MERIS observations of phytoplankton phenology in the Baltic Sea

Abstract

The historical data from the MEdium Resolution Imaging Spectrometer (MERIS) is an invaluable archive for studying global waters from inland lakes to open oceans. Although the MERIS sensor ceased to operate in April 2012, the data capacities are now re-established through the recently launched Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development of a consistent time series for investigating phytoplankton phenology features is crucial if the potential of MERIS and OLCI data is to be fully exploited for inland water monitoring. This study presents a time series of phytoplankton abundance and bloom spatial extent for the highly eutrophic inland water of the Baltic Sea using the 10-year MERIS archive (2002-2011) and a chlorophyll-a based Summed Positive Peaks (SPP) algorithm. A gradient approach in conjunction with the histogram analysis was used to determine a global threshold from the entire collection of SPP images for identifying phytoplankton blooms. This allows spatio-temporal dynamics of daily bloom coverage, timing, phytoplankton abundance and spatial extent to be investigated for each Baltic basin. Furthermore, a number of meteorological and hydrological variables, including spring excess phosphate, summer sea surface temperature and photosynthetically active radiation, were explored using boosted regression trees and generalised additive models to understand the ecological response of phytoplankton assemblages to environmental perturbations and potential predictor variables of summer blooms. The results indicate that the surface layer excess phosphate available in February and March had paramount importance over all other variables considered in governing summer bloom abundance in the major Baltic basins. This finding allows new insights into the development of early warning systems for summer phytoplankton blooms in the Baltic Sea and elsewhere.

Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals

“…The time series of total coverage of surface and subsurface blooms in summer is presented in Figure 10 for the whole basin. Overall, the summer bloom coverage increased from 1998 to 2005, decreased from 2005 to 2012, and then increased from 2012 to date, showing oscillations without a consistent decadal trend, as observed from 1979 to date [23,86,88] At the sub-basin scale (Figure 12 and Figure S1), several summer bloom events (e.g., in 2002, 2003, and 2005) occurred in the central regions (WGB, EGB, NBP), as also reported in [21,85]. In these central basins, the surface and subsurface summer blooms were of similar extents and the areas where both thresholds were exceeded ranged on average 10-20% of the total coverage.…”

“…These lower estimates may also depend on the use of only SeaWiFS in 1998-2001, while two or three satellite sensors were available for the multi-sensor products merging from 2002 onwards, as a higher number of sensors could imply that a larger fraction of the basin with bloom events is captured. As observed in previous satellite-based studies (e.g., [21,85,86]), the bloom spatiotemporal coverage varies across the sub-basins in the various years. Figure 8 presents as examples the bloom coverage for 2007, 2008, 2015, and 2018, with 2008 and 2018 showing the typical spring bloom spatial distribution and its time persistence.…”

“…Seasonal changes in phytoplankton composition and the occurrence of extensive phytoplankton surface blooms in this basin depend on various drivers, including nutrient inputs from natural and human sources, salinity, ice coverage, and temperature stratification in the water column [16,19]. The dominant phytoplankton group of spring blooms is usually associated with diatoms and then dinoflagellates [19][20][21], while the summer months are characterized by extensive surface or near-surface blooms dominated by nitrogen-fixing cyanobacteria, particularly in July and August [20,22,23]. The Chl-a retrieval in the Baltic Sea is hampered by two complementary difficulties [24][25][26][27].…”

Using Remote Sensing to Understand the Total Suspended Matter Dynamics in Lakes Across Inner Mongolia

“…Meanwhile, some satellite missions were designed for land applications, they also sparked a new interest from the water quality remote sensing community for high resolution imagery [12], [13], especially the Landsat satellite series [14], [15]. With much higher spatial and temporal coverages than the conventional sampling methods, these remote sensing sensors combined with corresponding algorithms have been applied for tracking several environmental processes [16], and for estimating some water quality parameters including water clarity and TSM [17]- [19]. From the long-term satellite data, the expected behavior and the natural variability of the investigated signal can be identified at pixel level [20].…”

“…Remote sensing retrieval of inland water quality is based on the assumption that the relationship between the reflectance and the concentration of water quality constituents is known a priori [21]. The application of satellite remote sensing for water quality assessment and monitoring requires accurate water reflectance received from the water in order to retrieve reliable water quality parameters [17]. However, signals reaching a sensor above water are often weakened by the undesired atmospheric effects caused by absorption and scattering [22].…”