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This study evaluates the effectiveness of 12 machine and deep learning algorithms in modeling vegetation indices (VIs) in tallgrass prairie ecosystems, focusing on the enhanced vegetation index (EVI) and land surface water index (LSWI). Results indicate that ensemble methods like XGBoost and random forest outperform other models, revealing complex relationships between climatic factors and prairie vegetation dynamics. The findings aim to improve management strategies for tallgrass prairie in the context of

Author

amigoaguilacalva

Language

EN