中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
南海海洋研究所 [1]
地理科学与资源研究所 [1]
自动化研究所 [1]
生态环境研究中心 [1]
采集方式
OAI收割 [4]
内容类型
期刊论文 [4]
发表日期
2024 [1]
2022 [1]
2020 [1]
2013 [1]
学科主题
Meteorolog... [1]
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Applicability of Precipitation Products in the Endorheic Basin of the Yellow River under Multi-Scale in Time and Modality
期刊论文
OAI收割
REMOTE SENSING, 2024, 卷号: 16, 期号: 5, 页码: 872
作者:
Zhu, Weiru
;
Liang, Kang
  |  
收藏
  |  
浏览/下载:31/0
  |  
提交时间:2024/04/08
multi-source precipitation datasets
multiple time scales
extreme precipitation
extreme climate indices
endorheic basin
Multisource Remote Sensing Data-Based Flood Monitoring and Crop Damage Assessment: A Case Study on the 20 July 2021 Extraordinary Rainfall Event in Henan, China
期刊论文
OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 22, 页码: 5771-1-17
作者:
Zhang, Minghui
;
Liu, Di
;
Wang, Siyuan
;
Xiang, Haibing
;
Zhang, Wenxiu
  |  
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2023/02/02
time-series datasets synthesized from multiple sources
Hisea-1 data
characteristics of flooding
Google Earth Engine (GEE)
damaged crop species
yield reduction
Hippocampal and Amygdalar Morphological Abnormalities in Alzheimer's Disease Based on Three Chinese MRI Datasets
期刊论文
OAI收割
CURRENT ALZHEIMER RESEARCH, 2020, 卷号: 17, 期号: 13, 页码: 1221-1231
作者:
Wei, Yuanyuan
;
Huang, Nianwei
;
Liu, Yong
;
Zhang, Xi
;
Wang, Silun
  |  
收藏
  |  
浏览/下载:20/0
  |  
提交时间:2021/04/21
Alzheimer's disease
mild cognitive impairment
shape analysis
MMSE
large deformation diffeomorphic metric mapping
multiple datasets
Performances of seven datasets in presenting the upper ocean heat content in the South China Sea
期刊论文
OAI收割
ADVANCES IN ATMOSPHERIC SCIENCES, 2013, 卷号: 30, 期号: 5, 页码: 1331-1342
[Chen Xiao
;
Yan Youfang
;
Cheng Xuhua
;
Qi Yiquan] Chinese Acad Sci, State Key Lab Trop Oceanog, South China Sea Inst Oceanol, Guangzhou 510301, Guangdong, Peoples R China
;
[Chen Xiao] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
收藏
  |  
浏览/下载:42/0
  |  
提交时间:2015/01/14
South China Sea
ocean heat content
multiple datasets
interannual variability