Phytoplankton is the basis of marine food webs, driving both ecological processes and global biogeochemical cycles. Despite their ecological and climatic significance, accurately simulating phytoplankton dynamics remains a major challenge for biogeochemical numerical models due to limited parameterizations, sparse observational data, and the complexity of oceanic processes. Here, we explore how deep learning models can be used to address these limitations predicting the spatio-temporal distribution of phytoplankton biomass in the global ocean based on satellite observations and environmental conditions. First, we investigate several deep learning architectures. Among the tested models, the UNet architecture stands out for its ability to reproduce the seasonal and interannual patterns of phytoplankton biomass more accurately than other models like CNNs, ConvLSTM, and 4CastNet. When using one to two months of environmental data as input, UNet performs better, although it tends to underestimate the amplitude of low-frequency changes in phytoplankton biomass. Thus, to improve predictions over time, an auto-regressive version of UNet was also tested, where the model uses its own previous predictions to forecast future conditions. This approach works well for short-term forecasts (up to five months), though its performance decreases for longer time scales. Overall, our study shows that combining ocean physical predictors with deep learning allows for reconstruction and short-term prediction of phytoplankton dynamics. These models could become powerful tools for monitoring ocean health and supporting marine ecosystem management, especially in the context of climate change.

翻译：浮游植物是海洋食物网的基础，驱动着生态过程和全球生物地球化学循环。尽管具有重要的生态和气候意义，但由于参数化有限、观测数据稀疏以及海洋过程的复杂性，准确模拟浮游植物动态仍然是生物地球化学数值模型面临的主要挑战。本文探讨了如何利用深度学习模型，基于卫星观测和环境条件预测全球海洋浮游植物生物量的时空分布，以应对这些局限性。首先，我们研究了多种深度学习架构。在测试的模型中，UNet架构因其能够比CNN、ConvLSTM和4CastNet等其他模型更准确地再现浮游植物生物量的季节和年际变化模式而表现突出。当使用一到两个月的环境数据作为输入时，UNet表现更佳，尽管它倾向于低估浮游植物生物量低频变化的幅度。因此，为了改进时间序列上的预测，我们还测试了UNet的自回归版本，该模型利用其自身先前的预测来预报未来状况。这种方法在短期预测（最多五个月）中效果良好，但在更长时间尺度上性能会下降。总体而言，我们的研究表明，将海洋物理预测因子与深度学习相结合，可以实现对浮游植物动态的重建和短期预测。这些模型可能成为监测海洋健康和支撑海洋生态系统管理的强大工具，尤其是在气候变化的背景下。