Remote Sensing Hyperspectral Inversion of Chlorophyll a in Water Quality Based on SSA-XGBoost Algorithm
This study focuses on the important regulation and storage lake on the eastern route of the South to North Water Diversion Project-Shangji Lake.In response to the water quality characteristics such as seasonal algal blooms,high eu-trophication risks,and turbid water bodies,field experiments were conducted on remote sensing hyperspectral water quality chlorophyll a inversion.The hyperspectral data and water quality data were synchronously obtained.Four spectral data prepro-cessing methods,including normalization,first-order differentiation,pairwise ratio,and four band model,were used to improve the quality of hyperspectral information.Pearson correlation analysis was used to select six optimal sensitive charac-teristic bands or combination bands for inverting water quality chlorophyll a as model input factors.SSA-XGBoost coupling algorithm was used to construct a high-precision water quality chlorophyll a inversion model suitable for the turbid water characteristics of the Lake.The results indicate that,SSA-XGBoost model is superior to the SVM model and BP neural net-work model,with a goodness of fit of 0.946,an average absolute error of 2.83 mg/m3,and a root mean square error of 3.69 mg/m3.The model has high prediction accuracy and good application effect.This study can provide data support and tech-nical support for the monitoring and control of eutrophication in the water body.
万方数据
维普
