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A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest

  • Published:2023-12 Issue: Volume:299 Page:113880
  • ISSN:0034-4257
  • Container-title:Remote Sensing of Environment
  • language:en
  • Short-container-title:Remote Sensing of Environment

Author:

Yang Qi,Liu Licheng,Zhou Junxiong,Ghosh Rahul,Peng Bin,Guan Kaiyu,Tang Jinyun,Zhou Wang,Kumar Vipin,Jin Zhenong

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference104 articles.

1. Enhancing hydrologic data assimilation by evolutionary particle filter and Markov chain Monte Carlo;Abbaszadeh;Adv. Water Resour.,2018

2. The quest for model uncertainty quantification: a hybrid ensemble and variational data assimilation framework;Abbaszadeh;Water Resour. Res.,2019

3. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions;Ball,1987

4. The digital revolution of Earth-system science;Bauer;Nat. Comp. Sci.,2021

5. Sequential data assimilation techniques in oceanography;Bertino;Int. Stat. Rev.,2003

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