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地理科学与资源研... [141]
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专题:地理科学与资源研究所
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Improving terrestrial evapotranspiration estimation across China during 2000-2018 with machine learning methods
期刊论文
JOURNAL OF HYDROLOGY, 2021, 卷号: 600, 页码: 18
作者:
Yin, Lichang
;
Tao, Fulu
;
Chen, Yi
;
Liu, Fengshan
;
Hu, Jian
收藏
  |  
浏览/下载:57/0
  |  
提交时间:2021/11/05
Evapotranspiration
Machine learning
process-based ET
ET integration
China
Gaussian process regression
Discrepant responses between evapotranspiration- and transpiration-based ecosystem water use efficiency to interannual precipitation fluctuations
期刊论文
AGRICULTURAL AND FOREST METEOROLOGY, 2021, 卷号: 303, 页码: 10
作者:
Gu, Chunjie
;
Tang, Qiuhong
;
Zhu, Gaofeng
;
Ma, Jinzhu
;
Gu, Chunli
收藏
  |  
浏览/下载:67/0
  |  
提交时间:2021/06/10
Ecosystem water use efficiency
Eddy covariance
PT-JPL model
PM model
Precipitation fluctuations
Evapotranspiration partition
Water availability
Differential Influence of No-Tillage and Precipitation Pulses on Soil Heterotrophic and Autotrophic Respiration of Summer Maize in the North China Plain
期刊论文
AGRONOMY-BASEL, 2020, 卷号: 10, 期号: 12, 页码: 12
作者:
Du, Kun
;
Li, Fadong
;
Leng, Peifang
;
Li, Zhao
;
Tian, Chao
收藏
  |  
浏览/下载:14/0
  |  
提交时间:2021/03/15
soil respiration components
precipitation
no-tillage
automatic chamber
maize
North China Plain
Incorporating Spatial Variations in Parameters for Improvements of an Evapotranspiration Model
期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 2020, 卷号: 125, 期号: 11, 页码: 21
作者:
Wu, Genan
;
Hu, Zhongmin
;
Keenan, Trevor F.
;
Li, Shenggong
;
Zhao, Wei
收藏
  |  
浏览/下载:19/0
  |  
提交时间:2021/03/15
evapotranspiration models
ecosystem models
parameter optimization
FLUXNET
Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and FLUXCOM
期刊论文
JOURNAL OF HYDROLOGY, 2020, 卷号: 590, 页码: 14
作者:
Ma, Ning
;
Szilagyi, Jozsef
;
Jozsa, Janos
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2021/03/15
Complementary relationship
FLUXCOM
Large-scale evapotranspiration
Benchmarking
Water balance
Importance of Shaded Leaf Contribution to the Total GPP of Canadian Terrestrial Ecosystems: Evaluation of MODIS GPP
期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 2020, 卷号: 125, 期号: 10, 页码: 22
作者:
Chen, Bin
;
Arain, M. Altaf
;
Chen, Jing M.
;
Wang, Shaoqiang
;
Fang, Hongliang
收藏
  |  
浏览/下载:19/0
  |  
提交时间:2021/03/16
gross primary productivity
light use efficiency
the two‐
leaf model
MODIS
Canadian land surface scheme
Isolating the Impacts of Land Use/Cover Change and Climate Change on the GPP in the Heihe River Basin of China
期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 2020, 卷号: 125, 期号: 10, 页码: 14
作者:
You, Nanshan
;
Meng, Jijun
;
Zhu, Lijun
;
Jiang, Song
;
Zhu, Likai
收藏
  |  
浏览/下载:14/0
  |  
提交时间:2021/03/16
GPP
land use and land cover change
climate change
attritube analyses
interannual variability
VPM
Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network
期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 17, 页码: 27
作者:
Wang, Shaofei
;
Zhou, Ji
;
Lei, Tianjie
;
Wu, Hua
;
Zhang, Xiaodong
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2021/03/16
land surface temperature
neural network
deep belief network
convolutional neural network
passive microwave
Effects of diffuse photosynthetically active radiation on gross primary productivity in a subtropical coniferous plantation vary in different timescales
期刊论文
ECOLOGICAL INDICATORS, 2020, 卷号: 115, 页码: 12
作者:
Han, Jiayin
;
Guo, Chuying
;
Ye, Shu
;
Zhang, Leiming
;
Li, Shenggong
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2021/03/18
Diffuse fraction
Eddy covariance
Light-use efficiency model
Path analysis
Timescale effects
Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation
期刊论文
SUSTAINABILITY, 2020, 卷号: 12, 期号: 7, 页码: 18
作者:
Ma, Hanqing
;
Ma, Chunfeng
;
Li, Xin
;
Yuan, Wenping
;
Liu, Zhengjia
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2021/03/23
sensitivity analysis
flux-based ecosystem model
extended Fourier amplitude sensitivity test (EFAST)
Howland forest
Markov chain Monte Carlo
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