First Experiences in Mapping Lake Water Quality Parameters with Sentinel-2 MSI Imagery

The importance of lakes and reservoirs leads to the high need for monitoring lake water quality both at local and global scales. The aim of the study was to test suitability of Sentinel-2 Multispectral Imager’s (MSI) data for mapping different lake water quality parameters. In situ data of chlorophyll a (Chl a), water color, colored dissolved organic matter (CDOM) and dissolved organic carbon (DOC) from nine small and two large lakes were compared with band ratio algorithms derived from Sentinel-2 Level-1C and atmospherically corrected (Sen2cor) Level-2A images. The height of the 705 nm peak was used for estimating Chl a. The suitability of the commonly used green to red band ratio was tested for estimating the CDOM, DOC and water color. Concurrent reflectance measurements were not available. Therefore, we were not able to validate the performance of Sen2cor atmospheric correction available in the Sentinel-2 Toolbox. The shape and magnitude of water reflectance were consistent with our field measurements from previous years. However, the atmospheric correction reduced the correlation between the band ratio algorithms and water quality parameters indicating the need in better atmospheric correction. We were able to show that there is good correlation between band ratio algorithms calculated from Sentinel-2 MSI data and lake water parameters like Chl a (R2 = 0.83), CDOM (R2 = 0.72) and DOC (R2 = 0.92) concentrations as well as water color (R2 = 0.52). The in situ dataset was limited in number, but covered a reasonably wide range of optical water properties. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for lake monitoring and research, especially taking into account that the data will be available routinely for many years, the imagery will be frequent, and free of charge.

[1]  Kaishan Song,et al.  Assessment of Chlorophyll-a Concentration and Trophic State for Lake Chagan Using Landsat TM and Field Spectral Data , 2007, Environmental monitoring and assessment.

[2]  K. Ruddick,et al.  Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8 , 2015 .

[3]  Lars J. Tranvik.  Allochthonous dissolved organic matter as an energy source for pelagic bacteria and the concept of the microbial loop , 1992 .

[4]  J. Pulliainen,et al.  Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons. , 2001, The Science of the total environment.

[5]  Jeffrey A. Cardille,et al.  Remote sensing of lake CDOM using noncontemporaneous field data , 2013 .

[6]  S. Peters,et al.  Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. , 2001, The Science of the total environment.

[7]  M. Futter,et al.  Variability in spectral absorbance metrics across boreal lake waters. , 2012, Journal of environmental monitoring : JEM.

[8]  G. Likens,et al.  Spectral reflectance and water quality of Adirondack mountain region lakes , 1989 .

[9]  A. Gitelson,et al.  Quantitative remote sensing methods for real-time monitoring of inland waters quality , 1993 .

[10]  Ants Erm,et al.  Optical Properties of Dissolved Organic Matter in Finnish and Estonian Lakes , 2003 .

[11]  P. Dillon,et al.  Colour - mass balances and colour - dissolved organic carbon relationships in lakes and streams in central Ontario , 1997 .

[12]  Anatoly A. Gitelson,et al.  Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data—successes and challenges , 2009 .

[13]  Tiit Kutser,et al.  Mapping lake CDOM by satellite remote sensing , 2005 .

[14]  Jouni Pulliainen,et al.  Landsat ETM+ Images in the Estimation of Seasonal Lake Water Quality in Boreal River Basins , 2008, Environmental management.

[15]  L. Edler,et al.  Recommendations on methods for marine biological studies in the Baltic Sea. Phytoplankton and chlorophyll , 1979 .

[16]  T. Nõges,et al.  Spatial and temporal variations in coloured dissolved organic matter in large and shallow Estonian waterbodies , 2009 .

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

[18]  M. Bauer,et al.  Landsat-based Remote Sensing of Lake Water Quality Characteristics, Including Chlorophyll and Colored Dissolved Organic Matter (CDOM) , 2005 .

[19]  R. Vogt,et al.  NOM increase in Northern European source waters: discussion of possible causes and impacts on coagulation/contact filtration processes , 2004 .

[20]  B. Koetz,et al.  Capability of the Sentinel 2 mission for tropical coral reef mapping and coral bleaching detection , 2012 .

[21]  Renato Ferreira,et al.  Mapping inland water carbon content with Landsat 8 data , 2016 .

[22]  Tiit Kutser,et al.  Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters , 2016, Remote. Sens..

[23]  Tiit Kutser,et al.  Estimating lake carbon fractions from remote sensing data , 2015 .

[24]  T. Kutser,et al.  Dissolved organic carbon and its potential predictors in eutrophic lakes. , 2016, Water research.

[25]  M. Bauer,et al.  A 20-year Landsat water clarity census of Minnesota's 10,000 lakes , 2008 .

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

[27]  T. Nõges,et al.  Contributions of autochthonous and allochthonous sources to dissolved organic matter in a large, shallow, eutrophic lake with a highly calcareous catchment , 2013 .

[28]  K. Moffett,et al.  Remote Sens , 2015 .

[29]  Xiaodong Li,et al.  Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band , 2016, Remote. Sens..

[30]  M. Matthews,et al.  An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters , 2012 .

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

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

[33]  S. Sterckx,et al.  OPERA: An Atmospheric Correction for Land and Water , 2015 .

[34]  R. Bukata,et al.  Optical water quality model of Lake Ontario. 2: Determination of chlorophyll a and suspended mineral concentrations of natural waters from submersible and low altitude optical sensors. , 1981, Applied optics.

[35]  K. Sand‐Jensen,et al.  Lake metabolism scales with lake morphometry and catchment conditions , 2011, Aquatic Sciences.

[36]  M. Bauer,et al.  Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters , 2015 .

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

[38]  A. Gitelson,et al.  Chlorophyll distribution in Lake Kinneret determined from Landsat Thematic Mapper data , 1995 .

[39]  C. Verpoorter,et al.  A global inventory of lakes based on high‐resolution satellite imagery , 2014 .

[40]  Alo Laas,et al.  Continuous and high-frequency measurements in limnology: history, applications, and future challenges , 2016 .

[41]  Tiit Kutser,et al.  The possibility of using the Landsat image archive for monitoring long time trends in coloured dissolved organic matter concentration in lake waters , 2012 .

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

[43]  R. Bukata,et al.  Optical water quality model of Lake Ontario. 1: Determination of the optical cross sections of organic and inorganic particulates in Lake Ontario. , 1981, Applied optics.

[44]  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 .

[45]  Tiit Kutser,et al.  Impact of iron associated to organic matter on remote sensing estimates of lake carbon content , 2015 .

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

[47]  John M. Melack,et al.  Lakes and reservoirs as regulators of carbon cycling and climate , 2009 .