Abstract:

The concentration of chlorophyll-a in marine waters is one of the key indicators for assessing the ecological environment and water quality of oceans, which plays a crucial role in marine resource management and protection.Focusing on the Yellow Sea, this study constructs a chlorophyll-a concentration inversion model based on 2022 VIIRS remote sensing data using a BP neural network machine learning method.The model features a three-hidden-layer architecture with 4-6-4 nodes and takes multi-band remote sensing reflectance and sea surface temperature during the same period as input data.The results are compared with commonly used models, such as OC2, OC3, linear, and cubic models.The comparison shows that all five models can be used for chlorophyll-a concentration inversion in the Yellow Sea, but the model designed in this study exhibits smaller inversion errors (with relative errors generally less than 12%, and an average relative error of 8.2%), outperforming the other models.To further assess the model’s performance, a comprehensive evaluation index was applied, demonstrating that the inversion results (MAE=0.122, RMSE=0.153, R²=0.937) meet the expected accuracy.Additionally, the model’s generalization ability was tested using VIIRS remote sensing data and sea surface temperature data from February 2021.The validation shows that the model has good generalization ability and can be used for inversion tasks in other periods within the same region.This study demonstrates the feasibility of using a BP neural network model based on VIIRS data for monitoring ocean chlorophyll-a, which provides a foundation for related practices and research.

Key words: Yellow Sea, machine learning, backpropagation neural network, chlorophyll-a, sea surface temperature, marine remote sensing