Monitoring blooms and surface accumulation of cyanobacteria in the Curonian Lagoon by combining MERIS and ASAR data Mariano Bresciani a,b, ⁎, Maria Adamo c , Giacomo De Carolis a , Erica Matta a , Guido Pasquariello c , Diana Vaičiūtė d , Claudia Giardino a a Optical Remote Sensing Group, CNR-IREA, Via Bassini 15, 20133 Milano, Italy b University of Parma, Environmental Science Department, Viale Usberti 11/A, 43100 Parma, Italy c Institute of Intelligent Automatic Systems, CNR-ISSIA, Via Amendola 122/D-O, 70126 Bari, Italy d Coastal Research and Planning Institute, Klaipėda University, Herkaus Manto str. 84, LT-92294 Klaipėda, Lithuania abstract article info Article history: Received 29 September 2012 Received in revised form 12 July 2013 Accepted 24 July 2013 Available online 5 October 2013 Keywords: MERIS ASAR Cyanobacteria Scum Chlorophyll-a Water quality Multi-sensor integration This study shows how different remote sensing techniques can be used to distinguish between surface accumulations (scum) and dense blooms of cyanobacteria in the Curonian Lagoon, the largest lagoon in Europe. Cyanobacteria blooms are a major concern in this region due to water quality issues interfering with the conservation of the whole ecosystem. Chlorophyll-a (Chl-a) concentrations can be extremely high (up to about 500 mg/m 3 in some cases) during cyanobacteria blooms, which are often associated with a surface accu- mulation of algae. The Medium Resolution Imaging Spectrometer (MERIS) was used to acquire 52 images cover- ing the summers of 2004 to 2011. These images were analyzed to map Chl-a concentrations and the presence of scum using two different band ratio algorithms applied to atmospherically-corrected data. The results identify wind speed as the main driving factor in the surface accumulation of algae, as well as in the spatial distribution of Chl-a. The utility of microwave images was also assessed, as since any cloud cover obviously hampers the use of optical data. Advanced Synthetic Aperture Radar (ASAR) images were collected synchronously with the MERIS data and the normalized radar cross-section (NRCS) signal was corrected for the contribution of wind for the purposes of correlating the results with the MERIS-derived Chl-a concentrations. In general, there was a stepwise decrease in the NRCS for high values of Chl-a (N 50 mg/m 3 ) with wind speeds in the range of 2 to 6 m/s. Under these conditions, our results demonstrate that optical and microwave signals can be used in com- bination to improve our understanding of cyanobacteria blooming. © 2013 Elsevier Inc. All rights reserved. 1. Introduction Algal blooming is one of the factors with the greatest impact on the quality and accessibility of bodies of water in aquatic ecosystems, and on the preservation of existing ecological balances (Wetzel, 1983). Some bloom phenomena can be particularly dangerous for life forms (humans, flora and fauna) because they produce toxins that negatively affect water quality and threaten its consumers (Backer et al., 2008; Codd et al., 2005; Wasmund, Nausch, & Voss, 2012). Cyanobacteria are the most characteristic taxonomic group of potentially toxic blooms oc- curring in freshwater ecosystems (Falconer, 2001; Johnk et al., 2008). The intensity of cyanobacteria blooming can be influenced by various factors, including the biological and adaptive characteristics of this plankton group (Herrero & Flores, 2008), the stoichiometric ratio of the principal nutrients (N, P, and Si) in the water and sediment (Fulweiler & Nixon, 2012), climate change (Paerl & Huisman, 2009), and the morphological conditions of the ecosystems considered (Paerl, 1996). Cyanobacteria blooming is characterized by very complex tem- poral dynamics, because this algal group is capable of rapid vertical mi- gration within the water column and very fast replication (Walsby, 1994). To plan possible measures for managing and protecting natural ecosystems, it is important to obtain a significant amount of information on cyanobacteria blooms in time and space, especially when they are particularly extensive, and when they appear and disappear quickly (Agha, Cirés, Wörmer, Domínguez, & Quesada, 2012; Sellner, Doucette, & Kirkpatrick, 2003). It has been demonstrated, however, that the spa- tial and temporal frequencies of conventional water-sampling methods are unable to meet the requirements in terms of costs (Liu, Islam, & Gao, 2003; Nausch, Nehring, & Nagel, 2008). They may also not suffice to reveal changes in phytoplankton biomass, especially in blooming condi- tions (Kutser, 2004; Rantajärvi, Olsonen, Hällfors, Leppänen, & Raateoja, 1998); for instance, layers of surface scum can be altered by the ship's passage so samples collected in situ may not be representative of the event. A good alternative, increasingly used in recent decades as a source of data for integrating field measurements, is remote sensing: once validated and processed, satellite images can provide helpful Remote Sensing of Environment 146 (2014) 124–135 ⁎ Corresponding author at: Optical Remote Sensing Group, CNR-IREA, Via Bassini 15, 20133 Milano, Italy. Tel.: +39 0223699298; fax: +39 0223699300. E-mail address: bresciani.m@irea.cnr.it (M. Bresciani). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.07.040 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse