Estimating the Optical Properties of Inorganic Matter-Dominated Oligo-to-Mesotrophic Inland Waters

Many studies over the years have focused on bio-optical modeling of inland waters to monitor water quality. However, those studies have been conducted mainly in eutrophic and hyper-eutrophic environments dominated by phytoplankton. With the launch of the Ocean and Land Colour Instrument (OLCI)/Sentinel-3A in 2016, a range of bands became available including the 709 nm band recommended for scaling up these bio-optical models for productive inland waters. It was found that one category of existing bio-optical models, the quasi-analytical algorithms (QAAs), when applied to colored dissolved organic matter (CDOM) and detritus-dominated waters, produce large errors. Even after shifting the reference wavelength to 709 nm, the recently re-parameterized QAA versions could not accurately retrieve the inherent optical properties (IOPs) in waterbodies dominated by inorganic matter. In this study, three existing versions of QAA were implemented and proved inefficient for the study site. Therefore, several changes were incorporated into the QAA, starting with the re-parameterization of the empirical steps related to the total absorption coefficient retrieval. The re-parameterized QAA, QAAOMW showed a significant improvement in the mean absolute percentage error (MAPE). MAPE decreased from 58.05% for existing open ocean QAA (QAALv5) to 16.35% for QAAOMW. Considerable improvement was also observed in the estimation of the absorption coefficient of CDOM and detritus from a MAPE of 91.05% for QAALv5 to 18.87% for QAAOMW. The retrieval of the absorption coefficient of phytoplankton ( a ϕ ) using the native form of QAA proved to be inaccurate for the oligo-to-mesotrophic waterbody due to the low a ϕ returning negative predictions. Therefore, a novel approach based on the normalized a ϕ was adopted to maintain the spectral shape and retrieve positive values, resulting in an improvement of 119% in QAAOMW. Further tuning and scale-up of QAAOMW to OLCI bands will aid in monitoring water resources and associated watershed processes.

[1]  Jun Zhao,et al.  Inherent and apparent optical properties of the complex estuarine waters of Tampa Bay: What controls light? , 2013 .

[2]  W. Dodds,et al.  Land use influence on raw surface water quality and treatment costs for drinking supply in Sao Paulo State (Brazil) , 2016 .

[3]  Palanisamy Shanmugam,et al.  An evaluation of inversion models for retrieval of inherent optical properties from ocean color in coastal and open sea waters around Korea , 2010 .

[4]  Xin Wang,et al.  Bio‐optical properties and estimation of the optically active substances in Lake Tianmuhu in summer , 2009 .

[5]  William F. Ritter,et al.  Agricultural Nonpoint Source Pollution : Watershed Management and Hydrology , 2000 .

[6]  E. Espíndola,et al.  Environmental gradient in reservoirs of the medium and low Tietê River: limnological differences through the habitat sequence , 2014 .

[7]  K. Baker,et al.  Optical properties of the clearest natural waters (200-800 nm). , 1981, Applied optics.

[8]  R. Arnone,et al.  Uncertainties of Optical Parameters and Their Propagations in an Analytical Ocean Color Inversion Algorithm , 2010 .

[9]  Anatoly A. Gitelson,et al.  The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration , 1992 .

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

[11]  A. Dekker,et al.  Validity of SeaDAS water constituents retrieval algorithms in Australian tropical coastal waters , 2007 .

[12]  Jin Chen,et al.  Retrieval of Inherent Optical Properties for Turbid Inland Waters From Remote-Sensing Reflectance , 2013, IEEE Transactions on Geoscience and Remote Sensing.

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

[14]  J. Kindle,et al.  Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment , 2009 .

[15]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[16]  G. Perbiche-Neves,et al.  Phytoplankton structure in two contrasting cascade reservoirs (Paranapanema River, Southeast Brazil) , 2011, Biologia.

[17]  R. Arnone,et al.  Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. , 2002, Applied optics.

[18]  Igor Ogashawara,et al.  Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[19]  André Morel,et al.  In-water and remote measurements of ocean color , 1980 .

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

[21]  E. Loos,et al.  Inherent optical properties and optical mass classification of the waters of the Strait of Georgia, British Columbia, Canada , 2010 .

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

[23]  Vittorio E Brando,et al.  Adaptive semianalytical inversion of ocean color radiometry in optically complex waters. , 2012, Applied optics.

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

[25]  R. Henry,et al.  Spatial and temporal heterogeneity in the Jurumirim Reservoir, São Paulo, Brazil , 1999 .

[26]  Guofeng Wu,et al.  Absorption and backscattering coefficients and their relations to water constituents of Poyang Lake, China. , 2011, Applied optics.

[27]  C. A. Coelho,et al.  Precipitation diagnostics of an exceptionally dry event in São Paulo, Brazil , 2016, Theoretical and Applied Climatology.

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

[29]  S. Benassi,et al.  Itaipu reservoir limnology: eutrophication degree and the horizontal distribution of its limnological variables , 2011 .

[30]  Claudio Clemente Faria Barbosa,et al.  Spatial and seasonal variation in diffuse attenuation coefficients of downward irradiance at Ibitinga Reservoir, São Paulo, Brazil , 2016, Hydrobiologia.

[31]  Dariusz Stramski,et al.  Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe , 2003 .

[32]  H. Gordon.  Remote sensing of ocean color: a methodology for dealing with broad spectral bands and significant out-of-band response. , 1995, Applied optics.

[33]  Ying Zhao,et al.  Evaluation of the Quasi-Analytical Algorithm (QAA) for Estimating Total Absorption Coefficient of Turbid Inland Waters in Northeast China , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Roland Doerffer,et al.  Concentrations of chlorophyll, suspended matter, and gelbstoff in case II waters derived from satellite coastal zone color scanner data with inverse modeling methods , 1994 .

[35]  Stuart J. Khan,et al.  Extreme weather events: Should drinking water quality management systems adapt to changing risk profiles? , 2015, Water research.

[36]  Taikan Oki,et al.  Spatial and temporal variation in nutrient parameters in stream water in a rural-urban catchment, Shikoku, Japan: effects of land cover and human impact. , 2011, Journal of Environmental Management.

[37]  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.

[38]  Paul V. Zimba,et al.  Remote Sensing Techniques to Assess Water Quality , 2003 .

[39]  K. Carder,et al.  Marine humic and fulvic acids: Their effects on remote sensing of ocean chlorophyll , 1989 .

[40]  Lars-Anders Hansson,et al.  Environmental issues in lakes and ponds: current state and perspectives , 2002, Environmental Conservation.

[41]  Sarah A. Green,et al.  Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters , 1994 .

[42]  K. Carder,et al.  Reflectance Model for Quantifying Chlorophyll- a in the Presence of Productivity Degradation Products , 1991 .

[43]  Memet Varol,et al.  Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey , 2012 .

[44]  Robert F. Chen,et al.  Estimation of chromophoric dissolved organic matter in the Mississippi and Atchafalaya river plume regions using above‐surface hyperspectral remote sensing , 2011 .

[45]  C. Barbosa,et al.  A contribution to understanding the turbidity behaviour in an Amazon floodplain , 2010 .

[46]  T. Platt,et al.  Remote sensing of phytoplankton pigments: A comparison of empirical and theoretical approaches , 2001 .

[47]  Nilton Nobuhiro Imai,et al.  Analyzing the status of submerged aquatic vegetation using novel optical parameters , 2016 .

[48]  Craig S. Tucker,et al.  Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: A quasi-analytical approach , 2013 .

[49]  Judit Padisák,et al.  The Cascading Reservoir Continuum Concept (CRCC) and its application to the river Tietê-basin, São Paulo State, Brazil , 1999 .

[50]  E. Welch,et al.  Ecological effects of wastewater : applied limnology and pollution effects , 1996 .

[51]  Igor Ogashawara,et al.  Terminology and classification of bio-optical algorithms , 2015 .

[52]  Stelvio Tassan,et al.  An alternative approach to absorption measurements of aquatic particles retained on filters , 1995 .

[53]  F. Muller‐Karger,et al.  Remote sensing of particle backscattering in Chesapeake Bay: a 6-year SeaWiFS retrospective view , 2007 .

[54]  S. Tassan,et al.  A sensitivity analysis of the 'Transmittance-Reflectance' method for measuring light absorption by aquatic particles , 2002 .

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

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

[57]  Hai Xu,et al.  Controlling harmful cyanobacterial blooms in a hyper-eutrophic lake (Lake Taihu, China): the need for a dual nutrient (N & P) management strategy. , 2011, Water research.

[58]  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 .

[59]  A. Soares,et al.  Water Quality in the Tietê River Reservoirs (Billings‚ Barra Bonita‚ Bariri and Promissão‚ SP-Brazil) and Nutrient Fluxes across the Sediment-Water Interface (Barra Bonita) , 2006 .

[60]  Michele Scardi,et al.  Assessing the Uncertainties of Model Estimates of Primary Productivity in the Tropical Pacific Ocean Revised , 2008 .

[61]  M. Petrere,et al.  Defining a fish bio-assessment tool to monitoring the biological condition of a cascade reservoirs system in tropical area , 2014 .

[62]  C. Barbosa,et al.  Parametrization and calibration of a quasi-analytical algorithm for tropical eutrophic waters , 2016 .

[63]  Howard R. Gordon,et al.  Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery , 1983, Lecture Notes on Coastal and Estuarine Studies.

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

[65]  Deyong Sun,et al.  Validation of a Quasi-Analytical Algorithm for Highly Turbid Eutrophic Water of Meiliang Bay in Taihu Lake, China , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[66]  Hugh L. MacIntyre,et al.  Relation between inherent optical properties and land use and land cover across Gulf Coast estuaries , 2015 .

[67]  ZhongPing Lee,et al.  Bio-Optical Inversion in Highly Turbid and Cyanobacteria-Dominated Waters , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[68]  Remote-Sensing Reflectance.  Absorption, Scattering, and Remote-Sensing Reflectance Relationships in Coastal Waters: Testing a New Inversion , 2001 .

[69]  E. Alcântara,et al.  DELINEAMENTO AMOSTRAL EM RESERVATÓRIOS UTILIZANDO IMAGENS LANDSAT-8/OLI: UM ESTUDO DE CASO NO RESERVATÓRIO DE NOVA AVANHANDAVA (ESTADO DE SÃO PAULO, BRASIL) , 2016 .

[70]  Palanisamy Shanmugam,et al.  A New Inversion Model to Retrieve the Particulate Backscattering in Coastal/Ocean Waters , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[71]  Stephanie C. J. Palmer,et al.  Remote sensing of inland waters: Challenges, progress and future directions , 2015 .