Machine learning-based remote sensing retrieval model for MODIS chlorophyll-a concentration in adjacent waters of the Yellow River Estuary

Hao Jiawen; Liu Huihui; Gao Zhiqiang; Wang De; Wang Yueqi

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Hao Jiawen，Liu Huihui，Gao Zhiqiang, et al. Machine learning-based remote sensing retrieval model for MODIS chlorophyll-a concentration in adjacent waters of the Yellow River Estuary[J]. Haiyang Xuebao，2025, 47(x)：1–14

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Machine learning-based remote sensing retrieval model for MODIS chlorophyll-a concentration in adjacent waters of the Yellow River Estuary

Hao Jiawen^{1, 2
,},
Liu Huihui³,
Gao Zhiqiang¹,
Wang De¹,
Wang Yueqi^{1
,
,}

1.
Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Geography Teaching and Research Group, Yantai Number Four Middle School, Yantai 264013, China

Received Date: 2024-12-30
Rev Recd Date: 2025-04-14

Available Online: 2025-05-13

Abstract

Abstract

: Chlorophyll-a (Chl-a) concentration in the ocean is an important indicator of the marine phytoplankton biomass and serves as a direct reflection of marine ecological and environmental changes. Accurate and efficient estimation of Chl-a is essential for oceanographic research. Satellite remote sensing facilitates large-scale, high-frequency monitoring of Chl-a, offering important support for understanding evolution of marine ecosystem. However, due to the complex bio-optical properties, remote sensing retrievals of Chl-a in coastal, turbid waters are often uncertain, requiring the use of extensive in situ observations for validation and optimization. In this study, in situ Chl-a observations from 45 cruises conducted between 2010 and 2023 were integrated with synchronous satellite remote sensing reflectance data to develop a machine learning (ML) model for estimating Chl-a concentrations in the Yellow River Estuary adjacent sea areas. The results demonstrate that, compared to traditional standard algorithms and previous regional models, ML algorithms achieve higher accuracy. Among ML models, the Gaussian Process Regression (GPR) model yielded the best performance (R² = 0.62, RMSE = 0.21 mg/m³), effectively capturing the spatial temporal Chl-a patterns of this area. The chlorophyll-a (Chl-a) concentration in this sea area demonstrates a spatial pattern of nearshore areas being higher than offshore areas, with seasonal variation showing a distinct single-peak structure characterized by higher values in summer and lower values in winter. From 2003 to 2023, the average Chl-a concentration increased at an annual rate of 0.02 mg/m³. This research advances remote sensing retrieval algorithms for Chl-a concentration in coastal waters, expands the application of ML models, and provides both methodological and data supports for evaluating the marine ecological environment in the Yellow River Estuary and its adjacent areas.
- Chlorophyll-a concentration,
- remote sensing retrieval model,
- MODIS,
- machine-learning,
- Yellow River Estuary adjacent sea areas

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References(43)

References

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