A Four-Band Semi-Analytical Model for Estimating Phycocyanin in Inland Waters From Simulated MERIS and OLCI Data

Existing remote-sensing algorithms to estimate the phycocyanin (PC) concentration in turbid inland waters have high associated uncertainties, especially at low PC concentrations in diverse phytoplankton communities. This paper provides the theoretical framework for a four-band semi-analytical algorithm (FBA_PC) which isolates PC absorption from second-order variability caused by yellow matter and other phytoplankton pigment absorption. The algorithm suits the band configuration of both the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3 Ocean and Land Color Instrument (OLCI). Calibration of the algorithm was based on absorption data from 12 inland water bodies in the USA, The Netherlands, and China, combined with measurements from laboratory-grown cultures, which demonstrated that the assumptions underlying FBA-PC are an improvement over existing three-band approaches. Validation of FBA_PC in seven inland water bodies in the USA, The Netherlands, and China showed good agreement of FBA_PC adjusted to the MERIS/OLCI band configuration with measured PC, with root-mean-square error =27.691<inline-formula> <tex-math notation="LaTeX">$\text {mg} \cdot \text {m}^{-3}$ </tex-math></inline-formula>, mean absolute percentage error = 172.863%, and coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}) = 0.730$ </tex-math></inline-formula>. FBA_PC outperformed previously proposed PC algorithms that can be applied to MERIS or OLCI data, and is expected to be more robust when applied to a wider range of water bodies.

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

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

[3]  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.

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

[5]  Machteld Rijkeboer,et al.  Effect of a Waveband Shift on Chlorophyll Retrieval from MERIS Imagery of Inland and Coastal Waters , 2004 .

[6]  Hongbin Li,et al.  Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks , 2007, Ecol. Informatics.

[7]  G H Huang,et al.  A system dynamics approach for regional environmental planning and management: a study for the Lake Erhai Basin. , 2001, Journal of environmental management.

[8]  Peter D. Hunter,et al.  Spectral discrimination of phytoplankton colour groups: The effect of suspended particulate matter and sensor spectral resolution , 2008 .

[9]  Nathan S. Bosch,et al.  Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions , 2013, Proceedings of the National Academy of Sciences.

[10]  M. Rast,et al.  The ESA Medium Resolution Imaging Spectrometer MERIS a review of the instrument and its mission , 1999 .

[11]  Sachidananda Mishra,et al.  A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms , 2014 .

[12]  Junqian Zhang,et al.  Bioaccumulation of microcystins in two freshwater gastropods from a cyanobacteria-bloom plateau lake, Lake Dianchi. , 2012, Environmental pollution.

[13]  Wei Li,et al.  Cyanobacterial bloom management through integrated monitoring and forecasting in large shallow eutrophic Lake Taihu (China). , 2015, Journal of hazardous materials.

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

[15]  Marten Scheffer,et al.  On the Dominance of filamentous cyanobacteria in shallow, turbid lakes , 1997 .

[16]  Rogier van der Velde,et al.  MONITORING BLUE-GREEN ALGAE IN THE IJSSELMEER USING REMOTE SENSING AND IN-SITU MEASUREMENTS , 2012 .

[17]  O. Seehausen,et al.  Eutrophication causes speciation reversal in whitefish adaptive radiations , 2012, Nature.

[18]  Gokare A. Ravishankar,et al.  Phycocyanin from Spirulina sp: influence of processing of biomass on phycocyanin yield, analysis of efficacy of extraction methods and stability studies on phycocyanin , 1999 .

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

[20]  Lin Li,et al.  An inversion model for deriving inherent optical properties of inland waters: Establishment, validation and application , 2013 .

[21]  Adam Krezel,et al.  Empirical Model for Phycocyanin Concentration Estimation as an Indicator of Cyanobacterial Bloom in the Optically Complex Coastal Waters of the Baltic Sea , 2016, Remote. Sens..

[22]  Heiko Balzter,et al.  Validation of Envisat MERIS algorithms for chlorophyll retrieval in a large, turbid and optically-complex shallow lake , 2015 .

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

[24]  Xiangdong Yang,et al.  Surface sediment diatom assemblages and epilimnetic total phosphorus in large, shallow lakes of the Yangtze floodplain: their relationships and implications for assessing long-term eutrophication , 2008 .

[25]  Hans W Paerl,et al.  Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. , 2011, The Science of the total environment.

[26]  Jinhui Huang,et al.  Spatial risk assessment and sources identification of heavy metals in surface sediments from the Dongting Lake, Middle China , 2013 .

[27]  R. Rosenberg,et al.  Spreading Dead Zones and Consequences for Marine Ecosystems , 2008, Science.

[28]  Ronghua Ma,et al.  A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations , 2014 .

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

[30]  Ronghua Ma,et al.  Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China , 2012 .

[31]  Liu Yongding,et al.  Mechanical removal of heavy cyanobacterial bloom in the hyper-eutrophic lake Dianchi , 2004 .

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

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

[34]  Lucie Guo,et al.  Doing Battle With the Green Monster of Taihu Lake , 2007, Science.

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

[36]  H. Paerl,et al.  Climate change: links to global expansion of harmful cyanobacteria. , 2012, Water research.

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

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

[39]  Kunimitsu Kaya,et al.  Cyanobacterial toxins, exposure routes and human health , 1999 .

[40]  Igor Ogashawara,et al.  A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters , 2013, Remote. Sens..

[41]  Micheli,et al.  Eutrophication, Fisheries, and Consumer-Resource Dynamics in Marine Pelagic Ecosystems. , 1999, Science.

[42]  Anatoly A. Gitelson,et al.  Towards a unified approach for remote estimation of chlorophyll‐a in both terrestrial vegetation and turbid productive waters , 2003 .

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

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

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

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

[47]  Hu Zheng-Yu,et al.  Studies on eutrophication problem and control strategy in the Three Gorges Reservoir , 2006 .