛧䛯኱Ẽ୰KϮ
䜺䝇䛾㉥እ྾཰ィ 䜢ᐇ᪋z ϵϬϬ䡚ϰϴϬϬ Ŷŵ䛾୰㉥እ^ග※䜢฼⏝䛧䛶䚸ϮϬϬϬ ŵŵ䜎䛷䛾኱Ẽఏᦙ㊥㞳䛷䛾㉥እ䝇䝨䜽䝖䝹ィ 䜢ᐇ᪋z ග㊰㛗ϯϬϬ ŵŵ䛾䝇䝨䜽䝖䝹䛷つ᱁໬䛧䚸Ϯ䛴䛾ǆƉ䜹䞊䝤䛷㏆ఝ䛩䜛䛣䛸䛷኱Ẽ
୰KϮ⃰ᗘ䛾ホ౯䛜ྍ⬟

;ďͿ

;ĂͿ

ŽƵďůĞĞǆƉŽŶĞŶƚŝĂů^ŝŶŐůĞĞǆƉŽŶĞŶƚŝĂů

ϯϬϬŵŵ
ϳϬϬŵŵ

ϭϳϬϬŵŵ

୰㉥እ^ග※䜢฼⏝䛩䜛䛣䛸䛷䚸▷䛔྾཰
ග㊰㛗䛷」ᩘ䛾 ᐊຠᯝ䜺䝇䛾⃰ᗘホ౯
䛜ྍ⬟

௒ᚋ䛾ㄢ㢟
z 」ᩘ䜺䝇䛾ྠ᫬ィz 䛥䜙䛻㛗Ἴ㛗ഃ䜎䛷䝇䝨䜽䝖䝹ᖜ䜢ᗈ䛢䛯SCග※䛾฼⏝ᵏᵑ୊յ&(5H6εϱϛζΤϞ 

ࢦࣅ◁₍࡟࠾ࡅࡿ
ࢲࢫࢺᾘᩓಀᩘ㸫㉁㔞ኚ᥮ಀᩘ㸦MECF㸧ࡢ◊✲ߗൻ‫࣏ݓ‬ɼՑ߻ ‫ܜ‬ɼਈ ‫޻ܜ‬ɼ௥໌ୣ༦ɼ('DYDDQ\DP ໌‫ݻ‬Բ୉ָ‫ڂݜָڧ؂‬Ռɼ ࠅཱི‫ॶڂݜڧ؂‬ɼએཁ୉ָ୉ָӅ޽ָ‫ڂݜ‬ӅɼϠϱβϩ৚ๅʀ‫ـ‬েਭช‫ॶڂݜڧ؂‬‫ڂݜ‬͹ಊ‫ͳؽ‬໪ద• ξηφ࣯ྖΝԗֶదͶ؏଎ͤΖ͞ͳ͗Ͳ͘Ηͻสཤʙ༙༽Ͳ͍Ζɽϧ΢ξʖ
͖ΔಚΔΗΖভࢆ‫ͳ਼ܐ‬ξηφ࣯ྖͳ͹‫ܐؖ‬ʤ0(&)ʥ͗‫ع‬எͲ͍Ηͻɼϧ΢ξʖ؏଎͖Δξηφ࣯ྖΝԗֶదͶ࿊କ؏଎ͤΖ͞ͳ͗Ͳ͘Ζɽ• Ԧธ Ansmann et al. 2012, 2019࡯࠿ Ώೖຌ Sugimoto et al.2003; Shimizu etal.2011 Ͳ΍0(&)Ͷ‫ͤؖ‬Ζ‫ߨ͗ڂݜ‬ΚΗͱ͏Ζɽ͖͢͢͵͗Δɼξηφ࣯ྖΝࣰ଎ͪ͢‫ྭڂݜ‬ͺঙ͵͏ɽ• ຌๅͲͺɼβϑ࠯റͲࣰࢬ͠Ηͪξηφ‫ͳځـཻܐ‬ϧ΢ξʖ͹ಋ࣎؏଎͖Δ
ಚΔΗͪ0(&)ΝๅࠄͤΖ ɽ͞͹τʖϜͺຌεϱϛζΤϞͲ΍Ձౕ͖ๅࠄ͢
ͱͪ͗͘ɼࡤ೧݆Ώͮͳ࿨ชͳ͢ͱΉͳΌΖ͞ͳ͗Ͳͪ͘ Kai et al., 2023 ɽKai, K., K. Kawai, K. Ohara, Y. Minamoto, Y. Jin, T. Maki, J. Noda, T. Shiina, and E. Davaanyam: MassExtinction Conversion Factor (MECF) over the Gobi Desert by a Tethered-balloon-based OPC and a Ceilometer.
SOLA, 19, 269-273, 2023.

ξηφ‫ͳځـཻܐ‬εʖϫϟʖνʖ ϧ΢ξʖ͹ಋ࣎؏଎೧݆ೖɼξϧϱδχΪχ
‫ـ‬েୈͲ͹ξηφ‫؏ځـཻܐ‬଎ξϧϱδχΪχ‫ـ‬েୈͲ͹εʖϫϟʖνʖ؏଎ξηφ࣯ྖͳভࢆ‫਼ܐ‬͹؏଎$೧݆ೖ࣎ɼϠϱβϩೈ෨͹ξϧϱδ
χΪχ‫ـ‬েୈͲ‫ځـཻܐ‬Ͷޭࢆཛྷཽࣞࢢ‫ܯ‬23& $LU\7HFKQRORJ\3 Ν౧ࡎ͢ɼஏන͖
Δ৏‫ۯ‬PΉͲ͹ξηφ͹ཽ‫ܚ‬พཽࢢ਼ືౕΝ଎ఈͪ͢ɽʾξηφ࣯ྖ% ؏଎Ͷ࢘༽ͪ͢ϧ΢ξʖʤεʖϫϟʖνʖʥͺɼ9DLVDODऀ੣͹&/ͲɼഀௗQPɼ࣎ؔ
෾մ೵භɼ‫ؔۯ‬෾մ೵P͹‫ݰ‬ਲ‫ޛ‬๏ࢆཛྷ‫਼ܐ‬Ўb VUP ͗ಚΔΗΖɽʾভࢆ‫਼ܐ‬‫ځـཻܐ‬Ͷ౧ࡎͪ͢23&͹࢕༹㼜㼍㼞㼠㼕㼏㼘㼑

፦যԋईἜ὚ᾄώᾌᢈ᧺ྙ
ॄὣୖࠪἨἰἕź

䪭
ග※

䝺䞊䝄䞊ග

䪧

ֻႵᆋἼὀ‫פ‬ᓞἸἓ὜Ẫග᳨ฟჾ

Airy Technology P611

• ޭ‫ݱ‬ʁϫϱήϧ΢ϓϪʖδʖξ΢Ψʖχ• ࢾྋ‫ྖـۯ‬ʁ/PLQ• ࠹୉ೳౕʁ‫ ݺ‬/• ཽ‫ۢܚ‬෾ʁИPҐ৏ɼИPҐ৏ɼИPҐ
৏ɼИPҐ৏ɼИPҐ৏ɼИPҐ৏͹ஊ֌଎ఈ͢ɼ෾ຘ͹ཽ֦‫ܚ‬͹ཽࢢ਼Ν‫ى‬࿧ͤΖɽ

23&͹υʖνմੵ˛ޭࢆཛྷཽࣞࢢ‫ܯ‬23& $LU\7HFKQRORJ\3 ͺɼ௜‫ܚ‬γ΢θ Pm Pm Pm
Pm Pm Pm͹οϡϱϋϩͲɼΦΠϫμϩ͹ཽ‫ܚ‬พཽࢢ਼ືౕΝ଎ఈͤΖɽ˛ξηφ͹࣯ྖ0ͺɼξηφືౕН JFPΝՀఈ͢ɼ࣏ࣞͲ‫ͪ͢ࢋܯ‬ɽ͞͞Ͳɼ' L ͺ‫ش‬Ձฑ‫ۋ‬௜‫ܚ‬ɼοϡϱϋϩL    Pm ͖Δʤ! PmʥͲ͍Ζɽ
Q L ͺοϡϱϋϩL͹ཽࢢ਼Ͳ͍Ζɽ˛ξηφ࣯ྖ͹‫Ͳࢋܯ‬ͺɼ࣏͹ՀఈΝͪ͢ɽ̏ʥ௜‫ܚ‬PmҐԾ͹ξηφͺɼໃࢻͪ͢ɽ
̐ʥοΥϱϋϩ ʤ! Pmʥ͹‫Ͳࢋܯ‬ͺɼՀ͹৏‫ݸ‬௜‫ ܚ‬PmΝՀఈͪ͢ɽ
̑ʥ23&ͺඬ६‫ߏͲࢢཽܙځ‬ਜ਼͠Ηͱ͏Ζ͹Ͳɼඉ‫ܙځ‬ξηφ͹‫ܯ‬଎Ͷͺ‫͍͗ࠫޣ‬Ζɽξηφཽ‫ܚ‬෾ා߶ౕ90, 180, 270, 360, 450 and 540 m

ϧ΢ξʖ͹υʖνմੵ˛ϧ΢ξʖʤ9DLVDODऀ੣εʖϫϟʖνʖ&/ʥ͖ΔɼഀௗQPɼ࣎ؔ෾մ೵භɼ‫ۯ‬ؔ෾մ೵P͹‫ݰ‬ਲ‫ޛ‬๏ࢆཛྷ‫਼ܐ‬Ўb ]  VUP ͗ಚΔΗΖɽ

͞͞Ͳɼ Ў ] ͺ‫ޛ‬๏ࢆཛྷ‫਼ܐ‬ɼ W ͺޭָద޲͠Ͳ͍Ζɽ‫ޛ‬๏ࢆཛྷ‫ͳ਼ܐ‬ভࢆ‫਼ܐ‬ͺɼΦΠϫμϩͶΓΖϝʖࢆཛྷ੔෾

ͳ୉‫ـ‬෾ࢢͶΓΖϪʖϨʖࢆཛྷ੔෾
Ͷ෾͜ΔΗΖɽ˛)HUQDOG  ͹๏๑ͶΓΕɼϧ΢ξʖർΝՀఈ͢ͱɼΦΠϫμϩ ϝʖࢆཛྷ ͹‫ޛ‬๏ࢆཛྷ‫਼ܐ‬Ўͳভࢆ‫਼ܐ‬Ѝ NP Ν‫ͪ͢ࢋܯ‬ɽᾭὨᾄ̵Ἷ໛௚Л௝Ἔ὚ᾄώᾌᢈ᧺ྙॄἝୖࠪἸ἞὜˛ξηφভࢆ‫਼ܐ‬ʷ࣯ྖร‫਼ܐ׷‬0(&) ȘȘ 06Ў

JP ͺ࣏ࣞͲಚΔΗΖɽ

0Ѝξηφ࣯ྖೳౕͳভࢆ‫਼ܐ‬
߶ౕ90, 180, 270, 360, 450 and 540 m

ξηφ࣯ྖೳౕ0ͳভࢆ‫਼ܐ‬Ѝ͹૮‫਼ܐؖ‬ͺɼ༙қͲʤʥ͍Εɼ ࡇࡢMECFࡢ᪉ἲ
Ș

0Ѝ

͹༙ް੓ΝࣖࠨͤΖഀௗร‫׷‬
ഀௗQPΝ࢘ͮͪ࿨ชͺևໃͲ͍ΖɽഀௗQP͖ΔQP΃͹ร‫׷‬ΝࢾΊͪɽ6D\QVKDQGͺ'DODQ]DGJDG͹౨๼౨NPͶҒ஖ͤΖɽ

϶

 Ў݃Վ̏ʥ0(&)ͺ൅ਫ਼‫ݱ‬Ͷۛ͏ͳ͞ΘͲͺ୉͚͘ɼ෫ԾଈͲͺঘ͠͏ɽ
̐ʥ0(&)͹஍ͺξηφ͹ཽ‫ܚ‬෾ා͗୉͚͖͖͘Κͮͱ͏Ζɽ̑ʥͤ͵Κͬɼཽ‫ܚ‬෾ා͹Ϡʖχ͗୉͘͏Άʹɼ0(&)ͺ୉͘͏͞ͳ͗༩૟͠ΗΖɽ࣯ྖͺ୉ཽࢢ͹Ӫ‫ڻ‬

Г

0 Ѝ
ভࢆ‫਼ܐ‬ͺঘཽࢢ͹Ӫ‫ڻ‬౨ΠζΠͶ͕͜Ζ0(&)͹ஏཀྵద෾ා

ξηφ͹ཽ‫͗ܚ‬୉͘͏ͳ0(&)ͺ୉͚͘͵Ζ͖ʃ
0(&)ͳ༙ްൔ‫ܚ‬͹‫ܐؖ‬*DVWHLJHUHWDO

ξηφ͹༙ްൔ‫͗ܚ‬୉͚͘͵Ζͳɼ0(&)΍୉͚͘͵Ζɽ
Gasteiger et al. (2011)ͳ
Ansmann et al.(2012)ͺɼξηφ࣯ྖΝ௜ં଎ఈͪ͢͹Ͳͺ͵͚ɼϧ΢ξʖʙඏ෼ཀྵಝ੓͹‫ٱ‬ਬఈ͖Δ‫ٽ‬Όͪɽຌ࿨ช͹༑Ғ੓ͺʹ͞Ͷ͍Ζ͖ʃ

ࠠ߻૜Հઈʁࠠ߻૜಼͹ΦΠϫμϩͳԻҒΰྜᒙ㧗ᗘ䝷䜲䝎䞊㔜䛺䜚㛵ᩘ䠙䠍䜶䜰䝻䝌䝹⃰ᗘ

఩(㼻K)ஏ৏͹ΦΠϫμϩ
γϱϕϨϱή

ΰྜᒙ䛷䛿䚸ᑐὶ䛻䜘䜚䚸 ఩䞉Ỉ⵨Ẽ䞉䛭䛾௚≀㉁䛾⃰ᗘ䛜䜋䜌୍ᵝ䛻䛺䜛(Garratt,1992)䚹
䛣䛾䛣䛸䛿䚸ᆅୖ䛷኱Ẽởᰁ≀㉁䛜ᨺฟ䛥䜜䜛䛸䚸ΰྜᒙ䝖䝑䝥䜎䛷㍺㏦䛥䜜䜛䛣䛸䜢ព࿡䛩䜛䚹ᅜ❧⎔ቃ◊✲ᡤ䛾AD-net䝷䜲䝎䞊䛷䛿䚸ΰྜᒙ௬ㄝ䜢⏝䛔䛶䚸MECF䜢᥎ᐃ䛧䛶䛔䜛

(Shimizu et al. 2011)䚹

ΉͳΌຌ‫Ͳڂݜ‬ͺɼβϑ࠯റͲࣰࢬͪ͢ξηφ‫ͳځـཻܐ‬ϧ΢ξʖ͹ಋ࣎؏଎͖Δɼξηφভࢆ‫ྖ࣯਼ܐ‬ร‫਼ܐ׷‬ʤ0(&) Νਬఈͪ͢ɽ
̏ɿξηφ࣯ྖPͳভࢆ‫਼ܐ‬Ѝ͹૮‫਼ܐؖ‬ͺɼͲ͍ͮͪ ༙қʥɽ̐ɿξϧϱδχΪχ βϑ࠯റ ͹0(&)ͺɼഀௗͲɼഀௗͶ‫ͤࢋ׷‬Ζ
ͳͲ͍ͮͪɽ
̑ɿ੊ֆͲ؏଎͠Ηͪ݃ՎΝർֳͤΖͳɼ0(&)ͺ൅ਫ਼‫ݱ‬Ͷۛ͏ͳ͞ΘͲͺ୉͚͘ɼ෫ԾଈͲͺঘ͠͏ɽ͞͹݃Վ͖Δɼ0(&)͹஍ͺξηφ͹ཽ‫ܚ‬෾ා͗୉͚͘
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ʤ&-ߗൻ‫࣏ݓ‬ʥɼྫ࿪೧ౕ௙ख୉ָ‫ף‬૫ஏ‫ڂݜ‬ιϱνʖ‫ڠ‬ಋ‫& ڂݜ‬Ց߻‫ ܜ‬͹ঁ੔Νणͪ͜ɽClassification of Air Pollution Levels in
Urban Areas Using Satellites
Imagery and CORS GNSS and Machine
Learning Techniques
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Air Pollution&NW VZFQNY^ HTSXNXY TK \FYJW [FUTW ,FXJX FSI UFWYNHQJX 8TRJ TK YMJ FNW HTSYJSY FWJ UTQQZYNTSX YMFY MFX
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Overview••••

Moisture content, particularly water vapor, significantly
influences how pollutants spread and react in theatmosphere.
Moisture, including water vapor, can be quantified using
GNSS (Global Navigation Satellite System) technology.
GNSS analyzes delays in satellite signals as they
traverse the troposphere, allowing the measurement ofwater vapor.
Delays in GNSS signals involve two main components:
the dry component (ZHD) and the wet component(ZWD) that results from the presence of water vapor in
the atmosphere.

Research Objectives
•
•

•
•
•

Developing an Integrated Approach: Create a sophisticated and integrated method to
combine satellite imagery data (MODIS, Sentinel-5P, Himawari) with PWV data obtained from
Continuous Operating Reference Station Global Navigation Satellite System (CORS GNSS).
Enhanced Air Quality Monitoring: This approach will significantly improve our ability tomonitor and classify air pollution.
Utilizing Precise Machine Learning Techniques: Apply appropriate machine learning techniques
to process and analyze the integrated data effectively.
Accurate Pollution Prediction: The goal is to generate accurate machine learning models thatcan predict air pollution levels with a high degree of precision.
Measuring Model Performance: Evaluate the performance of the developed machine learningmodels using relevant evaluation metrics such as accuracy, precision, recall, and F1-Score.

Flow ChartRemote Sensing Satellite
1.Sentinel-5p:•Objective: Conduct global monitoring of air quality to understand atmospheric pollution.
•Main Instrument: Equipped with TROPOMI, a tool that can measure concentrations of
nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3)..
2.MODIS:•Objective: Used for remote sensing in climate studies and understanding atmosphericconditions.
•Main Instrument: MODIS produces Aerosol Optical Depth (AOD) data which provides
information about the aerosol content in the atmosphere..
3.Himawari 8:•Objective: Geostationary meteorological satellite focused on the Asia-Pacific region.
•Main Instrument: With the Advanced Himawari Imager (AHI) instrument, Himawari 8
provides data on particulate matter (PM), including PM 2.5 and PM 10, for weather andatmosphere monitoring in the region.

Himawari 8 product level 4 datasets

File type
Latest version
Area
Temporal

NetCDF
Version Beta
Global
1-hour (Level 4)

resolution
Spatial resolution

Longitude 0.375 deg., Latitude 0.37147 toPM 2.50.37461 deg. (Gaussian) (Pixel number: 960,
Line number: 480)
Data

Aerosol optical thickness at 550 nm (Sulfate, BC,
Organic Aerosol, Sea Salt, Dust), PM2.5 surface
conc., PM10 surface conc.

PM 10

Sentinel 5p dan MODIS Product Datasets
Satellite

Polution

Google Earth Engine Catalog

Sentinel-5p

CO

COPERNICUS/S5P/OFFL/L3_CO

Sentinel-5p

NO2

COPERNICUS/S5P/NRTI/L3_NO2

Sentinel-5p

O3

COPERNICUS/S5P/OFFL/L3_O3

Sentinel-5p

SO2

COPERNICUS/S5P/OFFL/L3_SO2

Modis

AOD

MODIS/061/MCD19A2_GRANULES

CO

NO2

O3

SO2

AOD

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9MJKTWRZQFKTW33NXJ]UWJXXJIFXKTQQT\X݂ேே ൌ ݂ሺ  ‫ ݔ‬െ ‫ݔ‬௜‫" ݔ‬ZSPST\SUTNSY‫ݔ‬௜ "PST\SUTNSYXଶሻ"*ZHQNIJFS INXYFSHJ݂ "PST\S[FQZJଶData Interpolation Result

CORS GNSS Station•••

Zenith Wet Delay (ZWD) is a parameter derived from Global
Navigation Satellite System (GNSS) data collected by CORS(Continuously Operating Reference Station) stations, which are
typically part of national or regional GNSS networks.
CORS GNSS stations are equipped with dual-frequency
receivers. They receive signals on two different frequencies (L1and L2), which are affected differently by the atmosphere. By
comparing the signals on these two frequencies, it's possible toseparate the dry component of the delay from the wet
component.
The ZWD calculation involves observing the residuals, or thedifferences in signal travel time, on the L1 and L2 frequencies.
By analyzing these residuals, the wet component of the delay
can be determined.

GNSS Precipitable Water Vapor (PWV) Analysis
ܹܸܲ ൌ ܳ ൈ ܼܹ‫ܦ‬ܳൌͳ ൈ ͳͲ଺
݇ߩఠ ܴ௩ ଷ ൅ ݇ଶᇱܶ௠ܶ௠ ൌ ͹ͲǤʹ ൅ ͲǤ͹ʹܶ௦ߩఠ "\FYJW[FUTWUWJXXZWJܴ௩ "\FYJW[FUTWHTSXYFSY݇ଷ "FYRTXUMJWH WJKWFHYNTSHTSXYFSY݇ଶᇱ "FYRTXUMJWH WJKWFHYNTSHTSXYFSYܶ௦ "QFSIXZWKFHJYJRUJWFYZWJCORS GNSS PWV Result
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2&*

Data Extraction and Correlation Analysis
Data Fusion Phase:In this phase, processed satellite imagery data from Sentinel 5P and MODIS is aligned with theobservational points of CORS GNSS (CSBY).
Pixel Value Extraction:
Pixel values from the satellite images are extracted at the CORS GNSS observation stations.
ZWD Data Aggregation:
Simultaneously, the Zenith Wet Delay (ZWD) data from CORS GNSS is aggregated to obtain daily
averages. This ensures that it is compatible with daily satellite data.
Correlation Analysis:
The final step involves analyzing the correlation between the satellite imagery data and CORS
GNSS-derived ZWD.

Datasets on CORS CJKT PointCorrelation Analysis
CO

NO2

O3

SO2

PM25

PM10

AOD

Water
Vapor

CO
NO2

O3
SO2
PM25
PM10
AOD

1,000
0,402

1,000

0,252

0,075

1,000

-0,0020,005-0,054

1,000

0,253

0,279

0,017

0,003

1,000

0,275

0,296

0,015

0,003

0,994

1,000

0,316

0,277

0,031

-0,011

0,251

0,260

1,000

-0,385-0,329-0,005-0,062-0,262-0,291-0,196

Water

Vapor

1,000

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Hiperparameter&ĞĂƚƵƌĞƐ͗ ^ĂƚĞůůŝƚĞĚĂƚĂƐĞƚƐ;^ĞŶƚŝŶĞůͲϱW͕DŽĚŝƐ͕,ŝŵĂǁĂƌŝͿĂŶĚ
KZ^'E^^WtsĚĂƚĂƐĞƚ͘ĂƚĂ>ĂďĞů͗'ƌŽƵŶĚͲďĂƐĞĚŵĞĂƐƵƌĞŵĞŶƚƐĨƌŽŵ</:ĂŬĂƌƚĂŝƌYƵĂůŝƚǇDŽŶŝƚŽƌŝŶŐ^ƚĂƚŝŽŶ;^W<hͿͲ <ĞůĂƉĂ 'ĂĚŝŶŐ

NO2

O3

Water
vapor

SO2

PM25

WDϭϬ

K

0.033261 0.000168 0.126112

5.44068E-05

ϲ͕ϴϰϲ

ϲ͘ϵϮϱ

Ϯϲϴ

33.439 Medium

0.029858 7.13E-05 0.119236

0.000683666

20

ϭϯ͘ϵϵϮ

ϰϰϯ

0.000203878

88.4

ϭϭ͘ϳϲϬ

ϲϭϴ

33.344 Medium
33.250 Medium

0.030986 8.17E-05 0.122962 -6.66607E-05

156.8

ϭϯ͘ϳϳϬ

ϳϰϬ

33.251 Good

ϳ͘ϲϳϳ

ϳϮϵ͘ϱ

0.030422 8.92E-05 0.120613

Value

smote__k_neighbor[3, 5, 7]

rf__n_estimator[50, 100, 150]

Rf_max_depth[None, 5, 10, 15]Labels (Y)Features (X)COParameter

0.040017 0.000197 0.122623 -0.000144743 398.75

33.252Caregory

UnhealthyModel Training ResultĐĐƵƌĂĐǇ͗Ϭ͘ϴϬϭ
precision

recall

Good

0.83

0.91

0.42

242

Medium

0.78

0.59

0.86

232

0.79

0.91

0.26

211

Unhealthy

Training result in DKI Jakarta

f1-support supportZĂŶĚŽŵ&ŽƌĞƐƚDŽĚĞůdĞƐƚŝŶŐ
Ѭ

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Ѭ

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precision

recall

f1-score support

Good 1.00
Medium 0.84

1.00

1.00

3

1.00

0.91

16

Unhealt 0.00
hy

0.00

0.00

3

Conclusion
1.

Accuracy in the training stage reached 80.17%, while in the testing stage it increased to 86.36%. This showsthat the model succeeded in generalizing well on the test data.

2.

The model shows good performance in classifying the majority class 'GOOD' 1.00 and 'MEDIUM' 0.84, withhigh precision and recall values in both datasets.

3.

The 'UNHEALTHY' class shows unsatisfactory performance, as seen from the low precision values of 0.00
and recall of 0.00 at both training and testing stages.

4.

Improving model performance, especially for the 'UNHEALTHY' class, is the main focus. Further evaluationand model adjustments need to be carried out to improve the ability to recognize this class.

Validation of satellite NO2 and HCHO with
MAXDOAS data - GOME-2(A/B/C), OMI,TROPOMI and GEMS
Gaia Pinardi, Hitoshi Irie,
Steven Compernolle, Tijl Verhoelst, Isabelle De Smedt, Bavo
Langerock, Jean-Christopher Lambert, and Michel Van Roozendael
And many data contributors:

The 26th CEReS Environmental Remote Sensing Symposium , 15 February 2024

NO2 and HCHOBoth are central components of tropospheric chemistry: related to tropospheric ozone (link to air
quality and climate change) and hydroxyl radical OH (main cleaning agent of our troposphere)Æ can be measured by satellites and ground-based remote sensing in the UV-vis (DOAS technique)Æ Long datasets available (e.g.: GOME-2 on MetopA/B/C, 2007 to now, http://acsaf.org)
Æ Recent satellites (TROPOMI/S5p, GEMS/GK2B): high spatial/temporal resolution

NO2

HCHO

Satellite instruments~800km100km• LEO: Heliosynchronouspolar orbits• Altitude: ~800 km• 14 orbits/day

GOME-2 daily coverage
spectral coverage

GEOstationnary orbit

Ground-based instruments
MAXDOAS
NO2 & HCHO

national networks in the past decade, growing n° of
instruments and ongoing projects for harmonization

stratosphereLowertroposphere

DirectSun
FTIR, PGN

Total column
measurements during
the day, when sunny

Troposphericmeasurements
during the day

FTIR network since ‘90(instruments in NDACC,HCHO prod. (Vigouroux etal., 2018; 2020))
profiles in the 0-4
km altitude range
with DOFS
between 1.5 and 3

H. Irie
PGN network(~2018) and few
e.g.: Irie et al., 2008; 2009; 2011;2019; 2021; Damiani et al. 2022

before (NASA +ESA)

Use of MAXDOAS for satellite validation
Comparison method: Typically: select satellite pixel(s)close to the site (/average withinradius, eg for HCHO)

Challenging aspects:
Small-scale gradients in both time and space (4-D)
 Rapid temporal variations:

SCIAMACHYOMI

 Average/interpolate the
MAXDOAS at the SAT overpass time Use same VAL approachat all the sites
 ideally: harmonization
within the sites/groups

 Horizontal gradients:
NIDFORVALS5PVTMAXDOAS: Several retrieval methods exists: geometricalapproximation, Optimal Estimation and parametrized profiling –mostly focus on VCDtropo
DirectSun: Mostly harmonized instruments & retrievals (Pandora
PGN & FTIR instruments– VCDtot)

Spatial averaging effect on
pixels size:
OMI: 13x24 km²
SCIAMACHY: 60x30 km²

SCIAMACHY

Brinksma et al., 2008; Celarier et al. 2008

Use of MAXDOAS for satellite validation

NO2e.g.: for GOME-2
comparisons

Large effort on deployment & harmonization since the S5p launch

HCHO

Validation illustration- Comparisons of NO2 and HCHO GOME-2 GDP 4.8 to MAXDOAS in Chiba:

MAXDOAS
SAT

Pinardi et al. 2020

NO2

40x80km²~9h30LT overpass

HCHO
Metop

Validation illustration- Comparisons of higher resolution satellites: TROPOMI and GEMSMAXDOAS
SAT

HCHO

NO2

Oct 2017 -> …
3.5x5.5km²~13h30LT overpass

TROPOMI/Sentinel-5P

Feb 2020 -> …
3.5x8km²
hourly daytime

GEMS/GK2B

NO2

NO2 Validation results in Chiba

1UT, ~9LT~13h30LT~9h30LT~13h30LT~9h30LTGEMS NO2 over-estimation is« atypical »; NIST confirmed
residual problems into thecurrent v2.0 GEMS L2 dataset
for the stratospheric correction.

HCHOHCHO Validation results in Chiba

1UT,
~9LT

~9h30LGEMS HCHO under-estimationis coherent with other satellite
products.

HCHO

HCHO Validation results in Chiba

GEMS HCHO under-estimationof the GB is also found withOMI and TROPOMI early
afternoon products (~13h30LT).

Update of De Smedt et al., AMT 2021

HCHO

GEMS HCHO Validation summary

GMAP 2021
SIJAQ 2022

NO2 Validation overview
NO2
MAXDOAS

Higly
polluted>15x1015<2 x1015
clean
sites

The NO2 comparison results strongly depend on the location of
the station; GOME-2 smaller than GBÆPartly due to the impact of local pollution sources, notadequately sampled at the coarse resolution of GOME-2(representativeness mismatch of the horizontal NO2 variability& dilution in 100km wide pixels)

Update of Verhoelst et al., AMT 2021; ROCVRs (#21)

Update of Pinardi et al., AMT 2020; ACSAF VR 2023

HCHO Validation overview
HCHO
Higly polluted>8x 1015

clean sites<2,5 x1015

OMI QA4ECVThe HCHO comparison results strongly
depend on the location of the station:
GOME2: 50/26/71 %;- positive bias for clean stations (< 2.5
S5p: +34%
Pmolec/cm2)- negative bias for high emission
GOME2: -50/-43/-35 %;stations (> 8 Pmolec/cm2)
S5p: -30%
Vigouroux 2020; De Smedt et al 2021

Pinardi et al. ACSAF VR 2023

Conclusions
Long-term validation of GOME-2, OMI, S5p, GEMS NO2 and HCHO columns using ~70 ground-based
stations (FTIR, MAXDOAS and PGN):- Importance of harmonized approach for validation to allow checking coherence among the satellites &highlighting effect of station location:•

NO2: generally under-estimated by SAT (in part due to spatial inhomogeneities & representativenessÆ prb!)
mismatch within large satellites pixels), except GEMS v2.0 that over-estimate (Æ•HCHO: negative bias in polluted conditions (> 8 Pmolec/cm2) and positive bias for clean stations (< 2.5
Pmolec/cm2)- A-SKY MAXDOAS data are key for the long-term monitoring and validation in Asia – link between
historical and recent sensors, such as GEMS.

- Future: the creation of NO2 and HCHO TCDR (ESA CCI+precursor project) for the improvement of thee
retrievals & better consistency between SAT instruments Æ need for long-term validation work!
- Essential harmonization/homogeneity of the GB network(s) to use them as Fiducial Reference
Measurements Æ FRM4DOAS: MAXDOAS centralized processing system (including profiles retrievals,
that are a key (unknown) assumption for the satellite retrievals) + PGN, FTIR networks Æ ground-based
instruments intercomparison and synergy!

Thank you! Questions? gaia.pinardi@aeronomie.be 
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静止気象衛星 「ひまわり」の海面温度による海洋熱波の高精度検出
XIAO QI 1, YANG WEI 1, QIAO ZHI 1 (1.CHIBA UNIVERSITY)
Keywords：Marine Heatwave, Sea Surface Temperature, Himawari, NOAA OISST【研究背景】【結果】

海洋熱波ーー数日から数年にわたり急激に海水温が上昇する現象• 開始日と終了日を持つ• 連続して5日以上• 海面温度（SST）が季節変動の90パーセンタイルを超える• 2つの海洋熱波現象の間隔が3日未満の場合 → 単一の海洋熱波❏ ひまわり SSTデータによる海洋熱波の検出と分析❏ 被害▪ 漁業収入の損失。
▪ 海の食物連鎖網を破壊し、生物多様性を一変させてしまう。
▪ 2016年の海洋熱波後、サンゴ礁の大量死や変化が観察された。
❏ 現状▪ 海洋熱波の発生は20世紀中期以降、50％以上増加している。
▪ 海洋熱波の検出には主にNOAA OISSTを利用する。
(Hobday, A. et al.,2016)

NOAA OISST

Fig.3. The effects of time series length on the maximum intensity of focal MHWs from in-situ dataset in 2017 at#5200087.

HIMAWARI SST

観測記録：
42年（1982-2023）

観測記録：
8.5年(2015.7-2024.1)

時間解像度：
0.5d/1回

時間解像度：
10min/1回❏問題点▪ 低時間解像度→
極端な温度変化を見逃し▪ 持続的な曇り→
曇が多い地域で観測不能▪ 平滑化→
検出結果の精密度が低い❏利点▪ 高時間解像度→検出が精密的に可能❏疑問▪ 短期間の観測記録が十分かどうか（従来の研究は少なくとも20
年間のデータを利用する）

Fig.1. Comparison between NOAA OISST & Himawari
SST data.

Fig.4. The spatial distribution map showing the frequency of MHWs in 2016-2023 with
Himawari SST data.

❏ NOAA OISSTデータによる検出結果との比較

Fig.2. The difference between GEO
satellites and LEO satellites.

【目的】
将来の海洋熱波予測に、より精度高いデータを提供するため、ひまわり
SSTデータを利用して、2016年から2023年までの海洋熱波の高精度検出を行い、その結果を分析する。

Fig.5. The spatial distribution map showing the frequency of MHWs in 2016 with (a) Himawari SST data and (b)NOAA OISST. And (c) The pixel counts of MHWs frequency in 2016 comparing Himawari and NOAA OISST.

❏ 混合層深度との関連性分析【研究方法】❏ ひまわり SSTデータによる海洋熱波の検出と分析① 閾値の算出
𝑇90 𝑗

𝑃90 𝑋

𝑤ℎ𝑒𝑟𝑒 𝑃90 𝑖𝑠 𝑡ℎ𝑒 90𝑡ℎ 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒
𝑎𝑛𝑑 𝑃90 𝑋 𝑤ℎ𝑒𝑟𝑒 𝑋
𝑇 𝑦, 𝑑 |𝑦𝑠 𝑦 𝑦𝑒, 𝑗 5

𝑑

𝑗

5

(Eric C.J. et.al, 2018)② 時系列長さの評価
時系列の短縮で、時系列の長さが海洋熱波の検出に与える影響の評価

Fig.6. Time series of SST (black line) and mixed layer depth (purple line) for Himawari SST and NOAA OISST
between January and December 2016 of one pixel in the study area. The red shaded areas represent the MHW periods.

❏ NOAA OISSTデータによる検出結果との比較▪ 30年間のNOAA OISSTデータによる結果と比較される【結論】▪ ひまわり八号のSSTデータは２５kmにリサンプリングする▪ ２０１６ー２０２２年海洋熱波の発生は減少しました。その減少は、海面温度と非常に強い関連性があります。
▪ 海洋熱波の発生はエルニーニョ/ラニーニャ現象の影響を受けている。
▪ 両方の結果により、重大な海洋熱波がほぼ同じ地域で検出されます。
▪ NOAA OISSTデータにより、２０１６ー２０２２年海洋熱波の減少を示していますが、ひまわりでの結果ほど顕著ではない。
▪ NOAA OISSTの結果は、ひまわりの結果と比較して混合層深度との関連性が低い。
▪ より短い年数のデータを使用しても、ひまわりでの検出結果は依然として高い信頼性を持っている▪ 比較する海洋熱波の特性ーー頻度❏ 混合層深度との関連性分析
混合層にエネルギーを貯蔵

淺化

より少ない
水量に蓄積

水温上昇

混合層の深さと海面温度の時系列グラフを作成し、海洋熱波が発生する期
間中の変化と関連性について分析します。
ひまわりが海洋熱波検出において信頼性が高いことを証明するために行う。

E-mail: Yvette9624@gmail.com
Yang lab., Center for Environmental Remote Sensing, Chiba University

Reference:[1] Hobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, et al. 2016. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr.
141:227–38.
[2] OLIVER Eric C J，DONAT M G，BURROWS M T，et al. Longer and more frequent marine heatwaves over the past century[J]. Nature
Communications，2018，9：1324.

Monitoring active volcanoes by means geostationary
satellite observations
GENZANO NICOLA 1,*, FALCONIERI A.2,3 HATTORI K.4,5, HIGUCHI A.5, MARCHESE F. 2,3, PERGOLA N. 2,3, TRAMUTOLI V. 6,3
1 Department ABC (Architecture, Built Environment and Construction Engineering) , Politecnico di Milano, Via Ponzio 31, 20133 Milano, Italy
2 Institute of Methodologies for Environmental Analysis, National Research Council, C. da S. Loja, 85050 Tito Scalo (Pz), Italy
3 Space Technologies and Application Centre (STAC), 85100 Potenza (Italy)
4 Graduate School of Science, Chiba University, Yayoi 1-33, Inage, Chiba, 263-8522, Japan
5 Center for Environmental Remote Sensing, Chiba University, Yayoi 1-33, Inage, Chiba, 263-8522, Japan
6 School of Engineering, University of Basilicata, Via dell’ Ateneo Lucano, 10, 85100 Potenza, Italy* Contact author: nicola.genzano@polimi.it
Several studies have shown the important role that geostationary satellite sensors can play in the monitoringof active volcanoes. Indeed, the geostationary attitude allows for a quasi-continuous observation of the
volcanically active regions on Earth, favoring the rapid detection and the early warning of new phase ofthermal unrest, as well as the effective monitoring of short-lived eruptive events. Among the geostationary
satellite sensors, SEVIRI (Spinning Enhanced Visible and Infrared Imager), onboard the European Meteosat
Second Generation (MSG) geostationary satellites, has largely been used to monitor thermal volcanic activity,
thanks to its channels, in the medium infrared (MIR) and thermal infrared (TIR) regions of the electromagnetic
spectrum, and temporal (i.e., a high frequency of observation, with refreshing time of 15 minutes) features,
despite the coarse spatial resolution (3 km at nadir) (e.g., [1]). The ABI (Advanced Baseline Imager) and AHI(Advanced Himawari Imager) radiometers, respectively aboard the GOES-R [2] and Himawari-8 [3] satellites,by providing data every 10 min, with a 2 km spatial resolution in the MIR and TIR bands, have further
increased the monitoring capabilities of active volcanoes from space (e.g., [4]; [5]).
In this study, we investigate the recent and intense volcanic eruption occurred at Home Reef (Tonga)
submarine volcanoes, which produced new island emerged from oceans. Specifically, we show the results
retrieved using GOES-R ABI data and by applying the well-known RST (Robust Satellite Techniques; [6]) multitemporal approach. The latter is an advanced scheme of satellite data analysis which was largely used todetect and monitor volcanic thermal features in different geographic areas (e.g., [7]; [8]).
In more detail, RST is a change detection approach comparing the satellite signal to be investigated with an “unperturbed” background, which is computed by means of a long-term
statistical analysis of multi-annual cloud-free satellite records, acquired in similar observational conditions (e.g., same season/month and time/hour of acquisition). The main added value of
RST is the independence from the fixed thresholds, including those generally used to identify volcanic thermal anomalies by means of MIR and/or TIR data. Fixed thresholds, althoughimplemented also by some well-established methods performing operationally (e.g., MODVOLC; [9]) are generally effective in specific conditions (e.g., in presence of intense thermalfeatures). The RST approach, by automatically identifying statistically significant changes of the MIR/TIR signals, without a priori definition of the thresholds, is capable of guaranteeing highperformances in detecting hotspots (with a high trade-off between reliability and sensitivity) regardless of environmental/observational conditions (e.g., [10]).
These features are confirmed by results shown and discussed in this work. They indicate that RST was capable of promptly detecting the onset of the sub-marine Home Reef (Tonga)
eruption by means of GOES-R ABI. Porting the RST scheme to GOES-R ABI observations enabled a prompt detection (about 2h after the official eruption starting, identified only by means of
the volcanic gas plume emitted) of the first thermal signs of the Home Reef eruption which took place on 10 September 2022 at 01:39 LT.
Moreover, the continuous monitoring of the eruptive activities, performed thanks to the high temporal resolution offered by the geostationary satellites, allowed for an effective analysis of
the space-time evolution of the thermal anomalies occurring in the hours following the main event.

RST METHODOLOGYThe RST method ([6]) uses a local variation index, defined after processing satellite images in thespace-time domain named Absolutely Local Index of Change of the Environment (ALICE), todetect thermal anomalies ([7]):MIR( x, y , t )> TMIR ( x, y, t )  P MIR ( x, y) @(1)

V MIR ( x, y )In equation (1), TMIR(x,y,t) is the brightness temperature measured in the MIR (Medium Infrared)
band at time t for each pixel (x,y) of analyzed satellite scenes. The terms μMIR(x,y) and σMIR(x,y)
represent the temporal mean and standard deviation calculated at the pixel level after processing
multiyear time series of homogeneous (i.e., same spectral channel/s; same month, same overpasstime), cloud-free satellite records and removing possible signal outliers (e.g., hot targets) bymeans of the kσ clipping filter.

INVESTIGATIONSS OVER
R HOMEE REEFF (TONGA)) DURINGG SEPTEMBER
R 20222 BYY USINGG GOES-R/ABII OBSERVATIONS
The time series of the ALICE index computed over the Home Reef (Tonga)
area, in the period 9 September at 18:00 LT 10 September 2022 at 06:30
LT, shows that before the early morning of 10 September values of  MIR(x,y) < 2 mostly occurred (see blue bars). A first significant increment of
the index ( MIR (x,y) > 2.6) was recorded on 10 September at 03:40 LT (seered bars and the MIR image, bright pixels, on the right) indicating the time
(2h after the eruption start) when the thermal anomaly at the Home Reef
became detectable by satellite through GOES-R ABI observations. Negativevalues of the same index were observed in presence of clouds.

September 10, 2022
03:40 LT

REFERENCES[1] Pergola, N., Marchese, F., Tramutoli, V., Filizzola, C., Ciampa, M. (2008). Advanced satellite technique for volcanic activity monitoring and early warning. Annals of Geophysics, 1, 51.
[2] Schmit, T. J., Griffith, P., Gunshor, M., M., Daniels, J., M., Goodman, S., J., Lebair, W., J. (2017). A Closer Look at the ABI on the GOES-R Series. Bullettin of the Americal Meteorological Society, Vol. 98, Issue 4.
[3] Cheng D. (2015). Preliminary assessment of the Advanced Himawari Imager (AHI) measurement onboard Himawari-8 geostationary satellite. Remote Sensing Letters, Vol. 6, Issue 8.
[4] Thompson, J. O., Contreras-Arratia, R., Befus, K. S., Ramsey, M. S. (2022). Thermal and seismic precursors to the explosive eruption at La Soufrière Volcano, St. Vincent in April 2021. Earth and Planetary Science Letters, 592, 117621.
[5] Falconieri, A., Genzano, N., Mazzeo, G., Pergola, N., Marchese, F. (2022). First Implementation of a Normalized Hotspot Index on Himawari-8 and GOES-R Data for the Active Volcanoes Monitoring: Results and Future Developments. Remote Sensing, 14(21), 5481.
[6] Tramutoli, V. (2007). Robust satellite techniques (RST) for natural and environmental hazards monitoring and mitigation: Theory and applications. International workshop on the analysis of multi-temporal remote sensing images, Leuven, Belgium, 18-20 July 2007, (pp. 1-6).
[7] Pergola, N., Marchese, F., Tramutoli, V. (2004). Automated detection of thermal features of active volcanoes by means of infrared AVHRR records. Remote Sensing of Environment, 93(3), 311-327.
[8] Marchese, F., Filizzola, C., Genzano, N., Mazzeo, G., Pergola, N., Tramutoli, V. (2011). Assessment and improvement of a robust satellite technique (RST) for thermal monitoring of volcanoes. Remote Sensing of Environment, 115(6), 1556-1563.
[9] Wright, R., Flynn, L., Garbeil, H., Harris, A., Pilger, E. (2002). Automated volcanic eruption detection using MODIS. Remote sensing of environment, 82(1), 135-155.
[10] Marchese, F., Falconieri, A., Pergola, N., Tramutoli, V. (2014). A retrospective analysis of the Shinmoedake (Japan) eruption of 26–27 January 2011 by means of Japanese geostationary satellite data. Journal of volcanology and geothermal research, 269, 1-13.

ひまわり 8/9 号エアロゾルプロダクトを用いた
陸域大気補正アルゴリズムの検討

P-08*山本浩万1), 入江 仁士2)

1) 産業技術総合研究所デジタルアーキテクチャ研究センター
2) 千葉大学環境リモートセンシング研究センター
背景・目的・静止気象衛星ひまわり8/9号に搭載されているマルチスペクトルセンサーAHIセンサの空間分解能は0.5km〜2kmであるが、10分の高時間分解能で地球の半球を観測することができるため、雲コンタミの影響を回避することが期待できる。
・一方、陸域植生に関する高次プロダクトにおいて、地表面反射率を導出する大気補正アルゴリズムは重要である。また、JAXA EORCではエアロゾルプロダクト(光学的厚さ、オングストローム指数)の配布を始め、大気補正アルゴリズムの高精度化も期待できるようになった。
・大気補正アルゴリズムにおいては、エアロゾルなどのパラメータについて再解析プロダクトや他衛星プロダクトを用いることが多いが、ひまわり8/9号エアロゾルプロダクトは空間解像度や準リアルで処理できる点で大気補正処理に組み込む上で非常に有利である。
・本研究は、地上観測値および他衛星センサのプロダクトを用いたひまわり 8 号 AHI エアロゾルプロダクトの精度評価を示し、さらに大気補正アルゴリズムに入力した際の影響評価などを行った

POM-02が設置されている検証サイト

   

   

-

•
•
•
•

.
 $

)

.
$
.
$–2021年1月21日、2月6日、3月24日、4月20日、5月4日、6月9日、7月19日、8月5日、9月23日、10月6日、11月5日•ひまわり8/9号L2ARP (10min)、L3ARP (HourlyおよびDaily)

TKY•Terra/Aqua MODIS MOD08_D3、MYD08_D3 (Daily)• エアロゾルパラメータによる大気補正アルゴリズム評価• 6sV2.1を用いた大気上端、エアロゾル補正あり/なし反射率の計算• 放射照度 : Kurucz (2006)モデル• 大気モデルFHK
図.2 検証サイト–オゾン：Aura OMI OMTO3e(0.25deg) (2014-2022)

表.4 POM02の仕様 !4*(.+.(&7.32
&0++.*0)3+9.*:
.2.1816(&77*5.2,&2,0*

図.1 AHIセンサバンド1~6の応答関数$&9*0*2,7-6

$&9*0*2,7-6*0*(7.32*7*(735+'+0

,+

      /+ +'+0

, 

, 

表.3 ひまわり8/9号エアロゾルプロダクトと他のエアロ
ゾルプロダクトとの比較
-+#0"/

$),+- (-$.+(0/'+* 
%(+! (-$ * (3.'.

"

+'+0

,$

,$

 

"

+'+0

,,') 1 -' &! $#%

–時間平均および日平均

TGF(%(*• -.
,$• -.
,•SKYNET @ TGF, TKY, FHK–09:00~15:00 (JST)–Interval : 約 10 分

•岐阜大高山試験地(TKY) : N36.14527 [deg], E137.4234 [deg], 2004 ~–天空画像の目視判読およびTerra/Aqua MODIS画像     0++#
- ",- " ) ( %( *• 
 (  (  ",•ひまわり8/9号エアロゾルプロダクトの検証•富士北麓フラックス観測サイト(FHK) : N35.44352 [deg], E138.76472 [deg], 2004 ~表.2 ひまわり8/9号エアロゾルプロダクト   

用いたデータと方法•筑波大CRiES(TGF) : N36.11434 [deg], E140.09599 [deg], 2002~

Band6

Band5

CEReS  
SpatialBand gridded   resolution
data
[km]

1 VIS010.47063
1
2 VIS02
0.51
1
3 EXT01
0.63914
0.5
4 VIS030.85671
5 SIR01
1.6101
2
6 SIR022.25682
7 TIR01
3.8853
2
8 TIR026.24292
9 TIR03
6.941
2
10 TIR04
7.3467
2
11 TIR05
8.5926
2
12 TIR06
9.6372
2
13 TIR07
10.4073
2
14 TIR08
11.2395
2
15 TIR09
12.3806
2
16 TIR10
13.2807
2

Band4

Band1
Band2

表.1 ひまわり8/9号AHIセンサ仕様

Band3

ひまわり8/9号AHIセンサ仕様とエアロゾルプロダクト

*&685*1*2713)*6

, /' (-$.+(0/'+* '(3#$%2 #$%&+0-(3#$%2

5.9*5
<.187-&2,0*%*2.7-&2,0*!826*2635

#$%

31182.(&7.3227*5+&(*
3:*5(3268147.32
3:*5$*.,-7

TGF̲Tsukuba–水蒸気：Terra/Aqua MODIS MOD08_D3/MYD08_D3 (2014-2022)• エアロゾル• AOT@550nm (POM02 AOT@500nm)• オングストローム指数 + 複素屈折率 (POM02)• ユンゲ分布を仮定• 地表面反射率)*,5**6
)*,5**6
:&9*0*2,7-6         
21
.07*5:-**01*7-3)!-357:&9*0*2,7-6!.0.(324-373).3)*32,:&9*0*2,7-62&6*0*1*27
860*13735
  )*,5**6
 )*,5**6
*0*1*2760.(326*2635
.5*(7630&55&).&7.3213)*
!(&77*5.2,630&55&).&7.3213)*!
$ #  # !/;5&).31*7*5 /,– Fir_Tree IH91-2 (USGS Digital Spectral Library splib05a)– Brown fine sandy loam (ECOSTRESS Spectral Library Version 1.0)

&'0**7( /,+0-(3 '(3
+*/&(3 #$%2 #$%1  %1#

分野横断型プロダクト提供システム（P-Tree）により配布されている

&'0**64*(.+.(&7.32
&'0*0*2,7$*.,-7
3:*5(&'0*).&1*7*5
31182.(&7.32(&'0*1 147.32
/,!#" (35* =!#!#(35* ).&1*7*5

 =図.3 POM02、CE318Tおよび天空Camera (ADFC)

図.4 天空画像に

図.5 地表面反射よる目視判読率のサンプル

図.6 フローチャート

結果

AOT@500nm=0.02-0.5

AE=0.8-1.7

図.7 SKYNETデータとひまわり8/9号エアロゾルプロダクト(
図デ タと まわり 号
ゾ プ ダクト L2ARP, L3ARP
Hourly, and L3ARP Daily)との比較

continental

maritimeurban

biomass_burning

図.12 エアロゾル補正あり反射率(地表面反射率)とエアロゾル補正
なし反射率との相対誤差 (Fir(モミ), SZA=25.44 [deg], VZA=0[deg],

図.8 2014~2022年のOMTO3eオゾン全量観測

図.9 MOD08_D3/MYD08_D3水図.10. 2014~2022年のSKYNET

図.11 2014~2022年のSKYNET 複素屈折

値およびMOD08_D3/MYD08_D3水蒸気量プ

蒸気量プロダクトとAERONET

AOTとAEの日毎の平均値(@TGF,

率(@TGF, TKY, FHK)の日変化

ロダクトの日変化

水蒸気量(TGF_Tsukuba)の比較

TKY, FHK)(a)水蒸気量およびオゾン量の違いによる変化(夏季(b)エアロゾル光学的厚さおよびオングストローム(c)水蒸気量およびオゾン量の違いによる変化(冬季(d)エアロゾル光学的厚さおよびオングストローム

における植生)指数の違いによる変化(夏季における植生)における土壌)指数の違いによる変化(冬季における土壌)

RAA=0.0 [deg],H=0.0 [m], H2O=0.419g/cm2, O3=480DU)

図.13 エアロゾル補正あり反射率(地表面反射率)、エアロゾル補正なし反射率、大気上端反射率

結論

1. 2021年のFHK,TGF,TKYサイトにおいて雲がほとんどない(天空画像の目視判読による判別)SKYNETデータおよびTerra/Aqua MODIS MOD08_D3/MYD08_D3プロダクトとひまわり8/9号 AHI L2ARP, L3APRを比較した。
•晴天の条件下では、 SKYNETデータとL2ARP, L3APRの比較ではL3ARP DailyのAOT@500nmおよび AE プロダクトが最も相関が良かった。
•晴天の条件下では、 L3ARP Daily AOT@500nmはMOD08_D3/MYD08_D3とはよく一致する。AEはほとんど相関がない。
•POM02で捉えられていると思われる黄砂がL2ARP, L3APRで現れていない。
•今後は、天空画像による雲スクリーニングを行い比較データセットを増やしていく。

2. 2014年以降のFHK,TGF,TKYサイトにおけるSKYNETデータ、Terra/Aqua MODIS MOD08_D3/MYD08_D3、 Aura OMI OMTO3eプロダクトを用いて、ひまわり8/9号AHI L3ARP Dailyプロダクトをパラメータとすることを想定した陸域向け大気補正アルゴ
リズムのパフォーマンスを評価した。
•高いAOT@500nmおよび低いAEにおいて地表面反射率推定に大きな影響を及ぼす可能性があり、大気補正アルゴリズムではAOTおよびエアロゾルタイプを考慮する必要がある。
•今回エアロゾル粒径分布と複素屈折率（実数部と虚数部）を固定値にしたが、今後はこれらも考慮した陸域向け大気補正アルゴリズムの評価を行う予定。

謝辞
本研究は千葉大学環境リモートセンシング研究センター共同利用研究により実施されました(2023)。また、本研究はJSPS科研費JP21K12229、JP22H05004の助成を受けたものです。ひまわり8/9号エアロゾルプロダクト(L2ARP, L3ARP Hourly, Daily)は宇宙航空研究開発機構（JAXA）の分野横断型プロダクト提供サービス（P-Tree）より提供を受けました。筑波大の奈佐原顕郎准教授、海洋研究開発機構の永井信主任研究員、産業技術総合研究所の土田聡招聘研究員らが中心となって取得・管理しているPEN（Phenological Eyes Network）の協力を受けました。ここに感謝の意を表します。

Utilizing
g Geostationaryy Satellitee Dataa forr Ultraa Short--termm Forecastingg off Solarr
Irradiancee byy a Multivariatee Long--Short-Termm Memoryy Recurrentt Neurall Network
Nifatt Sultana,, Narumasaa Tsutsumida*
Saitamaa University
Graduate school of Science & Engineering
255 Shimo-Okubo, Sakura ward, Saitama, Japan, 338-8570

Overview

Methodology

Results

A Hybrid
d Approachh that integrates Machinee Learning (ML) techniqueswith physics-driven models to forecast Clear-skyy Irradiancee in near realtime using GOES-16
6 Geo-stationaryy Satellitee Imagery and data fromSun-synchronous Earth-orbiting Atmospheric Satellite- AURA.

By employing Artificiall Neurall Networkk (ANN) and Gradientt Boostingg Modell(GBM) trained on outputs from physics-driven models - clear-sky Global
Horizontal Irradiance(GHI) is predicted. Prior to that the most influential
meteorological features are extracted and then forecasted with
Long-Shortt term
Longm memoryy Recurrentt Neurall Networkk (LSTM-RNN)

Type C models showed the best overall performances with up to 0.94 R2
score. The models enhanced prediction accuracy by 10% overreanalysis-data-based models. The performance of ANN-SATELLITE-AOD
was close to Type B models predicting nearly 85% of the test samples
within 100 W/m2 error range (Figure 6). Although the number of
positive errors were quite higher than the negative ones (Figure 6)
which is presumably due to the minor impact of clouds.

AURA
A Satellitee
At a Sun-synchronous
orbit (430 miles away)

24 hours temporal and 28 km Spatial resolution

Start
Meteorological Data
streamMost Influential Features

Atmospheric Data
(Aerosol-Vapor-Air)

LSTM-RNN
Model
Forecasted
Features

Clear-sky Models(MAC2 and REST2)ML Models (ANN and GBM)
being Trained

TrainedML Models
Predicted
clear sky GHI.

Clear-sky GHI (Labels)
A polar orbit
120-600 miles awayEnd

5 min temporal and 2 km Spatial resolution

GOES-16
6 Satellitee
At a Geo-stationary orbit(22300 miles away)

Figure 3: Operational Overview of Clear-sky GHI forecasting

The LSTM model takes the last 3 hours of meteorological data as input
and forecasts those for next 1 hour at 5 min granularity. The daily trend
for 3 consecutive days for all meteorological data is presented in Figure 4
where Aerosol Optical Depth (Aerosol) showed strongest seasonality.

1 month temporal and 75km spatial resolution

Reanalysiss Dataa

Physics-driven
Models

Meteorological Data
StreamFigure 5: Clear-sky GHI of all models during the sunshine hours on 6th July 2023 at
the SURFRAD station in Bondville, Illinois (USA)Calculate
Clear Sky IrradianceTrain ML models(Neural Networks)
Foreca Clear Sky
Forecast
Irradiance
Irr
Compare With Respect toGround Truth
Figure 4: Meteorological parameters obtained from GOES 16, AURA, and MERRA 2 for3rd to 6th July 2023 at the SURFRAD station in Bondville Illinois (USA)

Prediction models are classified into 3 types. Type A and B are physicsdriven models predicting GHI from Reanalysis and Satellite data
respectively. Type C are ML models trained on Satellite data (as input
features) and outputs from Type B models (as labels). Table 1 compares
the prediction performances of different models while Table 2 shows themeteorological data (from satellite) usage of those models.

Pyranometer
SURFRAD Weather Station
At Bondville Illinois (USA)Figure 1: Project Overview

Table 1: Assessments of different irradiance prediction models
with respect to ground-truth (SURFRAD)

Figure 2 Demonstrates the Data Collection and Processing Mechanism
as a block diagram
Geostationary Satellite(GOES-16)

Ozone (O3)

Precipitable Water
Vapor

Georeferencing
Outlier
RemovalAngstrom
Exponent

NitrogenDioxide (NO2)

Aerosol Optical
Depth
Extraction at 2 km
resolution with 5-mingranularity

Reanalysis
DATA(MERRA2)

Orbiting Satellite(AURA)

Land Surface
AlbedoPressure

Extraction at 28 km
resolution with 24-hour
granularity

Extraction at 75
km resolution

Timelocalization

Merging (bytime)

Re-sampling

Interpolate

Figure 6: ANN-SATELLITE-AOD Model Error Histogram

Adjust Unit

Normalize
Data

Final Meteorological Data Stream at 5-minute Granularity

Type
A

B
C

Model Name
CAMS
MAC2-MERRA2
REST2-MERRA2
MAC2-SATELLITEREST2-SATELLITE
ANN-SATELLITE-ALL
ANN-SATELLITE-AOD
GBM-SATELLITE-ALL
GBM-SATELLITE-AOD

MSE
0.015
0.016
0.016
0.007
0.006
0.005
0.007
0.007
0.007

RMSE
0.123
0.127
0.128
0.081
0.075
0.073
0.085
0.086
0.085

R2
0.84
0.83
0.83
0.93
0.94
0.94
0.92
0.92
0.92

r
0.975
0.977
0.976
0.984
0.984
0.983
0.982
0.980
0.980

Table 1 evaluation metrics include mean squared error (MSE), root meansquared error (RMSE), coefficient of determination (R2), and Pearson
correlation coefficient (r). Type B models outperformed Type A models inall evaluation metrics. As REST2-SATELLITE performed better between the
two Type B models - the corresponding clear-sky GHI from this model wasused as labels to train Type C models that turned out to be the best
performing ones. All Type C models were validated for 3 consecutive days
(3rd to 5th July 2023)

Figure 7: Scatter plot between the prediction by ANN-SATELLITE-AOD Model andground truth (SURFRAD)

Conclusion

Table 2: Usage of the meteorological parameters by different models
Meteorological Parameters Used
Model Name

Aerosol Land
Total
Ozone Nitrogen Angstrom
dioxide Exponent
Optical Surface Precipitable (O3)(NO2)
Depth Albedo Water(AE)(AOD) (LSA)
(TPW)

Pres
sure
(PS)

MAC2-SATELLITE

○

○

○

○

○

○

REST2-SATELLITE

○

○

○

○

○

○

○

ANN-SATELLITE-ALL

○

○

○

○

○

GBM-SATELLITE-ALL ○

○

○

○

○

ANN-SATELLITE-AOD ○The proposed models works well for clear sky GHI forecasting. The next
step will be to incorporate cloud characteristics for actual GHI forecasting

Acknowledgements*

Thanks to NEDO (23200859-0) for funding this project.

Contactt Information
Narumasaa Tsutsumida

GBM-SATELLITE-AOD ○Figure 2: Data Collection and Processing System

www.meteconferences.org

only AOD was used as meteorological data to train two Type C models:
ANN-SATELLITE-AOD and GBM-SATELLITE-AOD. These AOD based ML
models were also up to 3 times faster among all Type B and C models

Contact Info

Tel: +81 Email: narut@mail.saitama-u.ac.jp

⾨ᫍ䝸䝰䞊䝖䝉䞁䝅䞁䜾䜢⏝䛔䛯䝋䞊䝷䞊䝟䝛䝹᳨ฟ䜰䝹䝂䝸䝈䝮䛾᳨ド䞉ᨵၿ᳜⏣ᬕ㽄䞉᳿೧㼽㽄༓ⴥ኱Ꮫ⌮Ꮫ㒊ᆅ⌫⛉Ꮫ⛉ 㼽⎔ቃ䝸䝰䞊䝖䝉䞁䝅䞁䜾◊✲䝉䞁䝍䞊◊✲⫼ᬒ+46$0ࢆ⏝࠸ࡓᨵၿᡭἲ• ෌⏕ྍ⬟࢚ࢿࣝࢠ࣮ࡢᬑཬ࡟క࠸㸪ࢯ࣮࣮ࣛࣃࢿࣝࡀከࡃࡢሙᡤ࡛タ⨨ࡉࢀ㸪㟁ຊ๭ྜࡶ㧗ࡲࡗ࡚࠸ࡿ㸬>㈨※࢚ࢿࣝࢠ࣮ᗇ@㸬• ᪥ᮏᅜෆࡢࢯ࣮࣮ࣛࣃࢿࣝࡢ✵㛫ศᕸ࡟ࡘ࠸࡚ࡢ◊✲ࡢከࡃࡀࢣ࣮ࢫࢫࢱࢹ࢕
࡛࠶ࡾ>ᮧᯇ࡯࠿ᔲ⏣ ➉ෆ@㸪඲ᅜࡢࢯ࣮࣮ࣛࣃࢿࣝศᕸࢆᐃ㔞ⓗ࡟ㄪᰝࡋࡓ౛ࡣᩘᑡ࡞࠸㸬• ඲⌫ࡢࢯ࣮࣮ࣛࣃࢿࣝࢆ࣐ࢵࣆࣥࢢࡋࡓ◊✲ࡀⓎ⾲ࡉࢀࡓ>/.UXLWZDJHQ HWDO@㸬ᮏ◊✲࡛ࡣ㸪3ODQHW6FRSH⏬ീࢆ⏝࠸࡚㸪ࡇࡢ◊✲࡟ࡼࡾᥦ౪ࡉࢀࡓࢹ࣮ࢱ㸦඲⌫ࢹ࣮ࢱ㸧ࡀ㸪࡝ࡢ⛬ᗘࡢ᳨ฟ⢭ᗘ࠿ࢆ᳨ドࡍࡿ࡜࡜ࡶ࡟㸪඲⌫ࢹ࣮ࢱࡢಙ㢗ᛶ࡟␲ၥࡀ࠶ࡿሙྜ㸪ࢯ࣮࣮ࣛࣃࢿ᳨ࣝฟ࡟ࡼࡾ㐺ࡋࡓ࢔ࣝࢦࣜࢬ࣒ࡢᥦ᱌ࢆ⾜࠺㸬+46$0 +LJK4XDOLW\6HJPHQW$Q\WKLQJ0RGHO >/.H HWDO@࡜ࡣ͐
9 ῝ᒙᏛ⩦ࡢ⏬ീศ๭ࣔࢹࣝࡢࡦ࡜ࡘ࡛࠶ࡿ6$0ࡢ⢭ᗘࢆୖ᪼ࡉࡏࡓࣔࢹࣝ
9 ᩍᖌࢹ࣮ࢱ࡞ࡋ࡛࠶ࡽࡺࡿ≀యࢆ⏬ീศ๭ࡍࡿ]HURVKRWỗ໬ᛶ⬟࡟ඃࢀࡿ
9 ᩍᖌ࠶ࡾᏛ⩦ࢆ㉸࠼ࡿ⢭ᗘࢆฟࡍࡇ࡜ࡶ
ධຊࢹ࣮ࢱࡢ๓ฎ⌮ SODQHWࢹ࣮ࢱ࠿ࡽྛ࣏ࣜࢦࣥࡢ㔜ᚰᗙᶆࢆ⟬ฟࡍࡿ㸬 ᗙᶆࢆ୰ᚰ࡟୍㎶ࡀ㹫ࡢṇ᪉ᙧ࡛3ODQHW6FRSH⏬ീࢆࢡࣜࢵࣉࡍࡿ㸬 ࢡࣜࢵࣉ⏬ീ࡟ࢥࣥࢺࣛࢫࢺᖹᆒ໬ࢆ⾜࠸㸪ࢥࣥࢺࣛࢫࢺࢆᙉㄪࡍࡿ㸬 ⏬ീ࡜࡜ࡶ࡟࣏ࣜࢦࣥࡢ㔜ᚰᗙᶆࢆࣔࢹࣝ࡟ධຊࡍࡿࡇ࡜࡛㸪ࢯ࣮࣮ࣛࣃࢿࣝ
ࡢ఩⨨ࢆ᫂☜࡟♧ࡍ㸬ධຊࢹ࣮ࢱ࡜ࡋ࡚౑⏝ࡋࡓ⏬ീ• ⏬ീD㸸ࢺ࣮࣮ࢗࣝ࢝ࣛ⏬ീ• ⏬ീE㸸㟷ࠊ㉥ࠊ㏆㉥እࣂࣥࢻࢆࡑࢀࡒࢀ㟷㸪⥳㸪㉥࡟๭ࡾᙜ࡚ࡓ⏬ീࣇ࢛࣮ࣝࢫ࣮࢝ࣛ⏬ീ• ⏬ീF㸸୺ᡂศศᯒ 3&$ ࢆ⾜࠸㸪➨୺ᡂศࢆ㟷㸪➨୺ᡂศࢆ⥳㸪➨୺ᡂศࢆ㉥࡟๭ࡾᙜ࡚ࡓ⏬ീ 3&$⏬ീᨵၿ⤖ᯝ䛸⪃ᐹ)LJXUH ᕥ 㸸༓ⴥ┴༳᪠἟ὶᇦ࡟࠾ࡅࡿㄪᰝᆅᇦ ⏬ീ㸸ᖺ*RRJOHPDS⏬ീ)LJXUH ྑ 㸸⇃ᮏ┴໭す㒊࡟࠾ࡅࡿㄪᰝᆅᇦ ⏬ീ㸸ᖺ*RRJOHPDS⏬ീ௒ᅇ᳨ドࢆ⾜ࡗࡓᆅⅬ ༓ⴥ┴༳᪠἟ὶᇦ͐⌧ᅾ㌟㏆࡞ሙᡤ࡛࠶ࡾ㸪᭱ึ࡟ㄪᰝࡍ࡭ࡁሙᡤ
ኴ㝧ගⓎ㟁ᡤࡢᩘࡀ඲ᅜ఩ >㈨※࢚ࢿࣝࢠ࣮ᗇ@ ⇃ᮏ┴໭す㒊͐ఫᏯ⏝ࢯ࣮࣮ࣛࣃࢿࣝᬑཬ⋡඲ᅜ఩ >⥲ົ┬⤫ィᒁ@࣓࢞ࢯ࣮࣮ࣛࡀ࠿ᡤ௨ୖ✌ാ୰ >⇃ᮏ┴@ࣅࢽ࣮ࣝࣁ࢘ࢫࡢタ⨨㠃✚඲ᅜ఩ >㎰ᯘỈ⏘┬@⏬ീ࡟ࡼࡽࡎṇ☜࡞᳨ฟࡀ࡛ࡁࡓࢯ࣮࣮ࣛࣃࢿࣝࡶ࠶ࢀࡤ )LJXUH㹼 㸪⏬ീ࡟ࡼࡗ᳨࡚ฟ⢭ᗘ࡟ࡤࡽࡘࡁࡀ࠶ࡿࡶࡢࡶ࠶ࡗࡓ㸬ࡑࡇ࡛㸪ྛ⏬ീࢆ⏝࠸ࡓࣔࢹࣝ
ࡢ᳨ฟ⋡ࢆㄪᰝࡋࡓ㸬᳨ドᡭἲ )LJXUH࣭)LJXUHෆࡢ㉥࠸࢚ࣜ࢔ෆࡢࢯ࣮࣮ࣛࣃࢿࣝ࡟ᑐࡋ࡚㸪┠どุูࢹ࣮ࢱࢆసᡂࡍࡿ㸬ᖺᮎࡢ3ODQHW6FRSH⏬ീࢆ౑⏝ࡋࡓ㸬 ┠どุูࢹ࣮ࢱ SODQHWࢹ࣮ࢱ ࡜඲⌫ࢹ࣮ࢱࢆ㔜ࡡ࡚࣏ࣜࢦࣥࡢ᳨ฟ⢭ᗘࢆㄪᰝࡍࡿ㸬 ࿘㎶ࡢᅵᆅ฼⏝࣭ഴᩳ࣭ᶆ㧗ࡸࢯ࣮࣮ࣛࣃࢿࣝࡢⰍࠊᙧ≧ࡀഇ㝜ᛶ㸪ഇ㝧ᛶࡢཎᅉ࡟࡞ࡗ࡚࠸࡞࠸࠿*RRJOH0DS⏬ീ࣭$/26:RUOG' $:' ࡢᶆ㧗࣭ᆅᙧࢹ࣮ࢱࢆ࣮࢜ࣂ࣮ࣛࢵࣉࡋ࡚☜ㄆࡍࡿ㸬 ㄪᰝ⠊ᅖࢆ੊ᅄ᪉ࡢࢢࣜࢵࢻ࡛ᶵᲔⓗ࡟༊ษࡾ㸪༊⏬ෆࡢ࣏ࣜࢦࣥࡢ㠃✚ࢆ⟬ฟࡋ㸪ศᕸࡢ೫ࡾࢆ᳨ドࡍࡿ )LJXUH࣭)LJXUHෆࡢ⥳ࡢ࢚ࣜ࢔ 㸬ࢺ࣮࣮ࢗࣝ࢝ࣛ⏬ീࣇ࢛࣮ࣝࢫ࣮࢝ࣛ⏬ീ

3&$⏬ീ

ඖ⏬ീ

᳨ド⤖ᯝ䛸⪃ᐹ)LJXUH ᕥ 㸸༓ⴥ┴༳᪠἟ὶᇦ࡟࠾ࡅࡿ඲⌫ࢹ࣮ࢱ࡜SODQHWࢹ࣮ࢱࡢศᕸ ⏬ീ㸸ᖺ3ODQHW6FRSH⏬ീ)LJXUH ྑ 㸸⇃ᮏ┴໭す㒊࡟࠾ࡅࡿ඲⌫ࢹ࣮ࢱ࡜SODQHWࢹ࣮ࢱࡢศᕸ ⏬ീ㸸ᖺ3ODQHW6FRSH⏬ീ㯤ᯟ㸸඲⌫ࢹ࣮ࢱ ࣆࣥࢡ㸸SODQHWࢹ࣮ࢱ⥲䝫䝸䝂䞁ᩘ
㼜㼘㼍㼚㼑㼠䝕䞊䝍䠖䠕䠓䠌඲⌫䝕䞊䝍䠖䠕䠔

⥲䝫䝸䝂䞁ᩘ
㼜㼘㼍㼚㼑㼠䝕䞊䝍䠖䠍䠑䠎䠑඲⌫䝕䞊䝍䠖䠍䠏䠎)LJXUH ᕥ 㸸༓ⴥ┴༳᪠἟ὶᇦ࡟࠾ࡅࡿࢢࣜࢵࢻࡈ࡜ࡢ඲⌫ࢹ࣮ࢱ࡜SODQHWࢹ࣮ࢱࡢ㠃✚ẚ㍑)LJXUH ྑ 㸸⇃ᮏ┴໭す㒊࡟࠾ࡅࡿࢢࣜࢵࢻࡈ࡜ࡢ඲⌫ࢹ࣮ࢱ࡜SODQHWࢹ࣮ࢱࡢ㠃✚ẚ㍑
㟷ࡢⅬ㸸ࢢࣜࢵࢻ㸯༊⏬ ࢜ࣞࣥࢪࡢᐇ⥺㸸ᅇᖐ┤⥺ ⥳ࡢ◚⥺㸸ᑐࡢ┤⥺ ༓ⴥ┴༳᪠἟ὶᇦ
඲⌫ࢹ࣮ࢱࡣSODQHWࢹ࣮ࢱࡢ⛬ᗘࡋ࠿᳨ฟ࡛ࡁ࡚࠸࡞࠸㸬ഇ㝧ᛶࡣⓎぢ࡛ࡁ࡞࠿ࡗࡓ㸬ഇ㝜ᛶࡢ࣏ࣜࢦࣥ࿘㎶ࡢᅵᆅ฼⏝➼࡟ἲ๎ᛶࡣぢࡽࢀ࡞࠿ࡗࡓ㸬ࢢࣜࢵࢻࡈ࡜࡟ࢯ࣮࣮ࣛࣃࢿࣝࡢ㠃✚ࢆẚ㍑ࡋࡓ᳨ド࡛ࡣ㸪 )LJXUH ඲⌫ࢹ࣮ࢱࡣ㐣ᑠホ౯ࡢഴྥ࡟࠶ࡿࡇ࡜ࡀ♧ࡉࢀࡓ㸬ࡲࡓࢹ࣮ࢱ㛫ࡢ┦㛵ࡀⰋ࠸ࡓࡵ㸪ࢢࣜࢵࢻࡈ࡜࡟೫ࡾࡀ࡞࠸㸬ࡋࡓࡀࡗ࡚㸪ㄗศ㢮࡟ᆅᇦᕪࡀ࡞ࡃ㸪ࣛࣥࢲ
࣒࡟Ⓨ⏕ࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿ㸬 ⇃ᮏ┴໭す㒊඲⌫ࢹ࣮ࢱࡣSODQHWࢹ࣮ࢱ࡜ẚ㍑ࡋ࡚௨ୖࢯ࣮࣮ࣛࣃࢿࣝࢆ᳨ฟ࡛ࡁ࡚
࠸࡞࠸㸬ࣅࢽ࣮ࣝࣁ࢘ࢫ࡜ࡢΰྠ࡟ࡼࡿഇ㝧ᛶࢆ࠿ᡤⓎぢࡋࡓ㸬ഇ㝜ᛶࡢ࣏ࣜࢦࣥ࿘㎶ࡢᅵᆅ฼⏝➼࡟ἲ๎ᛶࡣぢࡽࢀ࡞࠿ࡗࡓ㸬ࢢࣜࢵࢻࡈ࡜ࡢ᳨ド࡛ࡣ㸪༓ⴥྠᵝ㸪඲⌫ࢹ࣮ࢱࡀ㐣ᑠホ౯࡛࠶ࡿࡇ࡜㸪ㄗศ㢮࡟ᆅᇦᕪࡀ࡞࠸ࡇ࡜ࢆ⾲ࡋ࡚࠸ࡿ )LJXUH 㸬⤖ᯝ࠿ࡽ㸪඲⌫ࢹ࣮ࢱࡣᆅᇦ࡟ࡼࡽࡎഇ㝜ᛶࡀⓎ⏕ࡍࡿࡇ࡜ࡀศ࠿ࡗࡓ㸬ഇ㝜ᛶࡣ୍⯡࡟ධຊࢹ࣮ࢱ࡟⏤᮶ࡋ࡚࠾ࡾ㸪௒ᅇࡢሙྜ㸪ධຊ⏬ീࡢゎീᗘ࡟ၥ㢟ࡀ࠶ࡿ࡜⪃࠼ࡽࢀࡿ㸬ࡋࡓࡀࡗ࡚㸪6HQWLQHO⏬ീࡼࡾࡶ✵㛫ศゎ⬟ࡢ㧗࠸3ODQHW6FRSH⏬ീࢆ౑⏝ࡍࡿࡇ࡜ࡀ᭷ຠ࡛࠶ࡿ࡜⪃࠼ࡽࢀࡿ㸬)LJXUH  ᕥୖ 㸸⏬ീD ࢺ࣮࣮ࢗࣝ࢝ࣛ⏬ീ ࡛ࡢฟຊ)LJXUH  ྑୖ 㸸⏬ീE ࣇ࢛࣮ࣝࢫ࣮࢝ࣛ⏬ീ ࡛ࡢฟຊ)LJXUH  ᕥୗ 㸸⏬ീF 3&$⏬ീ ࡛ࡢฟຊ)LJXUH ྑୗ 㸸3ODQHW6FRSH⏬ീ ඖ⏬ീ㟷࠸㡿ᇦ㸸ฟຊ⤖ᯝ⥳ࡢᫍ㸸㔜ᚰᗙᶆ

᳨ฟ⋡ DFFXUDF\ ࡣ౑⏝ࡋࡓ⏬ീࡢ඲࡚࡛඲⌫ࢹ࣮ࢱࢆୖᅇࡗࡓ㸬఩⨨᝟ሗࡢྲྀᚓ࡟ㄢ㢟ࡀ࠶ࡿࡀ㸪6$0ࡣࢯ࣮࣮ࣛࣃࢿ᳨ࣝฟ࢔ࣝࢦࣜࢬ࣒ࡢ࣏ࢸࣥࢩࣕࣝࡀ࠶ࡿ࡜ゝ࠼ࡿ㸬ࡲࡓ㸪ྛ⏬ീ࡟ࡼࡿࣔࢹࣝࡢ࠸ࡎࢀ࠿࡛ṇࡋ࠸᳨ฟࡀ⾜ࢃࢀ࡚࠸ࡿሙྜࡢ᳨ฟ⋡ࡶ⟬ฟࡋࡓ FRPELQDWLRQ 㸬ࡍࡿ࡜㸪᳨ฟ⋡ࡀྛ⏬ീ༢⊂ࡢሙྜࡼࡾࡶୖ᪼ࡋ㸪᭱ࡶ㧗࠸್ࢆ♧ࡋࡓ㸬ࡋࡓࡀࡗ࡚㸪ࡇࢀࡽࡢ⏬ീࢆ⤌ࡳྜࢃࡏࡿࡇ࡜࡟ࡼࡾ㸪ࡼࡾṇ☜࡞᳨ฟࡀྍ⬟࡟࡞ࡿ࡜ゝ࠼ࡿ㸬
7DEOH㸸ྛ⏬ീ࡟ࡼࡿࣔࢹࣝ࡜඲⌫ࢹ࣮ࢱࡢ᳨ฟ⋡ྛฟຊ⤖ᯝ࡜SODQHWࢹ࣮ࢱࢆẚ㍑ࡋ㸪ᴫࡡṇࡋ࠸᳨ฟࡀ࡛ࡁ࡚࠸ࡿ࡜ุ᩿ࡋࡓሙྜࡣ͇R͇㸪࡛ࡁ࡚࠸࡞࠸
ሙྜࡣ͇[͇ࡢホ౯ࢆ⾜ࡗࡓ㸬ẚ㍑ࡢࡓࡵ࡟඲⌫ࢹ࣮ࢱ JOREDOGDWD ࡟ࡘ࠸࡚ࡶྠᵝࡢホ౯ࢆ⾜ࡗࡓ㸬

ࢺ࣮ࢗࣝ
࣮࢝ࣛ⏬ീ

ࣇ࢛࣮ࣝࢫ
࣮࢝ࣛ⏬ീ

3&$⏬ീ

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• ඲⌫ࢹ࣮ࢱࡣᐇ㝿ࡢࢯ࣮࣮ࣛࣃࢿࣝศᕸࡼࡾࡶ㐣ᑡホ౯࡛࠶ࡿ㸬• +46$0࡟3ODQHW6FRSH⏬ീ࣭ࢯ࣮࣮ࣛࣃࢿࣝࡢ఩⨨ᗙᶆࢆධຊࡍࡿࡇ࡜࡛㸪඲⌫ࢹ࣮ࢱ௨ୖࡢṇ☜ࡉ࡛ࢯ࣮࣮ࣛࣃࢿࣝࢆ᳨ฟࡍࡿࡇ࡜ࡀྍ⬟࡛࠶ࡿ㸬• +46$0࡟ࡼࡿ୍ᐃࡢࢯ࣮࣮ࣛࣃࢿ᳨ࣝฟ⢭ᗘࢆฟࡍࡓࡵ࡟㸪ࢥࣥࢺࣛࢫࢺᖹᆒ
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Transition zoneਁྜྷ಼‫ؔۯ‬Ϗνʖϱͳඏ‫ـ‬ে͹‫ܐؖ‬ʕ੪༆‫װ‬ଢ࿢ɾՅ౽‫ ݨ‬એཁ୉ָ ɾ஦ౣ࢛༦ ఽฎӶ೸Ԅ ɾੋ‫ߔ޳ރ‬ঞ ‫౐ښ‬୉ָʕഐ‫ܢ‬ʀ໪ద
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B

A

ਁྜྷ

ᶅਁྜྷ಼Ͷ͕͜Ζ‫ۯ‬伳͹ಝ௅ (A↔B)ᶆ‫ۯ‬伳ͳඏ‫ـ‬ে͹‫ܐؖ‬੓ (B↔C)͹2఼Ν໎Δ͖ͶͤΖʕ‫ۯ‬伳͹ఈٝ

ຌ‫ڂݜ‬

‫ۯ‬伳

C
ຌ‫ڂݜ‬

‫ـ‬ে

‫ع‬ԡ‫ڂݜ‬ʕ௒ࠬஏʀυʖνखಚ๏๑ຌ‫ڂݜ‬Ͷ͕͏ͱ‫ۯ‬伳ͺDSM(਼஍ඬ߶Ϡυϩ)Ґ಼͹൥ҕͲ༙‫ܙ‬෼͗ଚࡑ͢͵͏৖ॶͳఈٝͪ͢ɽ
ͨ͹ͪΌɾध‫ץ‬͹Ծ͹‫ؔۯ‬Ώध‫಼ץ‬͹ࢮཁ͹伳ؔ͵ʹ͹‫ؔۯ‬Ͷ‫ۯ‬伳͗ਫ਼੔͠ΗΖɽ
'60 'LJLWDO6XUIDFH0RGHOஏන໚ͳͨ͹৏Ͷ͍Ζஏ෼න໚͹ඬ߶͖Δ͵Ζ࣏‫ݫ‬υʖν౐ཱི໼ࢃ๼ʀ࿣ಕࢃޮԄʀޮԄ಼Ͳ18ϕϫρφΝઅఈʀϕϫρφͺߑ੔धझΏ֌૜ߑଆͶยΕ͗ड़͵͏Γ͑મఈʀex)߁ཁधྜྷɾਓ߁ࠠިྜྷ(ືౕ߶)ɾਓ߁ࠠިྜྷ(ືౕఁ)ਁྜྷߑଆʁஏ৏Ϫʖδʖ଎ఈߴ໪ʁ‫ـ‬Իɾ૮ଲౕ࣬
଎ఈֶؔʁ1෾ຘ଎ఈ‫ؔغ‬ʁ2023/8/5~2023/10/27

DSMॄ‫ܯ‬๏๑
ᶅ1ೖ͹஦͹࠹߶஍ɾ࠹ఁ஍ɾೖֳࠫΝபड़
ᶆ8,9݆(Ո‫ͳ)ل‬10݆(व‫Ͳ)ل‬ฑ‫ۋ‬஍Νࢋड़

‫ۯ‬伳

ʕմੵघ๑

100%

մੵߴ໪

఼‫܊‬υʖν͹ॴཀྵᶅ ϚέιϩԿʁ3࣏‫ؔۯݫ‬Νཱི๏ରͲۢ઀Εɾ఼‫͗܊‬ଚࡑ
ͤΖ৖߻ϚέιϩΝਫ਼੔ᶆ 4֌૜෾ׄʁ࠹߶ஏ఼͹߶͠Ν100ˍͲਜ਼‫و‬Կ͢ɾ࢝෾ҒͲ෾ׄ

ඏ‫ـ‬েʁ‫ـ‬েιϱγʖ

75%
50%
ϚέιϩԿ

25%

ध໨

‫ۯ‬伳

ᶅਁྜྷ಼ߑଆ෼ͳ‫ۯ‬伳͹ർֳ֌૜พ͹ध໨Ϛέιϩ਼ʹ‫ۯ‬伳Ϛέιϩ਼ᶆ‫ۯ‬伳ͳඏ‫ـ‬ে͹ർֳ֌૜พ͹‫ۯ‬伳Ϛέιϩ਼ʹ‫ـ‬Իʀౕ࣬(࠹߶஍ɾ࠹ఁ஍ɾೖֳࠫ)ʹ2࣎‫(غ‬Ոʀव)ʕ݃Վʀߡࡱ
න-1 ֌૜พ͹‫ۯ‬伳Ϛέιϩ਼ͳՈ‫ـل‬েυʖν͹૮‫਼ܐؖ‬
࣎ࢡࢭࣝᩘ
0-25%
25-50%
50-75%
75-100%* p <0.05

Ẽ (Ԩ)
᪥㍑ᕪ
᭱㧗
᭱ప
0.516*
0.237-0.545*0.361
0.066 -0.618**
0.119-0.169 -0.669**
0.084-0.085-0.483*** p <0.01

᪥㍑ᕪ
0.342
0.132
-0.224-0.236

‵ᗘ(%)
᭱㧗
0.122
0.057
-0.132-0.139᭱ప-0.269-0.0980.122
0.079

ਦ-2 ‫ـ‬Իͳౕ࣬͹ೖรಊ

න-2 ֌૜พ͹‫ۯ‬伳Ϛέιϩ਼ͳव‫ـل‬েυʖν͹૮‫਼ܐؖ‬࣎ࢡࢭࣝᩘਦ-1 ‫ۯ‬伳Ϛέιϩ਼(ॐ࣢)ͳध໨Ϛέιϩ਼(ԥ࣢)͹‫ܐؖ‬

0-25%
25-50%
50-75%
75-100%* p <0.05

0-50%ʁෝ͹૮‫ؖ‬50-75%ʁ૮‫͢͵ܐؖؖ‬→ध߶͹ͻΔͯ͘ͶΓΕध‫ͳ಼ץ‬ध‫ץ‬Ծ͹‫ۯ‬伳͗ࠠࡑ

75-100%ʁਜ਼͹૮‫ؖ‬→ߑ੔෼͗ध‫ץ‬৏෨ɾ‫ۯ‬伳͗ध‫಼ץ‬͹ΊͶਫ਼੔

Ẽ (Ԩ)
᪥㍑ᕪ
᭱㧗
᭱ప
᪥㍑ᕪ
0.433
-0.025 -0.593** 0.538**
0.210-0.297 -0.702** 0.429-0.096 -0.609** -0.763** 0.191
0.009-0.310-0.4630.151** p <0.01

‵ᗘ(%)
᭱㧗
0.509*
0.528*
0.415
0.250

᭱ప
-0.090
0.092
0.281
0.118

೦

๎ࣻྮ‫֕٭‬೨ਦʕ࣎‫غ‬ʀ֌૜ͶΓΔͥ‫ۯ‬伳Ϛέιϩ਼ͳ࠹ఁ‫ـ‬ԻͶෝ͹૮‫ؖ‬→࠹ఁ‫ـ‬Իͺ໎͜๏Ͷ؏଎͠ΗΖɽ‫ۯ‬伳͹ଁՅ͗๎ࣻྮ‫٭‬͹ްՎΝঁௗ
ʕ75-100%͹֌૜ʁ‫ۯ‬伳ଁʻध໨ଁ
→ध‫ץ‬ͶΓΕཛྷླྀ൅ਫ਼ɾ೦Νӣ൘ஏͤ΄ΕͶଲͤΖध໨͹ܑ͘ͳౖն͹‫ܐؖ‬ऑྜྷೖࡌʤએཁ୉ָԄָܵ෨ʥʀՅ౽‫ݨ‬ʤએཁ୉ָ୉ָӅԄָܵ‫ڂݜ‬Ռʥʀ‫־‬Ҭඔ੪ʤ๼քಕ୉ָ ୉ָӅ೸ָ‫ڂݜ‬Ӆʥʀ૥ઔ༡࿼ʤ๼քಕ୉ָ ୉ָӅஏ‫ڧ؂ځ‬Ռָ‫ڂݜ‬Ӆʥ݃Վʀߡࡱഐ‫ܢ‬ʀ໪ద
ۛ೧͹ҡ৙‫ـ‬েͶΓΕౖ࠯ࡄ֒͹‫ݧث‬੓͗ଁՅ͢ͱ͏ΖͪΌɼஏͤ΄Ε͹ಊ͘Ν೼ѴͤΖ͞ͳͺ॑གྷͲ͍Ζɽஏͤ΄ΕஏͲͺध໨ܑ͗͏ͱ͏Ζ͗ɼಋͣஏͤ΄Ε൥ҕ಼Ͳ΍৖ॶͶΓͮͱध໨͹ܑ͘๏͗ҡ͵Ζɽͨ͹ͪΌɼຌ‫Ͳڂݜ‬ͺౖն͹ಊ͘ͳध໨͹ܑ͘͹‫ܐؖ‬Ν໎Δ͖ͶͤΖ͞ͳΝ໪దͳͤΖɽਦᶅஏ໚͹ಊ͘ͳध໨͹ਪ௜໚͹ܑ͘͹รԿਦᶆஏ໚͹ಊ͘ͳध໨͹ਭฑ໚͹ܑ͘͹รԿ௒ࠬघ๑ஏ໚͹ಊ͘Ν೼ѴͤΖͪΌͶɼ௒ࠬஏͶϘήΝ120ຌઅ஖
͢ɼͨΗΔΝ6݆ͳ10݆͹್ౕ଎ྖͤΖ͞ͳͲ4͖݆ؔ͹ஏͤ΄Ε͹ಊ͘Νଌ͓ͪɽΉͪɼஏ໚͹ಊ͘ͳध໨͹รԿΝ
ർֳͤΖͪΌͶɼ֦ϘήͶۛ͏ध໨͹ܑ͘Ν଎ఈͪ͢ɽ

ਦᶇࡒ੷พஏ໚͹ಊ͘ͳध໨͹ਭฑ໚͹ܑ͘͹รԿ ਦᶈਓཁध߁ཁधพஏ໚͹ಊ͘ͳध໨͹ਭฑ໚͹ܑ͘͹รԿ௒ࠬ‫ؔغ‬ʁ6݆ʀ10݆௒ࠬஏʁ๼քಕ࠭ླྀ‫܌‬͹ΨνϨϜρϕ
ʤ୊ࢀ‫ن‬૜ஏͤ΄Εʥ→೧ؔ1mఖಊ͚ᶅϘή͹અ஖
40mʹ230m͹൥ҕͶ‫ܯ‬120ຌʤ5ʹ24ຌʥઅ஖→ήϨρχয়Ͷॐԥ10ֶ̾ؔͲઅ஖
ࣾᶆϘή͹Ғ஖͹‫ى‬࿧֦Ϙή͹Ғ஖ΝφʖνϩητʖεϥϱʤφϕαϱGM107ʥʀ
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͹ਭฑ໚͹ܑ͘͹รԿ͗୉͚͘͵Ζ͞ͳ͗Κ͖ͮͪɽΉͪɼध໨͹୉͘͠ΏझྪͶΓΔͥɼஏ໚ͳध໨ͺรԿ͢ͱ͏Ζ͞ͳ΍Κ͖ͮͪɽஏ໚͹ಊ͘ͳध໨͹ಊ͘ͶΓͮͱౖն͹‫ڧ‬ֆΝ೼ѴͤΖ͞ͳ͗Ͳͪͪ͘Όɼध໨ͺஏ໚͹ಊ͘ΝͳΔ͓ͱܑ͗͘รԿͤΖͳߡ͓ΔΗΖɽ

Estimation of Foliage Clumping Index
Using Himawari Satellite Land Surface Reflectance Data
QIAO ZHI 1 , YANG WEI 1, (1.CHIBA UNIVERSITY)
Keywords：：Clumping Index (CI), Himawari Satellite, BRDF simulation, Normalized difference between hotspot and darkspot (NDHD) index❏ Hot-spot detection

Introductions❏ Foliage Clumping Index: What and why- characterizes the grouping of foliage relative to its random spatial
distribution.
- critical in determining the foliage radiation transfer, photosynthesis, andhydrological processes (Peng et al,2018).
- very useful in ecological and meteorological models(estimation of true LAI;
separation of sunlit and shaded leaf groups; GPP modeling) (J.Pisek et al, 2015)

CI=1

Fig1. Foliage clumping happens at different hierarchical levels.

CI >1

When θ (scattering angle)> 175°,
considered as a hot-spot
Hot-spot calculate equation:cos(θ) = -cos(θS) cos(θv)-cos(φs-φv)sin(θS)sin(θv)(E. F.VERMOTE, 2002)
θ: Scattering angle, θS: sun zenith angle, θv: view zenith angle, φs: sun
azimuth angle, φv: view azimuth angle

CI <1

Fig.2 Different spatial distribution and corresponding CI❏ Field measurement
In field measurements, CI can be estimated by Digital
hemispherical photographs (DHP) (Fang, 2021).
Fig.6 Counts of hotspots observed by Himawari in 2022

❏ Remote Sensing method
Fig.3 Image of DHP

Fig7. Example of hotspot effect❏ BRDF-fitting: Enhanced Rahman-Pinty-Verstraete model

CI = a(θ)×NDHD + b(θ)
NDHD=(ρhot-ρdark) / (ρhot+ρdark)
ρhot /ρdark :the reflectancevalues at the hot-spot / darkspot in the principal plane,
respectively.

Hot-spot(Chen et al., 2005)

Fig.4 Hot-spot and dark-spot in the principle plane

To find dark-spot❏ Previous studies

Fig.8 Model fitting (top) and Principle plane fitting (bottom)❏ New opportunity from Geostationary (GEO) meteorologicalsatellites-Prove the opporturnity of detection of
more hot-spots use GEO.

Fig.5 Hot-spot detectived by Goes-16（Li, DP Roy, et al, 2021)

Objective
To explore the capability of Himawari geostationary satellite for
improving foliage CI estimation

Methods&Results
Step1

Hot-spot detection

Step2

BRDF-fitting

Step3

CI computation

Step4

Evaluation

Fig.9 Comparison results of 4 BRDF models❏ CI computation
For cone/cylinder crown shapes (coniferous forests):
CI = -0.47*NDHD+0.80
For ellipsoid crown shapes (the other vegetation classes):
CI = -1.23*NDHD+1.34(Jiao et al, 2018)❏ Evaluation

CI calculated by
MODIS CI PRODUCT In-situ data
Himawari Land Surface Reflectance(Wei et al,2019)（千葉大 市井研）
ꞏSite ScaleꞏArea Scale

In-situ data

MODIS

Himawari

ZHANG BEI
(China,0.83(2002/08)
Pastures)0.833(2002/08)
0.867(2019/10)

0.811(Close to in-situ
data, 2019/10)
0.383(Closer to in-situ
data, 2017/03)

Gnangara(Australia,
EBF)

0.44(2004/03)

0.542(2004/03)
0.512(2017/03)

Camerons(Australia,
EBF)

0.49(2004/03)

0.326(2004/03)
0.39(2019/03)

0.53(Closer to in-situ
data, 2019/03)

Location:
43.785°-44.3°N
112.76°-113.335°E
Date: 2022.10.10
Land cover: grassland

Himawari- MODIS, MISR, POLDER etc.
- Hot-spot can not be always observed because of the observe mode and cloudcontamination.

MODIS
Fig.10 Scatter plot of Himawari CI and MODIS CI in Small
hot-spot area with single vegetation type and same SCA

Tab.1 Comparison result with in-situ data, MODIS CI
product and CI calculated from Himawari

Conclusion & Discussion
ꞏ Himawari has the feasibility of calculating CI where and whenhot-spots can be observed.
Conclusion ꞏ Himawari CI may be more accurate than MODIS CI product.
ꞏ By comparing with in-situ data, it was found that the CI
calculated by Himawari has a certain degree of accuracy.
ꞏ An effective Cloud mask is needed.
Discussion ꞏ More valid datasets are needed.
ꞏ Temporal and spatial distributions of CI need to be discussed.
Future · Valid data collection and cloud mask. · Analyse temporal and spatial distributions of CI. · CI mapping developmnt.
References

E-mail: qiaozhi0401@163.com TEL: 090-3355-6777
Yang lab., Center for Environmental Remote Sensing, Chiba University

Thanks to the CeRES Ichii Lab. at Chiba University for providing the atmospheric correction method and reanalysis data.1) Jan Pisek, Liming He, Jing Ming Chen, Ajit Govind, Michael Sprintsin, et al.. Characterization, validation and intercomparison of Clumping Index maps from POLDER, MODIS, MISR satellite dataover reference sites. AGU Fall Meeting 2015, Dec 2015, San Francisco, United States. , n.p., 2015. hal-027979952) Shanshan Wei, Hongliang Fang, Crystal B. Schaaf, Liming He, Jing M. Chen, Global 500 m clumping index product derived from MODIS BRDF data (2001–2017), Remote Sensing of Environment, Volume232, 20193) J.M. Chen, C. Menges, S. Leblanc, Global mapping of foliage clumping index using multi-angular satellite data , pp. 447-457, Remote Sens. Environ., 97 (2005)4) Ziti Jiao, Yadong Dong, Crystal B. Schaaf, Jing M. Chen, Miguel Román, Zhuosen Wang, Hu Zhang, Anxin Ding, Angela Erb, Michael J. Hill,
Xiaoning Zhang, Alan Strahler, An algorithm for the retrieval of the clumping index (CI) from the MODIS BRDF product using an adjusted version of the kernel-driven BRDF model,Remote Sensing of Environment, Volume 209,2018,Pages 594-611,5) Hongliang Fang, Canopy clumping index (CI): A review of methods,
characteristics, and applications, Agricultural and Forest Meteorology, Volume 303,20217) E.F. Vermote, D.P. Roy, Land surface hot-spot observed by MODIS over Central Africa, Int. J. Remote Sens., 23 (11) (2002), pp. 2141-2143

੫ࢯ‫ـ‬েӶ੗ͽΉΚΕ̖ߺժ଀͹ͪΌ͹Ғ஖‫ݗࠫޣ‬ड़घ๑͹‫ݗ‬౾ʕฑؔୣ໷  ౣ㟔ඛਕ ࢤҬ࿪ਖ ࢃຌ༦ฑ ໨ߍ௣޽‫߶ۂ‬౵઒໵ָߏ
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ീࡢᗄఱ⢭ᗘࡣゎᯒ⤖ᯝ࡟኱ࡁ࡞ᙳ㡪ࢆཬࡰࡍせᅉࡢ୍ࡘ࡛࠶ࡾ㸪㟼ṆẼ㇟⾨ᫍࡦࡲࢃࡾ8ྕ࡟㛵ࡋ࡚ࡶ࠸ࡃࡘ࠿ࡢ◊✲ࡀሗ࿌ࡉࢀ࡚࠸ࡿ㸬ࡋ࠿ࡋ㸪ࡇࢀࡽࡢホ౯ᮇ㛫ࡣᡴࡕୖࡆᖺᗘࡢ2015ᖺ࠿ࡽ2016ᖺ࡟㞟୰ࡋ࡚࠾ࡾ㸪㛗ᮇⓗ࡞ኚືഴྥࡀ᫂ࡽ࠿࡟ࡉࢀ࡚࠸࡞࠸1,2)㸬㟼Ṇ⾨ᫍࡢ⏬ീ࡜ゎീᗘࡢ㧗࠸ᆅ⌫ほ ⾨ᫍࡢ⏬ീࢆ⤌ࡳྜࢃࡏ࡚౑⏝ࡍࡿሙྜࡸ㸪✵㛫ⓗ࡟」㞧࡞ᅵᆅ⿕そࢆ᭷ࡍࡿᆅᇦࡢ㝣ᇦゎᯒࢆ⾜࠺㝿࡟ࡣ㸪㟼Ṇ⾨ᫍࡢ⏬
ീ࡟ᑐࡋ࡚㸪௒௨ୖ࡟㧗࠸ᗄఱ⢭ᗘࡀせồࡉࢀࡿ㸬ࡑࡢࡓࡵ㸪ᗈ⠊ᅖ࠿ࡘ㛗ᮇⓗ࡞ᗄఱ⢭ᗘࡢホ౯ࢆ⾜࠸㸪ᗄఱ⿵ṇࢆ᪋ࡍᚲせࡀ࠶ࡿ㸬ᮏ◊✲࡛ࡣ㸪ࡦࡲࢃࡾ㸶ྕ⏬ീ࡟ྵࡲࢀࡿ఩⨨ㄗᕪࢆᐃ㔞ホ౯ࡍࡿࡓࡵࡢ㸪᭱㐺࡞ᡭἲ࠾ࡼࡧࣃ࣓࣮ࣛࢱࢆ≉ᐃࡍࡿࡇ࡜ࢆ┠ⓗ࡜ࡍࡿ㸬ᚓࡽࢀࡓ▱ぢࡣ㸪௒ᚋᐇ᪋ணᐃ࡛࠶ࡿࡦࡲࢃࡾ㸶ྕ඲⌫⏬ീ࡟࠾ࡅࡿᗄఱ⢭ᗘホ౯㸪ᘏ࠸࡚ࡣ⢭ᐦᗄఱ⿵ṇ➼࡟ᙺ❧࡚ࡽࢀࡿ㸬

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㞼 పῶ⏬ീ㸪⢭ᗘホ౯࡟ࡣLandsat8/9ࡢC2L2ࣉࣟࢲࢡࢺ 3) ࠿ࡽసᡂࡋࡓ㞼࢚ࢵࢪᙉㄪ⏬ീ㸪㍤㍤ᗘㄪᩚ⏬ീࢆ⏝࠸ࡿ(ᅗ1)㸬
4)඲࡚ࡢ⏬ീࢆNormalized Geostationary Projection ࡟ኚ᥮
30mゎീᗘࡢࣆࢡࢭࣝ༢఩࡛Ⓨ⏕ࡉࡏࡓ௵ពࡢ఩⨨ࡎࢀࢆ㸪ࡦࡲࢃࡾࡢゎീᗘ(500m)࡜Landsatࡢゎീᗘ(30m)ࡢ㸪௵ពࡢ୰㛫ゎീᗘ࡟࠾࠸
࡚࡝ࢀ࡯࡝ṇ☜࡟᳨ฟ࡛ࡁࡿ࠿ホ౯఩⨨ࡎࢀ᳨ฟᡭἲࡣࢸ
ࢸࣥࣉ࣮ࣞࢺ࣐ࢵࢳࣥࢢ࡜Scale Invariant Feature
Transform(SIFT) 5)
᳨ドࣃ࣓࣮ࣛࢱࡣ❆❆ࢧ࢖ࢬ࡜⏬
⏬ീゎീᗘ᳨ドゎീᗘࡣ30m, 50m, 60m, 75m, 100m, 125m, 150m, 250m, 300m, 375m,
500mࡢィ11ಶ᳨ド❆ࢧ࢖ࢬࡣ5000m, 7500m, 10000m, 12500m, 15000mࡢ5ࡘ(ᅗ2)㸬
࡞࠾㸪SIFTࡣ30000m࡟⤫୍ࡋࡓ㸬඲⌫⏬ീࡢ⠊ᅖ࡟タࡅࡓ642ಶࡢᇶ‽Ⅼ࠿ࡽ50ಶࢆ↓సⅭ࡟ᢳฟࡋ㸪᳨ドᇶ‽Ⅼ࡜ࡍࡿ(ᅗ3)㸬
ࢸࣥࣉ࣮ࣞࢺ࣐ࢵࢳࣥࢢ࡛ࡣ ఩┦㝈ᐃ┦㛵ἲ㸦POCἲ㸧࡟ࡼࡿ࣐ࢵࢳࣥ ࢢ ࡜ ḟ ᘧ ࡛ ᐃ ⩏ ࡉ ࢀ ࡿ Zero
Zero-mean
mean Normalized Cross Correlation(ZNCC)ࢆ౑⏝ࡋࡓ᪉ἲࢆ᳨ドCܼܰ‫݅ ܥܥ‬ǡ ݆ ൌσ௞ǡ௟ ‫ܫ‬௜ା௞ǡ௝ା௟ െ ߤூ ܶ௞ǡ௟ െ ߤ ்σ௞ǡ௟ ‫ܫ‬௜ା௞ǡ௝ା௟ െ ߤூzz

ᅗ1. ᮏ◊✲࡛⏝࠸ࡓ⏬ീࡢ౛.
ᕥ࠿ࡽ㞼పῶ⏬ീ㸪࢚ࢵࢪᙉㄪ⏬ീ㸪㍤ᗘㄪᩚ⏬ീଶσ௞ǡ௟ ܶ௞ǡ௟ െ ߤ ்ଶࡇࡇ࡛㸪‫ܫ‬௜ା௞ǡ௝ା௟ ࡣ኱ࡁ࡞㡿ᇦࢆᣢࡘ⏬ീࡢሺ݅ ൅ ݇ǡ ݆ ൅ ݈ሻ఩⨨ࡢࣆࢡࢭࣝ
್㸪ܶ௞ǡ௟ ࡣࢸࣥࣉ࣮ࣞࢺ⏬ീࡢሺ݇ǡ ݈ሻ఩⨨ࡢࣆࢡࢭ್ࣝ㸪ߤூ ࡜ߤ ் ࡣࡑࢀࡒࢀࡢ⏬ീࡢᖹᆒࣆࢡࢭ್࡛ࣝ࠶ࡿ㸬ࡼࡾ⢭⦓࡞ࢧࣈࣆࢡࢭࣝ᥎ᐃࢆ⾜࠺ࡓࡵ㸪஧஧ ḟඖࡢࣃࣛ࣎ࣛࣇ࢕ࢵ
ࢸ࢕ࣥࢢࢆ᪋ࡍ
ࢸࣥࣉ࣮ࣞࢺ⏬ീ࡟ࡣࡦࡲࢃࡾࡢゎീᗘ࡬୍ᗘపゎീᗘ໬ࡍࡿࡶࡢ࡜
┤᥋୰㛫ゎീᗘ࡟పゎീᗘ໬ࡍࡿࡶࡢࢆ⏝ពࡋ᳨ド㸦ᅗ4㸧

ᅗ2. ᳨ド❆ࢧ࢖ࢬᅗ3. ᳨ドᇶ‽Ⅼᅗ4. ఩⨨ࡎࢀ᳨ฟᛶ⬟ࡢホ౯᪉ἲ݃Վ㞼పῶ⏬ീ(ZNCC, పゎീᗘ໬ฎ⌮࡞ࡋ)㞼పῶ⏬ീ(ZNCC, పゎീᗘ໬ฎ⌮࠶ࡾ) ࢚ࢵࢪᙉㄪ⏬ീ(ZNCC, పゎീᗘ໬ฎ⌮࡞ࡋ) ࢚ࢵࢪᙉㄪ⏬ീ(ZNCC, పゎീᗘ໬ฎ⌮࠶ࡾ)㍤ᗘㄪᩚ⏬ീ
㍤ᗘㄪᩚ⏬ീ
(SIFT㸪పゎീᗘ໬ฎ⌮࡞ࡋ㸪❆ࢧ࢖ࢬᅛᐃ) (SIFT㸪పゎീᗘ໬ฎ⌮࠶ࡾ㸪❆ࢧ࢖ࢬᅛᐃ)㞼పῶ⏬ീ
(POC㸪పゎീᗘ໬ฎ⌮࡞ࡋ)㞼పῶ⏬ീ
(POC㸪పゎീᗘ໬ฎ⌮࠶ࡾ)

ᅗ5. ྛᡭἲ࡟ࡼࡿゎീᗘࡈ࡜ࡢRMSE࠾ࡼࡧ ┦㛵ಀᩘ݃࿨ʀࠕ‫ޛ‬͹ఴ๮
9 ᳨ฟᛶ⬟ࡣ㞼పῶ⏬ീࢆ⏝࠸ࡓࢸࣥࣉ࣮ࣞࢺ࣐ࢵࢳࣥࢢ㸦ZNCC㸪పゎീᗘ໬ฎ⌮࡞ࡋ㸧ࡀ᭱ࡶ㧗ࡃ࡞ࡗࡓ㸬≉࡟㸪30m,50m, 75mࡢゎീᗘ࡛
RMSEࡀᑠࡉࡃ࡞ࡾ㸪❆ࢧ࢖ࢬࡀ10000mࢆ㉸࠼ࡿ࡜㸪30m⛬ᗘࡢㄗᕪ࡛ࡢ఩⨨ࡎࢀ᳨ฟࡀྍ⬟࡜࡞ࡗ࡚࠸ࡓ㸬
9 ᭱㐺࡞ᡭἲ࠾ࡼࡧࣃ࣓࣮ࣛࢱࡣ㸪75mゎീᗘ㸪❆ࢧ࢖ࢬ10000m㸦134 pixel㸧ᅄ᪉ࡢ㞼పῶ⏬ീࢆ౑⏝ࡋࡓࢸࣥࣉ࣮ࣞࢺ࣐ࢵࢳࣥࢢ࡛࠶ࡗࡓ㸬
9 ࢸࣥࣉ࣮ࣞࢺ⏬ീࡶᇶ‽⏬ീྠᵝ࡟୍ᗘ500mゎീᗘ࡟పゎീᗘ໬ࡍࡿࡇ࡜࡛┦㛵ಀᩘࡣ඲యⓗ࡟㧗ࡃ࡞ࡿࡶࡢࡢ㸪RMSEࡣ┤᥋పゎീᗘ໬ࡋࡓ
⏬ീࢆ⏝࠸ࡓ᪉ࡀ㧗ࡃ࡞ࡗࡓ㸬
9 ௒ᚋࡣᐇ㝿࡟ࡦࡲࢃࡾ8ྕ඲⌫⏬ീࡢᗄఱ⢭ᗘࢆホ౯ࡍࡿ࡜࡜ࡶ࡟ᗄఱ⿵ṇࡢ᪉ἲ࡟ࡘ࠸࡚ࡶ᳨ウࡍࡿணᐃ࡛࠶ࡿ㸬ࢂߡช‫ݛ‬

1) W. Takeuchi, Assessment of geometric errors of Advanced Himawari-8 Imager (AHI) over one year operation, IOP Conf. Ser.: Earth Environ. Sci. Vol.37, 2016.
2) M. Matsuoka, A Method to Improve Geometric Accuracy of Himawari-8/AHI “Japan Area” Data. J. Jpn. Soc. Photogramm. Remote Sens. Vol.54, pp.280–289. 2015.
3) USGS.Landsat Collection 2 Level-2 Science Products. https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products. Accessed Jan. 4, 2024.
4) EUMETSAT, Coordination Group for Meteorological Satellites LRIT/HRIT Global Specification. CGMS/DOC/12/0017 Issue 2.8, 30 Oct. 2013.
5) Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, Vol.60, pp91–110, 2004

第26回CEReS環境リモートセンシングシンポジウム

Cooley TukeyアルゴリズムのSIMD並列化
北九州市立大学

山崎 進

背景Cooley Tukeyアルゴリズム

SAR信号処理では，受信信号をフーリエ変換してCooley Tukeyアルゴリズムは次の2処理を行う．

参照スペクトルと乗算して逆フーリエ変換を行う

1. Radix-2 DIT: The radix-2 decimation-in-time(DIT) fast Fourier transform (FFT)

ような処理を行うことで画像を得る．フーリエ変換を離散化されたデジタル信号に対して行うことを離散フーリエ変換(DFT)と呼ぶ．2のn乗サイズの離散フーリエ変換を高速に行うアルゴリズムは
高速フーリエ変換(FFT)として知られている．高速

2. Bit-Reversal
Radix-2 DITは次のような再帰関数である．
• サイズが1の場合は，入力をそのまま出力する

フーリエ変換の古典的アルゴリズムの1つがCooley Tukeyアルゴリズムである．本研究はこのCooley Tukeyアルゴリズムについて，細粒度のSIMD並列化を行った．

• そうでない場合は次の処理を行う

1. 入力を2分割して，前をa，後をbとする
2. wk =exp(-2πi/N k) を計算する
3. 出力として次の2つを繋げたものを返す

結果
wkの計算はサイズnが決まると値が決まる定数ベ
クトルである．また，サイズ2nの時のwkを間引く• aとbの和ベクトルを入力としてRadix-2 DITを再帰呼び出しした結果• aとbの差ベクトルとwkの積ベクトルを入力としてRadix-2 DITを再帰呼び出しした結果

ベクトル計算で，サイズnのwkを求めることができる．十分な計算資源がある時には，どちらの計

Radix-2 DITの最終結果は得たいFFTの結果の要素

算もO(1)で行える．

の順番を規則的に入れ替えたものになっているので，Bit-Reversalで適切に順番を入れ替えること

Radix-2 DITの本質的な計算は，再帰的にサイズn

で，FFTの結果を得る．

のベクトル計算を行う処理として表すことができる．したがって，十分な計算資源がある前提でCooley Tukeyアルゴリズムの計算量はO(n log n)

SIMD並列化すると，サイズnに対して，計算量はである．

O(log n)とすることができる．
またBit-Reversalは行列変換として定義できるので，計算量をO(1)にすることができる．

© 2024 Susumu Yamazaki
本研究はJSPS科研費 22K04657の助成を受けたものです以上より，Cooley Tukeyアルゴリズムを理想的に
SIMD並列化すると計算量はO(log n)となる．

本研究は令和5年度千葉大学環境リモートセンシング研究センター
共同研究利用の助成を受けたものです8$9࿌ൗ&36$5઺൸૪৶३५ॸ঒भ
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The 26th CEReS Environmental Remote Sensing Symposium༲ྖ੓ԃ‫ܙ‬ฑ൚ΠϱτψΝ༽͏ͪLFଵ߁ଵҮ‫ױ‬ম‫ܯ‬͹֋൅ͳୱોࢢͲ؏଎͠ΗͪLFଵుഀ͹ಝ௅ଢీ༖Ҳ࿗ϭͿ͕ࢀӞ݊৵ϮͿ͕٤໼એ‫ܛ‬ϯͿ͕ෲ෨ࠂິ ϯ͕ϰ͕ϱͿ͕ࠕेଉ೯ϲͿϭ͘એཁ୉ָ୉ָӅ༧߻ཀྵ޽ָැ Ϯ͘એཁ୉ָཀྵָ෨ஏ‫ځ‬ՌָՌ ϯ͘એཁ୉ָ୉ָӅཀྵָ‫ڂݜ‬Ӆϰ͘એཁ୉ָ‫ڧ؂‬ϨϠʖφιϱεϱή‫ڂݜ‬ιϱνʖ ϱ͘એཁ୉ָࡄ࣑֒ྏָ‫ ॶڂݜ‬ϲ͘ೖຌٗढ़࢞ճએཁ‫ࢩݟ‬෨
1. ‫ڂݜ‬ഐ‫ܢ‬

3. ΠϱτψεητϞ֕གྷ

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ஏਔ൅ਫ਼‫͛ͤޛ‬Ͷ௪৙ϪϗϩͶ໯ͮͪ (Gokhberg et al., 1982).
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1/14

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࣎
ؔ
౲
ͪ
Ε
͹
Ϗ
ϩ
η
਼

ᶅᶆ
Dϰ͘ϵ



8QNQRZQඉབྷϏϩη͹Ί͹Ϗϩη਼รಊਦ

1RGDWD




࣎
ؔ
౲
ͪ
Ε
͹
Ϗ
ϩ
η
਼

1RQ&*'


3%ɾ.HYHQWͺۛંབྷ /) Ͷ௧ՅབྷϏϩηঈ‫ڊ‬઴͹Ϗϩη਼รಊਦ
1RGDWD༁ϭϬϬђƐֶؔͲୱҲ͹Ϗϩη͗൅ਫ਼ώʖηφয়͹
Ϗϩη྽਼͗ϭϬϬђƐ‫ܩ‬କྈ‫ۅ‬੓Ϗϩη྽਼͗ϭϬϬђƐ‫ܩ‬କ

I

G

H

E

ᶉ
Dϰ͘ϵ








ᶅᶆ
Dϰ͘ϵ


ᶇ
ᶈ
Dϱ͘Ϯ Dϲ͘Ϯ



ᶉ
Dϰ͘ϵ


























ਦ ϭϭ͘෾ྪ݃ՎΝ੷Ί৏ࣞ͝Ͳනͪ͢ϭ࣎ؔ౲ͪΕ͹Ϗϩη਼รಊᶅʛᶊͺஏਔ൅ਫ਼࣎ؔɿօ৯͹Ӫͺܿଝ‫ؔغ‬ɿ΀ඉབྷϏϩη਼͹࣎ؔรಊ΁• hŶŬŶŽǁŶ͹ҡ৙ଁՅͺ౛ͳॡͶঙ͵͚ɾՈɼवͶॄ஦͢ͱ͏Ζܑ޴͗ΊΔΗͪɿ• ೖ஦ͳ໹ؔͶ͕͜Ζҡ৙ଁՅ൅ਫ਼յ਼Νർֳͪ͢ͳ͞Θɾ໹ؔ͹յ਼͗༁ϭ͘ϳഔଡ͖ͮͪɿ
΀ஏਔ൅ਫ਼઴Ͷ͕͜ΖඉབྷϏϩη͹ҡ৙ଁՅ͹༙ໃ΁• ᶇɾᶉΝঈ͚ϲͯ஦ϰͯ͹ஏਔ൅ਫ਼͹Ϯुؔ઴Ͷ͕͏ͱ ඉབྷϏϩη͹ҡ৙ଁՅ͗ΊΔΗͪɿ




















ϲʛϭϴ࣎ ;ೖ஦ͿͶ͕͜Ζ
ҡ৙ଁՅ൅ਫ਼յ਼

ϭϴʛϲ࣎ ;໹ؔͿͶ͕͜Ζ
ҡ৙ଁՅ൅ਫ਼յ਼

‫ܯ‬

ϰϵ

ϴϯ

ϭϯϮ

ΉͳΌ

ࢂߡช‫ݛ‬• ৏Ғϭϱй͹৾෱Ν΍ͯϏϩηͶ͕͜Ζௗ‫غ‬υʖνմੵͶΓͮͱɾඉབྷϏϩη͹ʰϏϩη਼͹࣎ؔรಊʱ͕Γ;ʰଠ͹ஏਔ൅ਫ਼઴͹ҡ৙ଁՅ͹༙ໃʱΝ௒ࠬͪ͢ɿ• ηϘέφϩͳഀ‫ܙ‬͹ಝ௅Ͷ‫ͱ͏Ͱخ‬ɾଲஏབྷ͕Γ;ͨΗҐ֐͹‫ع‬எ͹བྷϏϩη͹ঈ‫ڊ‬ΝࢾΊͪɿ• ඉབྷϏϩη਼͹࣎ؔรಊͶͯ͏ͱͺɼՈɼवͶଁՅܑ޴Ͷ͍Εɼ͖ͯ໹ؔͶଁՅͤΖΓ͑͵΀ΖΉ͏͗ΊΔΗͪɽ• ᶇɾᶉΝঈ͚ϲͯ஦ϰͯ͹ஏਔ൅ਫ਼͹Ϯुؔ઴Ͷ͕͏ͱ ඉབྷϏϩη͹ҡ৙ଁՅ͗ΊΔΗͪɿ• ຌմੵͲͺɾඉབྷϏϩη͹รಊͳஏਔͳ͹໎Δ͖͵‫ܐؖ‬Ν‫ݗ‬ड़ͤΖͶͺࢺΔ͵͖ͮͪͪΌɾΓΕ৆ࡋ͵մੵ͗චགྷͲ͍Ζɿࠕ‫ޛ‬͹ఴ๮• ࠕյ͹བྷϏϩηঈ‫ڊ‬๏๑ͲͺΉͫঈ‫͘͢ڊ‬Η͵͏བྷϏϩη΍ଚࡑͪ͢ ਦ ˢ‫ؽ‬փָसΝ༽͏ͪΓΕ੠ౕ͹ྒྷ͏Ϗϩηঈ‫ڊ‬๏๑΃͹րྒྷ• ৾෱͹ओ͏৶ߺ ԾҒ ͕Γ;஦ఖౕ͹৾෱Ν΍ͯ৶ߺ ʛ Ͷͯ͏ͱ͹Ϗϩη਼௒ࠬ• ుࣕഀ๎ࣻ‫ݱ‬ਬఈΝՆ೵ͳͤΖ/)ଵ߁ଵҮ‫ױ‬ম‫ܯ‬͹֋൅ ਦ‫ױ‬ম‫ܯ‬ͶΓΖుࣕഀ‫ݱ‬ਬఈ΍ࠕ‫ޛ‬஥घ͢ͱ͏͚༩ఈ
D
E
F
G








*RNKEHUJ 0%HWDO-*HRSK\V5HV<DPDGDDQG2LNH-3K\V(DUWK
-,]XWX7HUU$WPRV2FHDQ6FL  ߶༆/)ଵ߁ଵҮ‫ױ‬ম‫ܯ‬͹֋൅ͳབྷ๎ు؏଎Ͷ‫ͤؖ‬Ζ‫ڂݜ‬ଢీ༖Ҳ࿗Δ-$(.

؏଎఼ (༩ఈ)‫ع‬ଚ؏଎఼

[བྷ๎ుͶΓΖుࣕഀ]઴તΏӤͳ‫ڠ‬ͶҢಊਦ ϭϯ͘hŶŬŶŽǁŶͶ‫ޣ‬෾ྪ͠ΗͪབྷϏϩηͳηϘέφϩɿ;ĂͿɾ;ďͿۛંབྷʂ;ďͿɾ;ĚͿԗֶབྷਦ ϭϮ͘ϭ࣎ؔ౲ͪΕ͹hŶŬŶŽǁŶϏϩη਼รಊ͕Γ;ᶅʛᶊͺஏਔ൅ਫ਼࣎ؔɿօ৯͹Ӫͺܿଝ‫ؔغ‬ɿ੪‫؛‬ͺஏਔϮुؔ઴Ґ಼Ͷ൅ਫ਼ͪ͢ҡ৙ଁՅɿ[ஏਔͶΓΖుࣕഀ]ਔԟ෉ۛͶॄ஦ʃਦ ϭϰ͘ࠕ‫ޛ‬͹‫ױ‬ম‫ܯ‬ોࢢઅ஖ߑ૟ਦ

ःࣛ؏଎εητϞ͹ࡠ੔Ͷ͟‫ͪ͏ͫͪ͏ྙڢ‬ਈ‫߶ۂ޽ཱིࢤރ‬౵઒໵ָߏ
͹஦ଞՄ‫ܡ‬०‫گ‬दۛ‫ق‬୉ָ͹ਁຌ݊ࢦ‫گ‬दͶ਄͚‫״‬ः஗͢Ήͤɿ┠ⓗⅆ࡛ᫍࡢࢲࢫࢺᕳࡁୖࡆ࡟ࡣࠊ኱ᇦⓗ࡞㢼௨እ࡟ࡶ኱ᑠࡢࡘࡴࡌ㢼࡞࡝ࡢᐤ୚㔞ࡀ኱ࡁ࠸࡜ゝ࠺ᣦ᦬ࡀ࠶ࡿࠋࡑࡢ ᐃࡢࡓࡵ࡟ⅆᫍࡢ᪥୰࡛ࡶ౑⏝࡛ࡁࡿᑠᆺ࡛ప㟁ຊࡢLIDARࢆ㛤Ⓨࡋ࡚࠸ࡿࠋ
ⅆᫍ⏝࡟㛤Ⓨࡋ࡚࠸ࡿᑠᆺLIDARࡢᛶ⬟ࢸࢫࢺࢆ⾜ࡗࡓࠋ࣭ᒇእ࡛ኴ㝧ගୗ࡛࡝ࢀࡄࡽ࠸ ࢀࡿ࠿ࢆࢸࢫࢺࡍࡿࠋ
࣭ᕳୖࡆ㐣⛬ࡢẼ㇟ほ ࢆ⾜࠸ࠊ㢼㏿࡜ᕳୖࡆ㔞ࡢ㛵ಀࢆㄪ࡭ࡿࠋ

10cm3 Mini-lidarⅆᫍࡢࢲࢫࢺࢹࣅࣝ㸦ࡘࡴࡌ㢼㸧NASAࡢspirit ࣮ࣟࣂ࣮࡛᧜ᙳࡉࢀࡓࡶࡢ

Parameters of the transmitter and the receiver.

sss

Peak Power: 0.75W = 7.5nJ/10ns
Average Power: < 5mW+ High Speed & High Resolution Photon Counter (Original)ᐇ㦂ሙᡤ㸸㫽ྲྀ◁ୣ᭶㠃ᐇドࣇ࢕࣮ࣝࢻ
ࣝࢼࢸࣛࢫ㸦㫽ྲྀ኱Ꮫ஝⇱ᆅ◊✲ࢭࣥࢱ࣮ෆ㸧ࡢ⮬⏤タィࢰ࣮ࣥ㸦45 × 50m㸧ࢆ฼⏝

Two versions of the compact LED lidar
for near-range ground experiment:
ձ version 1, and ղ version 2.
They were setup in a biaxial
configuration
with angle adjuster.
ኴ㝧ගࢫ࣌ࢡࢺࣝ(NREL) 㸸㉥⥺ࡀᆅ⾲㠃,㯮⥺ࡀ኱Ẽᅪእ
385nmࡣUVA㡿ᇦ࡛5%࡯࡝࡟ῶ⾶㸦O3࡟ࡼࡿ㸧

⤖ᯝ

ࡼࡇࡀࡁ

miniLIDAR࡟ࡼࡿほ 㸸LIDAR࠿ࡽ10m௨ୖ㞳ࢀࡓᆅⅬ࡛ேᕤⓗ࡟◁ሻࢆⓎ⏕ࡉࡏࡓ㢼࡟࡞ࡀࡉࢀࡿ◁ሻࡀ᪥୰࡟ほ ࡛ࡁ࡚࠸ࡓ㸦ࡇࡢ౛ࡣ᪥ἐᚋࡔࡀ࡯ࡰྠ⛬ᗘ࡟ほ ࡛ࡁࡓ㸧
㢼㏿࡟ẚ࡭࡚◁ሻࡢ⛣ື㏿ᗘࡣᑠࡉࡃ⢏ᚄࡣ300ੇ⛬ᗘ࠶ࡿ࡜ぢ✚ࡶࡽࢀࡿࠋ
ேᕤⓗ࡟㉳ࡇࡋࡓ௨እࡢࢲࢫࢺࡢᕳࡁୖࡆࡣほᮇ㛫୰࡟ࡣᤊ࠼ࡽࢀ࡞࠿ࡗࡓࠋ

ჾࡢ㓄⨨㛵ಀ㸸ࣇ࢕࣮ࣝࢻࡢ༡ᮾ➃࠿ࡽ໭す࡟ྥ࠿࠸ࣛ࢖ࢲ࣮࡛ ᐃࠊど⥺࡟ἢ࠺ࡼ࠺࡟⚟ᒸ኱ࠊ┤஺ࡍࡿࡼ࠺࡟
ᒸᒣ኱ࡢẼᅽィࢆ୪࡭ࡓࡲ࡜ࡵ• ᆅ⌫࡛ࡣࠊ᪥୰࡛ࡶィ ࡛ࡁࡓࠋࡶ࠺ᑡࡋ▷Ἴ㛗࡛࠶ࢀࡤⅆ࡛ᫍࡶࠊ ᐃ࡛ࡁࡿࠋ
385nmࡢἼ㛗ࡢሙྜࠊ1᱆⛬ᗘኴ㝧ගࡀᙉ࠸࡜ᛮࢃࢀࡿࡢ࡛ࡇࡢࡲࡲ࡛
ᐃ࡛ࡁࡿ࠿࡟ࡘ࠸࡚ࡣឤᗘࡢ᳨ドࡀᚲせࠋ• ࡘࡴࡌ㢼ࡣほ ࡛ࡁ࡞࠿ࡗࡓࠋ
ࡘࡴࡌ㢼ࡣ㉳ࡇࡗ࡚࠸ࡓࡀࢲࢫࢺࡸ◁ࢆᕳୖࡆࡿ࡯࡝ࡢᙉࡉࡀ࡞࠸࠿ࠊ
㣕ࡧ࡟ࡃ࠸ᅵተࢥࣥࢹ࢕ࢩࣙࣥࡔࡗࡓ࡜⪃࠼ࡽࢀࡿࠋ• 㫽ྲྀ◁ୣ࡛ࡣࠊ┠࡛ぢ࠼ࡿࡄࡽ࠸㣕ࢇ࡛࠸ࡓࡀࠊᩚഛ┤ᚋ࡛‵ࡗ࡚࠸ࡓ㸽Ꮫᰯࡢࢢࣛࣥࢻ࡞࡝ࡀࡼ࠸࠿ࡶࡋࢀ࡞࠸ࠋ• Ẽᅽィ࡛ࡣࡘࡴࡌ㢼࡟ࡼࡿ࡜ᛮࢃࢀࡿẼᅽࡢ㝆ୗࡣ ᐃ࡛ࡁࡓࠋ
2⛊࡯࡝࡛2㹫⛬ᗘ⛣ືࡋ࡚࠸ࡿ࡜ᛮࢃࢀࡿࠋ

28᪥15᫬

29᪥0᫬

29᪥12᫬

29᪥0᫬

30᪥8᫬

ほ ᮇ㛫୰ࡢẼᅽ㸸඲ ჾ࡯ࡰྠᵝ࡛࠶ࡗࡓࠋ
y㍈ࡣほ ᮇ㛫ࡢᖹᆒ࠿ࡽࡢᕪ࡛ x㍈ࡣ᫬㛫Ẽᅽࡢ㝆ୗࡀぢࡽࢀࡿ౛㸸㹶㍈ࡣ⿦⨨ࡢ఩⨨ࠊy㍈ࡣᖹᆒ࠿ࡽࡢẼᅽᕪࢱ࢖ࢺࣝࡣ⤒㐣᫬㛫࡛༢఩ࡣ⛊Ẽᅽࡢ୍␒ୗࡀࡗ࡚࠸ࡿ࡜ࡇࢁࢆ୰ᚰ࡜ࡋ࡚㞄ࡢẼᅽィ࡜ࡢ㛫࡛ィ⟬ࡋࡓẼᅽഴᗘຊ࡜ࣂࣛࣥࢫࡋ࡚࠸ࡿ࡜ࡋ࡚㢼㏿ࢆぢ✚ࡶࡿ࡜ᕥഃࡢ౛࡛4.4m/s ྑഃࡢ౛࡛ 3.2m/s ࡜ぢ✚ࡶࡽࢀࡿࠋ

ㅰ㎡
ࡇࡢᐇ㦂ࡣ㫽ྲྀ◁ୣ᭶㠃ᐇ㦂ࣇ࢕࣮ࣝࢻ ࣝࢼࢸࣛࢫࢆ฼⏝ࡋ࡚⾜࠸ࡲࡋࡓࠋ
ࣇ࢕࣮ࣝࢻࢆ฼⏝ࡉࡏ࡚࠸ࡃࡔࡉࡗࡓ㫽ྲྀ┴࠾ࡼࡧ㫽ྲྀ኱Ꮫ஝⇱ᆅ◊✲ࢭࣥࢱ࣮࡟ࡣཌࡃ࠾♩⏦ࡋୖࡆࡲࡍࠋ
ࡲࡓࠊᐇ㦂࡟ࡣࠊ༓ⴥ኱Ꮫ࠿ࡽ4ྡᒸᒣ኱Ꮫ࠿ࡽ1ྡ⚟ᒸ኱Ꮫ࠿ࡽࡣ2ྡࡢᏛ⏕ࡀཧຍࡋᶵჾࡢタ⨨㐠⏝࠾ࡼࡧࢹ࣮ࢱゎᯒ࡟༠ຊࡃࢀࡲࡋࡓࠋ

எ㒊┤ே*1, ᳝ྡ㐩⏨*2, ༓⛅༤⣖*3, ࡣࡋࡶ࡜ࡌࡻ࣮ࡌ*4, ᒾᒣ㝯ᐶ*1*1 ⚟ᒸ኱Ꮫ⌮Ꮫ㒊 *2༓ⴥ኱Ꮫ኱Ꮫ㝔ᕤᏛ◊✲⛉*3༓ⴥᕤᴗ኱Ꮫᝨᫍ᥈ᰝ◊✲ࢭࣥࢱ࣮ *4ᒸᒣ኱Ꮫ኱Ꮫ㝔⮬↛⛉Ꮫ◊✲⛉

P-32 ⅆᫍẼ㇟ࢭࣥࢧ㛤Ⓨ࡟ྥࡅࡓࣇ࢕࣮ࣝࢻヨ㦂॥টॼም॑அिশ਋৴ਢ௴೾पेॊ
঩মभপਞ୆ੀಽরभ੸෷৲ᆼ້෯২૗৿भਏ౤ੰ৥
*৪દ Ⅰ୬1(aywa2283@chiba-u.jp), ো௖ึ૒1
1:૸୴প৾୭୆জঔ‫ॺش‬७থ३থॢଢ଼஢७থॱ‫ش‬

੥ટ

ూବ
1

ৰ஍ଣೝਘ਑ৡ(IPCC AR6,2021)

ਈপ12ফ৑भ৴ਢশ਋ॹ‫॑ॱش‬੭ॊऒधऋदऌञ
5१ॖॺभ2019ٕ2022ফभ঩রఙகभຢऱऑ௕

඾჆৛ྱౌ
඾჆৛ྱౌ
ਧঀਣྱౌ
ਈुরఙகऋৈःभम
૸୴१ॖॺ呍ऒोम62ൾল౺भ
੅ਢ৓ऩ୶஭॑ਭऐथःॊ
ञीदँॊधઅ௄
呍્पযನ৓ऩൾল౺प
੍ଦ৓ऩવ্਱पା৯

඙ਣྱౌ
ਧঀਣྱౌ

‫ٴ‬ൾল౺भऺध॒न॑ग़ॿঝॠ‫ش‬ਓ঵ृੵ঵ऋಚीथःॊ
‫॔ূٴ‬४॔৉ୠदभൾল୤ऋਈु੗ः ,3&&$5 ‫ٴ‬঩মमણౌ৛भਯऋ਼ੀਸ਼ਜ਼

62भ૗৿ਏ౤॑৥ैऊपघॊऒधम੎ਏ
৯৓জঔ‫ॺش‬७থ३থॢૼ୒॑৷ःथ঩মभপਞ୆ੀಽর
भ੸෷৲ᆼ້෯২૗৿भਏ౤॑ੰ৥घॊ

ૣ௖

ஐ঩

૸୴

णऎय

෽઎

૸୴१ॖॺવ্਱भ2019–2020ফभারఙகभ௓୎

What’s MAX-DOAS ?

59٫भ૶ౕऩ੖૘
59পਞ୆ੀಽभ௴೾भ્ඉ˖ ௴೾ॹ‫ॱش‬भ૬৑৻਀ਙऋৈः˖ শ਋৑भ৴ਢ৓ऩ௴೾ऋ୔खः

MAX-DOAS
Multi–Axisٛ2019ফ9া‫ع‬ٜ

Differential Optical
Absorption
Spectroscopy૸୴ౌৡ৅ਗ਼ਚ
೒ஸध૸୴भଲளਚ

ٛ2020ফ3া‫ع‬ٜপਞ୆ੀಽ೒ஸभଲளਚ

๶୤ਞ৬भ્ඉ৓ऩลઽ५ঌॡॺঝଡୗ॑ਹ৷ख‫؜‬෯২॑଑লघॊ୷ীลઽী୾১
ളਯभ଩ྛഓ೾৒ਃચ॑ਸइञऒधदफ़५ृग़॔ট॰ঝभ๗ઉ஋઴୤
टऐदऩऎৈ২ীഘु଑ল૭ચ

ྲྀ૊ౌৡ৅ਗ਼ਚ

পਞ୆ੀಽभ௴೾पిखथःॊ

॥টॼምपेॊ
ளभ೽ਏ੖૘

⋉ೠ৵ઈૡ

਌ਏऩ62ൾল౺भ೿ૃ‫؞‬
ೠ৵ઈૡ
⋇
઎௯,
॥টॼም,
⋈
઱ਝभഠᄺ৲
⋉ഠᄺ৲ഓ২‫؟‬௯சपेॉ੎ातऐखथ੷ीञ1঩भ਴಑௯਱
রੱऊैभ෱௞‫؞‬౦‫؟‬SO2෯২঩রఙக
উটॵॺभপऌऔ‫؟‬1঩भ਴಑௯ச

૸୴१ॖॺदम্਱भ0$;'2$6॑ਝ઼खञ

਷਴५ॣ‫ش‬ঝ
7.6 km(c) Google Map

઎௯15ಀपेॊ
শ਋৑भ೿ૃ

૸୴१ॖॺ2021/7–2023/10ऽदभSO2෯২ध௯਱भঢ়બ

௴೾१ॖॺधੰෲ্১

౮ऩॊ৉৶৓્ඉ॑੅ण१ॖॺ

⋈⋇વਧभ௯ऋ฾ःञधऌपSO2෯২ऋৈऎऩॊ
ൊ਱ऋँॊ呍ূ਎೓ఢఈभౌৡ৅ਗ਼ਚ‫؞‬௩࿹ऩनभ୶஭
दँॊ૭ચਙ੥૛(c) Google Earthপਞ୆ੀಽभSO2෯২૗৿ਏ౤पम৉৶৓ऩ્ਙऋખ಩औोथःञ
‫س‬ঽேକ౺भ୶஭॑ਭऐथःञ१ॖॺ‫ق‬ஐ঩‫ૣ؞‬௖‫ك‬‫؞‬ఢఈपણౌ৛ऋ੗ःधःअ્ඉऊैྱ໛भ୶஭
(c) Google Earth(c) Google Earthੰෲ্১
SO2෯২भ঩রఙகभ௓୎ऋ
્౮৓ऩ঩॑ർघ

ংॵॡॺছ४ख़ॡॺজ‫ش‬ੰෲ॑ষअৈ෯২भਏ౤पणःथ
ੰෲ॑ষअ‫س‬যನକ౺भ୶஭॑ਭऐथःञ१ॖॺ‫؞୴૸ق‬णऎय‫؞‬෽઎‫ك‬‫ृৃੵ؞‬৅ਗ਼ਚऩनऋ੗ऎਜ਼઼घॊ௃ृূ਎೓भ୶஭
‫؞‬2019ফ9াਰఋ਴಑द59٫भSO2෯২੖૘‫ك୴૸ق‬‫؞‬વਧभ௯ऋ฾ःञधऌपSO2෯২ऋৈऎऩॊൊ਱‫ك୴૸ق‬Quantifying Anthropogenic Impacts on Global CO₂:
A Robust Isotopic Modelling Approach
Uddalak CHAKRABORTY*(ceha1867@chiba-u.jp), Naoko SAITOH, Prabir PATRA, Dmitry BELIKOV, Naveen CHANDRA
Carbon Dioxide as a Greenhouse Gas

CO2 Isotopes and their Applications• Anthropogenic CO2 emissions from fossil fuels and cement production
increased atmospheric CO2 by 38% from 1850-1900 to 2010-2019,reaching 398 ppm. In 2010–2019, 46% remained in the atmosphere, 23%
in oceans, and 31% in vegetation.
• The most recent concentration is 422.80 ppm in the atmosphere by theMauna Loa Observatory in January 2024.

•

What are the CO2 Isotopes?
12C , 13C , 14C
6
6
6

•

Isotopic values differentiate CO2 sources
and sinks; fossil fuels deplete δ13C andΔ14C, impacting atmospheric isotopes.

•

δ13C reflects 13C/12C deviations; Δ14C
indicates 14C/12C variations, adjusted forisotopic fractionation and radioactive
decay.

ǻ14C Dynamics
į13C Dynamics• Radiocarbon, with a half-life of 5,700± 30 years, occurs naturally anddecays over time. Fossil fuels lack
14C, hence, combustion releases only
stable isotopes 12C and 13C into theatmosphere.

• The isotopic composition of carbon in fossil
fuels, characterized by δ13C values ranging from-44‰ to -19‰, differs from atmospheric CO2,
which typically ranges from -8.5‰ to -4‰ overgeological timescales. This discrepancy arises
from photosynthetic fractionation during plantmaterial formation, where 13C is selectively
rejected. Notably, C3 plants, predominant among
trees, exhibit greater discrimination (-18‰) thanC4 plants like grasses (-4‰). Atmospheric CO2
modifications via gas transfer induce isotopic
fractionation, influencing the δ13C signature of
dissolved inorganic carbon (DIC) in surface
ocean waters.

• In the 1950s, nuclear weapons testing
spiked atmospheric 14C levels, halting
its declining trend ("aging"). This
surge, known as "bomb" 14C, nearly
doubled atmospheric 14C content.
• Nuclear bomb test sites and atomic
bomb explosion zones from 1940 to1990 mark the primary sources ofthese point releases.

• Marine ecosystems contribute to variations inocean δ13C through processes such as net
photosynthesis and respiration, which decreaseδ13C values at depth. Overall, these phenomena
underscore the complex interplay between
biological processes and environmental factors
in shaping the isotopic composition of carbon in
Earth's carbon cycle.

Box Model Results

Box Model and MIROC4.0-ACTM• Isotopic and CO2 exchange among boxes follows fixed factors over time. Physical parameterslike box thickness and exchange rates guide calculations to align with observed data.

• The Box Model dissects Earth into
compartments—Atmosphere, Biosphere, andOcean—to scrutinize CO2 and isotopic mixing. It
further subdivides into 48 sub-boxes for precise
concentration analysis.

d(Δ14C)/dt (per mil/year)(Special acknowledgement to Prof. Shinji Morimoto of Tohoku University for providing us with the Box Model Code)Δ 14C per mil• MIROC4-ACTM, part of the MIROC Earthsystem model, precisely simulates long-lived
atmospheric gases like CO2 and CH4 with T42
resolution and 67 vertical layers. It accurately
simulates long-lived atmospheric gases,
facilitating inverse modelling for estimating landand oceanic fluxes.

• Initial CO2, 13C, and 14C concentrations, set by Nakazawa et al. (1997) from 1727 and adjusted
by Morimoto et al. to 2018, underpin the Box Model simulation.

MIROC4.0-ACTM Results• Total CO2 concentrations are represented by
gc3t and gvjf for the 258 sites that can be
retrieved based on the CCGG (Carbon Cycle
Greenhouse Gas) observation database of
NOAA.

Chandra et al., 2022

• The CO2 concentrations are then plotted as amonthly average for 2022 from January to
December for both gc3t and gvjf.
• Observation data from NOAA’s CO2 observation
database are added along with the simulated
data for the specific sites.
• Offset is calculated as a difference between
Model – Observed Data

Future Works• As previously mentioned, measurements of δ13C and Δ14C in atmospheric CO2 and other carbon
reservoirs have provided crucial information on the carbon cycle as well as atmospheric andoceanic circulation. Scientific research would have benefited greatly from even more observations
made during the uniquely powerful period of nuclear bomb testing, across a wider range ofenvironments. This would have included additional measurements of the atmosphere and ocean
as well as the carbon in soils, rivers, and lakes.
• We are entering yet another critical time as Δ14CO2 falls below 0°C and either stabilizes or keeps
falling to extremely low levels. Future atmospheric change simulations show that some applicationsof Δ14C, and maybe δ13C, will inevitably lose part of their effectiveness over time. The particularapplications impacted will vary based on the emissions strategy chosen. As time goes on, at least
some applications' utility decreases, hence observations made today will typically be morevaluable than those that are yet to be produced. For instance, if Δ14CO2 stabilizes, the need for
Δ14C observations to determine the decadal-scale turnover of terrestrial carbon stocks will nolonger be necessary.
• The next task involves preparing fluxes for isotopes 13C and 14C for simulation in the MIROC4.0
Atmospheric Chemistry Transport Model (ACTM). The endeavor aims to seamlessly integratetheoretical insights from the Box Model with practical application in the MIROC4.0 ACTM, fosteringa harmonious synergy between model-based simulations and empirical observations for continualrefinement and optimization.

μ͞ඬᾤᾐᾥᵋᾢᾐᾡဇ
ᾧἢὅἛό͞ඬἰỶἁἿἋἚἼἕἩỴἾὊỴὅἘἜỉ᧏ႆ௥Ҿʁʴᵏᵊᴾᵨᶍᶑᵿᶎᶆᵿᶒᴾᵲᶃᶒᶓᶉᶍᴾᵱᶐᶇᴾᵱᶓᶋᵿᶌᶒᶗᶍᵏᵊᴾඡ˲‫ٽ‬ᵐᵊᴾ˙ᕲπɟᵏᵊᴾᵱᵌᴾᵥᵿᶍᵑᵊᴾᩊඬɟ᠗ᵏᵏ Ҙᓶ‫ܖٻ‬ᵊᴾᵐ ܴᖅ߻ಅ‫ܖٻ‬ᵊ ᵑ ᬐลɶ૨‫ܖٻ‬ᄂᆮᏑ୎ỴὅἘἜỉ˺ᙌể౨ᚰ

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ࡢไ⣙ࡀ࠶ࡾࠊ⅏ᐖⓎ⏕┤ᚋ࡟௵ពࡢሙᡤࢆほ ࡍࡿࡇ࡜ࡣ࡛ࡁ
࡞࠸ࠋᑠᆺࡢ8$9ࡣ㐠ᦙࡀྍ⬟࡛ࠊ⅏ᐖ≧ἣ࡟ྜࢃࡏࡓ㐠⏝ࡀྍ⬟࡛࠶ࡿࠋኪ㛫ほ ࡛ࡁࡿ6$5࡛࠶ࢀࡤࠊ⅏ᐖⓎ⏕࠿ࡽ▷࠸᫬㛫࡛ከࡃࡢ᝟ሗࢆ㞟ࡵࡿࡇ࡜ࡀ࡛ࡁࠊ8$96$5࡛ࡢ⅏ᐖ᫬ほ ࡟ᮇᚅࡉࢀ࡚࠸ࡿࠋ

タィࡋࡓ࢔ࣥࢸࢼࡣẚㄏ㟁⋡ࡢࣉࣜࣉࣞࢢㄏ㟁యᇶᯈ࡛స〇ࡋࠊ฼ᚓࡀG%LFࠊ,%:ࡀ0+]㸦㸧ࠊ$5%:ࡀ0+]㸦㸧ࠊࣅ࣮࣒ᖜࡀᗘ࡛࠶ࡗࡓࠋ

ᄂᆮႸႎ࢔ࣥࢸࢼタィᅗస〇ࡋࡓ࢔ࣥࢸࢼ

5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0

0.0
-5.0
-10.0
S11 [dB]

Axial Ratio [dB]༓ⴥ኱Ꮫ࡛ࡣࠊ/ࣂࣥࢻ&ࣂࣥࢻࡢ෇೫Ἴ6$5ࢩࢫࢸ࣒ࢆ8$9࡟ᦚ
㍕ࡍࡿ◊✲ࢆ⾜ࡗ࡚ࡁࡓ>@ࠋ⅏ᐖ᫬ほ ࡟ᑐᛂࡍࡿ8$96$5ࢩ
ࢫࢸ࣒ࡣࠊᚑ᮶ࢩࢫࢸ࣒ࡼࡾࡶᑠᆺࠊ㧗ゎീᗘࠊ㧗ศ㢮⬟࡞࡝ࡢᐇ⌧ࡀㄢ㢟࡜࡞ࡗ࡚࠸ࡿࠋ
ᥦ᱌ࡍࡿ;ࣂࣥࢻ෇೫Ἴ࣐࢖ࢡࣟࢫࢺࣜࢵࣉ࢔࣮ࣞ࢔ࣥࢸࢼࡣࠊ
ᚑ᮶ࢩࢫࢸ࣒ࡼࡾ▷࠸Ἴ㛗ࢆ౑࠺ࡇ࡜࡛㧗ศゎ⬟࡟࡞ࡾࠊࢩࢫࢸ
࣒඲యࢆᑠᆺ໬ࡉࡏࡿࠋࡉࡽ࡟6$5⏬ീࢆ㧗ゎീᗘ໬ࡉࡏࡿࡓࡵ
࡟ᗈᖏᇦ໬ࡉࡏࡓ෇೫Ἴ࢔ࣥࢸࢼࡣࠊ඲೫Ἴ࣮ࣔࢻ࡛౑⏝ࡍࡿࡇ
࡜࡛೫Ἴ6$5࡟ࡼࡿ㧗ศ㢮⬟໬ࡀྍ⬟࡜࡞ࡿࠋᮏ◊✲࡛ࡣࠊᗈᖏ
ᇦ࡞඲೫Ἴ8$96$5ࢩࢫࢸ࣒ࡢࢭࣥࢧ࡜ࡋ࡚ࠊ;ࣂࣥࢻ෇೫Ἴ࣐࢖
ࢡࣟࢫࢺࣜࢵࣉ࢔࣮ࣞ࢔ࣥࢸࢼࡢ㛤Ⓨࢆ⾜࠺ࠋ

-20.0
-25.0
-30.0
-35.0
-40.0

8.6

8.8

9.0

9.2
9.4
9.6
9.8
Frequency [GHz]
Simulation
Measurement

10.0

8.6

10.2

8.8

LHCP Radiation Pattern (9.4 GHz)
[dBic]

ỴὅἘἜỉᚘဒểᚨᚘ
Gain [dBic]

18
15
12
9
6
3
0
8.6

8.8

9.0

9.2
9.4
9.6
9.8
Frequency [GHz]
Simulation
Measurement

10.0

10.2

24

10.2

16
12
8
4
0
-4
-8
0

60

120
180
240
E-plane Theta Angle [deg]Simulation
Measurement

300

360

/+&3ᨺᑕࣃࢱ࣮ࣥ

μ͞ඬᵱᵟᵰ᬴ܱ

࢔ࣥࢸࢼࡢ฼ᚓྥୖࡉࡏࡿࡓࡵ࡟ࡣ࢔࣮ࣞ໬ࡀᚲ㡲࡛࠶ࡿࡀከẁ࡟࡞ࡿ࡜ࣅ࣮࣒ᖜࡀ⊃ࡃ࡞ࡿࡓࡵࠊ[࢔࣮ࣞ࡜ࡍࡿࠋ෇೫Ἴ࢔
ࣥࢸࢼࡣ࢖ࣥࣆ࣮ࢲࣥࢫᖏᇦ㸦,%:㸧࡜㍈ẚᖏᇦ㸦$5%:㸧ࡢ୧❧ࡀ㔜せ࡛࠶ࡾࠊᗈᖏᇦ໬ࡢࡓࡵ࡟෇೫Ἴࢆບ᣺ࡍࡿ⦰㏥ศ㞳⣲Ꮚࡢᙧ≧ࢆ᭱㐺໬ࡋࠊࢩ࣮ࢣࣥࢩࣕࣝᅇ㌿ᢏ⾡࡛0+]ࡢᖏᇦࢆ┠ᶆ࡜ࡍࡿࠋ
⦰㏥ศ㞳⣲Ꮚࡣᨺᑕ㠃࡜ࡢ㠃✚ẚࡔࡅ࡛࡞ࡃࠊᙧ≧࡟ࡼࡗ࡚㍈ẚࡀኚ໬ࡍࡿࡇ࡜ࡀࢩ࣑࣮ࣗࣞࢩࣙࣥ
࠿ࡽุ᫂ࡋࡓࡓࡵࠊࡢ㠃✚ẚ
࡜ &7K ࡜ &7Z ࡢㄪᩚ࡟ࡼࡗ࡚㍈ẚࡢ᭱㐺໬ࢆ⾜ࡗࡓࠋ

10.0

Angular Width (3dB): 15 degrees

20฼ᚓ‫ܪ‬ௌ஺ோܴ݉ ൌܿ‫݅ߠݏ݋‬

9.2
9.4
9.6
9.8
Frequency [GHz]
Simulation
Measurement

6

21

ଷ
1 ܴܵܰ 4ߨ ଷ ܴ௠‫ܨܤܶܭ‬4ߥ‫ܮ‬௦ ‫ߠ݊݅ݏ‬௜‫ܩ‬ൌߟܲ௧ ߣଷ ܴܲ‫ߪ ܨ‬°ܿ߬௣

9.0

㍈ẚ
24

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ಙ࢔ࣥࢸࢼࢆࡘᦚ㍕ࡋࠊ࢜ࣇࢼࢹ࢕࢔ゅࢆᗘ࡜ࡍࡿࠋ8$96$5
ࡣ㧗ᗘP࠿ࡽほ ࡍࡿࡇ࡜࠿ࡽࠊࢩࢫࢸ࣒≉ᛶࡢྛࣃ࣓࣮ࣛࢱ
࠿ࡽ࢔ࣥࢸࢼࡢᚲせ฼ᚓࢆḟᘧ࡟ࡼࡾG%ࡀᚲせ࡜ࡉࢀࡓ>@ࠋ-15.0

ᨺᑕ⣲Ꮚ࡜⦰㏥ศ㞳⣲Ꮚࡢᙧ≧

඲೫Ἴ6$5࡛ᚓࡽࢀࡓ✀㢮ࡢࢱ࣮ࢤࢵࢺ࠿ࡽᚓࡽࢀࡓᩓ஘ࢹ࣮ࢱ
ࢆ㏫ᢞᙳ࢔ࣝࢦࣜࢬ࣒࡛⏬ീ໬ࡋࡓࠋ6$5⏬ീ࡜ࡋ࡚≉ᚩⓗ࡞Ⅼᩓ஘యࢆ☜ㄆ࡛ࡁࠊᩓ஘ᙉᗘࡣࢱ࣮ࢤࢵࢺࡢᩓ஘≉ᚩ࡜୍⮴ࡋࡓࠋᩓ஘ࢹ࣮ࢱ࠿ࡽ᣺ᖜ࡜఩┦ࢆࢧࣥࣉࣜࣥࢢࡋࠊ⌮ㄽᩓ஘⾜ิ࡜ẚ
㍑ࡋࡓ>@ࠋ⌮ㄽᩓ஘⾜ิ࡜ィ ᩓ஘⾜ิࡢẚ㍑
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ࡓࡵ࡟ࡣᰯṇࡀᚲせ࡛࠶ࡿ࡜⪃࠼ࡿࠋ

6$5ࢩࢫࢸ࣒≉ᛶ࡜┠ᶆ฼ᚓParameter

Mounting to
the right side

Bottom of body

210 mmθN

Mounting
Mounting to the left side
60 mm
110 mmOff nadir angle 30 deg

Full Polarimetric Antenna Configuration
Tx
LHCP

Tx
RHCP

Rx
LHCP

Rx
RHCP

Forward110 mm

110 mm

440 mm8$9࡬ࡢᦚ㍕᪉ἲ

Symbol

Value

Unit

Antenna GainG

20.006dB

Signal Noise Ratio

SNR

35

dB

Transmission Power

Pt

100

W

Antenna Efficiency

ɻ

0.85

Wavelengthʄ0.0319m

Pulse Frequency

PRF

1500

Hz

Backscattering Coefficient

ʍ°

-35

dB

Speed of Lightc299792458

m/s

Pulse Length

ʏp

10

ʅs

Platform Altitude

HSAR

150

m

Slant Range Distance

Rm

173

m

Boltzmann ConstantK

1.38*10^(-23)

J/K

Temperature

T

280

K

Bandwidth

B

800

MHz

Receive Noise Figure

F

3

dB

Platform Speed

v

150

km/h

System Attenuation
Angle of incidence=Off Nadir angle

Ls

3

dB

ɽi=ɽo *

30

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[LR]

[LL]

[LR]

[LL]

[LR]

[RL]

[RR]

[RL]

[RR]

[RL]

[RR]

඲೫Ἴ6$5ᐇ㦂࡟࠾ࡅࡿ⏕ᡂ⏬ീộểỜᥦ᱌ࡋࡓ࢔ࣥࢸࢼࡣᇶᮏⓗ≉ᛶࡢ┠ᶆ௙ᵝࢆ‶㊊ࡉࡏࠊ඲೫Ἴ
6$5ᐇ㦂࡟࠾ࡅࡿᩓ஘ࢹ࣮ࢱࡀྛ೫Ἴ࣮ࣔࢻࡢ᣺ᖜ࡜఩┦ࡢ㛵ಀࡀ⌮ㄽᩓ஘࡜ࡢẚ㍑࡟࠾࠸୍࡚⮴ࡀ☜ㄆ࡛ࡁࡓࠋࡇࢀࡽ࡟ࡼࡗ࡚
ᥦ᱌ࡋࡓ࢔ࣥࢸࢼࢆ⏝࠸ࡓ6$5ࢩࢫࢸ࣒ࡣไᚚࡉࢀࡓ⎔ቃୗ࡛೫
Ἴ6$5ࢹ࣮ࢱရ㉁ࢆᥦ౪࡛ࡁࡿ࡜⤖ㄽ௜ࡅࡿࠋࡑࡢ⤖ᯝࠊᥦ᱌ࡋ
ࡓ࢔ࣥࢸࢼࡣࠊ8$96$5⏝඲೫Ἴྜᡂ㛤ཱྀ࣮ࣞࢲࡢ࢔ࣥࢸࢼ࡜ࡋ
࡚ࡢᛶ⬟ࢆ㐩ᡂࡋ࡚࠸ࡿࡇ࡜ࡀ☜ㄆࡉࢀࡓ>@ࠋ