Evaluation of Empirical and Semianalytical Spectral Reflectance Models for Surface Suspended Sediment Concentration in the Highly Variable Estuarine and Coastal Waters of East China

Development and validation of the surface suspended sediment concentration (SSC) models derived from the surface remote-sensing reflectance spectra [Rrs(λ)] are important in satellite monitoring of estuarine and coastal waters. Seven empirical and seven semianalytical spectral reflectance models for evaluation of the surface SSC were compared with one another and with laboratory tank (one dataset) and in situ measurements (two datasets) performed in different natural waters of East China. All models were presented in the form of Rrs spectral ratios, in which wavelengths were selected from the list of NASA's satellite sensor, MODIS unsaturated central wavelengths. A statistical analysis has been performed to find the best models and spectral ratios for remote-sensing monitoring purposes. Analysis has shown that empirical models are generally superior to the semianalytical models for solution existence, prediction accuracy, and correlation with the observed SSC values. However, all semianalytical models using the red to green spectral ratio have demonstrated approximately the same accuracy and correlation as empirical models, what provides an additional support for using more simple easily calculated empirical models. Additionally, relationships between SSC and inherent optical properties (IOPs) (absorption and backscattering coefficients) and between IOPs and Rrs(λ) provided by the semianalytical models have their own benefits for aquatic optics and remote sensing purposes.

[1]  James W. Brown,et al.  A semianalytic radiance model of ocean color , 1988 .

[2]  Zheng Lu Optical absorption of pure water in the blue and ultraviolet , 2007 .

[3]  N. K. Højerslev,et al.  Analytic remote-sensing optical algorithms requiring simple and practical field parameter inputs. , 2001, Applied optics.

[4]  Zhongbo Su,et al.  Remote-sensing reflectance characteristics of highly turbid estuarine waters – a comparative experiment of the Yangtze River and the Yellow River , 2010 .

[5]  L. Kou,et al.  Refractive indices of water and ice in the 0.65- to 2.5-µm spectral range. , 1993, Applied optics.

[6]  E. Fry,et al.  Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements. , 1997, Applied optics.

[7]  David Doxaran,et al.  A reflectance band ratio used to estimate suspended matter concentrations in sediment-dominated coastal waters , 2002 .

[8]  C. Mobley,et al.  Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. , 1999, Applied optics.

[9]  S. Effler,et al.  Spectral absorption properties of mineral particles in western Lake Erie: Insights from individual particle analysis , 2013 .

[10]  Xiaodong Zhang,et al.  Scattering by pure seawater: effect of salinity. , 2009, Optics express.

[11]  Jun Chen,et al.  A Split-Window Model for Deriving Total Suspended Sediment Matter From MODIS Data in the Bohai Sea , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

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

[14]  M. Montes-Hugo,et al.  OCEAN COLOUR AND DISTRIBUTION OF SUSPENDED PARTICULATES IN THE ST. LAWRENCE ESTUARY , 2012 .

[15]  M. Kahru,et al.  Ocean Color Chlorophyll Algorithms for SEAWIFS , 1998 .

[16]  R. Bukata,et al.  Remote sensing reflectance and its relationship to optical properties of natural waters , 1996 .

[17]  L. Sokoletsky,et al.  Optical properties of the Dead Sea , 2013 .

[18]  L. Sokoletsky Optical Methods for the Estimation of Phytoplankton Concentration , 2011 .

[19]  Xu Sun,et al.  Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance , 2015, Remote. Sens..

[20]  Peter Gege,et al.  Inversion of irradiance and remote sensing reflectance in shallow water between 400 and 800 nm for calculations of water and bottom properties. , 2006, Applied optics.

[21]  Hendrik Buiteveld,et al.  Optical properties of pure water , 1994, Other Conferences.

[22]  Eric F. Vermote,et al.  Improved Understanding of Suspended Sediment Transport Process Using Multi-Temporal Landsat Data: A Case Study From the Old Woman Creek Estuary (Ohio) , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  Fang Shen,et al.  Satellite multi-sensor mapping of suspended particulate matter in turbid estuarine and coastal ocean, China , 2014 .

[24]  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.

[25]  Mhd. Suhyb Salama,et al.  Satellite Estimates of Wide-Range Suspended Sediment Concentrations in Changjiang (Yangtze) Estuary Using MERIS Data , 2010 .

[26]  E. Aas,et al.  Estimates of radiance reflected towards the zenith at the surface of the sea , 2010 .

[27]  Leonid G. Sokoletsky,et al.  Optical closure for remote-sensing reflectance based on accurate radiative transfer approximations: the case of the Changjiang (Yangtze) River Estuary and its adjacent coastal area, China , 2014 .

[28]  Ratheesh Ramakrishnan,et al.  Suspended Sediment Concentration Profiles From Synoptic Satellite Observations , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Ping Shi,et al.  Using in situ and Satellite Hyperspectral Data to Estimate the Surface Suspended Sediments Concentrations in the Pearl River Estuary , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Jianhua Zhu,et al.  Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters , 2016, Remote. Sens..

[31]  Zhongping Lee,et al.  Use of hyperspectral remote sensing reflectance for detection and assessment of the harmful alga, Karenia brevis. , 2006, Applied optics.

[32]  Ross S. Lunetta,et al.  MERIS Retrieval of Water Quality Components in the Turbid Albemarle-Pamlico Sound Estuary, USA , 2011, Remote. Sens..

[33]  L. Sokoletsky,et al.  Comparative analysis of radiative transfer approaches for calculation of diffuse reflectance of plane-parallel light-scattering layers. , 2013, Applied optics.

[34]  Ross S. Lunetta,et al.  Assessment of the water quality components in turbid estuarine waters based on radiative transfer approximations , 2012 .

[35]  Leonid G. Sokoletsky,et al.  Suspended sediment concentration mapping based on the MODIS satellite imagery in the East China inland, estuarine, and coastal waters , 2017, Chinese Journal of Oceanology and Limnology.

[36]  B Gentili,et al.  Diffuse reflectance of oceanic waters. II Bidirectional aspects. , 1993, Applied optics.

[37]  D. Menzies,et al.  Remote-sensing reflectance determinations in the coastal ocean environment: impact of instrumental characteristics and environmental variability. , 2000, Applied optics.

[38]  D. Doxaran,et al.  Remote-sensing reflectance of turbid sediment-dominated waters. Reduction of sediment type variations and changing illumination conditions effects by use of reflectance ratios. , 2003, Applied optics.

[39]  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 .