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

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.