基于连续小波系数的叶绿素a浓度估测模型

Estimation Model of Chlorophyll-a Concentration Based on Continuous Wavelet Coefficient

叶绿素a浓度是估算浮游植物生物量的重要指标,连续小波变换是一种重要的多尺度光谱分析方法。本文以粤西、珠江口为案例区,基于地面高光谱和实测叶绿素a的浓度数据,选取10种不同的母小波基函数对高光谱反射率数据进行连续小波变换。运用偏最小二乘回归(PLSR)方法构建叶绿素a浓度的反演模型,分析比较了不同小波变换系数对建模结果的影响。研究结果表明:采用经过多种小波变换后的小波系数与实测叶绿素a浓度进行相关性分析,相关性均高于原始光谱;基于不同的小波变换系数进行建模,反演精度差异较大,其中,基于sym6小波系数的偏最小二乘回归模型的精度最高(决定系数R2=0.732,均方根误差为6.457μg/L,相对分析误差为2.600),相较于基于光谱特征的传统反演方法精度明显提升。本研究为今后进行二类水体叶绿素a浓度模型构建过程中的小波基优选提供了参考。

Chlorophyll-a concentration is an essential indicator for estimating phytoplankton biomass,and continuous wavelet transforms is an essential multi-scale spectral analysis method.In this study,taking western Guangdong and Pearl River Estuary as the study area,ten mother wavelet functions are selected based on the water surface hyperspectral and measured chlorophyll-a concentration data to perform continuous wavelet transformation on hyperspectral reflectance data.The partial least square regression(PLSR)method was used to develop the chlorophyll-a concentration inversion model.Besides,the influence of different wavelet transformation coefficients on the modeling results was analyzed and compared.The results showed that the correlation between wavelet coefficients after various wavelet transformations and measured chlorophyll-a concentration is higher than that between the original spectrum and measured chlorophyll-a concentration.Besides,the results showed that the inversion accuracy varies greatly with models based on different wavelet coefficients.The partial least squares regression(PLSR)model based on sym6 wavelet coefficients has the best accuracy(determination coefficient R2 is0.732,root-mean-square error is 6.457μg/L,relative percent deviation is 2.600).Compared with the traditional inversion method based on spectral characteristics,it performs better and provides a wavelet-based optimization selection in the construction of the chlorophyll-a concentration model of caseⅡwater in the future.