文献
J-GLOBAL ID:202202242579713929
整理番号:22A0430658
AMSR2測定からの北極海氷上の寒冷季節積雪深さを検索するための深層学習アプローチ【JST・京大機械翻訳】
A deep learning approach to retrieve cold-season snow depth over Arctic sea ice from AMSR2 measurements
著者 (14件):
Li Haili
(Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China)
,
Li Haili
(Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China)
,
Li Haili
(Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China)
,
Ke Chang-Qing
(Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China)
,
Ke Chang-Qing
(Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China)
,
Ke Chang-Qing
(Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China)
,
Zhu Qinghui
(Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China)
,
Zhu Qinghui
(Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China)
,
Zhu Qinghui
(Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China)
,
Li Mengmeng
(Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China)
,
Shen Xiaoyi
(Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China)
,
Shen Xiaoyi
(Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China)
,
Shen Xiaoyi
(Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China)
,
Li Mengmeng
(Henan Academy of Big Data, Zhengzhou University, Zhengzhou 450001, China)
資料名:
Remote Sensing of Environment
(Remote Sensing of Environment)
巻:
269
ページ:
Null
発行年:
2022年
JST資料番号:
C0252B
ISSN:
0034-4257
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)