2002年,Lee et al.提出了多波段准分析算法(multi-band Quasi-Analytical Algorithm-QAA)。多波段准分析算法目前只能用于光学深水情况,可以快速反演得到总吸收系数和粒子回向散射系数,以及浮游植物和CDOM(有色可溶有机物)的吸收系数。...
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2002年,Lee et al.提出了多波段准分析算法(multi-band Quasi-Analytical Algorithm-QAA)。多波段准分析算法目前只能用于光学深水情况,可以快速反演得到总吸收系数和粒子回向散射系数,以及浮游植物和CDOM(有色可溶有机物)的吸收系数。但是在整个计算流程中并没有考虑非弹性散射的影响,例如Raman散射、CDOM荧光。当水体属于清澈的大洋水时,Raman散射的贡献变得不可忽视;当CDOM浓度较大时,CDOM荧光作用也同样值得考虑。本文通过辐射传递方程模拟的办法对Raman散射及CDOM荧光对多波段准分析算法的影响进行模拟。只有在比较清澈的大洋Ⅰ类水域,Raman散射贡献才值得考虑,所以进行数值模拟时需要设定比较小的叶绿素浓度。非弹性散射部分进行了以下四种情况的模拟:(1)既没有Raman散射也没有CDOM荧光、(2)只考虑Raman散射、(3)只考虑CDOM荧光、(4)同时考虑Raman散射和CDOM荧光。只有当CDOM浓度达到一定程度后,CDOM荧光作用才能表现出来。非弹性散射部分进行了以下两种情况的模拟:(1)既没有Raman散射也没有CDOM荧光、(2)只考虑CDOM荧光。结果表明,Raman散射对遥感反射比的影响(～9%～25%)主要集中在400-500nm。CDOM荧光的影响(～1%-15%)主要集中在400-550nm;Raman散射和CDOM荧光对多波段准分析算法的影响在2%左右,在反演过程中不需要对此进行校正。
The recently developed quasi-analytical algo-rithm (QAA) is a promising algorithm for deriving inherentoptical properties from ocean color. Unlike the conventionalsemi-analytical algorithm, QAA does not need a priorik...
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The recently developed quasi-analytical algo-rithm (QAA) is a promising algorithm for deriving inherentoptical properties from ocean color. Unlike the conventionalsemi-analytical algorithm, QAA does not need a prioriknowledge of the spectral shape of chlorophyll ***, several empirical relations, which may not be uni-versally applicable and can result in low noise tolerance, areinvolved in QAA. In this study, the Bayesian inversion theoryis introduced to improve the performance of QAA. In theestimation of total absorption coefficient at the referencewavelength, instead of empirical algorithms used in the QAAthe Bayesian approach is employed in combination with anoptical model that uses separate parameters to account ex-plicitly for the contribution of molecular and particle scat-terings to remote sensing reflectance, a priori knowledgeproduced by the QAA, the Akaike’s Bayesian informationcriterion (ABIC) for choosing the optimal regularizationparameter, and genetic algorithms for global *** at other wavelengths are then derived using anempirical estimate of particle backscattering spectral *** applied to a simulated dataset synthesized by IOCCG,the Bayesian algorithm outperforms QAA algorithm, espe-cially in higher chlorophyll concentration waters. The rootmean square errors (RMSEs) between the true and the de-rived a(440) and bb(440) are reduced from 0.918 and 0.039m–1 for QAA-555 to 0.367 and 0.023 m–1 for Bayes-555, 0.205and 0.007 m–1 for QAA-640 to 0.092 and 0.005 m–1 forBayes-640, and 0.207 and 0.007 m–1 for QAA-blending to0.096 and 0.005 m–1 for Bayes-blending. Results of noise sen-sitivity analysis show that the Bayesian algorithm is morerobust than QAA.
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