Particle composition-specific approach to estimate the particulate organic carbon concentration off the coastal areas of Guangdong and eastern Hainan Island

Author:

Wei Panpan1,Zhou Wen2,Li Cai2,Deng Lin3ORCID,Zheng Wendi2,Xu Zhantang2,Shi Zhen,Wang Zihui1,Qin Min1,Yang Zeming2,Cao Wenxi2

Affiliation:

1. University of Chinese Academy of Sciences

2. South China Sea Institute of Oceanology

3. Sun Yat-sen University

Abstract

High optical complexity caused by the variability of marine particles poses a major challenge to the development of bio-optical algorithms for particulate organic carbon (POC) concentration retrievals from optical measurements in coastal waters. Here, we developed a particle composition-specific approach to estimate POC off the coastal areas of Guangdong and eastern Hainan Island, China. The ratio of phytoplankton absorption to detritus absorption coefficient aph(443)/ad(443) was used to optically discriminate water types. The samples with aph(443)/ad(443) ≤ 4.9 showed a significant correlation between POC and absorption line height at 676 nm aLH(676) (R2= 0.75, n = 70, p < 0.01). In contrast, aph-dominant samples with aph(443)/ad(443) > 4.9 had a high covariance between POC and particle scattering coefficient at 675 nm bp(675) (R2 = 0.85, n = 37, p < 0.01). Validation with an independent dataset yielded a small positive bias (R2 = 0.81, APD = 23.10%, RMSE = 29.01 mg m–3, RPD = 16.31%). The approach provided a better estimation of POC concentration in coastal waters compared with univariate algorithms. A depth-resolved index aLH(676)/bbp(442) was defined as the ratio of absorption line height to particle backscattering coefficient. Using the depth-resolved index instead of aph(443)/ad(443) for optical water type classification can be utilized to represent the vertical variations of POC in 1 m bins, and can complement remote sensing observations to accurately characterize the three-dimensional structure of POC distribution in the oceans.

Funder

Science and Technology Planning Project of Guangzhou Nansha District Guangzhou City China

National Natural Science Foundation of China

Science and Technology Fundamental Resources Investigation Program

Guangdong Basic and Applied Basic Research Foundation

Publisher

Optica Publishing Group

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