Yang, Qi, Liu, Licheng, Zhou, Junxiong, Ghosh, Rahul, Peng, Bin, Guan, Kaiyu, Tang, Jinyun, Zhou, Wang, Kumar, Vipin, and Jin, Zhenong. A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest. Retrieved from https://par.nsf.gov/biblio/10481179. Remote Sensing of Environment 299.C Web. doi:10.1016/j.rse.2023.113880.

Yang, Qi, Liu, Licheng, Zhou, Junxiong, Ghosh, Rahul, Peng, Bin, Guan, Kaiyu, Tang, Jinyun, Zhou, Wang, Kumar, Vipin, & Jin, Zhenong. A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest. Remote Sensing of Environment, 299 (C). Retrieved from https://par.nsf.gov/biblio/10481179. https://doi.org/10.1016/j.rse.2023.113880

Yang, Qi, Liu, Licheng, Zhou, Junxiong, Ghosh, Rahul, Peng, Bin, Guan, Kaiyu, Tang, Jinyun, Zhou, Wang, Kumar, Vipin, and Jin, Zhenong. "A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest". Remote Sensing of Environment 299 (C). Country unknown/Code not available: Elsevier. https://doi.org/10.1016/j.rse.2023.113880. https://par.nsf.gov/biblio/10481179.

@article{osti_10481179,
place = {Country unknown/Code not available}, title = {A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest}, url = {https://par.nsf.gov/biblio/10481179}, DOI = {10.1016/j.rse.2023.113880}, abstractNote = {}, journal = {Remote Sensing of Environment}, volume = {299}, number = {C}, publisher = {Elsevier}, author = {Yang, Qi and Liu, Licheng and Zhou, Junxiong and Ghosh, Rahul and Peng, Bin and Guan, Kaiyu and Tang, Jinyun and Zhou, Wang and Kumar, Vipin and Jin, Zhenong}, }