Preprints
https://doi.org/10.5194/essd-2024-219
https://doi.org/10.5194/essd-2024-219
02 Jul 2024
 | 02 Jul 2024
Status: this preprint is currently under review for the journal ESSD.

High-spatiotemporal reconstruction of biogeochemical dynamics in Australia integrating satellites products and in-situ observations (2000–2022)

Xiaohan Zhang, Lizhe Wang, Jining Yan, and Sheng Wang

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)

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Xiaohan Zhang, Lizhe Wang, Jining Yan, and Sheng Wang

Status: open (until 22 Aug 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Xiaohan Zhang, Lizhe Wang, Jining Yan, and Sheng Wang

Data sets

Monthly Product of Marine Chemical Data in Australian Waters from 2000 to 2022 Xiaohan Zhang, Lizhe Wang, Jining Yan, and Sheng Wang https://doi.org/10.57760/sciencedb.09331

Xiaohan Zhang, Lizhe Wang, Jining Yan, and Sheng Wang

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Short summary
This study tackles the limitations of marine biogeochemical monitoring by integrating CANYON-B and Random Forest regression methods to reconstruct key biogeochemical concentrations across Australia from 2000 to 2022 at a 1-kilometre resolution. Utilizing multi-source in-situ observations and MODIS products, this approach showcases machine learning's effectiveness in enhancing large-scale ocean chemistry data reconstruction.
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