Bio‐optical model with optimal parameter suitable for Taihu Lake in water colour remote sensing

Sixty‐seven samplings were collected, almost covering all over the Taihu Lake, in one campaign in October 2004. At each station, the backscattering coefficients and the field spectra were measured in situ, respectively, with a HS‐6 and a FieldSpec 931 spectroradiometer (ASD Inc.). Almost concurrently, water samples were fetched with Niskin water‐fetching equipment and then returned to the laboratory for concentration and absorption measurement. The whole lake was divided into different areas according to some indexes. Three models were used to calculate remote sensing reflectance R rsc for the waters where the in situ remote sensing reflectance R rsm was beyond the bottom effect, which was considered as optically deep waters. By comparison of R rsc and R rsm, the best model suitable for optically deep waters in Taihu Lake, together with its optimal experiential parameter, were selected and developed, which was very important and helpful to develop a universal model to estimate accurately remote sensing reflectance for the whole lake in the next step.

[1]  C. Yentsch.  MEASUREMENT OF VISIBLE LIGHT ABSORPTION BY PARTICULATE MATTER IN THE OCEAN1 , 1962 .

[2]  H. Gordon,et al.  Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. , 1975, Applied optics.

[3]  L. Prieur,et al.  Analysis of variations in ocean color1 , 1977 .

[4]  L. Prieur,et al.  Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains1 , 1981 .

[5]  H. Gordon,et al.  Clear water radiances for atmospheric correction of coastal zone color scanner imagery. , 1981, Applied optics.

[6]  L. Prieur,et al.  An optical classification of coastal and oceanic waters based on the specific spectral absorption curves of phytoplankton pigments, dissolved organic matter, and other particulate materials1 , 1981 .

[7]  B. Osborne,et al.  Light and Photosynthesis in Aquatic Ecosystems. , 1985 .

[8]  John T. O. Kirk,et al.  Dependence of relationship between inherent and apparent optical properties of water on solar altitude , 1984 .

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

[10]  L. Prieur,et al.  A three-component model of ocean colour and its application to remote sensing of phytoplankton pigments in coastal waters , 1989 .

[11]  B. G. Mitchell,et al.  Algorithms for determining the absorption coefficient for aquatic particulates using the quantitative filter technique , 1990, Defense, Security, and Sensing.

[12]  B Gentili,et al.  Diffuse reflectance of oceanic waters: its dependence on Sun angle as influenced by the molecular scattering contribution. , 1991, Applied optics.

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

[14]  A. Weidemann,et al.  Quantifying absorption by aquatic particles: A multiple scattering correction for glass-fiber filters , 1993 .

[15]  C. Davis,et al.  Model for the interpretation of hyperspectral remote-sensing reflectance. , 1994, Applied optics.

[16]  P. Harrison,et al.  Seaweed ecology and physiology: Light and photosynthesis , 1994 .

[17]  B. Gentili,et al.  Diffuse reflectance of oceanic waters. III. Implication of bidirectionality for the remote-sensing problem. , 1996, Applied optics.

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

[19]  Collin S. Roesler.  Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique , 1998 .

[20]  C. Mobley,et al.  Hyperspectral remote sensing for shallow waters. I. A semianalytical model. , 1998, Applied optics.

[21]  C. Mobley,et al.  Estimation of the remote-sensing reflectance from above-surface measurements. , 1999, Applied optics.

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

[23]  Prieur,et al.  Analysis of variations in ocean color’ , 2000 .

[24]  R. W. Austin,et al.  Ocean Optics Protocols for Satellite Ocean Color Sensor Validation , 2013 .

[25]  D. Pierson,et al.  A modelling approach to evaluate preliminary remote sensing algorithms: Use of water quality data from Swedish great lakes , 2000 .

[26]  D. Pierson,et al.  The effects of variability in the inherent optical properties on estimations of chlorophyll a by remote sensing in Swedish freshwaters. , 2001, The Science of the total environment.

[27]  M. A. Lodhi,et al.  A spectral analysis of bottom-induced variation in the colour of Sand Hills lakes, Nebraska, USA , 2001 .

[28]  N. Strömbeck,et al.  Water Quality and Optical Properties of Swedish Lakes and Coastal Waters in Relation to Remote Sensing , 2001 .

[29]  D. Keith,et al.  Spatial and Temporal Distribution of Coloured Dissolved Organic Matter (CDOM) in Narragansett Bay, Rhode Island: Implications for Phytoplankton in Coastal Waters , 2002 .

[30]  D. Pierson,et al.  Bio-optical modelling combined with remote sensing to assess water quality , 2002 .

[31]  Mark R. Miller,et al.  Ocean Optics Protocols For Satellite Ocean Color Sensor Validation, Revision 4, Volume III: Radiometric Measurements and Data Analysis Protocols , 2003 .

[32]  R. Ma,et al.  Investigation of chlorophyll‐a and total suspended matter concentrations using Landsat ETM and field spectral measurement in Taihu Lake, China , 2005 .