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Remote sensing estimation of colored dissolved organic matter (CDOM) from GOCI measurements in the Bohai Sea and Yellow Sea

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Abstract

Colored dissolved organic matter (CDOM) is the main constituent of dissolved organic matter (DOM), also a key indicator of water quality conditions. Accurate estimation of CDOM is essential for understanding biogeochemical processes and ecosystems in marine waters. The use of remote sensing to derive the changes in CDOM is vital technology that can be used to dynamically monitor the marine environment and to document the spatiotemporal variations in CDOM over a large scale. In the present study, we develop a simple approach to estimate the CDOM concentrations based on the in situ datasets from four cruise surveys over the Bohai Sea (BS) and Yellow Sea (YS). Eight band combination forms (using ** as a delegate, where i denotes the numerical order of band combination forms), including single bands, band ratios, and other band combinations by remote sensing reflectance, Rrs(λ), were trained to test the correlations with the CDOM concentrations. The obtained results indicated that X7, i.e., [Rrs(443) + Rrs(555)]/[Rrs(443)/Rrs(555)], was the optimal form, with correlation coefficient (R) values of 0.904 (p < 0.001). The X7-based fitting model was determined as the optimal model by the leave-one-out cross-validation method with relatively low estimation errors (mean relative error, MRE, 20%), and satellite match-up validation with in situ measurements indicated good performance MRE = 20.3%). Moreover, two spatial distribution patterns of CDOM in Jan. 2017 and Apr. 2018 (independent data) retrieved from Geostationary Ocean Color Imager (GOCI) data agreed well with those in situ observations. These results indicate that our proposed algorithm is feasible and robust for retrieving CDOM concentrations in this study region. In addition, we applied this method to GOCI data for the whole 2016 year in the BS and YS and produced the spatial distribution patterns from different temporal scales including monthly, seasonal, and annual scales. Overall, the findings of this study motivate the development and application of a simple but effective method of the CDOM estimation for those optically complex turbid coastal waters, like this study water areas.

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Acknowledgments

We acknowledge captains, officers, and crews of R/V Dongfanghong 2 for providing excellent assistance during field sampling and measurements. We acknowledge hard work received from Hailong Zhang, Cong ** Su, and Ying Mao in our field investigations. We thank the reviewers’ valuable suggestions and comments that help us greatly improve the paper quality.

Funding

This research was jointly supported by National Key Research and Development Program of China (No. 2016YFC1400904, 2016YFC1400901), National Natural Science Foundation of China (No. 41876203, 41576172), Jiangsu Six Talent Summit Project (No. JY-084), Jiangsu Provincial Programs for Marine Science and Technology Innovation (No. HY2017-5), the open fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography (No. QNHX1812), the Qing Lan Project, and NSFC Open Research Cruise (Cruise No. NORC 2018-01), funded by Ship-time Sharing Project of NSFC.

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Authors and Affiliations

  1. School of Marine Sciences, Nan**g University of Information Science & Technology, Nan**g, China

    Zunbin Ling, Deyong Sun, Shengqiang Wang, Zhongfeng Qiu, Yu Huan & Yijun He

  2. Chuzhou Meteorological Bureau, Chuzhou, Anhui, China

    Zunbin Ling

  3. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, 36 Bochubeilu, Hangzhou, 310012, China

    Deyong Sun & Zhihua Mao

  4. Jiangsu Research Center for Ocean Survey Technology, NUIST, Nan**g, China

    Deyong Sun, Shengqiang Wang, Zhongfeng Qiu & Yijun He

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  1. Zunbin Ling
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Correspondence to Deyong Sun.

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Ling, Z., Sun, D., Wang, S. et al. Remote sensing estimation of colored dissolved organic matter (CDOM) from GOCI measurements in the Bohai Sea and Yellow Sea. Environ Sci Pollut Res 27, 6872–6885 (2020). https://doi.org/10.1007/s11356-019-07435-6

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