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Performance evaluation of non-water absorption coefficient partitioning algorithms in optically complex coastal waters of Kochi and Goa, India
Oceanologia ( IF 2.9 ) Pub Date : 2021-05-24 , DOI: 10.1016/j.oceano.2021.05.001
Srinivas Kolluru , Shirishkumar S. Gedam , Arun B. Inamdar

Absorption coefficient partitioning algorithms (APAs) were developed to partition the total absorption coefficient (a(λ)) or total non-water absorption coefficient (anw(λ)) into the absorption subcomponents, i.e., absorption due to phytoplankton aph(λ), colored dissolved organic matter (CDOM) ag(λ) and non-algal particulate matter ad(λ), λ is the wavelength. Absorption coefficients of CDOM and non-algal particulate matter are generally combined due to a similarity in exhibited spectral shape and represented as colored detrital matter (CDM) absorption coefficient, adg(λ). This study focuses on the applicability of five APAs Schofield's, Lin's, Zhang's, Stacked Constraints Model (SCM) and Generalized Stacked Constraints Model (GSCM), in deriving the absorption subcomponents from anw(λ) in optically complex coastal waters of Kochi and Goa, India. The average spectral Mean Absolute Percentage Errors (MAPE) obtained for all models in the retrieval of aph(λ), ad(λ), ag(λ) and adg(λ) lie in the ranges of 26–44%, 37–45%, 34–65% and 42–56%. Slopes of adg(λ),ag(λ) and ad(λ) as indicated by Sdg,Sg and Sd are derivable from GSCM, Schofield and Lin's models only. GSCM model exhibited good retrieval capability of Sd with MAPE values of 22% and a correlation coefficient of 0.74. In retrieval of Sg parameter, none of the models demonstrated satisfactory performance. Overall, the GSCM and Schofield's models demonstrated good performance in the retrieval of absorption subcomponents, aph(λ),adg(λ), ad(λ) and Sd. Effect of applying baseline correction to ad(λ) on model performance is studied. Tuning with in situ data can further improve the absorption subcomponent and slope parameter retrieval capability of the models.



中文翻译:

印度 Kochi 和 Goa 光学复杂沿海水域非吸水系数划分算法的性能评估

开发了吸收系数分配算法 (APA) 来分配总吸收系数 (一种(λ)) 或总非吸水系数 (一种n(λ)) 进入吸收子成分,即由于浮游植物的吸收 一种H(λ), 有色溶解有机物 (CDOM) 一种G(λ) 和非藻类颗粒物 一种d(λ), λ是波长。CDOM 和非藻类颗粒物的吸收系数通常结合在一起,因为所显示的光谱形状相似,并表示为有色碎屑 (CDM) 吸收系数,一种dG(λ). 本研究侧重于 Schofield's、Lin's、Zhang's、Stacked Constraints Model (SCM) 和 Generalized Stacked Constraints Model (GSCM) 这五个 APAs 在推导吸收子组件中的适用性。一种n(λ)在印度高知和果阿的光学复杂的沿海水域。在检索的所有模型中获得的平均光谱平均绝对百分比误差 (MAPE)一种H(λ), 一种d(λ), 一种G(λ)一种dG(λ)位于 26-44%、37-45%、34-65% 和 42-56% 的范围内。坡度一种dG(λ),一种G(λ)一种d(λ) 正如所指出的 dG,Gd仅可从 GSCM、Schofield 和 Lin 的模型推导出来。GSCM 模型表现出良好的检索能力dMAPE 值为 22%,相关系数为 0.74。在检索G参数,没有一个模型表现出令人满意的性能。总体而言,GSCM 和 Schofield 的模型在吸收子组件的检索中表现出良好的性能,一种H(λ),一种dG(λ), 一种d(λ)d. 应用基线校正的效果一种d(λ)对模型性能进行了研究。使用原位数据进行调整可以进一步提高模型的吸收子分量和斜率参数检索能力。

更新日期:2021-05-24
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