A Review of Some Important Technical Problems in Respect of Satellite Remote Sensing of Chlorophyll-a Concentration in Coastal Waters

With the development of quantitative ocean color remote sensing, estimation of chlorophyll-a concentration in the coastal waters has aroused increasing attention from researchers. Currently, researches are confronted with difficulty in improving the accuracy of chlorophyll-a concentration estimation for turbid waters. Atmospheric correction, chlorophyll-a concentration modeling, and scale effect have already been identified as three critical factors affecting coastal water remote sensing. The in-depth exploration of them will accelerate the research progress of ocean color remote sensing. The ultimate objective of atmospheric correction and scale effect correction is to accurately estimate active constituents of turbid coastal waters in an optical way. Accordingly, the chlorophyll-a concentration modeling is a basic problem to be resolved, while atmospheric correction is the essential one. The scale effect problem arises during the modeling procedure where unrealistic homogeneous assumption is taken to measure chlorophyll-a concentration from the realistic non-homogeneous pixel. In the coastal remote sensing field, these three problems have become the most important topics in the current researches, and they will remain be the hot topics in the future.

[1]  Arnold G. Dekker,et al.  Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data , 2002 .

[2]  K. Ruddick,et al.  Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters. , 2000, Applied optics.

[3]  K. Carder,et al.  Semianalytic Moderate‐Resolution Imaging Spectrometer algorithms for chlorophyll a and absorption with bio‐optical domains based on nitrate‐depletion temperatures , 1999 .

[4]  G. M. Hale,et al.  Optical Constants of Water in the 200-nm to 200-microm Wavelength Region. , 1973, Applied optics.

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

[6]  Menghua Wang,et al.  Detection of turbid waters and absorbing aerosols for the MODIS ocean color data processing , 2007 .

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

[8]  Jun Chen,et al.  Systematic Underestimation of MODIS Global Chlorophyll-a Concentration Estimation Algorithm Associating With Scale Effect , 2013, IEEE Sensors Journal.

[9]  E. Vermote,et al.  Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer , 1997 .

[10]  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 .

[11]  Motoaki Kishino,et al.  Chlorophyll-specific absorption coefficients and pigments of phytoplankton off Sanriku, northwestern North Pacific , 1998 .

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

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

[14]  Ian S. Robinson,et al.  Measuring the Oceans from Space: The Principles and Methods of Satellite Oceanography , 2004 .

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

[16]  M. Raffy,et al.  Change of scale in models of remote sensing: A general method for spatialization of models , 1992 .

[17]  H. Gordon,et al.  Remote sensing optical properties of a stratified ocean: an improved interpretation. , 1980, Applied optics.

[18]  Gyanesh Chander,et al.  Evaluation and Comparison of the IRS-P6 and the Landsat Sensors , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Giulietta S. Fargion,et al.  Ocean Optics Protocols for Satellite Ocean Color Sensor Validation. Revised , 2000 .

[20]  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.

[21]  A. Gitelson,et al.  Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands , 2005 .

[22]  James V. Gardner,et al.  Mapping U.S. continental shelves , 1998 .

[23]  Alan H. Strahler,et al.  Simulation of scaling effects of thermal emission from non-isothermal pixels with the typical three-dimensional structure , 2003 .

[24]  K. Ruddick,et al.  Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements , 2011 .

[25]  K. Voss,et al.  Spectral optimization for constituent retrieval in Case 2 waters II: Validation study in the Chesapeake Bay , 2009 .

[26]  Kendall L. Carder,et al.  Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a , 2004 .

[27]  E. Ann Gallie,et al.  Specific absorption and backscattering spectra for suspended minerals and chlorophyll-a in chilko lake, British Columbia , 1992 .

[28]  A. Gitelson,et al.  Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data. , 2012, Water research.

[29]  F. Muller‐Karger,et al.  Bridging between SeaWiFS and MODIS for continuity of chlorophyll-a concentration assessments off Southeastern China , 2006 .

[30]  Chen Jun Nonhomogeneity:The Scale Error of Pixel in Remote Sensing Assimulation of Suspending Sediment Concentration , 2008 .

[31]  Mati Kahru,et al.  Phytoplankton absorption, photosynthetic parameters, and primary production off Baja California: summer and autumn 1998 , 2004 .

[32]  Annick Bricaud,et al.  In situ methods for measuring the inherent optical properties of ocean waters , 1995 .

[33]  G. C. Ewing Oceanography from Space , 1965 .

[34]  Wenting Quan,et al.  Scale Effects on Chlorophyll-A Concentration Retrieved: Assessment and Validation Using Indian Remote Sensing Satellite , 2013, Journal of the Indian Society of Remote Sensing.

[35]  F. Muller‐Karger,et al.  How precise are SeaWiFS ocean color estimates? Implications of digitization-noise errors , 2001 .

[36]  T Platt,et al.  Remote sensing of ocean chlorophyll: consequence of nonuniform pigment profile. , 1989, Applied optics.

[37]  Machteld Rijkeboer,et al.  Optical Teledetection of Chlorophyll a in Estuarine and Coastal Waters , 2000 .

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

[39]  Tinglu Zhang,et al.  Atmospheric correction of ocean color imagery over turbid coastal waters using active and passive remote sensing , 2009 .

[40]  S. Maritorena,et al.  Atmospheric correction of satellite ocean color imagery: the black pixel assumption. , 2000, Applied optics.

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

[42]  Anatoly A. Gitelson,et al.  Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters — The Azov Sea case study , 2012 .

[43]  B. Franz,et al.  Evaluation of shortwave infrared atmospheric correction for ocean color remote sensing of Chesapeake Bay , 2010 .

[44]  Jun Fu,et al.  An Atmospheric Correction Algorithm for Landsat/TM Imagery Basing on Inverse Distance Spatial Interpolation Algorithm: A Case Study in Taihu Lake , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[45]  B. Gentili,et al.  Bio-optical properties of high chlorophyll Case 1 waters and of yellow-substance-dominated Case 2 waters , 2006 .

[46]  Sonoyo Mukai,et al.  Atmospheric correction for ocean color data given by ADEOS/OCTS and POLDER , 2000 .

[47]  F. Muller‐Karger,et al.  Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method , 2000 .

[48]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

[49]  Menghua Wang,et al.  An assessment of the black ocean pixel assumption for MODIS SWIR bands , 2009 .

[50]  H. Gordon,et al.  Aerosol analysis with the Coastal Zone Color Scanner: a simple method for including multiple scattering effects. , 1989, Applied optics.

[51]  Guan Sheng Validation of satellite remote sensing product by ship-towed profiling system , 2010 .

[52]  Hermann Kaufmann,et al.  Determination of Chlorophyll Content and Trophic State of Lakes Using Field Spectrometer and IRS-1C Satellite Data in the Mecklenburg Lake District, Germany , 2000 .

[53]  Jong-Kuk Choi,et al.  GOCI, the world's first geostationary ocean color observation satellite, for the monitoring of temporal variability in coastal water turbidity , 2012 .

[54]  W. Nimmo-Smith,et al.  Light scattering by particles suspended in the sea: The role of particle size and density , 2009 .

[55]  M. Pinkerton,et al.  Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters , 2005 .

[56]  Bunkei Matsushita,et al.  Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data , 2009 .

[57]  Jun Fu,et al.  Theoretical Model for Estimating the Scaling Error of the Two-Band Ratio of Red to Near-Infrared in Inhomogeneous Pixels: Simulation Using a Moving Window , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[58]  Juan C. Jiménez-Muñoz,et al.  Atmospheric correction of optical imagery from MODIS and Reanalysis atmospheric products , 2010 .

[59]  Z. Li,et al.  Feasibility of land surface temperature and emissivity determination from AVHRR data , 1993 .

[60]  Y. Zha,et al.  A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China , 2009 .

[61]  Baojun Wang,et al.  Scale Correction of Two-Band Ratio of Red to Near-Infrared Using Imagery Histogram Approach: A Case Study on Indian Remote Sensing Satellite in Yellow River Estuary , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[63]  H. Gordon,et al.  Remote sensing of ocean color and aerosol properties: resolving the issue of aerosol absorption. , 1997, Applied optics.

[64]  Jun Chen,et al.  [Effect of remotely sensed data errors on the retrieving accuracy of territorial parameters--a case study on chlorophyll a concentration inversion of Taihu Lake]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[65]  R. H. Evans,et al.  Validation of a SeaWiFS dust-correction methodology in the Mediterranean Sea: Identification of an algorithm-switching criterion , 2009 .

[66]  Young-Heon Jo,et al.  Potential applications of geostationary ocean color imagery for physical-biological interactions , 2010, Asia-Pacific Remote Sensing.

[67]  Dimitra Kitsiou,et al.  Coastal marine eutrophication assessment: a review on data analysis. , 2011, Environment international.

[68]  S. Saitoh,et al.  Validation of ADEOS-II GLI ocean color products using in-situ observations , 2006 .

[69]  Jun Chen,et al.  A Multi‐Band Semi‐Analytical Algorithm for Estimating Chlorophyll‐a Concentration in the Yellow River Estuary, China , 2015, Water environment research : a research publication of the Water Environment Federation.

[70]  C. Mobley Light and Water: Radiative Transfer in Natural Waters , 1994 .

[71]  Ziauddin Ahmad,et al.  New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans. , 2010, Applied optics.

[72]  W. Philpot Radiative transfer in stratified waters: a single-scattering approximation for irradiance. , 1987, Applied optics.

[73]  Jun Chen,et al.  Spectral Geometric Triangle Properties of Chlorophyll-A Inversion in Taihu Lake Based on TM Data , 2011 .

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

[75]  Menghua Wang,et al.  The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing. , 2007, Optics express.

[76]  Jun Chen,et al.  The use of MODIS 250 m bands to improve the MODIS 1 km ocean color atmospheric correction algorithm in turbid water , 2013 .

[77]  M. Perry,et al.  Modeling in situ phytoplankton absorption from total absorption spectra in productive inland marine waters , 1989 .

[78]  H. Gordon,et al.  Surface-roughness considerations for atmospheric correction of ocean color sensors. I: The Rayleigh-scattering component. , 1992, Applied optics.

[79]  K. Stamnes,et al.  Comparison of numerical models for computing underwater light fields. , 1993, Applied optics.

[80]  C. Lorenzen,et al.  Spectra of Backscattered Light from the Sea Obtained from Aircraft as a Measure of Chlorophyll Concentration , 1970, Science.

[81]  P. Falkowski,et al.  Biogeochemical Controls and Feedbacks on Ocean Primary Production , 1998, Science.

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

[83]  R. Bukata,et al.  An assessment of MERIS algal products during an intense bloom in Lake of the Woods , 2011 .

[84]  Jun Chen,et al.  A simple ‘clear water’ atmospheric correction algorithm for Landsat-5 sensors. I: A spectral slope-based method , 2013 .

[85]  Stelvio Tassan,et al.  Variability of light absorption by aquatic particles in the near-infrared spectral region. , 2003, Applied optics.

[86]  D. Doxaran,et al.  Spectral signature of highly turbid waters: Application with SPOT data to quantify suspended particulate matter concentrations , 2002 .

[87]  Knut Stamnes,et al.  Monte Carlo and discrete-ordinate simulations of irradiances in the coupled atmosphere-ocean system. , 2003, Applied optics.

[88]  Özçelik Ceyhun,et al.  Remote sensing of water depths in shallow waters via artificial neural networks , 2010 .

[89]  H. Gons,et al.  MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes , 2008 .

[90]  Medhavy Thankappan,et al.  Assessing the effect of hydrocarbon oil type and thickness on a remote sensing signal: A sensitivity study based on the optical properties of two different oil types and the HYMAP and Quickbird sensors , 2009 .

[91]  T. Pitcher,et al.  Towards sustainability in world fisheries , 2002, Nature.

[92]  J. Ras,et al.  Validation of MERIS reflectance and chlorophyll during the BENCAL cruise October 2002: preliminary validation of new demonstration products for phytoplankton functional types and photosynthetic parameters , 2007 .

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

[94]  G. Tedeschi,et al.  Spatial variation of the aerosol concentration and deposition over the Mediterranean coastal zone , 2010 .

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

[96]  J. Mercado,et al.  Changes in nutrient concentration induced by hydrological variability and its effect on light absorption by phytoplankton in the Alborán Sea (Western Mediterranean Sea) , 2008 .

[97]  H. Gordon Radiometric considerations for ocean color remote sensors. , 1990, Applied optics.

[98]  Menghua Wang,et al.  Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. , 1994, Applied optics.

[99]  Donald C. Rundquist,et al.  Comparison of NIR/RED ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: A case study in a turbid reservoir , 1997 .

[100]  Anatoly A. Gitelson,et al.  Remote chlorophyll-a retrieval in turbid, productive estuaries : Chesapeake Bay case study , 2007 .

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

[102]  Kenneth J. Voss,et al.  MODIS Normalized Water-leaving Radiance Algorithm Theoretical Basis Document ( MOD 18 ) Version 4 Submitted by Howard , 1999 .

[103]  Minwei Zhang,et al.  Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery , 2010 .

[104]  M. Matthews A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters , 2011 .

[105]  Larry L. Stowe,et al.  Characterization of tropospheric aerosols over the oceans with the NOAA advanced very high resolution radiometer optical thickness operational product , 1997 .

[106]  F. Muller‐Karger,et al.  A comparison of ship and coastal zone color scanner mapped distribution of phytoplankton in the southeastern Bering Sea , 1990 .

[107]  Anatoly A. Gitelson,et al.  Remote estimation of chl-a concentration in turbid productive waters — Return to a simple two-band NIR-red model? , 2011 .

[108]  Wei Shi,et al.  MODIS‐derived ocean color products along the China east coastal region , 2007 .

[109]  Jun Chen,et al.  A Simple Atmospheric Correction Algorithm for MODIS in Shallow Turbid Waters: A Case Study in Taihu Lake , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[110]  Jun Chen,et al.  Estimation on Scale Error of SSC Retrieval Model Based on Scale Expansion Method , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[111]  H. Gordon,et al.  Remote sensing of ocean color: Assessment of the water-leaving radiance bidirectional effects on the atmospheric diffuse transmittance for SeaWiFS and MODIS intercomparisons , 2008 .

[112]  Zhao-Liang Li,et al.  Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling , 2009, Sensors.

[113]  Bertram Ostendorf,et al.  A review of methods for analysing spatial and temporal patterns in coastal water quality , 2011 .

[114]  M. Chami,et al.  Inversion of oceanic constituents in case I and II waters with genetic programming algorithms. , 2002, Applied optics.