A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

The bulk refractive index (np) of suspended particles, an apparent measure of particulate refraction capability and yet an essential element of particulate compositions and optical properties, is a critical indicator that helps understand many biogeochemical processes and ecosystems in marine waters. Remote estimation of np remains a very challenging task. Here, a multiple-step hybrid model is developed to estimate the np in the Bohai Sea (BS) and Yellow Sea (YS) through obtaining two key intermediate parameters (i.e., particulate backscattering ratio, Bp, and particle size distribution (PSD) slope, j) from remote-sensing reflectance, Rrs(λ). The in situ observed datasets available to us were collected from four cruise surveys during a period from 2014 to 2017 in the BS and YS, covering beam attenuation (cp), scattering (bp), and backscattering (bbp) coefficients, total suspended matter (TSM) concentrations, and Rrs(λ). Based on those in situ observation data, two retrieval algorithms for TSM and bbp were firstly established from Rrs(λ), and then close empirical relationships between cp and bp with TSM could be constructed to determine the Bp and j parameters. The series of steps for the np estimation model proposed in this study can be summarized as follows: Rrs (λ) → TSM and bbp, TSM → bp → cp → j, bbp and bp → Bp, and j and Bp → np. This method shows a high degree of fit (R2 = 0.85) between the measured and modeled np by validation, with low predictive errors (such as a mean relative error, MRE, of 2.55%), while satellite-derived results also reveal good performance (R2 = 0.95, MRE = 2.32%). A spatial distribution pattern of np in January 2017 derived from GOCI (Geostationary Ocean Color Imager) data agrees well with those in situ observations. This also verifies the satisfactory performance of our developed np estimation model. Applying this model to GOCI data for one year (from December 2014 to November 2015), we document the np spatial distribution patterns at different time scales (such as monthly, seasonal, and annual scales) for the first time in the study areas. While the applicability of our developed method to other water areas is unknown, our findings in the current study demonstrate that the method presented here can serve as a proof-of-concept template to remotely estimate np in other coastal optically complex water bodies.

[1]  Francesca Bovolo,et al.  Novel Spectra-Derived Features for Empirical Retrieval of Water Quality Parameters: Demonstrations for OLI, MSI, and OLCI Sensors , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Delu Pan,et al.  Deriving colored dissolved organic matter absorption coefficient from ocean color with a neural quasi‐analytical algorithm , 2017 .

[3]  Zhongfeng Qiu,et al.  Remote Sensing of Particle Cross-Sectional Area in the Bohai Sea and Yellow Sea: Algorithm Development and Application Implications , 2016, Remote. Sens..

[4]  Zhongfeng Qiu,et al.  Innovative GOCI algorithm to derive turbidity in highly turbid waters: a case study in the Zhejiang coastal area. , 2015, Optics express.

[5]  Palanisamy Shanmugam,et al.  Estimating the Bulk Refractive Index and Related Particulate Properties of Natural Waters From Remote-Sensing Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Jennifer P. Cannizzaro,et al.  Influence of a red band-based water classification approach on chlorophyll algorithms for optically complex estuaries , 2014 .

[7]  Palanisamy Shanmugam,et al.  A New Inversion Model to Estimate Bulk Refractive Index of Particles in Coastal Oceanic Waters: Implications for Remote Sensing , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Antonio Camacho,et al.  Empirical Relationships for Monitoring Water Quality of Lakes and Reservoirs Through Multispectral Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Yuhai Bao,et al.  Remote sensing retrieval of inorganic suspended particle size in the Bohai Sea , 2014 .

[10]  Eko Siswanto,et al.  Relationships of interannual variability in SST and phytoplankton blooms with giant jellyfish (Nemopilema nomurai) outbreaks in the Yellow Sea and East China Sea , 2013, Journal of Oceanography.

[11]  C. Chen,et al.  Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters , 2013 .

[12]  Yongming Shen,et al.  Numerical study of wave and longshore current interaction , 2012, Acta Oceanologica Sinica.

[13]  Ronald L. Vogel,et al.  The development of a new optical total suspended matter algorithm for the Chesapeake Bay , 2012 .

[14]  Li Fu,et al.  Applying support vector regression to water quality modelling by remote sensing data , 2011 .

[15]  J. Shutler,et al.  An assessment of chlorophyll-a algorithms available for SeaWiFS in coastal and open areas of the Bay of Bengal and Arabian Sea , 2011 .

[16]  Bunkei Matsushita,et al.  Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables , 2011 .

[17]  Junwu Tang,et al.  Backscattering ratio variation and its implications for studying particle composition: A case study in Yellow and East China seas , 2010 .

[18]  Tingwei Cui,et al.  Validation of MERIS ocean-color products in the Bohai Sea: A case study for turbid coastal waters , 2010 .

[19]  M. He,et al.  On the Recurrent Ulva prolifera Blooms in the Yellow Sea and East China Sea , 2010 .

[20]  B. Nechad,et al.  Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters , 2010 .

[21]  G. Cabello,et al.  The beta-adrenergic system is involved in the regulation of the expression of avian uncoupling protein in the chicken. , 2010, Domestic animal endocrinology.

[22]  C. Chen,et al.  Chemical and physical fronts in the Bohai, Yellow and East China seas , 2009 .

[23]  B. Franz,et al.  Regional and seasonal variability of chlorophyll-a in Chesapeake Bay as observed by SeaWiFS and MODIS-Aqua , 2009 .

[24]  A. Gitelson,et al.  A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation , 2008 .

[25]  R. Davies‐Colley,et al.  Predicting light penetration into river waters , 2008 .

[26]  S. Andréfouët,et al.  Optical Algorithms at Satellite Wavelengths for Total Suspended Matter in Tropical Coastal Waters , 2008, Sensors.

[27]  S. Hooker,et al.  Algorithm development and validation for satellite‐derived distributions of DOC and CDOM in the U.S. Middle Atlantic Bight , 2008 .

[28]  Gia Lamela,et al.  Optical scattering and backscattering by organic and inorganic particulates in U.S. coastal waters. , 2008, Applied optics.

[29]  E. Boss,et al.  Spectral variability of the particulate backscattering ratio. , 2007, Optics express.

[30]  Jean-François Berthon,et al.  Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea , 2007 .

[31]  Shailesh Nayak,et al.  An empirical method to estimate bulk particulate refractive index for ocean satellite applications , 2006, SPIE Asia-Pacific Remote Sensing.

[32]  Gavin C. Cawley,et al.  Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[33]  M. Kishino,et al.  Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data , 2005 .

[34]  Michael S Twardowski,et al.  Use of optical scattering to discriminate particle types in coastal waters. , 2005, Applied optics.

[35]  D. Stramski,et al.  Modeling the optical properties of mineral particles suspended in seawater and their influence on ocean reflectance and chlorophyll estimation from remote sensing algorithms. , 2004, Applied optics.

[36]  E. Boss,et al.  Spectral beam attenuation coefficient retrieved from ocean color inversion , 2003 .

[37]  Dariusz Stramski,et al.  Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration , 2003 .

[38]  Andrew H. Barnard,et al.  A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters , 2001 .

[39]  A. Bricaud,et al.  Modeling the inherent optical properties of the ocean based on the detailed composition of the planktonic community. , 2001, Applied optics.

[40]  Andrew H. Barnard,et al.  Spectral particulate attenuation and particle size distribution in the bottom boundary layer of a continental shelf , 2001 .

[41]  Y. C. Agrawal,et al.  Instruments for particle size and settling velocity observations in sediment transport , 2000 .

[42]  Dana Ron,et al.  Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation , 1997, Neural Computation.

[43]  D. Goodin,et al.  Mapping Reservoir Turbidity Patterns Using SPOT‐HRV Data , 1996 .

[44]  Eyvind Aas,et al.  Refractive index of phytoplankton derived from its metabolite composition , 1996 .

[45]  Menghua Wang,et al.  A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS☆ , 1994 .

[46]  Kenneth J. Voss,et al.  A spectral model of the beam attenuation coefficient in the ocean and coastal areas , 1992 .

[47]  A. Bricaud,et al.  Modeling the light attenuation and scattering by spherical phytoplanktonic cells: a retrieval of the bulk refractive index. , 1988, Applied optics.

[48]  C. Yentsch.  A note on the fluorescence characteristics of particles that pkss through glass‐fiber filters1 , 1983 .

[49]  J. Ronald,et al.  Method for the determination of the index of refraction of particles suspended in the ocean , 1973 .

[50]  Kendall L. Carder,et al.  a Technique for the Estimation of Indices of Refraction of Marine PHYTOPLANKTERS1 , 1972 .

[51]  Els Knaeps,et al.  A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters , 2015 .

[52]  Yong Q. Tian,et al.  An assessment of remote sensing algorithms for colored dissolved organic matter in complex freshwater environments , 2014 .

[53]  Guo Shujin,et al.  Phytoplankton Communities in the Northern Yellow Sea in Autumn 2011 , 2013 .

[54]  Bryan A. Franz,et al.  Chlorophyll aalgorithms for oligotrophic oceans: A novel approach based on three‐band reflectance difference , 2012 .

[55]  S. Ling.  The study on the distribution of suspended particulate matter in the Bohai Sea by remote sensing , 2009 .

[56]  M. Jonasz,et al.  Chapter 4 – Measurements of light scattering by particles in water , 2007 .

[57]  Michael S. Twardowski,et al.  Particulate backscattering ratio at LEO 15 and its use to study particle composition and distribution , 2004 .

[58]  J. Kirk,et al.  Monte Carlo study of the nature of the underwater light field in, and the relationships between optical properties of, turbid yellow waters , 1981 .

[59]  Diane S. Allen-Gipson,et al.  Digital Commons @ University of Digital Commons @ University of South Florida South Florida , 2022 .