Detection of Phytoplankton Temporal Anomalies Based on Satellite Inherent Optical Properties: A Tool for Monitoring Phytoplankton Blooms

[EN] The baseline of a specific variable defines the average behavior of that variable and it must be built from long data series that represent its spatial and temporal variability. In coastal and marine waters, phytoplankton can produce blooms characterized by a wide range of total cells number or chlorophyll a concentration. Classifying a phytoplankton abundance increase as a bloom depends on the species, the study area and the season. The objective of this study was to define the baseline of satellite absorption coefficients in Todos Santos Bay (Baja California, Mexico) to determine the presence of phytoplankton blooms based on the satellite inherent optical properties index (satellite IOP index). Two field points were selected according to historical bloom reports. To build the baseline, the data of phytoplankton absorption coefficients (aphy,GIOP ) and detritus plus colored dissolved organic matter (CDOM) (adCDOM,GIOP ) from the generalized inherent optical property (GIOP) satellite model of the NASA moderate resolution imaging spectroradiometer (MODIS-Aqua) sensor was studied for the period 2003 to 2016. Field data taken during a phytoplankton bloom event on June 2017 was used to validate the use of satellite products. The association between field and satellite data had a significant positive correlation. The satellite baseline detected a trend change from high values to low values of the satellite IOP index since 2010. Improved wastewater treatment to waters discharged into the Bay, and increased aquaculture of filter-feeding mollusks could have been the cause. The methodology proposed in this study can be a supplementary tool for permanent in situ monitoring programs. This methodology offers several advantages: A complete spatial coverage of the specific coastal area under study, appropriate temporal resolution and a tool for building an objective baseline to detect deviation from average conditions during phytoplankton bloom events.This research was funded by the Council of Science and Technology of Mexico (CONACYT by its acronym in Spanish) with a doctorate scholarship to J.A.A.-M., with the announcement number 291025 in 2015. Also, it was funded by the Spanish Ministry of Education Culture and Sports with a post-doctoral research grant to M.T.S.-F., number CAS18/00107, in support of her stay at the Universidad Autonoma de Baja California (Mexico). The APC was funded by the Secretariat of Public Education of Mexico (SEP) under the Program for Professional Development Teacher.Aguilar-Maldonado, J.; Santamaria-Del-Angel, E.; González-Silvera, A.; Sebastiá-Frasquet, M. (2019). Detection of Phytoplankton Temporal Anomalies Based on Satellite Inherent Optical Properties: A Tool for Monitoring Phytoplankton Blooms. Sensors. 19(15). https://doi.org/10.3390/s191533391915Carbonel H., C. A. A., & Valentin, J. L. (1999). Numerical modelling of phytoplankton bloom in the upwelling ecosystem of Cabo Frio (Brazil). Ecological Modelling, 116(2-3), 135-148. doi:10.1016/s0304-3800(98)00201-4Dore, J. E., Letelier, R. M., Church, M. J., Lukas, R., & Karl, D. M. (2008). Summer phytoplankton blooms in the oligotrophic North Pacific Subtropical Gyre: Historical perspective and recent observations. Progress in Oceanography, 76(1), 2-38. doi:10.1016/j.pocean.2007.10.002Cox, R. F., Issa, R. R. A., & Ahrens, D. (2003). Management’s Perception of Key Performance Indicators for Construction. Journal of Construction Engineering and Management, 129(2), 142-151. doi:10.1061/(asce)0733-9364(2003)129:2(142)Zheng, G., & DiGiacomo, P. M. (2017). Remote sensing of chlorophyll-a in coastal waters based on the light absorption coefficient of phytoplankton. Remote Sensing of Environment, 201, 331-341. doi:10.1016/j.rse.2017.09.008Cao, F., Tzortziou, M., Hu, C., Mannino, A., Fichot, C. G., Del Vecchio, R., … Novak, M. (2018). Remote sensing retrievals of colored dissolved organic matter and dissolved organic carbon dynamics in North American estuaries and their margins. Remote Sensing of Environment, 205, 151-165. doi:10.1016/j.rse.2017.11.014Blondeau-Patissier, D., Gower, J. F. R., Dekker, A. G., Phinn, S. R., & Brando, V. E. (2014). A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans. Progress in Oceanography, 123, 123-144. doi:10.1016/j.pocean.2013.12.008Garver, S. A., & Siegel, D. A. (1997). Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: 1. Time series from the Sargasso Sea. Journal of Geophysical Research: Oceans, 102(C8), 18607-18625. doi:10.1029/96jc03243Kahru, M., Lee, Z., Kudela, R. M., Manzano-Sarabia, M., & Greg Mitchell, B. (2015). Multi-satellite time series of inherent optical properties in the California Current. Deep Sea Research Part II: Topical Studies in Oceanography, 112, 91-106. doi:10.1016/j.dsr2.2013.07.023Kratzer, S., & Moore, G. (2018). Inherent Optical Properties of the Baltic Sea in Comparison to Other Seas and Oceans. Remote Sensing, 10(3), 418. doi:10.3390/rs10030418Werdell, P. J., McKinna, L. I. W., Boss, E., Ackleson, S. G., Craig, S. E., Gregg, W. W., … Zhang, X. (2018). An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Progress in Oceanography, 160, 186-212. doi:10.1016/j.pocean.2018.01.001Costa Goela, P., Icely, J., Cristina, S., Newton, A., Moore, G., & Cordeiro, C. (2013). Specific absorption coefficient of phytoplankton off the Southwest coast of the Iberian Peninsula: A contribution to algorithm development for ocean colour remote sensing. Continental Shelf Research, 52, 119-132. doi:10.1016/j.csr.2012.11.009Soja-Woźniak, M., Craig, S., Kratzer, S., Wojtasiewicz, B., Darecki, M., & Jones, C. (2017). A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters. Remote Sensing, 9(4), 343. doi:10.3390/rs9040343Pavlov, A. K., Taskjelle, T., Kauko, H. M., Hamre, B., Hudson, S. R., Assmy, P., … Granskog, M. A. (2017). Altered inherent optical properties and estimates of the underwater light field during an Arctic under-ice bloom of Phaeocystis pouchetii. Journal of Geophysical Research: Oceans, 122(6), 4939-4961. doi:10.1002/2016jc012471Aguilar-Maldonado, J. A., Santamaría-del-Ángel, E., & Sebastiá-Frasquet, M. T. (2017). Reflectances of SPOT multispectral images associated with the turbidity of the Upper Gulf of California. Revista de Teledetección, (50), 1. doi:10.4995/raet.2017.7795Binding, C. E., Greenberg, T. A., McCullough, G., Watson, S. B., & Page, E. (2018). An analysis of satellite-derived chlorophyll and algal bloom indices on Lake Winnipeg. Journal of Great Lakes Research, 44(3), 436-446. doi:10.1016/j.jglr.2018.04.001Aguilar-Maldonado, J., Santamaría-Del-Ángel, E., González-Silvera, A., Cervantes-Rosas, O., & Sebastiá-Frasquet, M.-T. (2018). Mapping Satellite Inherent Optical Properties Index in Coastal Waters of the Yucatán Peninsula (Mexico). Sustainability, 10(6), 1894. doi:10.3390/su10061894Brewin, R. J. W., Sathyendranath, S., Müller, D., Brockmann, C., Deschamps, P.-Y., Devred, E., … White, G. N. (2015). The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms. Remote Sensing of Environment, 162, 271-294. doi:10.1016/j.rse.2013.09.016Aguilar-Maldonado, J., Santamaría-del-Ángel, E., González-Silvera, A., Cervantes-Rosas, O., López, L., Gutiérrez-Magness, A., … Sebastiá-Frasquet, M.-T. (2018). Identification of Phytoplankton Blooms under the Index of Inherent Optical Properties (IOP Index) in Optically Complex Waters. Water, 10(2), 129. doi:10.3390/w10020129Cavole, L., Demko, A., Diner, R., Giddings, A., Koester, I., … Franks, P. (2016). Biological Impacts of the 2013–2015 Warm-Water Anomaly in the Northeast Pacific: Winners, Losers, and the Future. Oceanography, 29(2). doi:10.5670/oceanog.2016.32Di Lorenzo, E., & Mantua, N. (2016). Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nature Climate Change, 6(11), 1042-1047. doi:10.1038/nclimate3082Mkrtchyan, F. A., & Varotsos, C. A. (2018). A New Monitoring System for the Surface Marine Anomalies. Water, Air, & Soil Pollution, 229(8). doi:10.1007/s11270-018-3938-3Gan, R., Yang, Y., & Ma, Y. (2018). Modelling the impacts of the Pacific Ocean sea surface temperature anomalies on a drought event in southwestern China with a piecewise-integration method. International Journal of Climatology, 39(2), 799-813. doi:10.1002/joc.5843Yeh, S.-W., Kug, J.-S., Dewitte, B., Kwon, M.-H., Kirtman, B. P., & Jin, F.-F. (2009). El Niño in a changing climate. Nature, 461(7263), 511-514. doi:10.1038/nature08316Peña Manjarrez, J. (2009). Environmental factors influencing the variability of Lingulodinium polyedrum and Scrippsiella trochoidea (Dinophyceae) cyst production. Ciencias Marinas, 35(1), 1-14. doi:10.7773/cm.v35i1.1406Cepeda-Morales, J. (2017). Response of primary producers to the hydrographic variability in the southern region of the California Current System. Ciencias Marinas, 40(2), 123-135. doi:10.7773/cm.v43i2.2752Barocio-Leon, O. (2007). Phytoplankton primary productivity in the euphotic zone of the California Current System estimated from CZCS imagery. Ciencias Marinas, 33(1), 59-72. doi:10.7773/cm.v33i1.1037INEGI 2015 (Instituto Nacional de Estadística y Geografía/National Institute of Statistic and Geography)http://www.beta.inegi.org.mx/programas/intercensal/2015/default.html#TabuladosGutierrez-Mejia, E., Lares, M. L., Huerta-Diaz, M. A., & Delgadillo-Hinojosa, F. (2016). Cadmium and phosphate variability during algal blooms of the dinoflagellate Lingulodinium polyedrum in Todos Santos Bay, Baja California, Mexico. Science of The Total Environment, 541, 865-876. doi:10.1016/j.scitotenv.2015.09.081Mateos, E., Marinone, S. G., & Parés-Sierra, A. (2009). Towards the numerical simulation of the summer circulation in Todos Santos Bay, Ensenada, B.C. Mexico. Ocean Modelling, 27(1-2), 107-112. doi:10.1016/j.ocemod.2008.11.002SOMMER, U., & LENGFELLNER, K. (2008). Climate change and the timing, magnitude, and composition of the phytoplankton spring bloom. Global Change Biology, 14(6), 1199-1208. doi:10.1111/j.1365-2486.2008.01571.xWinder, M., & Sommer, U. (2012). Phytoplankton response to a changing climate. Hydrobiologia, 698(1), 5-16. doi:10.1007/s10750-012-1149-2Orozco-Borbón, M. V., Rico-Mora, R., Weisberg, S. B., Noble, R. T., Dorsey, J. H., Leecaster, M. K., & McGee, C. D. (2006). Bacteriological water quality along the Tijuana–Ensenada, Baja California, México shoreline. Marine Pollution Bulletin, 52(10), 1190-1196. doi:10.1016/j.marpolbul.2006.02.005Gregg, W. W., & Casey, N. W. (2004). Global and regional evaluation of the SeaWiFS chlorophyll data set. Remote Sensing of Environment, 93(4), 463-479. doi:10.1016/j.rse.2003.12.012Sebastiá, M.-T., Rodilla, M., Sanchis, J.-A., Altur, V., Gadea, I., & Falco, S. (2012). Influence of nutrient inputs from a wetland dominated by agriculture on the phytoplankton community in a shallow harbour at the Spanish Mediterranean coast. Agriculture, Ecosystems & Environment, 152, 10-20. doi:10.1016/j.agee.2012.02.006Wilson, C. (2003). Late Summer chlorophyll blooms in the oligotrophic North Pacific Subtropical Gyre. Geophysical Research Letters, 30(18). doi:10.1029/2003gl017770Villareal, T. A., Adornato, L., Wilson, C., & Schoenbaechler, C. A. (2011). Summer blooms of diatom-diazotroph assemblages and surface chlorophyll in the North Pacific gyre: A disconnect. Journal of Geophysical Research, 116(C3). doi:10.1029/2010jc00626