A centennial-scale data of suspended particulate matter over the global ocean

Abstract. Suspended Particulate Matter (SPM) is a crucial indicator of marine nutrient transport, the carbon cycle, and land-sourced pollution. However, SPM research has been constrained by limited data. While marine remote sensing has helped fill this gap since late 20th century, pre-2000 data remains scarce, particularly for global datasets with high spatiotemporal resolution. To address the shortage of in-situ SPM observations, we developed two empirical models for estimating SPM concentrations: one based on remote sensing reflectance (Rrs) and the other on Secchi depth (Zsd). Model evaluation shows that retrieval uncertainties do not exceed 35 %. To mitigate errors in highly turbid waters (>100 mg l-1), we applied data quality constraints by excluding samples above this threshold. The resulting dataset merges in-situ SPM observations, providing global coverage of SPM concentrations from 1890 to 2020. Temporal and spatial analysis reveals an overall increase in global SPM concentrations, with a sharp rise between 1920 and 1942, followed by a rapid return to pre-1920 levels. The underlying causes of this fluctuation warrant further investigation. This unique dataset holds considerable potential for marine resource management, climate modeling, human activity assessment, and environmental protection.

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Published
2025-09-01
DOI
https://doi.org/10.5194/essd-2025-484
Primary Topic
Marine and coastal ecosystems
Type
article
Field-Weighted Citation Impact
0.00

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article

A centennial-scale data of suspended particulate matter over the global ocean

Wenting Quan, Xiaofan Wang, Cheng Fan, Qianguo Xing et al.
Marine and coastal ecosystems
article

A centennial-scale data of suspended particulate matter over the global ocean

Wenting Quan, Xiaofan Wang, Cheng Fan, Qianguo Xing, Jun Chen, Liguo Liu, Qianni Deng, Shilin Tang
article en

Abstract

Abstract. Suspended Particulate Matter (SPM) is a crucial indicator of marine nutrient transport, the carbon cycle, and land-sourced pollution. However, SPM research has been constrained by limited data. While marine remote sensing has helped fill this gap since late 20th century, pre-2000 data remains scarce, particularly for global datasets with high spatiotemporal resolution. To address the shortage of in-situ SPM observations, we developed two empirical models for estimating SPM concentrations: one based on remote sensing reflectance (Rrs) and the other on Secchi depth (Zsd). Model evaluation shows that retrieval uncertainties do not exceed 35 %. To mitigate errors in highly turbid waters (>100 mg l-1), we applied data quality constraints by excluding samples above this threshold. The resulting dataset merges in-situ SPM observations, providing global coverage of SPM concentrations from 1890 to 2020. Temporal and spatial analysis reveals an overall increase in global SPM concentrations, with a sharp rise between 1920 and 1942, followed by a rapid return to pre-1920 levels. The underlying causes of this fluctuation warrant further investigation. This unique dataset holds considerable potential for marine resource management, climate modeling, human activity assessment, and environmental protection.

Chinese Academy of Sciences (CN), Ministry of Natural Resources (CN), Naval University of Engineering (CN), Institute of Oceanology (CN), South China Sea Institute Of Oceanology (CN), Yantai Institute of Coastal Zone Research (CN), Second Institute of Oceanography (CN), Xi'an Jiaotong University (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 76%
Marine and coastal ecosystems
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