海洋科学2025，Vol.49Issue(10)：1-13,13.DOI:10.11759/hykx20250118001
基于Sentinel-2卫星和优化RBF模型反演香港海域叶绿素a浓度
Inversion of chlorophyll a concentration in Hong Kong waters based on Sentinel-2 satellite and optimized RBF model
摘要
Abstract
Chlorophyll-a concentration is a crucial parameter for the aquatic environment.However,because of the complexity of the spectral characteristics of offshore Case-Ⅱ water,which affects the reliability of the inversion of chlorophyll-a concentration in them.To assess the suitability of the radial basis function(RBF)neural network,enhanced by the dynamic K-means clustering and particles swarm optimization,for chlorophyll-a concentration inversion in Case-Ⅱ water,Sentinel-2 multispectral image remote sensing data were employed.Hong Kong offshore served as the study area.Sampling points with matching remote sensing image data were chosen based on consistent chlorophyll-a collection times,ensuring cloud coverage rates below 10%.Remote sensing image data underwent preprocessing to obtain reflectance values aligned with the monitoring dates.On this basis,bands with high corre-lation and their combinations B2,1/B2,1/B3 and B2-B4 were selected to construct the optimized RBF model,and compared with the traditional empirical model and the traditional RBF model.Results indicated an R2 value of 0.90 for the optimized RBF model,surpassing the traditional RBF models and empirical models.Additionally,the opti-mized RBF model's suitability for chlorophyll-a concentration inversion in Case-Ⅱ water was confirmed.Using the trained and optimized RBF model,chlorophyll-a concentration inversion in Hong Kong's offshore waters was exe-cuted using Sentinel-2 MSI data.The spatial distribution exhibited a pattern of low-high-low from west to east.Notably,certain areas within Hong Kong offshore waters displayed higher chlorophyll-a concentrations compared to the surrounding external waters.关键词
叶绿素a浓度/RBF神经网络/动态K-means聚类/粒子群最优化算法/香港近海Key words
chlorophyll-a concentration/RBF neural network/dynamic K-means clustering algorithm/particle swarm optimi-zation/Hong Kong offshore waters分类
资源环境引用本文复制引用
张磊,赵宽,魏来,管守德,赵玮..基于Sentinel-2卫星和优化RBF模型反演香港海域叶绿素a浓度[J].海洋科学,2025,49(10):1-13,13.基金项目
国家重点研发计划项目(2022YFD2401304) the National Key R&D Program of China,No.2022YFD2401304 （2022YFD2401304）
