基于现场多波段激发荧光的浮游植物多种色素含量XGBoost反演

XGBoost-Based Inversion of Phytoplankton Pigment Concentrations from Field Measured Fluorescence Excitation Spectra

针对浮游植物的总叶绿素a和7种诊断色素(叶绿素b、岩藻黄素、多甲藻素、19-己酰基氧化盐藻黄素、19-丁酰基氧化盐藻黄素、别藻黄素和玉米黄素),基于现场多波段激发荧光光谱数据,通过构建激发荧光光谱特征表征量,利用极限梯度提升(XGBoost)机器学习算法,建立了浮游植物色素浓度的反演模型。验证结果表明,反演模型具有良好的估算精度,其中总叶绿素a的反演模型精度最高(决定系数为0.87,平均绝对相对百分比误差为28.1%,均方根误差为1.168 mg·m^(-3))。将建立的色素反演模型应用于东海典型断面处,成功获取了色素浓度的垂向分布特征。

In this study,inversion models of phytoplankton pigment concentrations are built for the total chlorophyll a and seven diagnostic pigments(i.e.,chlorophyll b,fucoxanthin,peridinin,19′-hexanoyloxyfucoxanthin,19′-butanoyloxyfucoxanthin,alloxanthin,and zeaxanthin).Specifically,given the field measured data of fluorescence excitation spectra,the feature representations of fluorescence excitation spectra are constructed,and the machine learning algorithm eXtreme Gradient Boosting(XGBoost)is employed to build these models.The validation indicates that the inversion models have good estimation accuracy,among which the inversion model of the total chlorophyll a has the highest accuracy(with the determination coefficient of 0.87,the mean absolute percentage error of 28.1%,and the root mean square error of 1.168 mg·m^(−3)).In addition,these pigment inversion models are applied to typical sections of the East China Sea,and vertical distribution features of pigment concentrations are obtained.