基于ISSA-SVR算法的近海典型水质参数遥感反演

化学需氧量（COD）和叶绿素a（Chl-a）浓度作为与光谱相关的典型水质参数，是反映水体污染程度和富营养化程度的重要指标。支持向量回归模型（Support Vector Regression，SVR）适用于小样本，广泛用于近海典型水质参数的遥感反演，但也存在模型参数选择困难、容易陷入局部最优解的问题。针对这一问题，本文构建融合反向学习和模拟退火的改进麻雀算法（Improved Sparrow Search Algorithm，ISSA），通过改进麻雀算法对SVR模型的惩罚系数和核函数参数进行参数寻优，提出了一种改进的支持向量回归模型（ISSA-SVR）。通过该模型利用实测水面光谱与水质参数数据建立COD和Chl-a浓度反演模型。利用Sentinel-2卫星遥感光谱数据对模型的精度进行验证，得到各水质参数浓度的反演精度。采用ISSA算法优化SVR建立的COD浓度预测模型和Chl-a浓度预测模型的平均相对误差（MRE）分别为20.02%和30.17%。反演结果均优于其他模型（线性回归、SVR和SSA-SVR模型）。实验结果证实，ISSA-SVR算法是实现COD浓度和Chl-a浓度遥感反演的有效方法，可为我国近海典型水质参数遥感反演及后续水体科学管理提供参考。

Remote Sensing Retrieval of Coastal Water Quality Parameters Based on ISSA-SVR Method

The chemical oxygen demand (COD) and chlorophyll a concentration, which are typical water quality parameters related to the spectrum, serve as important indicators for reflecting the degree of water pollution and eutrophication. Support Vector Regression (SVR) is suitable for small sample sizes and widely utilized in remote sensing retrieval of typical offshore water quality parameters; however, it faces challenges in model parameter selection and may easily fall into local optimal solutions. To address this issue, an Improved Sparrow Search Algorithm (ISSA) is developed by integrating reverse learning and simulated annealing. An enhanced support vector regression model (ISA-SVR) is proposed by refining the Sparrow algorithm to optimize the penalty coefficient and kernel parameters of the SVR model. Inversion models for COD and Chl-a concentrations are established using measured water spectra and data on water quality parameters. The accuracy of the model is validated using Sentinel-2 satellite remote sensing spectral data, yielding inversion accuracies for each water quality parameter concentration. The mean relative error (MRE) of the COD concentration prediction model and Chl-a concentration prediction model based on ISSA algorithm optimized SVR are 20.02% and 30.17%, respectively, outperforming other models such as linear regression, SVR, and SSA-SVR models. Experimental results demonstrate that ISA-SVR algorithm represents an effective approach for remotely sensed retrieval of COD and Chl-a concentrations while offering valuable insights for subsequent scientific management of offshore water quality.