Remote sensing retrieval of phytoplankton group in the eastern China seas

Zhao Haiyang; Shen Fang; Sun Xuerong; Wei Xiaodao

doi:10.12284/hyxb2022062

Volume 44 Issue 4

Apr. 2022

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Zhao Haiyang，Shen Fang，Sun Xuerong, et al. Remote sensing retrieval of phytoplankton group in the eastern China seas[J]. Haiyang Xuebao，2022, 44(4)：153–168 doi: 10.12284/hyxb2022062

Citation:

Zhao Haiyang，Shen Fang，Sun Xuerong, et al. Remote sensing retrieval of phytoplankton group in the eastern China seas[J]. Haiyang Xuebao，2022, 44(4)：153–168 doi: 10.12284/hyxb2022062

Citation:

Zhao Haiyang，Shen Fang，Sun Xuerong, et al. Remote sensing retrieval of phytoplankton group in the eastern China seas[J]. Haiyang Xuebao，2022, 44(4)：153–168 doi: 10.12284/hyxb2022062

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Remote sensing retrieval of phytoplankton group in the eastern China seas

doi: 10.12284/hyxb2022062

1.
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China
2.
Institute of Eco-Chongming, East China Normal University, Shanghai 202162, China

Received Date: 2021-08-17
Rev Recd Date: 2021-11-20
Publish Date: 2022-04-14

Abstract

Abstract

Remote sensing retrieval of phytoplankton group can provide important data for a comprehensive understanding of the role of phytoplankton in marine ecosystem. However, due to the complex optical characteristics, there are still great challenges in the remote sensing retrieval of phytoplankton group in offshore waters. In this study, the eastern China seas region, a complex optical class II water body, is taken as the research area. By using three modeling methods, namely band combination method, multiple linear regression method based on singular value decomposition (SVD+MLR) and XGBoost regression method based on singular value decomposition (SVD+XGBoost), the phytoplankton group is retrieved from remote sensing reflectance (R_rs) data. Verified by the in-situ measured data set, the chlorophyll a (Chl a) concentration retrieval model of eight phytoplankton groups by SVD+XGBoost has the highest accuracy, and the determination coefficient (R²) of Chl a concentration inversion model of diatoms and dinoflagellates in the validation set is greater than 0.7. In contrast, the accuracy of Chl a concentration of chlorophytes, cyanobacteria and chrysophytes estimated by the three modeling methods is low (the R² of the validation results is less than 0.45). At the same time, the applicability of three atmospheric correction methods of OLCI images (C2RCC, POLYMER and MUMM) in the eastern China seas is evaluated. The results show that compared with the other two atmospheric correction algorithms, C2RCC has better performance in each band (root mean square error is less than 0.0048 sr⁻¹). Finally, the performance of the retrieval model on satellite images is verified by the in-situ data. The validation results show that the diatoms Chl a concentration model established by SVD+MLR has better accuracy (the R² is 0.56), while the Chl a concentration inversion models of other phytoplankton groups have poor results.
- eastern China seas,
- phytoplankton group,
- remote sensing retrieval,
- OLCI,
- atmospheric correction

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