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Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetatio...

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Autores principales: Chen, Zhiting, Liu, Hongyan, Xu, Chongyang, Wu, Xiuchen, Liang, Boyi, Cao, Jing, Chen, Deliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216928/
https://www.ncbi.nlm.nih.gov/pubmed/34188816
http://dx.doi.org/10.1002/ece3.7564
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author Chen, Zhiting
Liu, Hongyan
Xu, Chongyang
Wu, Xiuchen
Liang, Boyi
Cao, Jing
Chen, Deliang
author_facet Chen, Zhiting
Liu, Hongyan
Xu, Chongyang
Wu, Xiuchen
Liang, Boyi
Cao, Jing
Chen, Deliang
author_sort Chen, Zhiting
collection PubMed
description Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.
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spelling pubmed-82169282021-06-28 Modeling vegetation greenness and its climate sensitivity with deep‐learning technology Chen, Zhiting Liu, Hongyan Xu, Chongyang Wu, Xiuchen Liang, Boyi Cao, Jing Chen, Deliang Ecol Evol Original Research Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature. John Wiley and Sons Inc. 2021-05-02 /pmc/articles/PMC8216928/ /pubmed/34188816 http://dx.doi.org/10.1002/ece3.7564 Text en © 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Chen, Zhiting
Liu, Hongyan
Xu, Chongyang
Wu, Xiuchen
Liang, Boyi
Cao, Jing
Chen, Deliang
Modeling vegetation greenness and its climate sensitivity with deep‐learning technology
title Modeling vegetation greenness and its climate sensitivity with deep‐learning technology
title_full Modeling vegetation greenness and its climate sensitivity with deep‐learning technology
title_fullStr Modeling vegetation greenness and its climate sensitivity with deep‐learning technology
title_full_unstemmed Modeling vegetation greenness and its climate sensitivity with deep‐learning technology
title_short Modeling vegetation greenness and its climate sensitivity with deep‐learning technology
title_sort modeling vegetation greenness and its climate sensitivity with deep‐learning technology
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216928/
https://www.ncbi.nlm.nih.gov/pubmed/34188816
http://dx.doi.org/10.1002/ece3.7564
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