Abstract. The marine biogeochemical time-series products, which include total alkalinity, inorganic carbon, nitrate, phosphate, silicate, and pH, constitute a foundational support mechanism for the ongoing surveillance of oceanic biogeochemical changes. These products play a critical role in facilitating research focused on dynamic monitoring of marine ecosystems and fostering sustainable oceanic development. However, existing monitoring methodologies are hampered by inherent limitations, notably the paucity of observational products that simultaneously offer high spatial and temporal resolutions. Furthermore, the interpolation methods typically employed in these contexts frequently prove low-effective on a large scale, resulting in data with extensive temporal and spatial expanses that are difficulty for applications aimed at monitoring large-scale ocean dynamics. A novel integration of the CANYON-B and Random Forest regression methods was explored to address these challenges in reconstructing key marine biogeochemical parameters. This work reconstructs the concentrations of these marine biogeochemicals at the sea surface within Australia's Exclusive Economic Zone over the period from 2000 to 2022 on a 1-kilometre scale. The approach involves the amalgamation of multi-source in-situ ocean chemistry time-series observations with MODIS Terra ocean reflectance imagery and ocean water colour product distributions. This research highlights the substantial capabilities of machine learning for the large-scale reconstruction of ocean chemistry data, introducing a new, viable method for utilising in-situ measurements and optical imagery in reconstructing marine biogeochemical elements, thereby significantly enhancing our ability to monitor large-scale ocean dynamics. The datasets generated and analysed in this study are available on Science Data Bank (https://doi.org/10.57760/sciencedb.09331) (Zhang et al., 2024)

中文翻译：

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结合卫星产品和现场观测的澳大利亚生物地球化学动力学高时空重建（2000-2022）


摘要。海洋生物地球化学时间序列产品包括总碱度、无机碳、硝酸盐、磷酸盐、硅酸盐和pH值，构成了持续监测海洋生物地球化学变化的基础支撑机制。这些产品在促进海洋生态系统动态监测和促进可持续海洋发展的研究方面发挥着关键作用。然而，现有的监测方法受到固有局限性的阻碍，特别是缺乏同时提供高空间和时间分辨率的观测产品。此外，在这些情况下通常采用的插值方法在大范围内经常被证明效率低下，导致数据具有广泛的时间和空间范围，这对于旨在监测大规模海洋动力学的应用来说是困难的。探索了 CANYON-B 和随机森林回归方法的新颖整合，以解决重建关键海洋生物地球化学参数时的这些挑战。这项工作以 1 公里尺度重建了 2000 年至 2022 年期间澳大利亚专属经济区内海面这些海洋生物地球化学物质的浓度。该方法涉及将多源原位海洋化学时间序列观测与 MODIS Terra 海洋反射图像和海水颜色产品分布相结合。这项研究凸显了机器学习在大规模重建海洋化学数据方面的强大能力，引入了一种新的、可行的方法，利用原位测量和光学图像重建海洋生物地球化学元素，从而显着增强我们监测大规模海洋化学数据的能力。尺度海洋动力学。 本研究生成和分析的数据集可在科学数据库 (https://doi.org/10.57760/sciencedb.09331) 上获取 (Zhang et al., 2024)