Adam Krężel

Empirical Model for Phycocyanin Concentration Estimation as an Indicator of Cyanobacterial Bloom in the Optically Complex Coastal Waters of the Baltic Sea

Commonly used parameters to assess cyanobacteria blooms are chlorophyll a concentration and cyanobacterial cell counts. Chlorophyll a is contained in all phytoplankton groups and therefore it is not a good estimator when only detection of cyanobacteria is desired. Moreover, laboratory determination of cyanobacterial cell counts is difficult and it requires a well-trained specialist. Instead of that, cyanobacterial blooms can be assessed using phycocyanin, a marker pigment for cyanobacteria, which shows a strong correlation with the biomass of cyanobacteria. The objective of this research is to develop a simple, remote sensing reflectance-based spectral band ratio model for the estimation of phycocyanin concentration, optimized for the waters of the Baltic Sea. The study was performed using hyperspectral remote sensing reflectance data and reference pigment concentration obtained in the optically complex coastal waters of the Baltic Sea, where cyanobacteria bloom occur regularly every summer, often causing severe damages. The presented two-band model shows good estimation results, with root-mean-square error (RMSE) 0.26 and determination coefficient (R2) 0.73. Moreover, no correlation with chlorophyll a concentration is observed, which makes it accurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing phytoplankton groups as well as for the waters with high colored dissolved organic matter (CDOM) concentration. The developed model was also adapted to spectral bands of the recently launched Sentinel-3 Ocean and Land Color Imager (OLCI) radiometer, and the estimation accuracy was comparable (RMSE = 0.28 and R2 = 0.69). The presented model allows frequent, large-scale monitoring of cyanobacteria biomass and it can be an effective tool for the monitoring and management of coastal regions.

Comparison of satellite chlorophyll a algorithms for the Baltic Sea

Abstract Chlorophyll a (chl- a ) products calculated using medium resolution imaging spectrometer (MERIS) data were tested. The satellite products were compared to chl- a concentrations measured in surface waters between 2003 and 2011 throughout the Baltic Sea. Image processing was performed with two neural-network-based MERIS data processors: the Case-2 Water Properties processor developed at the Freie Universität Berlin (FUB) and the Case-2 Regional processor of the German Institute for Coastal Research (C2R). Additionally, two algorithms for deriving chl- a concentrations from atmospherically corrected reflectances originally designed for Moderate Resolution Imaging Spectroradiometer and Sea-viewing Wide Field-of-view Sensor radiometers and adapted to Baltic Sea conditions were tested (algorithms denoted further as MD and SW respectively). The effectiveness of the Improved Contrast between Ocean and Land (ICOL) processor was also verified. Our results showed that the accuracy of chl- a concentration retrieval from satellite data varies depending on the location of the area. The difference in the statistical error between results from optically different coastal and open sea waters was as high as 200%. The most accurate results for the coastal zone were noted for the standard chl- a FUB processor product, while in open sea waters the highest accuracy was noted for the MD and SW algorithms with reflectance derived from the FUB processor.

Automatic Detection of Cloud Cover over the Baltic Sea

AbstractA simple detection method was proposed to increase the efficiency of automatic classification of a satellite image cell (clear/cloudy). A method is described on the basis of the Advanced Very High Resolution Radiometer (AVHRR) data, with a focus on highly active and highly reflecting targets (i.e., the Baltic Sea). Radiation input conditions and the dynamic threshold were used to reduce geometric influences of any daily scene supplied by the NOAA-17 and NOAA-18 platforms. They were carried out from statistic and histogram sequences of albedo and temperature maps. The presented approach is intended to primarily serve the activation of a nonsupervised system for oceanographic analyses (mainly SST), based on an accurate cloud mask.

Practical Applicability and Preliminary Results of the Baltic Environmental Satellite Remote Sensing System (Satbałtyk)

Abstract The SatBałtyk (Satellite Monitoring of the Baltic Sea Environment) project is being realized in Poland by the SatBałtyk Scientific Consortium, specifically appointed for this purpose, which associates four scientific institutions: the Institute of Oceanology PAN in Sopot - coordinator of the project, the University of Gdańsk (Institute of Oceanography), the Pomeranian Academy in Słupsk (Institute of Physics) and the University of Szczecin (Institute of Marine Sciences). The project is aiming to prepare a technical infrastructure and set in motion operational procedures for the satellite monitoring of the Baltic Sea ecosystem. The main sources of input data for this system will be the results of systematic observations by metrological and environmental satellites such as TIROS N/NOAA, MSG (currently Meteosat 10), EOS/AQUA and Sentinel -1, 2, 3 (in the future). The system will deliver on a routine basis the variety of structural and functional properties of this sea, based on data provided by relevant satellites and supported by hydro-biological models. Among them: the solar radiation influx to the sea’s waters in various spectral intervals, energy balances of the short- and long-wave radiation at the Baltic Sea surface and in the upper layers of the atmosphere over the Baltic, sea surface temperature distribution, dynamic states of the water surface, concentrations of chlorophyll a and other phytoplankton pigments in the Baltic waters, spatial distributions of algal blooms, the occurrence of coastal upwelling events, and the characteristics of primary production of organic matter and photosynthetically released oxygen in the water and many others. The structure of the system and preliminary results will be presented.

Daily Radiation Budget of the Baltic Sea Surface from Satellite Data

Abstract Recently developed system for assessment of radiation budget for the Baltic Sea has been presented and verified. The system utilizes data from various sources: satellite, model and in situ measurements. It has been developed within the SatBałtyk project (Satellite Monitoring of the Baltic Sea Environment - www.satbaltyk.eu) where the energy radiation budget is one of the key element. The SatBałtyk system generates daily maps of the all components of radiation budget on every day basis. We show the scheme of making daily maps, applied algorithms and empirical data collection within the system. An empirical verification of the system has been carried out based on empirical data collected on the oil rig placed on the Baltic Sea. This verification concerned all the components of the surface radiation budget. The average daily NET products are estimated with statistical error ca. 13 Wm-2. The biggest absolute statistical error is for LWd component and equals 14 Wm-2. The relative error in relation to the average annual values for whole Baltic is the biggest for SWu and reaches 25%. All estimated components have correlation coefficient above 0.91.

Use of satellite data in monitoring of hydrophysical parameters of the baltic sea environment

Abstract Intensive development of infrastructure for fast processing of outsized amount of space-borne data enables now to use the satellite data for operational controlling the state of its environment. In our presentation we show some examples of analysis of processes in marine environment which are possible due to satellite data and algorithms of its processing developed in SatBaltic Project. It concerns supporting of modelling of solar energy inflow to the sea with space-borne input data, identification and analysis of sea ice cover, supporting of oil spill detection, and identification of phenomena which modify spatial distribution of the sea surface temperature.