Remote Sensing of Environment 235 (2019) 111484 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse Simple methods for satellite identification of algal blooms and species using 10-year time series data from the East China Sea T ∗ Fang Shena,b, , Rugang Tanga, Xuerong Suna, Dongyan Liua a State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China b Institute of Eco-Chongming (IEC), 3663 Zhongshan N. Road, Shanghai, 200062, China ARTICLE INFO ABSTRACT Keywords: Long-term ocean color satellite missions have the ability to help monitor algal blooms. However, satellite Algal blooms identification of algal blooms and species in turbid coastal waters has been challenging. There is an urgent need Phytoplankton species for simple and effective methods to identify locations, areas, durations, and species present in algal blooms Diatom through satellite observation, to aid in the operational and emergency monitoring of the marine environment. In Dinoflagellate this study, based on a three-band blended model, we propose an indicator (Red tide Detection Index, RDI) that is MERIS suitable for the purpose of detecting algal blooms in turbid coastal waters using multi-source ocean color data. MODIS GOCI MERIS, MODIS, and GOCI data used for the detection of algal blooms demonstrate consistent results using the RDI indicator, and these results correlate with in situ investigations. Furthermore, based on a green-red spectral slope, we propose a method that uses MERIS data to identify dominant species of diatoms and dinoflagellates in algal blooms in the East China Sea (ECS). The 10-year time series MERIS data collected between 2003 and 2012 indicates that algal bloom occurrences are mainly distributed in the nearshore areas of the ECS, and possess a distinct climatological cycle. The 10-year time series MERIS data help discriminate diatom and dinoflagellate blooms in the ECS, and show the dissimilarity in the seasonal timing of their life cycles. We find that these two dominant species in algal blooms are usually distributed in different spatial locations in the ECS. Additionally, by defining a divergence index (DI), the limitations of spectral resolutions of satellite data used for algal bloom species differentiation are quantitatively assessed. 1. Introduction distribution, and dominant species present. Traditional methods have not been able to successfully monitor algal blooms, because algal Algal blooms can cause widespread ecological disasters across the blooms appear suddenly and algae can accumulate rapidly over a short world. The occurrence of algal blooms may lead to coastal hypoxia and period of time. The use of remote sensing stands out from other ap- toxin accumulation, water quality degradation, as well as, fishery and proaches since it can provide a synoptic view and continually monitor aquaculture activities collapses (Hallegraeff et al., 2003). The toxic of algal blooms every day or every hour at a large spatial scale. species present in algal blooms pose a threat to human health via the The detection of algal blooms through remote sensing has become aquatic food chain. The Marine Environment Quality Bulletin of the an important topic since ocean color satellite missions were launched at State Ocean Administration (SOA) reports that algal blooms occur al- the end of last century (IOCCG, 2014). There are several types of ap- most every year between April and October in the East China Sea. In the proaches to detect algal blooms using ocean color satellite imagery, bulletins of the SOA, algal blooms in the East China Sea are reported to such as chlorophyll a concentration (Chla) based approach (e.g., He be mainly dominated by the diatom species, Skeletonema costatum, and et al., 2013), Chla anomalies based approach (e.g., Stumpf et al., 2003), the dinoflagellate species, Prorocentrum donghaiense. Since 2005, toxic reflectance-blended indices based approach (e.g., Gower et al., 2005), species, such as the dinoflagellate, Karenia mikimotoi, have also been and absorption-trait based approach (e.g., Sathyendranath et al., 2004). occasionally observed. The Chla based approach for algal bloom detection is somewhat Due to the complexity of the formation process and the triggering challenging. Although higher Chla indicates a potential augmentation mechanisms of algal blooms, it is difficult to predict and prepare for in algal cells, Chla is not a counterpart for algal cell abundance due to their occurrence, in terms of time, location, evolution duration, the phytoplankton diversity, in terms of cell size and species. Moreover, ∗ Corresponding author. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China. E-mail address: [email protected] (F. Shen). https://doi.org/10.1016/j.rse.2019.111484 Received 3 February 2019; Received in revised form 15 October 2019; Accepted 17 October 2019 0034-4257/ © 2019 Published by Elsevier Inc. F. Shen, et al. Remote Sensing of Environment 235 (2019) 111484 in coastal waters, satellite estimation of Chla in high accuracy still re- documentation about a method that is capable of applying multi-sensor mains difficult (Hu, 2009). Satellite outputs regarding Chla may often satellite data and carrying out the cross-verification of multiple satellite be overestimated in turbid coastal waters, e.g., in coastal waters of the inversion results. The limitations are also attributed to the existing East China Sea (He et al., 2013; Shen et al., 2010a; Shi and Wang, differences in the spectral resolution and characteristic waveband po- 2012). Thus, there is a high risk of false identification when using Chla sitions of ocean color sensors, depending on the specific targets of the to identify algal blooms in the region (He et al., 2013). initial design and the possible considerations of technical costs and The anomaly of Chla, e.g., which was observed when a single Chla tradeoffs. value was compared to the 60-day running mean value, was success- In this case, developing a method for multi-source satellite data can fully used for routine detections of Karenia brevis blooms in the Gulf of increase the spatial and temporal coverage of ocean observations, and Mexico (Stumpf et al., 2003; Tomlinson et al., 2004). However, the fill the temporal gap that may exist between on-orbit and off-orbit anomaly cannot be easily detected if algal bloom species have low observations in satellite time series data. It can also provide a greater cellular chlorophyll a content (Siswanto et al., 2013), or when the time chance for the cross-verification and validation between ground truth series satellite data for the monthly averages are less representative, data and satellite observations. More importantly, it can provide op- due to the limited available imagery affected by the cloud coverage in portunities for the development of further studies on phytoplankton the coastal area. phenology and their response to climate change. There are several kinds of reflectance-based indices for algal bloom The purpose of this study is to: (1) develop an approach for de- detection. A red tide index (RI) was proposed by Ahn and Shanmugam tecting algal blooms in turbid coastal waters without the prerequisite of (2006) for the detection of algal blooms in turbid coastal waters by highly accurate atmospheric correction data and complicated proces- combining the normalized water-leaving radiance of the SeaWiFS bands sing procedures, aiming at multi-source satellite data applications (e.g., at 510, 555, and 443 nm. Its function was also dependent on a pre- MERIS, MODIS, GOCI, or their successors); (2) explore a method for requisite of high accuracy in atmospheric correction (Siswanto et al., discriminating dominant species present in algal blooms (e.g., domi- 2013), which still remains a challenging task in optically complex and nant diatom and dinoflagellate species) and (3) analyze and discuss the turbid coastal waters. Moreover, utilizing the blue-green spectral range limitations of spectral resolutions and their impact on the discrimina- for the RI estimation may also be impacted by absorptions due to co- tion of algal bloom species. lored dissolved organic matter (CDOM) and suspended particulate matter (SPM). The phytoplankton fluorescence line height (FLH) index 2. Data and the maximum chlorophyll index (MCI) have potential to detect algal blooms in coastal waters (Gower et al., 2005; Hu et al., 2005; 2.1. In situ data Ryan et al., 2009). However, their applications in turbid coastal waters may be hindered due to the presence of high scattering suspended solids The Changjiang (Yangtze) Estuary, the Zhejiang coast, and the in the waters (Hu et al., 2005; Shen et al., 2010a). nearshore of the South Yellow Sea (Fig. 1) are areas with frequent Furthermore, efforts to enhance the ability of satellites that can outbreaks of algal blooms. They are mainly affected by the Changjiang discriminate species of algal blooms have been made in oceanography riverine plumes and the sea front in the East China Sea (Wang et al., and ocean color remote sensing communities (IOCCG, 2014), although 2019), where the waters are turbid, eutrophic, and optically complex historic and current satellite ocean color sensors have limitations in (Lei, 2011). terms of the spectral resolution and characteristic waveband positions We carried out cruise campaigns in the Changjiang (Yangtze) used for differentiating various species of algal blooms (e.g., Blondeau- Estuary and its adjacent coast (YEC) in August 2013, and in the East Patissier et al., 2014; Mouw et al., 2015; and references therein). For China Sea (ECS) in June and July 2014. Algal bloom occurrences at a instance, a spectrally-resolved reflectance model that can be applied to total of 17 stations (13 stations in the YEC in August 2013, and 4 sta- the SeaWiFS data for the discrimination of diatom and non-diatom tions in the ECS in June 2014) were observed (Fig.