Preprints
https://doi.org/10.5194/egusphere-2024-464
https://doi.org/10.5194/egusphere-2024-464
07 Mar 2024
 | 07 Mar 2024
Status: this preprint is open for discussion.

DeepPhenoMem V1.0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander Winkler

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth’s surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different time scales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of phenology simulated by land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and canopy greenness at high temporal resolution collected taking advantage of digital repeat photography by the PhenoCam network. We compare a simple multiple linear regression model, a no-memory-effect, and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates of 50 sites and 317 site-year during 2009–2018, across deciduous broadleaf forests, evergreen needleleaf forests and grasslands. The deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957, and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands) corroborating the benefits of deep learning approach and the importance of multi-variate meteorological memory effects in phenology modelling. We also find that the LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that 1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and 2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander Winkler

Status: open (until 02 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2024-464', Matthew Garcia, 11 Mar 2024 reply
    • AC1: 'Reply on CC1', Guohua Liu, 13 Mar 2024 reply
  • CEC1: 'Comment on egusphere-2024-464', Juan Antonio Añel, 28 Mar 2024 reply
    • AC2: 'Reply on CEC1', Guohua Liu, 04 Apr 2024 reply
  • RC1: 'Comment on egusphere-2024-464', Anonymous Referee #1, 06 Apr 2024 reply
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander Winkler
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander Winkler

Viewed

Total article views: 466 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
359 89 18 466 23 6 7
  • HTML: 359
  • PDF: 89
  • XML: 18
  • Total: 466
  • Supplement: 23
  • BibTeX: 6
  • EndNote: 7
Views and downloads (calculated since 07 Mar 2024)
Cumulative views and downloads (calculated since 07 Mar 2024)

Viewed (geographical distribution)

Total article views: 486 (including HTML, PDF, and XML) Thereof 486 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Apr 2024
Download
Short summary
Our study employs Long Short-Term Memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlocking the secrets of vegetation phenology responses to climate change with deep learning techniques.