Chemical oxygen demand inversion in typical coastal breeding areas in Fujian based on Sentinel-2 data

doi:10.13474/j.cnki.11-2246.2025.0413

Abstract

Abstract: Chemical oxygen demand(COD) is an important indicator for evaluating the ecological and eutrophication levels of the water environment. Timely monitoring of COD concentrations in coastal waters is significant for marine environmental protection. This study utilizes field-measured data and Sentinel-2 MSI satellite remote sensing image data to identify the best band combination for inverting COD. It applies an empirical model based on statistical analysis and a machine learning model for inversion. The optimal inversion model for COD, applicable to Zhao'an Bay and Dongshan Bay in Fujian province, is determined. The spatiotemporal characteristics of water quality in these areas are also analyzed. The results show that the COD index regression model, constructed using the band reciprocal difference (BRD) combination, is the best inversion model for Zhao'an Bay and Dongshan Bay. This model achieves a determination coefficient R² of 0.82, a mean square error (MSE) of 1.85%, and a mean absolute percentage error (MAPE) of 11.55%. The inversion results indicate that the COD concentration in the study area remains relatively stable from 2017 to 2022, with high-value areas concentrated in nearshore regions and river estuaries. The COD concentration decreases in 2023, which is attributed to changes in aquaculture density resulting from local ecological protection measures implemented between 2022 and 2023.

Key words: COD, semi-empirical model, machine learning, Sentinel-2

CLC Number:

P237

CHEN Baofeng, CHEN Yunzhi, CHEN Hongmei. Chemical oxygen demand inversion in typical coastal breeding areas in Fujian based on Sentinel-2 data[J]. Bulletin of Surveying and Mapping, 2025, 0(4): 75-81.

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