Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere

The timing of vegetation spring onset is largely influenced by climate factors, making it sensitive to climate variation. Robust models that predict vegetation spring onset via the climate forcing data are needed in the land surface models for understanding the impacts of climate change on vegetatio...

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Bibliographic Details
Main Authors:	Li, W. (Author), Ruan, Y. (Author), Xin, Q. (Author), Zhang, Z. (Author), Zhou, X. (Author)
Format:	Article
Language:	English
Published:	Elsevier B.V. 2021
Subjects:	benchmarking calibration Climate change Climate factors climate forcing Climate models comparative study Forecasting Land surface models machine learning Machine learning Machine learning methods Machine learning models Machine-learning Model learning Natural vegetation Northern Hemisphere Northern Hemispheres numerical method Numerical methods numerical model Numerical models phenology Phenology modeling prediction Remote sensing Remote-sensing satellite altimetry spring (season) Surface measurement United States Vegetation vegetation dynamics
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Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere

Internet

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