Comparison of satellite chlorophyll a algorithms for the Baltic Sea

Abstract Chlorophyll a (chl- a ) products calculated using medium resolution imaging spectrometer (MERIS) data were tested. The satellite products were compared to chl- a concentrations measured in surface waters between 2003 and 2011 throughout the Baltic Sea. Image processing was performed with two neural-network-based MERIS data processors: the Case-2 Water Properties processor developed at the Freie Universität Berlin (FUB) and the Case-2 Regional processor of the German Institute for Coastal Research (C2R). Additionally, two algorithms for deriving chl- a concentrations from atmospherically corrected reflectances originally designed for Moderate Resolution Imaging Spectroradiometer and Sea-viewing Wide Field-of-view Sensor radiometers and adapted to Baltic Sea conditions were tested (algorithms denoted further as MD and SW respectively). The effectiveness of the Improved Contrast between Ocean and Land (ICOL) processor was also verified. Our results showed that the accuracy of chl- a concentration retrieval from satellite data varies depending on the location of the area. The difference in the statistical error between results from optically different coastal and open sea waters was as high as 200%. The most accurate results for the coastal zone were noted for the standard chl- a FUB processor product, while in open sea waters the highest accuracy was noted for the MD and SW algorithms with reflectance derived from the FUB processor.

[1]  Mati Kahru,et al.  Satellite detection of increased cyanobacteria blooms in the Baltic Sea: Natural fluctuation or ecosystem change? , 1994 .

[2]  Kai Sørensen,et al.  Validation of MERIS water products and bio‐optical relationships in the Skagerrak , 2007 .

[3]  T. Schroeder,et al.  Retrieval of atmospheric and oceanic properties from MERIS measurements: A new Case‐2 water processor for BEAM , 2007 .

[4]  Ragnar Elmgren,et al.  Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: interannual and spatial variability , 2007 .

[5]  Soo Chin Liew,et al.  EVALUATING THE VALIDITY OF SeaWiFS CHLOROPHYLL ALGORITHM FOR COASTAL WATERS , 2001 .

[6]  D. Stramski,et al.  An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea , 2004 .

[7]  A. Krezel,et al.  Toxic Nodularia spumigena blooms in the coastal waters of the Gulf of Gdańsk: a ten-year survey* , 2006 .

[8]  Roland Doerffer,et al.  Atmospheric correction algorithm for MERIS above case‐2 waters , 2007 .

[9]  B. Osborne,et al.  Light and Photosynthesis in Aquatic Ecosystems. , 1985 .

[10]  Mariano Bresciani,et al.  Validation of MERIS bio-optical products with in situ data in the turbid Lithuanian Baltic Sea coastal waters , 2012 .

[11]  Susanne Kratzer,et al.  Improvement of MERIS level 2 products in Baltic Sea coastal areas by applying the Improved Contrast between Ocean and Land processor (ICOL) - data analysis and validation , 2010 .

[12]  Mirosław Darecki,et al.  Algorithms for the remote sensing of the Baltic ecosystem (DESAMBEM). Part 2: Empirical validation , 2008 .

[13]  H. Siegel,et al.  Validation of MERIS Level‐2 products in the Baltic Sea, the Namibian coastal area and the Atlantic Ocean , 2007 .

[14]  M. Darecki,et al.  SeaWiFS ocean colour chlorophyll algorithms for the southern Baltic Sea , 2005 .

[15]  Bogdan Woźniak,et al.  Algorithm for the remote sensing of the Baltic ecosystem (DESAMBEM). Part 1: Mathematical apparatus , 2008 .

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

[17]  Roland Doerffer,et al.  Algorithm Theoretical Basis Document (ATBD) , 2008 .

[18]  P. Kowalczuk.  Seasonal variability of yellow substance absorption in the surface layer of the Baltic Sea , 1999 .

[19]  Carsten Brockmann,et al.  Using MERIS full resolution data to monitor coastal waters : A case study from Himmerfjärden, a fjord-like bay in the northwestern Baltic Sea , 2008 .

[20]  P. Kauppila,et al.  Seasonality of Coastal Phytoplankton in the Baltic Sea: Influence of Salinity and Eutrophication , 2005 .

[21]  Charles,et al.  SeaWiFS Postlaunch Calibration and Validation Analyses , Part 1 , 2000 .

[22]  N. Wasmund.  Occurrence of Cyanobacterial Blooms in the Baltic Sea in Relation to Environmental Conditions , 1997 .

[23]  Justyna Meler,et al.  Inherent optical properties of suspended particulate matter in the southern Baltic Sea , 2011 .

[24]  Menghua Wang,et al.  Seawifs Postlaunch Calibration and Validation Analyses , 2013 .

[25]  K. Sørensen,et al.  Monitoring the Bio-optical State of the Baltic Sea Ecosystem with Remote Sensing and Autonomous In Situ Techniques , 2011 .

[26]  P. Chauhan,et al.  Comparison of ocean color chlorophyll algorithms for IRS-P4 OCM sensor usingin-situ data , 2002 .