Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle
Research highlights
►We apply regionally and cluster specific chlorophyll a algorithms for MERIS data. ►We produce more accurate chlorophyll a maps of optically complex waters. ►We describe an upwelling cycle using in situ and satellite data. ►MERIS images capture the development and decay of a phytoplankton bloom.
Introduction
Although remote sensing tools can be used with a relatively high precision at global scale for the calculation of chlorophyll a (chla), they are not always totally accurate in local areas (Cota et al., 2004, Ruddick et al., 2008) and highly dynamic systems such upwelling regimes. Eastern boundary upwelling systems cover a small percentage of the ocean surface, but account more than 20% of the global fish catch.
In these high productive systems harmful algal events due to toxic phytoplankton species and/or high-biomass blooms pose an increasing threat for aquaculture and fishing industries, ecosystem health and diversity and have possible implications for human health and activities (Trainer et al., 2010). Harmful algal events in eastern boundary upwelling systems have been closely associated with the wind properties (Bates et al., 1998, Pitcher et al., 1998) and have become a focal point of numerous studies (e.g. Fawcett et al., 2007, Kudela et al., 2005, Pitcher and Nelson, 2006). For example, in their review on harmful algal events in upwelling systems, Pitcher et al. (2010) noticed that variations in wind-stress fluctuations and buoyancy inputs in upwelling systems are controlling factors of the bloom timing and pointed out the role of inner-shelf dynamics on the spatial distribution of the bloom.
Upwelling waters are characterized by considerable variability in the vertical distribution of phytoplankton (Brown & Hutchings, 1987) and in their optical properties (Morel & Prieur, 1977). Optically active water constituents such as SPM which are brought into the surface because of the strong mixing and vertical advection that takes place during upwelling events may vary independently of the surface chla, as they do in typically shallow estuarine case II waters.
In typical case II waters the traditional satellite-derived chla models (empirical: Aiken et al., 1995, Brown et al., 2008, Evans and Gordon, 1994, McClain et al., 2004, Muller-Karger et al., 1990, O'Reily et al., 2000 and semi-analytical: Carder and Steward, 1985, Carder et al., 1999) based on the ratio between the radiance of blue and green light reflected by the surface waters cannot be used for an accurate retrieval of chla (Gitelson et al., 2007, Gons, 1999, Morel and Prieur, 1977). However, chla algorithms that use green to red and near infrared band ratios have shown good performance in inland and coastal waters (Gilerson et al., 2010). In the effort for more accurate retrieval of water constituents in optically complex waters, neural network (NN) techniques can play an important role, since they seem ideal for multivariate, complex and non-linear data modeling (Thiria et al., 1993). Dransfeld et al. (2004) emphasized the role of NNs in the retrieval of water constituents especially in Case 2 waters. In recent decades the application of neural network (NN) techniques for the estimation of selected water quality parameters from ocean-color has increased (Atkinson and Tatnall, 1997, Dzwonkowski and Yan, 2005, Keiner and Yan, 1998, Shahraiyni et al., 2009, Zhang et al., 2003). NN based algorithms are currently used as standard products for the estimation of chla, suspended particulate matter (SPM) and yellow substances by the European Space Agency (ESA) for Medium Resolution Imaging Spectrometer (MERIS) data (Doerffer and Schiller, 2007, Doerffer and Schiller, 2008).
The Galician rias are V-like embayments along the northwest part of the Iberian Peninsula formed by sunken river valleys flooded by the sea, whose ecosystems are strongly influenced by oceanic conditions on the adjacent continental shelf. Interest in developing an accurate estimation of chla in these rias is considerable, mainly because of the economic and social importance of the extensive culture of mussels (Rodríguez Rodríguez et al., 2011), and the frequent occurrence of harmful algal events (GEOHAB, 2005).
Although MERIS is an ocean color sensor with characteristics considered suitable for chla monitoring and detection of HABs in coastal areas (Doerffer et al., 1999), to our knowledge the studies using MERIS data in the Galician rias are limited to those of Torres-Palenzuela et al., 2005a, Torres-Palenzuela et al., 2005b, Spyrakos et al., 2010, González Vilas et al., 2011. The latter authors developed a chla algorithm based on NNs and classification techniques from MERIS full resolution data for rias Baixas coastal waters. Previous ocean color studies by satellite sensors (CZCS, SeaWiFS, MODIS) during active upwelling in the Iberian system (Bode et al., 2003, Joint et al., 2002, McClain et al., 1986, Oliveira et al., 2009a, Oliveira et al., 2009b, Peliz and Fiuza, 1999, Ribeiro et al., 2005) played an important role in the identification of chla patterns and study of harmful algal blooms and primary production but were restricted to the ocean shelf because of insufficient spatial resolution. Another problem that affected many of these previous satellite remote sensing studies in the area was the failure of the algorithms used to provide reliable chla data during upwelling favorable conditions especially in the areas closest to the coast.
In the present paper a set of neural network-based chla algorithms previously developed for the Galician rias waters (within the rias and for coastal waters on the continental shelf) are applied for the first time in a short series of MERIS (FR) images delivered during an upwelling cycle in order to obtain maps of chla. This study tests the potential of the algorithms to map the spatial extent of possible algal blooms caused by coastal upwelling. Also, the temporal and spatial distributions of the chla patterns, captured in the MERIS images using the local adapted algorithm, are discussed in relation to the meteorological and oceanographic conditions in the area. Finally, the performance of the neural network-based chla algorithm is compared to in situ measurements.
Section snippets
Description of the study area
The rias Baixas constitute the southern part of the Galician rias (Fig. 1). They are formed by four large coastal embayments, from north to south: Muros y Noya, Arousa, Pontevedra and Vigo, all oriented in a SW–NE direction, and characterized by strong tides. Surface area covers approximately 600 km2 and water depths range from 5 to 60 m. This study focuses on three rias (Arousa, Pontevedra and Vigo), each connected to the open sea through two entrances, to the north and south of the islands
In situ data
Sea-truthing mean values of HPLC chla, SPM, percentage of inorganic matter and Secchi disk depth for the two samplings are given in Table 1, which also summarizes information about the available MERIS imagery. Water temperatures near surface ranged from 16.90 to 19.54 °C and from 16.58 to 18.91 °C, respectively, for the two samplings. Temperature at 10 m depth during the first campaign was between 15.33 and 17.70 °C, whereas temperatures dropped to 13.50–14.47 °C at the sampling stations on July 22.
Summary and conclusions
Three different states of meteorological and oceanographic periods were identified in the area during the July of 2008. Surface currents and winds off the rias Baixas affected the distribution of chla in the rias Baixas. At the beginning of July (State 1) the variable and weak wind and the resulting northward surface currents limited the high chla concentrations to the rias so that only low chla values were found in the offshore area. Differences in the topography of the rias, effects of local
Acknowledgments
MERIS data were obtained through ESA/ENVISAT project AO-623. We are very grateful to A. Acuña and D. Perez Estevez for their helpful assistance during the field work. This work was partially funded by the European Commission's Marie Curie Actions (project 20501 ECOsystem approach to Sustainable Management of the Marine Environment and its living Resources [ECOSUMMER]) through a grant supported ES. Part of this work has been financed by European Commission's EUFAR/RIAWATER project and by the
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