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Regional algorithms for the estimation of chlorophyll and suspended matter concentration in the Gulf of Finland from MODIS-Aqua satellite data

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Validation of algorithms for the retrieval of concentrations of chlorophyll (Chl) and total suspended matter (TSM) in the Gulf of Finland from satellite ocean colour data was carried out using field measurement data from summer 2012 and 2013. These data included spectral values of the remote sensing reflectance Rrs(λ), Chl and TSM concentrations. Testing of the existing algorithms (OC4v4, OC3M, and the Baltic regional algorithms developed by Polish specialists) showed that all of them overestimated Chl several times. The new regional algorithms were developed on the basis of measured values of Rrs(λ), Chl and TSM (40 stations in total). Direct comparison of Chl and TSM values, obtained from MODIS-Aqua data with the algorithms developed here, with their in situ values showed reasonable agreement. The spatial distributions of Chl and TSM concentrations were constructed from MODIS-Aqua data. Errors of the atmospheric correction were analysed.
Czasopismo
Rocznik
Strony
737--756
Opis fizyczny
Bibliogr. 12 poz., fot., mapki, tab., wykr.
Twórcy
autor
  • P. P. Shirshov Institute of Oceanology of the Russian Academy of Sciences (SIO RAS), 36 Nakhimovsky prospect, 117997 Moscow, Russia
  • Russian State Hydrometeorological University (RSHU), 98 Malookhtinsky prospect, 195196 St. Petersburg, Russia
autor
  • P. P. Shirshov Institute of Oceanology of the Russian Academy of Sciences (SIO RAS), 36 Nakhimovsky prospect, 117997 Moscow, Russia
  • Russian State Hydrometeorological University (RSHU), 98 Malookhtinsky prospect, 195196 St. Petersburg, Russia
  • P. P. Shirshov Institute of Oceanology of the Russian Academy of Sciences (SIO RAS), 36 Nakhimovsky prospect, 117997 Moscow, Russia
  • Russian State Hydrometeorological University (RSHU), 98 Malookhtinsky prospect, 195196 St. Petersburg, Russia
autor
  • P. P. Shirshov Institute of Oceanology of the Russian Academy of Sciences (SIO RAS), 36 Nakhimovsky prospect, 117997 Moscow, Russia
autor
  • Russian State Hydrometeorological University (RSHU), 98 Malookhtinsky prospect, 195196 St. Petersburg, Russia
autor
  • Russian State Hydrometeorological University (RSHU), 98 Malookhtinsky prospect, 195196 St. Petersburg, Russia
Bibliografia
  • [1]. Artemiev V. A., Burenkov V. I., Vortman M. I., Grigoriev A. V., Kopelevich O. V., Khrapko A. N., 2000, Sea-truth measurements of ocean color: a new floating spectroradiometer and its metrology, Oceanology, 40, 139-145, (translated from Okeanologiya, 40, 148-155).
  • [2]. Bailey S. W., Werdell P. J., 2006, A multi-sensor approach for the on-orbit validation of ocean color satellite data products, Remote Sens. Environ., 102 (1-2), 12-23, http://dx.doi.org/10.1016/j.rse.2006.01.015
  • [3]. Burenkov V. I., Ershova S. V., Kopelevich O. V., Sheberstov S. V., Shevchenko V. P., 2001, An estimate of the distribution of suspended matter in the Barents Sea waters on the basis of the SeaWiFS satellite ocean color scanner, Oceanology, 41 (5), 622-628, (translated from Okeanologiya, 2001, 41 (5), 653-659).
  • [4]. Darecki M., Ficek D., Krężel A., Ostrowska M., Majchrowski R., Woźniak S. B., Bradtke K., Dera J., Woźniak B., 2008, Algorithm for the remote sensing of the Baltic ecosystem (DESAMBEM). Part 2: Empirical validation, Oceanologia, 50 (4), 509-538.
  • [5]. Darecki M., Stramski D., 2004, An evaluation of MODIS and SeaWiFS bio- optical algorithms in the Baltic Sea, Remote Sens. Environ., 89 (3), 326-350, http://dx.doi.org/10.1016/j.rse.2003.10.012
  • [6]. Feldman G. C., 2013, Ocean color web, http://oceancolor.gsfc.nasa.gov. Kopelevich O. V., Sheberstov S. V., Sahling I. V., Vazyulya S. V., Burenkov V. I., 2013, Bio-optical characteristics of the Barents, White, Black, and Caspian Seas from data of satellite ocean color scanners, http://optics.ocean.ru.
  • [7]. Lee Z., Carder K. L., Mobley C. D., Steward R. G., Patch J. S., 1998, Hyperspectral remote sensing for shallow waters. 1. A semianalytical model, Appl. Opt., 37 (27), 6329-6338, http://dx.doi.org/10.1364/AO.37.006329
  • [8]. PND F, 2012, Quantitative chemical analysis of water. The method for measuring suspended solids and total amount of the admixture of natural and treated wastewater by gravimetric method, 14.1:2.110-97, Media-Service, Moscow, 11 pp., (in Russian).
  • [9]. Reinart A., Kutser T., 2006, Comparison of different satellite sensors in detecting cyanobacterial bloom events in the Baltic Sea, Remote Sens. Environ., 102 (1-2), 74-85, http://dx.doi.org/10.1016/j.rse.2006.02.013
  • [10]. Report of SCOR-UNESCO working group 17 on determination of photosynthetic pigments, 1966, Determination of photosynthetic pigments in sea-water, UNESCO, Paris, 9-16.
  • [11]. Woźniak B., Krężel A., Darecki M., 2008, Algorithm for the remote sensing of the Baltic ecosystem (DESAMBEM). Part 1: Mathematical apparatus, Oceanologia, 50 (4), 451-508.
  • [12]. Zibordi G., Holben B., Slutsker I., 2009, A network for the validation of ocean color primary products, J. Atmos. Ocean. Tech., 26 (8), 1634-1651, http://dx.doi.org/10.1175/2009JTECHO654.1
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-baf49aad-574c-4527-9adf-cda7b2d98df8
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