Remote sensing inversion of COD in Maowei Sea and nearshore aquaculture ponds based on machine learning

MO Jinying; TIAN Yichao; WANG Jiale; DU Jinze; ZHANG Qiang; ZHANG Yali; TAO Jin; LIN Junliang

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MO Jinying，TIAN Yichao，WANG Jiale, et al. Remote sensing inversion of COD in Maowei Sea and nearshore aquaculture ponds based on machine learning[J]. Haiyang Xuebao，2025, 47(x)：1–13

Citation:

MO Jinying，TIAN Yichao，WANG Jiale, et al. Remote sensing inversion of COD in Maowei Sea and nearshore aquaculture ponds based on machine learning[J]. Haiyang Xuebao，2025, 47(x)：1–13

Citation:

MO Jinying，TIAN Yichao，WANG Jiale, et al. Remote sensing inversion of COD in Maowei Sea and nearshore aquaculture ponds based on machine learning[J]. Haiyang Xuebao，2025, 47(x)：1–13

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Remote sensing inversion of COD in Maowei Sea and nearshore aquaculture ponds based on machine learning

MO Jinying^{1, 2
,},
TIAN Yichao^{1, 2, 3, 4, 5
,
,},
WANG Jiale²,
DU Jinze⁶,
ZHANG Qiang²,
ZHANG Yali²,
TAO Jin²,
LIN Junliang²

1.
School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China
2.
School of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China
3.
Guangxi Key Laboratory of Marine Environmental Change and Disaster in Beibu Gulf, Qinzhou 535011, China
4.
Gulf Ocean Development Research Center, Beibu Gulf University, Qinzhou 535011, China
5.
Key Laboratory of Marine Geographic Information Resources Development and Utilization in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China
6.
School of Marine Sciences, Beibu Gulf University, Qinzhou 535011, China

Received Date: 2024-07-14
Rev Recd Date: 2025-02-07

Available Online: 2025-04-14

Abstract

Abstract

COD is an critical parameter for measuring the degree of organic pollution in water bodies. Using remote sensing inversion to quickly obtain the spatiotemporal distribution of COD concentration is essential for aquaculture pollution control and nearshore ecological environment protection. This study used Sentinel-2 extracted single band, vegetation index (NDVI), and water index (NDWI) to evaluate the performance of six models, including catboost regression (CBR), gradient boost regression (GBR), k-nearest neighbor regression (KNNR), light generalized boosted regression (LGBM), random forest (RF), and extreme gradient boosting regression (XGBR). The performance of each model was evaluated with the coefficient of determination (R²) and root mean square error (RMSE). The spatiotemporal distribution characteristics of COD concentration Maowei Sea and its coastal aquaculture ponds were analyzed. The results showed that: (1) The XGBR model had the best predictive performance, with a test set R² of 0.9432 and an RMSE of 1.4033 mg/L. (2) B8a, B2, and NDVI contributed significantly to the XGBR inversion model. (3) During the period of 2019-2023, the annual average concentration of COD in the aquaculture pond water ranged from 16.23 to 17.39 mg/L, with a relatively uniform spatial distribution pattern; The annual average concentration of COD in Maowei Sea ranged from 2.30 to 2.88 mg/L, showing a distribution pattern of decreasing from the inner bay to the outer bay and higher near the shore than far shore. This study has validated the remarkable universal applicability of the XGBR model when applied simultaneously to COD inversion in two specific aquatic environments: aquaculture waters and their coastal seas. The relevant findings not only provide valuable insights for COD inversion under various complex water quality conditions but also offer robust technical support and a solid theoretical foundation for aquaculture activities and environmental management in coastal areas.
- COD,
- Machine learning algorithm,
- Sentinel-2,
- Aquaculture pond,
- Maowei Sea

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References(67)

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