Retrieving Phytoplankton Size Class from the Absorption Coefficient and Chlorophyll A Concentration Based on Support Vector Machine
Retrieving Phytoplankton Size Class from the Absorption Coefficient and Chlorophyll A Concentration Based on Support Vector Machine
复制标题
基于支持向量机的吸收系数和叶绿素A浓度反演浮游植物大小等级
DOI：
10.3390/rs11091054
复制
发表时间：
2019
期刊：
影响因子：
5
通讯作者：
Zhao Wenjing
中科院分区：
文献类型：
--
作者：
Deng Lin;Zhou Wen;Cao Wenxi;Zheng Wendi;Wang Guifen;Xu Zhantang;Li Cai;Yang Yuezhong;Hu Shuibo;Zhao Wenjing
浮游植物大小级(PSC)在海洋生物地球化学过程中起着重要作用。在这项研究中，提出了一个由总负吸水系数(At-w(λ))和叶绿素a浓度(Chla)反演垂直PSCs的区域模式。该模型首先从浮游植物吸收和叶绿素a(λ)数据中重建浮游植物吸收和Chla，然后利用支持向量机提取它们的PSC。利用2006年至2013年在南海区中国海域采集的现场生物光学数据对支持向量机进行训练。所提出的PSC模型随后使用2015年来自东北中国南海邮轮的独立PSC数据集进行了验证。结果表明，PSC模型优于三组分模型，r2值在0.35~0.66之间，绝对百分比差异在56%~181%之间。总体而言，我们的PSC模式在推断中国海南部的垂直PSC方面表现出了显著的实用性。
The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (at-w(λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla fromat-w(λ), and then extracting PSC from them using the support vector machine (SVM). In situ bio-optical data collected in the South China Sea from 2006 to 2013 were used to train the SVM. The proposed PSC model was subsequently validated using an independent PSC dataset from the Northeast South China Sea Cruise in 2015. The results indicate that the PSC model performed better than the three components model, with a value of r2between 0.35 and 0.66, and the absolute percentage difference between 56% and 181%. On the whole, our PSC model shows a remarkable utility in terms of inferring vertical PSCs from the South China Sea.