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Abstract: ALIVE-KGML: A Knowledge-Guided Machine Learning Framework for Coupled Carbon and Water Flux Estimation from Geostationary Satellites (AGU25)

ALIVE-KGML: A Knowledge-Guided Machine Learning Framework for Coupled Carbon and Water Flux Estimation from Geostationary Satellites

Sadegh Ranjbar, PhD Candidate, University of Wisconsin Madison, Biological Systems Engineering, Madison, United States, Danielle Losos, University of Wisconsin, SSEC/CIMSS, Madison, United States, Sophie Hoffman, University of Wisconsin Madison, Biological System Engineering, Madison, United States, Qunying Huang, University of Wisconsin Madison, Department of Geography, Madison, United States, Ankur R Desai, University of Wisconsin Madison, Atmospheric and Oceanic Sciences, Madison, United States and Paul C Stoy, University of Wisconsin-Madison, Madison, Biological Systems Engineering, Madison, United States
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