The marine chemical 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 chemical changes. These products play a critical role in facilitating research focused on dynamic monitoring of marine ecosystems and fostering sustainable oceanic development. The interpolation methods 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 in reconstructing the concentrations of these marine chemicals 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 chemical elements, thereby significantly enhancing our abil
The data is sourced from the Scientific Data Bank（ https://www.scidb.cn/en/detail?dataSetId=4991142399124803a68a67633cbec7d3 ）.
A novel integration exploration was conducted between CANYON-B and random forest regression methods, which involves merging multi-source in-situ ocean chemistry time series observation data with MODIS Terra ocean reflectance images and ocean color product distributions.
The data quality is good.
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CSTR:11738.11.NCDC.SCIDB.DB6698.2024
10.57760/sciencedb.09331