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CAS IR Grid
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成都山地灾害与环... [145]
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期刊论文 [101]
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专题:成都山地灾害与环境研究所
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Characterizing the effect of scaling errors on the spatial downscaling of mountain vegetation gross primary productivity
期刊论文
OAI收割
GEO-SPATIAL INFORMATION SCIENCE, 2023, 页码: 12
作者:
Xie, Xinyao
;
Li, Ainong
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2024/02/27
Gross primary productivity
spatial downscaling
scaling errors
mountainous areas
Land cover dataset of the China Central-Asia West-Asia Economic Corridor from 1993 to 2018
期刊论文
OAI收割
SCIENTIFIC DATA, 2023, 卷号: 10, 期号: 1, 页码: 12
作者:
Naboureh, Amin
;
Li, Ainong
;
Bian, Jinhu
;
Lei, Guangbin
;
Nan, Xi
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2024/03/04
无
How is the performance of satellite-based product suites in monitoring long-term dynamics of vegetation photosynthesis over global mountainous areas?
期刊论文
OAI收割
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 卷号: 119, 页码: 14
作者:
Xie, Xinyao
;
Li, Ainong
  |  
收藏
  |  
浏览/下载:5/0
  |  
提交时间:2023/10/09
Terrestrial photosynthesis
Global mountainous areas
Moderate -resolution Imaging
Spectroradiometer (MODIS)
Global LAnd Surface Satellite (GLASS)
Global Inventory Modeling and Mapping
Studies (GIMMS)
A fine spatial resolution estimation scheme for large-scale gross primary productivity (GPP) in mountain ecosystems by integrating an eco-hydrological model with the combination of linear and non-linear downscaling processes
期刊论文
OAI收割
JOURNAL OF HYDROLOGY, 2023, 卷号: 616, 页码: 12
作者:
Xie, Xinyao
;
Li, Ainong
;
Tian, Jie
;
Wu, Changlin
;
Jin, Huaan
  |  
收藏
  |  
浏览/下载:22/0
  |  
提交时间:2023/02/08
Gross primary productivity
Fine spatial resolution
Eco-hydrological models
Surface heterogeneity
Spatial downscaling
Modeling the Impact of Investment and National Planning Policies on Future Land Use Development: A Case Study for Myanmar
期刊论文
OAI收割
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2023, 卷号: 12, 期号: 1, 页码: 19
作者:
Jin, Yuan
;
Li, Ainong
;
Bian, Jinhu
;
Nan, Xi
;
Lei, Guangbin
  |  
收藏
  |  
浏览/下载:28/0
  |  
提交时间:2023/03/13
land use simulation
CGELUC model
FLUS model
updated mechanism
belt and road initiative
land sustainability
Myanmar
Estimation of Vegetation Leaf-Area-Index Dynamics from Multiple Satellite Products through Deep-Learning Method
期刊论文
OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 17
作者:
Liu, Tian
;
Jin, Huaan
;
Li, Ainong
;
Fang, Hongliang
;
Wei, Dandan
  |  
收藏
  |  
浏览/下载:18/0
  |  
提交时间:2022/12/28
time series
leaf-area index
long short-term memory
deep learning
SGOT: A Simplified Geometric-Optical Model for Crown Scene Components Modeling over Rugged Terrain
期刊论文
OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 8, 页码: 24
作者:
Hu, Guyue
;
Li, Ainong
  |  
收藏
  |  
浏览/下载:22/0
  |  
提交时间:2022/07/05
canopy reflectance
geometric-optical model
scene components
topographic effect
Bi-LSTM Model for Time Series Leaf Area Index Estimation Using Multiple Satellite Products
期刊论文
OAI收割
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 卷号: 19, 页码: 5
作者:
Liu, Tian
;
Jin, Huaan
;
Xie, Xinyao
;
Fang, Hongliang
;
Wei, Dandan
  |  
收藏
  |  
浏览/下载:23/0
  |  
提交时间:2022/11/02
Bidirectional long short-term memory (Bi-LSTM)
deep learning (DL)
leaf area index (LAI)
time series
Assessing the effects of irrigated agricultural expansions on Lake Urmia using multi-decadal Landsat imagery and a sample migration technique within Google Earth Engine
期刊论文
OAI收割
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 卷号: 105, 页码: 11
作者:
Naboureh, Amin
;
Li, Ainong
;
Ebrahimy, Hamid
;
Bian, Jinhu
;
Azadbakht, Mohsen
  |  
收藏
  |  
浏览/下载:58/0
  |  
提交时间:2021/12/13
Land cover
Irrigation expansions
Lake Urmia
Google Earth Engine
Sample migration
Landsat
Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China
期刊论文
OAI收割
REMOTE SENSING, 2021, 卷号: 13, 期号: 18, 页码: 25
作者:
Xie, Xinyao
;
Li, Ainong
;
Jin, Huaan
;
Bian, Jinhu
;
Zhang, Zhengjian
  |  
收藏
  |  
浏览/下载:45/0
  |  
提交时间:2021/12/10
GPP estimation
mountainous areas
ecosystem models
remote sensing