Abstract. Long time series of spatiotemporally continuous phytoplankton functional type (PFT) products are essential for understanding marine ecosystems, global biogeochemical cycles, and effective marine management. In this study, by integrating artificial intelligence (AI) technology with multi-source marine big data, we have developed a Spatial–Temporal–Ecological Ensemble model based on Deep Learning (STEE-DL), and then generated the first AI-driven Global Daily gap-free 4 km PFTs product from 1998 to 2023 (AIGD-PFT), significantly enhancing the accuracy and spatiotemporal coverage of quantifying eight major PFTs (i.e., Diatoms, Dinoflagellates, Haptophytes, Pelagophytes, Cryptophytes, Green Algae, Prokaryotes, and Prochlorococcus). The input data encompass physical oceanographic, biogeochemical, spatiotemporal information, and ocean color data (OC-CCI v6.0) that have been gap-filled using a Discrete Cosine Transform with a Penalized Least Square (DCT-PLS) approach. The STEE-DL model utilizes an ensemble strategy with 100 ResNet models, applying Monte Carlo and bootstrapping methods to estimate optimal PFT values and assess model uncertainty through ensemble means and standard deviations. The model's performance was validated using multiple cross-validation strategies—random, spatial-block, and temporal-block—combined with in-situ data, demonstrating STEE-DL's robustness and generalization capability. The daily updates and seamless nature of the AIGD-PFT product capture the complex dynamics of coastal regions effectively. Finally, through a comparative analysis using a triple-collocation (TC) approach, the competitive advantages of the AIGD-PFT product over existing products were validated. The AIGD-PFT product not only provides the foundation for detailed analyses of PFT trends, interannual variability, and the impacts of climate change on phytoplankton composition across various temporal and spatial scales, but also has the potential to facilitate precise quantification of marine carbon flux and enhances the accuracy of biogeochemical models. A video demonstration is available at https://doi.org/10.5446/67366 (Zhang and Shen, 2024a). The complete product dataset (1998–2023) can be freely downloaded at https://doi.org/10.11888/RemoteSen.tpdc.301164 (Zhang and Shen, 2024b).

中文翻译：

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AIGD-PFT：1998年至2023年首款人工智能驱动的全球每日无间隙4公里浮游植物功能型产品

摘要。时空连续浮游植物功能型（PFT）产品的长时间序列对于了解海洋生态系统、全球生物地球化学循环和有效的海洋管理至关重要。本研究通过将人工智能（AI）技术与多源海洋大数据相结合，开发了基于深度学习的时空生态集成模型（STEE-DL），进而生成了第一个人工智能驱动的全球海洋数据模型。 1998年至2023年每日无间隙4公里PFT产品（AIGD-PFT），显着提高了量化八种主要PFT（即硅藻、甲藻、附着藻、泥藻类、隐藻类、绿藻、原核生物和原绿球藻）的准确性和时空覆盖范围）。输入数据包括物理海洋学、生物地球化学、时空信息和海洋颜色数据 (OC-CCI v6.0)，这些数据已使用带有惩罚最小二乘法的离散余弦变换 (DCT-PLS) 方法进行了间隙填充。 STEE-DL 模型采用包含 100 个 ResNet 模型的集成策略，应用蒙特卡洛和自举方法来估计最佳 PFT 值，并通过集成均值和标准差评估模型不确定性。使用多种交叉验证策略（随机、空间块和时间块）结合现场数据对该模型的性能进行了验证，证明了 STEE-DL 的鲁棒性和泛化能力。 AIGD-PFT 产品的每日更新和无缝特性有效捕捉了沿海地区的复杂动态。最后，通过使用三重搭配（TC）方法的比较分析，验证了AIGD-PFT产品相对于现有产品的竞争优势。 AIGD-PFT产品不仅为详细分析PFT趋势、年际变化以及气候变化对不同时间和空间尺度的浮游植物组成的影响提供了基础，而且有可能促进海洋碳通量和碳通量的精确量化。提高生物地球化学模型的准确性。视频演示请访问 https://doi.org/10.5446/67366（Zhang 和 Shen，2024a）。完整的产品数据集（1998-2023）可以在 https://doi.org/10.11888/RemoteSen.tpdc.301164 免费下载（Zhang 和 Shen，2024b）。