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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Autores principales:	Wanjing Li, Qinchuan Xin, Xuewen Zhou, Zhicheng Zhang, Yongjian Ruan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Numerical models Machine learning Phenology modeling Remote sensing Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/ae90e7ff52654b1ead29ab1c09b4d372
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id	oai:doaj.org-article:ae90e7ff52654b1ead29ab1c09b4d372
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spelling	oai:doaj.org-article:ae90e7ff52654b1ead29ab1c09b4d3722021-12-01T04:59:29ZComparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere1470-160X10.1016/j.ecolind.2021.108126https://doaj.org/article/ae90e7ff52654b1ead29ab1c09b4d3722021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21007913https://doaj.org/toc/1470-160XThe 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 vegetation processes. In this study, we apply and assess both numerical phenology models and the machine learning models on predicting the timing of spring onset for different vegetation types, including deciduous vegetation, evergreen vegetation and stressed deciduous vegetation. We perform model calibration for numerical phenology models and machine learning models using both in-situ observations of spring onset dates from National Phenology Network in the United States and satellite-derived green-up dates in the Northern Hemisphere. Our experiment showed better performance of numerical models calibrated by ground phenology observations. Among all the numerical phenology models, the models developed based on Growing Season Index perform well on predicting the spring onsets of deciduous vegetation and stressed deciduous vegetation across the Northern Hemisphere. Machine learning models if trained appropriately could also capture the spatial variation of satellite-derived spring onset dates. Our study highlights the need of improvements on numerical phenology models for their uses in the land surface models. We also illustrate the benchmarking role of the machine learning models on predicting vegetation spring onsets via climate variables.Wanjing LiQinchuan XinXuewen ZhouZhicheng ZhangYongjian RuanElsevierarticleNumerical modelsMachine learningPhenology modelingRemote sensingEcologyQH540-549.5ENEcological Indicators, Vol 131, Iss , Pp 108126- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Numerical models Machine learning Phenology modeling Remote sensing Ecology QH540-549.5
spellingShingle	Numerical models Machine learning Phenology modeling Remote sensing Ecology QH540-549.5 Wanjing Li Qinchuan Xin Xuewen Zhou Zhicheng Zhang Yongjian Ruan Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere
description	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 vegetation processes. In this study, we apply and assess both numerical phenology models and the machine learning models on predicting the timing of spring onset for different vegetation types, including deciduous vegetation, evergreen vegetation and stressed deciduous vegetation. We perform model calibration for numerical phenology models and machine learning models using both in-situ observations of spring onset dates from National Phenology Network in the United States and satellite-derived green-up dates in the Northern Hemisphere. Our experiment showed better performance of numerical models calibrated by ground phenology observations. Among all the numerical phenology models, the models developed based on Growing Season Index perform well on predicting the spring onsets of deciduous vegetation and stressed deciduous vegetation across the Northern Hemisphere. Machine learning models if trained appropriately could also capture the spatial variation of satellite-derived spring onset dates. Our study highlights the need of improvements on numerical phenology models for their uses in the land surface models. We also illustrate the benchmarking role of the machine learning models on predicting vegetation spring onsets via climate variables.
format	article
author	Wanjing Li Qinchuan Xin Xuewen Zhou Zhicheng Zhang Yongjian Ruan
author_facet	Wanjing Li Qinchuan Xin Xuewen Zhou Zhicheng Zhang Yongjian Ruan
author_sort	Wanjing Li
title	Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere
title_short	Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere
title_full	Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere
title_fullStr	Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere
title_full_unstemmed	Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere
title_sort	comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the northern hemisphere
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/ae90e7ff52654b1ead29ab1c09b4d372
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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

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