Assimilation of Remotely Sensed LAI Into CLM4CN Using DART-Reference-Cited by-同舟云学术

Assimilation of Remotely Sensed LAI Into CLM4CN Using DART

Published:2019-08 Issue:8 Volume:11 Page:2768-2786
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J. Adv. Model. Earth Syst.

Author:

Ling X. L.¹²³^ORCID,Fu C. B.¹²^ORCID,Guo W. D.¹²^ORCID,Yang Z.‐L.³⁴^ORCID

Affiliation:

1. Institute for Climate and Global Change Research and School of Atmospheric SciencesNanjing University Nanjing China

2. Joint International Research Laboratory of Atmospheric and Earth System Sciences of Ministry of Education Nanjing China

3. Department of Geological Sciences, John A. and Katherine G. Jackson School of GeosciencesUniversity of Texas at Austin Austin TX USA

4. CAS Key Laboratory of Regional Climate‐Environment Research for Temperate East Asia, Institute of Atmospheric PhysicsChinese Academy of Sciences Beijing China

Funder

National Basic Research Program of China

National Natural Science Foundation of China

Jiangsu Collaborative Innovation Center for Climate Change

China Scholarship Council

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019MS001634

Reference112 articles.

1. Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France

2. Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

3. Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

4. An Ensemble Adjustment Kalman Filter for Data Assimilation

5. Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region;Journal of Geophysical Research: Biogeosciences;2024-09

2. Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations;Agricultural and Forest Meteorology;2024-08

3. Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau;Science of Remote Sensing;2024-06

4. Assimilation of Remotely Sensed Leaf Area Index for Improving Land Surface Simulation Performance at a Global Scale;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. Contrasting Leaf Phenologies at Two Highly Seasonal Tropical Forests;2024