Evaluating Machine Learning Algorithms to Model Time Series of Vegetation Indices in Tallgrass Prairie

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Published Dec 30, 2024
https://doi.org/10.35251/gjaas.2024.004
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Vol 5 No S1 (2024): Extended Abstract, NAPA 2024 Conference Special Issue 1

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Pradeep Wagle
Gopichandh Danala
Data Institute for Societal Challenges, University of Oklahoma, Norman, OK 73019, USA
Catherine Donner
Data Science and Analytics Institute, University of Oklahoma, Norman, OK 73019, USA
Xiangming Xiao
School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA
Corey Moffet
USDA, Agricultural Research Service, Oklahoma and Central Plains Agricultural Research Center, El Reno, OK 73036, USA
Stacey Gunter
USDA, Agricultural Research Service, Oklahoma and Central Plains Agricultural Research Center, El Reno, OK 73036, USA
Wolfgang Jentner
Data Institute for Societal Challenges, University of Oklahoma, Norman, OK 73019, USA
David Ebert
Data Institute for Societal Challenges, University of Oklahoma, Norman, OK 73019, USA

Abstract

Extended Abstract 

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How to Cite
Wagle, P., Danala, G., Donner, C., Xiao, X., Moffet, C., Gunter, S., Jentner, W., & Ebert, D. (2024). Evaluating Machine Learning Algorithms to Model Time Series of Vegetation Indices in Tallgrass Prairie . Global Journal of Agricultural and Allied Sciences, 5(S1), 1-2. https://doi.org/10.35251/gjaas.2024.004