A bio-optical approach to estimating chlorophyll-a concentration from hyperspectral remote sensing

Eagle Creek Reservoir is one of three central Indiana reservoirs supplying drinking water for the residents of Indianapolis. The occurrence of blue-green algae blooms resulting from high nutrient input has been a major public concern so that estimation of chlorophyll-a concentration of this reservoir is significantly important for assessing the reservoir's water quality. Empirical and semi-empirical methods were used in our previous studies for estimating CHL. Due to limitations to empirical and semi-empirical methods, a bio-optical model is tested in this study. Field campaigns were carried out in Eagle Creek Reservoir in central Indiana, and water samples analyzed for water quality parameter concentrations and their inherent optical properties (IOPs). A bio-optical model parameterized with these derived IOPs is used to estimate CHL concentration through a matrix inversion of hyperspectral data, and its performance is compared with those for empirical and semi-empirical models. The result demonstrates that the bio-optical model results in a higher correlation than empirical and semi-empirical models do.

[1]  A. L. Robertson,et al.  USING BAND RATIO, SEMI-EMPIRICAL, C URVE FITTING, AND PARTIAL LEAST SQUARES (PLS) MODELS TO ESTIMATE CYANOBACTERIAL PIGMENT CONCENTRATION FROM HYPERSPECTRAL REFLECTANCE , 2009 .

[2]  Vittorio E. Brando,et al.  Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality , 2003, IEEE Trans. Geosci. Remote. Sens..

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

[4]  André Morel,et al.  Report of the working group on water color , 1980 .

[5]  A. J. Allnutt.  Optical Aspects of Oceanography , 1975 .

[6]  Hee-Mock Oh,et al.  Alternative alert system for cyanobacterial bloom, using phycocyanin as a level determinant. , 2007, Journal of microbiology.

[7]  Deepak R. Mishra,et al.  A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach , 2009, Remote. Sens..

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

[9]  Delu Pan,et al.  Hyperspectral retrieval model of phycocyanin in case II waters , 2006 .

[10]  L. Backer,et al.  Cyanobacterial Harmful Algal Blooms (CyanoHABs): Developing a Public Health Response , 2002 .

[11]  Arnold G. Dekker,et al.  Retrieval of chlorophyll and suspended matter from imaging spectrometry data by matrix inversion , 1998 .

[12]  Tiit Kutser,et al.  Monitoring cyanobacterial blooms by satellite remote sensing , 2006 .

[13]  José Antonio Domínguez,et al.  Remote Sensing as a Basic Toolbox for Monitoring Water Quality Parameters and as a System of Surveillance of Cyanobacterial Harmful Algae Blooms (SCyanoHABs) , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[14]  H. Paerl,et al.  Climate change: a catalyst for global expansion of harmful cyanobacterial blooms. , 2009, Environmental microbiology reports.

[15]  H. Gons,et al.  Optical teledetection of chlorophyll a in turbid inland waters , 1999 .

[16]  Stefan G. H. Simis,et al.  Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water , 2005 .

[17]  Arnold G. Dekker,et al.  Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing , 1993 .

[18]  Machteld Rijkeboer,et al.  Towards airborne remote sensing of water quality in The Netherlands - validation and error analysis , 2002 .

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

[20]  Graham P. Harris,et al.  Detection, identification and mapping of cyanobacteria — Using remote sensing to measure the optical quality of turbid inland waters , 1994 .

[21]  Lin Li,et al.  Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin , 2008 .

[22]  Jamie Bartram,et al.  Toxic Cyanobacteria in Water: a Guide to Their Public Health Consequences, Monitoring and Management Chapter 2. Cyanobacteria in the Environment 2.1 Nature and Diversity 2.1.1 Systematics , 2022 .

[23]  John T. O. Kirk,et al.  Characteristics of the light field in highly turbid waters: A Monte Carlo study , 1994 .

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

[25]  Mohamed Sultan,et al.  Mapping Cyanobacterial Blooms in the Great Lakes Using MODIS , 2009 .

[26]  G. Habermehl,et al.  Toxic Cyanobacteria in Water , 2001 .

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

[28]  R. J. Ritchie,et al.  Universal chlorophyll equations for estimating chlorophylls a, b, c, and d and total chlorophylls in natural assemblages of photosynthetic organisms using acetone, methanol, or ethanol solvents , 2008, Photosynthetica.

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