An ultraviolet to visible scheme to estimate chromophoric dissolved organic matter absorption in a Case-2 water from remote sensing reflectance

Abstract

In a typical Case-2 coastal water environment (here, the Pearl River Estuary (PRE), China), chromophoric dissolved organic matter (CDOM) and suspended particulates dominate the water optical properties, and CDOM fluorescence contributes considerably to surface water reflectance. In this paper, an ultraviolet (UV) to visible scheme to retrieve CDOM absorption (a_g) is developed based on a set of in situ observations. First, the CDOM UV absorption and spectral slope (S_g) are derived directly from the visible remote sensing reflectance; then the S_g is extrapolated to obtain the spectrum from UV to visible spectral range. This algorithm performs well, with an overall mean absolute percent difference (MAPD) of ~30%, ~5% and ~6% for the estimation of a_g in 250–450 nm, S_g over 250–400 nm, and 250–700 nm, respectively. The effectiveness and stability of the algorithm is further demonstrated in capturing the distribution pattern of CDOM absorption in the PRE from satellite ocean color imagery with multiple spatial and spectral resolution, namely: the Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m/Multispectral), the Ocean and Land Color Instrument (OLCI) (300 m/Multispectral), the Hyperspectral Imager for the Coastal Ocean (HICO) (100 m/Hyperspectral), and the Landsat 8 Operational Land Imager (OLI) (30 m/Multispectral). The UV to visible scheme can benefit the CDOM absorption estimation in two aspects: 1) it is free from the disturbance of suspended matter; 2) it avoids uncertainties caused by the low signal-to-noise ratio (SNR) of a_g measurements in the visible range. The algorithm is effective in revealing multiple scales of variation of CDOM absorption from ocean color observations.

Keywords

Chromophoric dissolved organic matter / ocean color remote sensing / Pearl River Estuary / ultraviolet

Xia Lei was born in Shanxi, China, in 1988. She received the B.S. degree in geomatics from Jilin University, China in 2010, and the M.S. degree in signal processing from University of Chinese Academy of Sciences, China in 2013, and is currently pursuing the Ph.D. degree in earth system and geoinformation science at the Chinese University of Hong Kong, Hong Kong. From 2013 to 2015, she was a Research Assistant with the State Key laboratory of Remote Sensing Science, Chinese Academy of Sciences. Her research interests include the optical properties of natural waters and ocean color remote sensing. E-mail: [e-mail] J received the M.Sc. degree in optics from the Chinese Academy of Sciences (CAS), Hefei, China, in 1988 and the Ph.D. degree in physical oceanography from CAS, Qingdao, China, in 1996. He was with the University of Delaware, Newark, DE, USA; The University of Southern Mississippi, Stennis Space Center, MS, USA; Oregon Health and Science University, Portland, OR, USA; and Portland State University, Portland. Since 2009, he has been with the Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong. His study interests include marine remote sensing, coastal oceanography, river plume dynamics, etc. E-mail: [e-mail] T Devlin was born in Chicago, IL, USA, in 1977. He received the B.S. degree from the University of Oregon, Eugene, OR, USA, in 2001, the M.S. degree in applied physics from Appalachian State University, Boone, NC, USA, in 2003, and the Ph.D. degree in applied physics from Portland State University, Portland, OR, in 2016. He is a Postdoctoral Fellow with The Chinese University of Hong Kong, Shatin, Hong Kong. His research interests involve physical oceanography, climate analysis, and remote sensing of geophysical and oceanic variability and his main focus is on ocean tides and sea level variability. E-mail: [e-mail].

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 41376035), the General Research Fund of Hong Kong Research Grants Council (RGC) (Nos. CUHK 14303818, 402912, and 403113), the Hong Kong Innovation and Technology Fund (Nos. ITS/259/12 and ITS/321/13).

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