融合多源信息的大型湖泊营养状态遥感评估

Remote sensing assessment of trophic state of large lakes by integrating multi-source information

湖泊营养状态直接反映了水环境质量,但在复杂环境因素影响下,全面且准确评估大型湖泊的营养状态是一项挑战。为应对这一难题,综合运用卫星遥感数据和现场实测数据等多源信息,并采用光谱曲线和四分位距原则(IQR)清洗数据异常值,以反演湖泊营养状态表征指标:叶绿素a浓度(Chl-a)和营养状态指数(TSI)。通过统计分析选取关键环境因子,包括pH、温度(T)、平均风速(AWS)和含沙量(SC),基于反向传输神经网络(BP-NN)和麻雀搜索算法(SSA)构建营养状态反演模型SSA-BP-NN。结果表明:基于BP-NN模型的Chl-a和TSI反演精度分别为0.843和0.834,而经SSA算法优化后,二者反演精度提高至0.918和0.936。以洪泽湖为例,运用该模型阐明了大型湖泊营养状态的时空分布特性,即西、北部湖区高于东部湖区。此外,栅格点数据分析表明Chl-a和TSI具有较强的相关性,且TSI的变化范围更稳定。水华因其高度瞬态性而复杂,TSI作为综合性指标可反映水体富营养化的基本情况,为水华风险预警提供背景参考。本研究表明,通过引入与湖泊营养状态相关性较强的环境因子,可以显著提升遥感反演模型评估精度,为大型湖泊生态系统健康状况评估和风险预警提供参考。

The trophic state of lakes is a direct reflection of the water quality,but its comprehensive,accurate assessment is often a challenge if a large lake is influenced by complex environmental factors.This study synthesizes multi-source information such as remote sensing data and on-site measured data,and cleans the data outlines using the methods of spectral curves and the interquartile range principle(IQR),so as to invert lake trophic state characterization metrics:chlorophyll-a concentration(Chl-a)and the trophic state index(TSI).Key environmental factors-including pH,temperature,average wind speed,and sediment content-are selected through statistical analysis;A trophic state inversion model SSA-BP-NN is constructed based on the back-propagation neural network(BP-NN)and sparrow search algorithm(SSA).The results show that the inversion accuracies of Chl-a and TSI based on the BP-NN model are 0.843 and 0.834 respectively,while they are improved to 0.918 and 0.936 through optimization using the SSA algorithm.In the case study of Hongze Lake,this model has been applied to analysis of the spatiotemporal distribution characteristics of the trophic state:TSI is higher in the western and northern lake than the eastern lake.An analysis of raster point data shows that Chl-a and TSI are closely correlated,and the range of TSI change is more stable.In this lake,water blooms are complex due to their highly transient nature and the influence of various factors;TSI,as a comprehensive indicator that can reflect the basic situation of eutrophication in water bodies,provides as background information for early warning of water bloom risks.This study verifies that the assessment accuracy of a remote sensing inversion model can be improved significantly by introducing environmental factors correlated closely with the lake trophic state,so as to help useful assessment of the health state of lake ecosystems and risk early warning.