Advancing remote sensing of carbon fractions in coastal waters: retrieval algorithms and optical proxies for Sentinel-2 MSI and Sentinel-3 OLCI

A thorough understanding of the global carbon pools and cycle is essential for understanding and predicting the effects of climate change. Coastal waters play a key role in the global carbon cycle but remain poorly understood due to their optical complexity, high spatial variability, and sensitivity to climate change. Satellite remote sensing data can provide high spatial and temporal resolution for carbon monitoring at local, regional, and global scales. This study estimates the potential of Sentinel-2 MSI (S2) and Sentinel-3 OLCI (S3) for mapping carbon fractions in coastal waters. In situ hyperspectral reflectance measurements and concentrations of different carbon fractions, namely Total Organic Carbon (TOC), Dissolved Organic Carbon (DOC), Particulate Organic Carbon (POC), and Dissolved Inorganic Carbon (DIC), were used. Bio-optical and physical water parameters, as well as carbon fractions, were measured in the coastal areas of the eastern Baltic Sea from 2023 to 2024. In situ reflectance spectra were recalculated to S2 and S3 band reflectance using their respective spectral response functions. Regression models, including linear regression and machine learning methods, were developed and validated against in situ data to estimate carbon fractions. For S2, the combinations of green and red or red-edge bands achieved the best performance in carbon retrieval, with R 2 values of 0.67, 0.64, and 0.56 for TOC, DOC, and DIC, respectively. The corresponding MAPE values ranged from 14% to 18%, and biases were close to zero. Slightly higher accuracies were obtained with S3 simulations. The S3 models that used reflectance at 490, 620, and 665 nm produced the most accurate estimates for both TOC ( R 2 = 0.71) and DOC ( R 2 = 0.67). For DIC, the best predictive model combined information from 510, 665, and 709 nm bands, achieving R 2 of 0.62. Among the tested optical proxies, CDOM absorption at 443 nm showed the most consistent relationship with TOC, DOC, and DIC ( R 2 = 0.56–0.30). The study represents a significant step forward in the remote sensing of coastal waters, and if adopted, the proposed carbon-fraction products could enable substantial progress across various fields, from carbon-cycle research to monitoring and policymaking.

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Journal
Remote Sensing of Environment
Published
2026-11-02
DOI
https://doi.org/10.1016/j.rse.2026.115756
Primary Topic
Marine Plankton and Biogeochemistry
Type
article
Field-Weighted Citation Impact
0.00

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article

Advancing remote sensing of carbon fractions in coastal waters: retrieval algorithms and optical proxies for Sentinel-2 MSI and Sentinel-3 OLCI

Tiit Kutser, Alo Laas, Martin Ligi, Laura Argus et al.
1 links
Remote Sensing of Environment
Marine Plankton and Biogeochemistry
article

Advancing remote sensing of carbon fractions in coastal waters: retrieval algorithms and optical proxies for Sentinel-2 MSI and Sentinel-3 OLCI

Tiit Kutser, Alo Laas, Martin Ligi, Laura Argus, Tuuli Soomets, Kaire Toming, Ele Vahtmäe, Roberto Sabia
article en

Abstract

A thorough understanding of the global carbon pools and cycle is essential for understanding and predicting the effects of climate change. Coastal waters play a key role in the global carbon cycle but remain poorly understood due to their optical complexity, high spatial variability, and sensitivity to climate change. Satellite remote sensing data can provide high spatial and temporal resolution for carbon monitoring at local, regional, and global scales. This study estimates the potential of Sentinel-2 MSI (S2) and Sentinel-3 OLCI (S3) for mapping carbon fractions in coastal waters. In situ hyperspectral reflectance measurements and concentrations of different carbon fractions, namely Total Organic Carbon (TOC), Dissolved Organic Carbon (DOC), Particulate Organic Carbon (POC), and Dissolved Inorganic Carbon (DIC), were used. Bio-optical and physical water parameters, as well as carbon fractions, were measured in the coastal areas of the eastern Baltic Sea from 2023 to 2024. In situ reflectance spectra were recalculated to S2 and S3 band reflectance using their respective spectral response functions. Regression models, including linear regression and machine learning methods, were developed and validated against in situ data to estimate carbon fractions. For S2, the combinations of green and red or red-edge bands achieved the best performance in carbon retrieval, with R 2 values of 0.67, 0.64, and 0.56 for TOC, DOC, and DIC, respectively. The corresponding MAPE values ranged from 14% to 18%, and biases were close to zero. Slightly higher accuracies were obtained with S3 simulations. The S3 models that used reflectance at 490, 620, and 665 nm produced the most accurate estimates for both TOC ( R 2 = 0.71) and DOC ( R 2 = 0.67). For DIC, the best predictive model combined information from 510, 665, and 709 nm bands, achieving R 2 of 0.62. Among the tested optical proxies, CDOM absorption at 443 nm showed the most consistent relationship with TOC, DOC, and DIC ( R 2 = 0.56–0.30). The study represents a significant step forward in the remote sensing of coastal waters, and if adopted, the proposed carbon-fraction products could enable substantial progress across various fields, from carbon-cycle research to monitoring and policymaking.

Remote Sensing of EnvironmentVol. 348
Estonian University of Life Sciences (EE), European Space Agency (FR), European Space Research Institute (IT), University of Tartu (EE)
European Space Agency, Eesti Teadusagentuur
Climate action, Life below water
Openalex Percentile: Top 20%
Marine Plankton and Biogeochemistry
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