Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms’ indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R2 = 0.692, root mean square error (RMSE) = 21.4 mg m−3), Lake Kasumigaura (R2 = 0.866, RMSE = 11.3 mg m−3), and MERIS data over Lake Kasumigaura (R2 = 0.57, RMSE = 36.5 mg m−3). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura.

[1]  Wojciech M. Klonowski,et al.  Intercomparison of shallow water bathymetry, hydro‐optics, and benthos mapping techniques in Australian and Caribbean coastal environments , 2011 .

[2]  A. Morel.  Optical properties of pure water and pure sea water , 1974 .

[3]  I. Ioannou,et al.  Fluorescence component in the reflectance spectra from coastal waters. II. Performance of retrieval algorithms. , 2008, Optics express.

[4]  Robert A. Leathers,et al.  Optical remote sensing of benthic habitats and bathymetry in coastal environments at Lee Stocking Island, Bahamas: A comparative spectral classification approach , 2003 .

[5]  Dong Jiang,et al.  Evaluation of Hyperspectral Indices for Chlorophyll-a Concentration Estimation in Tangxun Lake (Wuhan, China) , 2010, International journal of environmental research and public health.

[6]  R. Doerffer,et al.  Neural network for retrieval of concentrations of water constituents with the possibility of detecting exceptional out of scope spectra , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[7]  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 .

[8]  Y. Yasuoka,et al.  Estimation of chlorophyll concentration in lakes and inland seas with a field spectroradiometer above the water surface. , 2002, Applied optics.

[9]  Yong Q. Tian,et al.  An assessment of remote sensing algorithms for colored dissolved organic matter in complex freshwater environments , 2014 .

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

[11]  Takashi Ishimaru,et al.  An improved method for the determination of phytoplankton chlorophyll using N, N-dimethylformamide , 1990 .

[12]  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 .

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

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

[15]  Hendrik Buiteveld,et al.  Optical properties of pure water , 1994, Other Conferences.

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

[17]  D. Mishra,et al.  Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters , 2012 .

[18]  D. Siegel,et al.  Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: 1. Time series from the Sargasso Sea , 1997 .

[19]  Yunxuan Zhou,et al.  Medium resolution imaging spectrometer (MERIS) estimation of chlorophyll-a concentration in the turbid sediment-laden waters of the Changjiang (Yangtze) Estuary , 2010 .

[20]  Robert F. Chen,et al.  Properties of the Water Column and Bottom Derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Data , 2001 .

[21]  Lisa R. Moore,et al.  Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples , 2000 .

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

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

[24]  Bunkei Matsushita,et al.  Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables , 2011 .

[25]  C. Mobley,et al.  Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. , 1999, Applied optics.

[26]  Loet Leydesdorff,et al.  The relation between Pearson's correlation coefficient r and Salton's cosine measure , 2009, ArXiv.

[27]  Jennifer P. Cannizzaro,et al.  Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary , 2013 .

[28]  Stuart R. Phinn,et al.  Efficient radiative transfer model inversion for remote sensing applications , 2009 .

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

[30]  Xiaohan Liu,et al.  Remote sensing of diffuse attenuation coefficient of photosynthetically active radiation in Lake Taihu using MERIS data , 2014 .

[31]  Wojciech M. Klonowski,et al.  Retrieving key benthic cover types and bathymetry from hyperspectral imagery , 2007 .

[32]  Lin Li,et al.  Remote chlorophyll-a estimates for inland waters based on a cluster-based classification. , 2013, The Science of the total environment.

[33]  R. W. Austin.  Gulf of Mexico, ocean-color surface-truth measurements , 1980 .

[34]  Trijntje Valerie Downes,et al.  Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. , 2005, Applied optics.

[35]  J. Gower,et al.  Interpretation of the 685nm peak in water-leaving radiance spectra in terms of fluorescence, absorption and scattering, and its observation by MERIS , 1999 .

[36]  M. P. Lesser,et al.  Bathymetry, water optical properties, and benthic classification of coral reefs using hyperspectral remote sensing imagery , 2007, Coral Reefs.

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

[38]  Roland Doerffer,et al.  Lake Water Algorithm for BEAM , 2007 .

[39]  Steve Groom,et al.  Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters. , 2016, The Science of the total environment.

[40]  Machteld Rijkeboer,et al.  Coupling of phytoplankton and detritus in a shallow, eutrophic lake (Lake Loosdrecht, The Netherlands) , 1992, Hydrobiologia.

[41]  David A. Siegel,et al.  Assessing requirements for sustained ocean color research and operations , 2011 .

[42]  Stuart R. Phinn,et al.  Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007 , 2011, Remote. Sens..

[43]  R. W. Austin,et al.  The Determination of the Diffuse Attenuation Coefficient of Sea Water Using the Coastal Zone Color Scanner , 1981 .

[44]  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 .

[45]  L. Bruzzone,et al.  Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data , 2012 .

[46]  Charles R. McClain,et al.  Spatial scales in CZCS‐chlorophyll imagery of the southeastern U.S. continental shelf1 , 1987 .

[47]  Giorgio Dall'Olmo,et al.  Effect of bio-optical parameter variability and uncertainties in reflectance measurements on the remote estimation of chlorophyll-a concentration in turbid productive waters: modeling results. , 2006, Applied optics.

[48]  Weichun Ma,et al.  Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China , 2014 .

[49]  Chris Roelfsema,et al.  A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data , 2009 .

[50]  R. Doerffer,et al.  The MERIS Case 2 water algorithm , 2007 .

[51]  T. Moore,et al.  An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. , 2014, Remote sensing of environment.

[52]  B. Matsushita,et al.  A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters , 2015 .

[53]  John T. O. Kirk,et al.  Dependence of relationship between inherent and apparent optical properties of water on solar altitude , 1984 .

[54]  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 .

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

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

[57]  Qiao Wang,et al.  Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes. , 2015, The Science of the total environment.

[58]  Mhd. Suhyb Salama,et al.  Remote sensing of euphotic depth in shallow tropical inland waters of Lake Naivasha using MERIS data , 2014 .

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

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

[61]  Deyong Sun,et al.  A semi-analytical approach for detecting suspended particulate composition in complex turbid inland waters (China) , 2013 .

[62]  M. Tamura,et al.  NEURAL NETWORK MODELING OF LAKE SURFACE CHLOROPHYLL AND SEDIMENT CONTENT FROM LANDSAT TM IMAGERY , 2001 .

[63]  B. Matsushita,et al.  A simple method for distinguishing global Case-1 and Case-2 waters using SeaWiFS measurements , 2012 .

[64]  Jin Chen,et al.  A Relaxed Matrix Inversion Method for Retrieving Water Constituent Concentrations in Case II Waters: The Case of Lake Kasumigaura, Japan , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[65]  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.

[66]  Kazuo,et al.  Estimation of Chlorophyll-a Concentration in Rich Chlorophyll Water Area from Near-infrared and Red Spectral Signature , 1996 .

[67]  Qian Shen,et al.  Algorithms and Schemes for Chlorophyll a Estimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[69]  Kazuo Oki,et al.  Why is the Ratio of Reflectivity Effective for Chlorophyll Estimation in the Lake Water? , 2010, Remote. Sens..

[70]  N. Welschmeyer.  Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments , 1994 .

[71]  Lin Li,et al.  Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model , 2013 .

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

[73]  Vittorio E. Brando,et al.  Assessment of water quality in Lake Garda (Italy) using Hyperion , 2007 .

[74]  Daniela Stroppiana,et al.  Assessing remotely sensed chlorophyll-a for the implementation of the Water Framework Directive in European perialpine lakes. , 2011, The Science of the total environment.

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

[76]  Hiroshi Kobayashi,et al.  Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands , 2017, Sensors.

[77]  D. Rundquist,et al.  The spectral responses of algal chlorophyll in water with varying levels of suspended sediment , 1994 .