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Abstrakty
Remote sensing studies published up to now show that the performance of empirical (band-ratio type) algorithms in different parts of the Baltic Sea is highly variable. Best performing algorithms are different in the different regions of the Baltic Sea. Moreover, there is indication that the algorithms have to be seasonal as the optical properties of phytoplankton assemblages dominating in spring and summer are different. We modelled 15,600 reflectance spectra using HydroLight radiative transfer model to test 58 previously published empirical algorithms. 7200 of the spectra were modelled using specific inherent optical properties (SIOPs) of the open parts of the Baltic Sea in summer and 8400 with SIOPs of spring season. Concentration range of chlorophyll-a, coloured dissolved organic matter (CDOM) and suspended matter used in the model simulations were based on the actually measured values available in literature. For each optically active constituent we added one concentration below actually measured minimum and one concentration above the actually measured maximum value in order to test the performance of the algorithms in wider range. 77 in situ reflectance spectra from rocky (Sweden) and sandy (Estonia, Latvia) coastal areas were used to evaluate the performance of the algorithms also in coastal waters. Seasonal differences in the algorithm performance were confirmed but we found also algorithms that can be used in both spring and summer conditions. The algorithms that use bands available on OLCI, launched in February 2016, are highlighted as this sensor will be available for Baltic Sea monitoring for coming decades.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
57--68
Opis fizyczny
Bibliogr. 78 poz., rys., tab., wykr.
Twórcy
autor
- Tartu Observatory, Nõo Parish, Tartu County, Estonia
autor
- Estonian Marine Institute, University of Tartu, Tallinn, Estonia
autor
- Finnish Environment Institute, Helsinki, Finland
autor
- Finnish Environment Institute, Helsinki, Finland
autor
- Finnish Environment Institute, Helsinki, Finland
autor
- Estonian Marine Institute, University of Tartu, Tallinn, Estonia
autor
- Estonian Marine Institute, University of Tartu, Tallinn, Estonia
autor
- Tartu Observatory, Nõo Parish, Tartu County, Estonia
Bibliografia
- [1] Alikas, K., Kango, K., Randoja, R., Philipson, P., Asuküll, E., Pisek, J., Reinart, A., 2015. Satellite-based products for monitoring optically complex inland waters in support of EU Water Framework Directive. Int. J. Remote Sens. 36 (17), 4446—4468, http://dx.doi.org/10.1080/01431161.2015.1083630.
- [2] Ammenberg, P., Flink, P., Lindell, T., Pierson, D., Strombeck, N., 2002. Bio-optical modelling combined with remote sensing to assess water quality. Int. J. Remote Sens. 23 (8), 1621—1638, http://dx.doi.org/10.1080/01431160110071860.
- [3] Anon, 2015. OCV6 (Ocean Color Chlorophyll (OC) v6), http://oceancolor.gsfc.nasa.gov/cms/reprocessing/r2009/ocv6.
- [4] Arst, H., Kutser, T., 1994. Data processing and interpretation of sea radiance factor measurements. Polar Res. 13 (1), 3—12.
- [5] Attila, J., Kallio, K., Kutser, T., Koponen, S., Simis, S., Böttcher, M., Brockmann, C., 2015. FerryScope D2.1 Hydrolight Baltic, Version 1.2. 30.06.2015.
- [6] Attila, J., Koponen, S., Kallio, K., Lindfors, A., Kaitala, S., Ylöstalo, P., 2013. MERIS Case II water processor comparison on coastal sites of the northern Baltic Sea. Remote Sens. Environ. 128, 138—149, http://dx.doi.org/10.1016/j.rse.2012.07.009.
- [7] Beltran-Abaunza, J. M., Kratzer, S., Brockmann, C., 2014. Evaluation of MERIS products from Baltic Sea coastal waters rich in CDOM. Ocean Sci. 10, 377—396, http://dx.doi.org/10.5194/os-10-377-2014.
- [8] Brezonik, P., Menken, K. D., Bauer, M., 2005. Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake Reserv. Manage. 21 (4), 373—382, http://dx.doi.org/10.1080/07438140509354442.
- [9] Darecki, M., Ficek, D., Krezel, A., Ostrowska, M., Majchrowski, R., Wozniak, S. B., Bradtke, K., Dera, J., Wozniak, B., 2008. Algorithms for the remote sensing of the Baltic ecosystem (DESAMBEM). Part 2: Empirical validation. Oceanologia 50 (4), 509—538.
- [10] Darecki, M., Kaczmarek, S., Olszewski, J., 2005. SeaWiFS ocean colour chlorophyll algorithms for the southern Baltic Sea. Int. J. Remote Sens. 26 (2), 247—260, http://dx.doi.org/10.1080/01431160410001720298.
- [11] 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.
- [12] Darecki, M., Weeks, A., Sagan, S., Kowalczuk, P., Kaczmarek, S., 2003. Optical characteristics of two contrasting Case 2 waters and their influence on remote sensing algorithms. Cont. Shelf Res. 23 (3—4), 237—250, http://dx.doi.org/10.1016/S0278-4343(02)00222-4.
- [13] Davies-Colley, R. J., Vant, W. N., 1987. Absorption of light by yellow substance in freshwater lakes. Limnol. Oceanogr. 32 (2), 416—425, http://dx.doi.org/10.4319/lo.1987.32.2.0416.
- [14] Dekker, A. G., Phinn, S. R., Anstee, J., Bissett, P., Brando, V. E., Casey, B., Fearns, P., Hedley, J., Klonowski, W., Lee, Z. P., Lynch, M., Lyons, M., Mobley, C., Roelfsema, C., 2011. Intercomparison of shallow water bathymetry, hydro-optics, and benthos mapping techniques in Australian and Caribbean coastal environments. Limnol. Oceanogr. Meth. 9 (9), 396—425, http://dx.doi.org/10.4319/lom.2011.9.396.
- [15] Dekker, A. G., Vos, R. J., Peters, S. W., 2002. Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data. Int. J. Remote Sens. 23 (1), 15—35, http://dx.doi.org/10.1080/01431160010006917.
- [16] Dierberg, F. E., Carriker, N. E., 1994. Field testing two instruments for remotely sensing water quality in the Tennessee Valley. Environ. Sci. Technol. 28 (1), 16—25, http://dx.doi.org/10.1021/es00050a004.
- [17] Doerffer, R., Schiller, H., 1997. Algorithm Theoretical Basis Document (ATBD 2.12). Pigment Index, Sediment and Gelbstoff Retrieval from Directional Water Leaving Radiance Reflectances using Inverse Modelling Technique. GKSS Research Centre.
- [18] Doerffer, R., Schiller, H., 2007. The MERIS Case 2 algorithm. Int. J. Remote Sens. 28 (3—4), 517—535, http://dx.doi.org/10.1080/01431160600821127.
- [19] Doxaran, D., Castaing, P., Lavender, S., 2006. Monitoring the maximum turbidity zone and detecting finescale turbidity features in the Gironde estuary using high spatial resolution satellite sensor (SPOT HRV, Landsat ETM) data. Int. J. Remote Sens. 27 (11), 2303—2321, http://dx.doi.org/10.1080/01431160500396865.
- [20] Doxaran, D., Cherukuru, R., Lavender, S., 2005. Use of reflectance band ratios to estimate suspended and dissolved matter concentrations in estuarine waters. Int. J. Remote Sens. 26 (8), 1763—1770, http://dx.doi.org/10.1080/01431160512331314092.
- [21] Doxaran, D., Froidefond, J. M., Castaing, P., 2002. A reflectance band ratio used to estimate suspended matter concentrations in sediment-dominated coastal waters. Int. J. Remote Sens. 23 (23), 5079—5085, http://dx.doi.org/10.1080/0143116021000009912.
- [22] Doxaran, D., Froidefond, J. M., Castaing, P., 2003. Remote-sensing reflectance of turbid sediment-dominated waters. Reduction of sediment type variations and changing illumination conditions effects by use of reflectance ratios. Appl. Opt. 42 (15), 2623—2634, http://dx.doi.org/10.1364/AO.42.002623.
- [23] Duan, H. T., Zhang, Y., Zhang, B., Song, K., Wang, Z., 2007. Assessment of chlorophyll-a concentration and trophic state for Lake Chagan using Landsat TM and field spectral data. Environ. Monit. Assess. 129 (1), 295—308, http://dx.doi.org/10.1007/s10661-006-9362-y.
- [24] Erm, A., Väli, G., Lips, I., Lips, U., 2008. Optical properties of northeastern Baltic Sea (Conf. paper). In: IEEE/OES US/EU-Baltic Int. Symp. 27—29 May 2008, Tallinn, EEE, http://dx.doi.org/10.1109/BALTIC.2008.4625547 7 pp.
- [25] ESS, 1993. EES Method 340.2: Total suspended solids, mass balance (dried at 103—1058C), volatile suspended solids (ignited at 5508C). Environ. Sci. Section, Madison 189—192.
- [26] Feistel, R., Günther, N., Wasmund, N., 2008. State and Evolution of the Baltic Sea, 1952—2005: A Detailed 50-year Survey. John Wiley & Sons, Hoboken, 441—481.
- [27] Garnesson, P., Krasemann, H., 2016. Quality Information Document. Ocean Colour. Baltic Chlorophyll Observation Products, CMEMS, http://marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-080-097.pdf.
- [28] Giardino, C., Candiani, G., Bresciani, M., Lee, Z., Gagliano, S., Pepe, M., 2012. BOMBER: a tool for estimating water quality and bottom properties from remote sensing images. Comput. Geosci. 45, 313—318, http://dx.doi.org/10.1016/j.cageo.2011.11.022.
- [29] Gitelson, A. A., Gurlin, D., Moses, W. J., Barrow, T., 2009. A biooptical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters. Environ. Res. Lett. 4 (4), 5 pp. 045003.http://iopscience.iop.org/article/10.1088/1748-9326/4/4/045003/meta.
- [30] Groetsch, P. M., Simis, S. G., Eleveld, M. A., Peters, S. W., 2014. Cyanobacterial bloom detection based on coherence between ferrybox observations. J. Marine Syst. 140 (A), 50—58, http://dx.doi.org/10.1016/j.jmarsys.2014.05.015.
- [31] Han, L., Jordan, K., 2005. Estimating and mapping chlorophyll a concentration in Pensacola Bay, Florida using Landsat ETM data. Int. J. Remote Sens. 26 (23), 5245—5254, http://dx.doi.org/10.1080/01431160500219182.
- [32] Hunter, P., Tyler, A. N., Willby, N. J., Gilvear, D. J., 2008. The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: a case study using high spatial resolution time-series airborne remote sensing. Limnol. Oceanogr. 53 (6), 2391—2406, http://dx.doi.org/10.4319/lo.2008.53.6.2391.
- [33] Härmä, P., Vepsäläinen, J., Hannonen, T., Pyhälahti, T., Kämäri, J., Kallio, K., Eloheimo, K., Koponen, S., 2001. Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland. Sci. Total Environ. 268 (1—3), 107—121, http://dx.doi.org/10.1016/S0048-9697(00)00688-4.
- [34] ISO 1.1., 1992. Water Quality — Measurement of Biochemical Parameters — Spectrophotometric Determination of Chlorophyll a Concentration.
- [35] Jiao, H. B., Zha, Y., Gao, J., Li, Y. M., Wei, Y. C., Huang, J. Z., 2006. Estimation of chlorophyll a concentration in Lake Tai, China using in situ hyperspectral data. Int. J. Remote Sens. 27 (19), 4267—4276, http://dx.doi.org/10.1080/01431160600702434.
- [36] Kallio, K., Attila, J., Härmä, P., Koponen, S., Pulliainen, J., Hyytiäinen, U.-M., Pyhälahti, T., 2008. Landsat ETM+ images in the estimation of seasonal lake water quality in boreal river basins. Environ. Manage. 42 (3), 511—522, http://dx.doi.org/10.1007/s00267-008-9146-y.
- [37] Kallio, K., Koponen, S., Pulliainen, J., 2003. Feasibility of airborne imaging spectrometry for lake monitoring — a case study of spatial chlorophyll a distribution in two meso-eutrophic lakes. Int. J. Remote Sens. 24 (19), 3771—3790, http://dx.doi.org/10.1080/0143116021000023899.
- [38] Kallio, K., Kutser, T., Hannonen, T., Koponen, S., Pulliainen, J., Vepsäläinen, J., Pyhälahti, T., 2001. Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons. Sci. Total Environ. 268 (1—3), 59—77, http://dx.doi.org/10.1016/S0048-9697(00)00685-9.
- [39] Koponen, S., Attila, J., Pulliainen, J., Kallio, K., Pyhälahti, T., Lindfors, A., Rasmus, K., Hallikainen, M., 2007. A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland. Cont. Shelf Res. 27 (2), 228—244, http://dx.doi.org/10.1016/j.csr.2006.10.006.
- [40] Kowalczuk, P., Darecki, M., Zabloka, M., 2010. Validation of empirical and semi-analytical remote sensing algorithms for estimating absorption by colored dissolved organic matter in the Baltic Sea from SeaWiFS and MODIS imagery. Oceanologia 52 (2), 171—196, http://dx.doi.org/10.5697/oc.52-2.171.
- [41] Kowalczuk, P., Olszewski, J., Darecki, M., Kaczmarek, S., 2005a. Empirical relationships between coloured dissolved organic matter (CDOM) absorption and apparent optical properties in Baltic Sea waters. Int. J. Remote Sens. 26 (2), 345—370, http://dx.doi.org/10.1080/01431160410001720270.
- [42] Kowalczuk, P., Ston-Egiert, J., Cooper, W. J., Whitehead, R. F., Duranko, M. J., 2005b. Characterization of chromophoric dissolved organic matter (CDOM) in the Baltic Sea by excitation emission matrix fluorescence spectroscopy. Mar. Chem. 96 (3—4), 273—292, http://dx.doi.org/10.1016/j.marchem.2005.03.002.
- [43] Kratzer, S., Brockmann, C., Moore, G., 2008. Using MERIS full resolution data to monitor coastal waters — a case study from Himmerfjärden, a fjord-like bay in the northwestern Baltic Sea. Remote Sens. Environ. 112 (5), 2284—2300, http://dx.doi.org/10.1016/j.rse.2007.10.006.
- [44] Kutser, T., 2004. Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing. Limnol. Oceanogr. 49 (6), 2179—2189, http://dx.doi.org/10.4319/lo.2004.49.6.2179.
- [45] Kutser, T., Herlevi, A., Kallio, K., Arst, H., 2001. A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes. Sci. Total Environ. 268 (1—3), 47—58, http://dx.doi.org/10.1016/S0048-9697(00)00682-3.
- [46] Kutser, T., Kallio, K., Eloheimo, K., Hannonen, T., Pyhälahti, T., Koponen, S., Pulliainen, J., 1999. Quantitative monitoring of water properties with the airborne imaging spectrometer AISA. Proc. Estonian Acad. Sci. Biol. Ecol. 48 (1), 25—36.
- [47] Kutser, T., Metsamaa, L., Strömbeck, N., Vahtmäe, E., 2006. Monitoring cyanobacterial blooms by satellite remote sensing. Estuar. Coastal Shelf Sci. 67 (1—2), 303—312, http://dx.doi.org/10.1016/j.ecss.2005.11.024.
- [48] Kutser, T., Paavel, B., Verpoorter, C., Ligi, M., Soomets, T., Toming, K., Casal, G., 2016. Remote sensing of black lakes and using 810 nm reflectance peak for retrieving water quality parameters of optically complex waters. Remote Sens. 8 (6), 497, http://dx.doi.org/10.3390/rs8060497.
- [49] Kutser, T., Pierson, D. C., Kallio, K. Y., Reinart, A., Sobek, S., 2005a. Mapping lake CDOM by satellite remote sensing. Remote Sens. Environ. 94 (4), 535—540, http://dx.doi.org/10.1016/j.rse.2004.11.009.
- [50] Kutser, T., Pierson, D. C., Tranvik, L., Reinart, A., Sobek, S., Kallio, K., 2005b. Using satellite remote sensing to estimate the colored dissolved organic matter absorption coefficient in lakes. Ecosystems 8 (6), 709—720, http://dx.doi.org/10.1007/s10021-003-0148-6.
- [51] Kutser, T., Vahtmäe, E., Paavel, B., Kauer, T., 2013. Removing glint effects from field radiometry data measured in optically complex coastal and inland waters. Remote Sens. Environ. 133, 85—89, http://dx.doi.org/10.1016/j.rse.2013.02.011.
- [52] Lorenzen, C. J., 1967. Determination of chlorophyll and phaeopigments; spectrophotometric equations. Limnol. Oceanogr. 12 (2), 343—346.
- [53] Mayo, M., Gitelson, A., Yacobi, Y. Z., Ben-Avraham, Z., 1995. Chlorophyll distribution in Lake Kinneret determined from Landsat Thematic Mapper data. Int. J. Remote Sens. 16 (1), 175—182, http://dx.doi.org/10.1080/01431169508954386.
- [54] Menken, K. D., Brezonik, P. L., Bauer, M. E., 2006. Influence of chlorophyll and colored dissolved organic matter (CDOM) on lake reflectance spectra: implications for measuring lake properties by remote sensing. Lake Reserv. Manage. 22 (3), 179—190, http://dx.doi.org/10.1080/07438140609353895.
- [55] Metsamaa, L., Kutser, T., Strömbeck, N., 2006. Recognising cyanobacterial blooms based on their optical signature: a modelling study. Boreal Environ. Res. 11 (6), 493—506.
- [56] Miller, R. L., McKee, B. A., 2004. Using MODIS Terra 250 m imagery to map concentrations of total suspended matter in coastal waters. Remote Sens. Environ. 93 (1—2), 259—266, http://dx.doi.org/10.1016/j.rse.2004.07.012.
- [57] Mobley, C. D., 1994. Light and Water: Radiative Transfer in Natural Waters. Academic Press, San Diego, 608 pp.
- [58] Mobley, C. D., Sundman, L. K., 2013a. HydroLight 5.2 — EcoLight 5.2 Technical Documentation. Sequoia Scientific. Inc., 115 pp.
- [59] Mobley, C. D., Sundman, L. K., 2013b. HydroLight 5.2 — EcoLight 5.2 Users' Guide. Sequoia Scientific. Inc., 109 pp.
- [60] Moses, W. J., Gitelson, A. A., Berdnikov, S., Povaznyy, V., 2009a. Satellite estimation of chlorophyll-a concentration using the red and NIR bands of MERIS — the Azov Sea case study. IEEE Geosci. Remote Sci. 6 (4), 845—849, http://dx.doi.org/10.1109/LGRS.2009.2026657.
- [61] Moses, W. J., Gitelson, A. A., Berdnikov, S., Povaznyy, V., 2009b. Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data — successes and challenges. Environ. Res. Lett. 4 (4), 045005, http://dx.doi.org/10.1088/1748-9326/4/4/045005.
- [62] Neukermans, G., Ruddick, K., Bernard, E., Ramon, D., Nechad, B., Deschamps, P.-Y., 2009. Mapping total suspended matter from geostationary satellites: a feasibility study with SEVIRI in the Southern North Sea. Opt. Express 17 (16), 14029—14052, http://dx.doi.org/10.1364/OE.17.014029.
- [63] Onderka, M., Pekarova, P., 2008. Retrieval of suspended particulate matter concentrations in the Danube River from Landsat ETM data. Sci. Total Environ. 397 (1—3), 238—243, http://dx.doi.org/10.1016/j.scitotenv.2008.02.044.
- [64] Östlund, C., Flink, P., Srömbeck, N., Pierson, D., Lindell, T., 2001. Mapping of the water quality of Lake Erken, Sweden, from imaging spectrometry and Landsat Thematic Mapper. Sci. Total Environ. 268 (1—3), 139—154, http://dx.doi.org/10.1016/S0048-9697(00)00683-5.
- [65] Pitarch, J., Volpe, G., Colella, S., Krasemann, H., Santoleri, R., 2015. Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multisensor data. Ocean Sci. 12 (2), 2283—2313, http://dx.doi.org/10.5194/os-12-379-2016.
- [66] 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.
- [67] Schalles, J. F., Gitelson, A. A., Yacobi, Y. Z., Kroenke, A .E., 1998. Estimation of chlorophyll a from time series measurements of high spectral resolution reflectance in an eutrophic lake. J. Phycol. 34 (2), 383—390.
- [68] Simis, S. G., Olsson, J., 2013. Unattended processing of shipborne hyperspectral reflectance measurements. Remote Sens. Environ. 135, 202—212, http://dx.doi.org/10.1016/j.rse.2013.04.001.
- [69] Simis, S. G. H., Ylöstalo, P., Kallio, K., Spilling, K., Kutser, T., Optical biogeochemical models of the Baltic Sea in spring and summer. PLOS ONE, submitted for publication.
- [70] Thiemann, S., Kaufmann, H., 2000. Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg lake district, Germany. Remote Sens. Environ. 73 (2), 227—235, http://dx.doi.org/10.1016/S0034-4257(00)00097-3.
- [71] Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B., Nõges, T., 2016. First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sens. 8, 640, 14 pp.
- [72] Wang, F., Han, L., Kung, H. T., Van Arsdale, R. B., 2006. Applications of Landsat-5 TM imagery in assessing and mapping water quality in Reelfoot Lake, Tennessee. Int. J. Remote Sens. 27 (23), 5269—5283, http://dx.doi.org/10.1080/01431160500191704.
- [73] Wang, X. J., Ma, T., 2001. Application of remote sensing techniques in monitoring and assessing the water quality of Taihu Lake. Bull. Environ. Contam. Toxicol. 67 (6), 863—870, http://dx.doi.org/10.1007/s001280202.
- [74] Wasmund, N., Uhlig, S., 2003. Phytoplankton trends in the Baltic Sea. ICES J. Mar. Sci. 60 (2), 177—186, http://dx.doi.org/10.1016/S1054-3139(02)00280-1.
- [75] Woźniak, A. B., 2014. Simple statistical formulas for estimating biogeochemical properties of suspended particulate matter in the southern Baltic Sea potentially useful for optical remote ensing applications. Oceanologia 56 (1), 7—39, http://dx.doi.org/10.5697/oc.56-1.007.
- [76] Woźniak, B., Krężel, A., Darecki, M., Woźniak, S. B., Majchrowski, R., Ostrowska, M., Kozłowski, L., Ficek, D., Olszewski, J., Dera, J., 2008. Algorithm for the remote sensing of the Baltic ecosystem (DESAMBEM). Part 1: Mathematical apparatus. Oceanologia 50 (4), 451—508.
- [77] Yacobi, Y. Z., Gitelson, A., Mayo, M., 1995. Remote sensing of chlorophyll in Lake Kinneret using high spectral-resolution radiometer and Landsat TM: spectral features of reflectance and algorithm development. J. Plankton Res. 17 (11), 2155—2173, http://dx.doi.org/10.1093/plankt/17.11.2155.
- [78] Zimba, P. V., Gitelson, A., 2006. Remote estimation of chlorophyll concentration in hypereutrophic aquatic systems: model tuning and accuracy optimization. Aquaculture 256 (1—4), 272—286, http://dx.doi.org/10.1016/j.aquaculture.2006.02.038.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-22cb24e5-ade7-452a-8e2b-758d514a04db