An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.

[1]  Thomas B. Bridgeman,et al.  A novel method for tracking western Lake Erie Microcystis blooms, 2002–2011 , 2013 .

[2]  J. Gower,et al.  Global monitoring of plankton blooms using MERIS MCI , 2008 .

[3]  P Jeremy Werdell,et al.  Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing. , 2010, Optics express.

[4]  K. Ruddick,et al.  Seaborne measurements of near infrared water‐leaving reflectance: The similarity spectrum for turbid waters , 2006 .

[5]  M. Matthews,et al.  An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters , 2012 .

[6]  M. Bauer,et al.  Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: the Mississippi River and its tributaries in Minnesota. , 2013 .

[7]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[8]  R. Bukata,et al.  An analysis of MODIS-derived algal and mineral turbidity in Lake Erie , 2012 .

[9]  S. Bradt.  Development of bio-optical algorithms to estimate chlorophyll in the Great Salt Lake and New England lakes using in situ hyperspectral measurements , 2012 .

[10]  Y. Zha,et al.  Remote estimation of chlorophyll a in optically complex waters based on optical classification , 2011 .

[11]  Antonio Ruiz-Verdú,et al.  Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass , 2007 .

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

[13]  Stéphane Maritorena,et al.  Optimization of a semianalytical ocean color model for global-scale applications. , 2002, Applied optics.

[14]  R. Bukata,et al.  Trends in Water Clarity of the Lower Great Lakes from Remotely Sensed Aquatic Color , 2007 .

[15]  I. Ioannou,et al.  Fluorescence component in the reflectance spectra from coastal waters. Dependence on water composition. , 2007, Optics express.

[16]  Janet W. Campbell,et al.  The lognormal distribution as a model for bio‐optical variability in the sea , 1995 .

[17]  David Dessailly,et al.  Optical classification of contrasted coastal waters , 2012 .

[18]  T. Wynne,et al.  Characterizing a cyanobacterial bloom in Western Lake Erie using satellite imagery and meteorological data , 2010 .

[19]  H. Loisel,et al.  Variability and classification of remote sensing reflectance spectra in the eastern English Channel and southern North Sea , 2007 .

[20]  Annick Bricaud,et al.  Light backscattering efficiency and related properties of some phytoplankters , 1992 .

[21]  Anatoly A. Gitelson,et al.  Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: Model tuning and accuracy optimization , 2006 .

[22]  R. Bukata,et al.  Suspended particulate matter in Lake Erie derived from MODIS aquatic colour imagery , 2010 .

[23]  A. Gitelson,et al.  Remote Estimation of Chlorophyll-a Concentration in Inland, Estuarine, and Coastal Waters , 2011 .

[24]  Janet W. Campbell,et al.  Are the world's oceans optically different? , 2011 .

[25]  David Doxaran,et al.  Spectral variations in the near-infrared ocean reflectance , 2011 .

[26]  James C. Bezdek,et al.  A geometric approach to cluster validity for normal mixtures , 1997, Soft Comput..

[27]  Hui Feng,et al.  Modeling spectral reflectance of optically complex waters using bio-optical measurements from Tokyo Bay , 2005 .

[28]  Ronghua Ma,et al.  Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China , 2010 .

[29]  C. Mouw,et al.  Evaluation and optimization of bio‐optical inversion algorithms for remote sensing of Lake Superior's optical properties , 2013 .

[30]  Peter D. Hunter,et al.  Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes , 2010 .

[31]  Antonio Ruiz-Verdú,et al.  An evaluation of algorithms for the remote sensing of cyanobacterial biomass , 2008 .

[32]  Giorgio Dall'Olmo,et al.  Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results. , 2005, Applied optics.

[33]  E. Carpenter,et al.  Detecting Trichodesmium blooms in SeaWiFS imagery , 2001 .

[34]  Tiit Kutser,et al.  Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing , 2004 .

[35]  Timothy S. Moore,et al.  A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product , 2009 .

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

[37]  J. Gower,et al.  Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer , 2005 .

[38]  Wesley J Moses,et al.  NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study. , 2011, Water research.

[39]  Davide D'Alimonte,et al.  Multi-sensor satellite time series of optical properties and chlorophyll- a concentration in the Adriatic Sea , 2011 .

[40]  Alexander A Gilerson,et al.  Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. , 2010, Optics express.

[41]  Hui Feng,et al.  AERONET-OC: A Network for the Validation of Ocean Color Primary Products , 2009 .

[42]  David Doxaran,et al.  Use of reflectance band ratios to estimate suspended and dissolved matter concentrations in estuarine waters , 2005 .

[43]  M. Bauer,et al.  A procedure for regional lake water clarity assessment using Landsat multispectral data , 2002 .

[44]  Cédric Jamet,et al.  Evaluation of four atmospheric correction algorithms for MODIS-Aqua images over contrasted coastal waters , 2013 .

[45]  Menghua Wang,et al.  Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data , 2009 .

[46]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[47]  Hui Feng,et al.  A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms , 2001, IEEE Trans. Geosci. Remote. Sens..

[48]  M. Schaepman,et al.  Review of constituent retrieval in optically deep and complex waters from satellite imagery , 2012 .

[49]  B. Franz,et al.  Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors , 2012 .

[50]  Kirk Knobelspiesse,et al.  Unique data repository facilitates ocean color satellite validation , 2003 .