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Testing the performance of empirical remote sensing algorithms in the Baltic Sea waters with modelled and in situ reflectance data

Ligi M., Kutser T., Kallio K., Attila J., Koponen S., Paavel B., Soomets T., Reinart A.

Oceanologia

2017

No. 59 (1)

57--68

Remote sensing studies published up to now show that the performance of empirical (band-ratio type) algorithms in different parts of the Baltic Sea is highly variable. Best performing algorithms are different in the different regions of the Baltic Sea. Moreover, there is indication that the algorithms have to be seasonal as the optical properties of phytoplankton assemblages dominating in spring and summer are different. We modelled 15,600 reflectance spectra using HydroLight radiative transfer model to test 58 previously published empirical algorithms. 7200 of the spectra were modelled using specific inherent optical properties (SIOPs) of the open parts of the Baltic Sea in summer and 8400 with SIOPs of spring season. Concentration range of chlorophyll-a, coloured dissolved organic matter (CDOM) and suspended matter used in the model simulations were based on the actually measured values available in literature. For each optically active constituent we added one concentration below actually measured minimum and one concentration above the actually measured maximum value in order to test the performance of the algorithms in wider range. 77 in situ reflectance spectra from rocky (Sweden) and sandy (Estonia, Latvia) coastal areas were used to evaluate the performance of the algorithms also in coastal waters. Seasonal differences in the algorithm performance were confirmed but we found also algorithms that can be used in both spring and summer conditions. The algorithms that use bands available on OLCI, launched in February 2016, are highlighted as this sensor will be available for Baltic Sea monitoring for coming decades.

Detecting cyanobacterial blooms in large North European lakes using the Maximum Chlorophyll Index

Alikas K., Kangro K., Reinart A.

Oceanologia

2010

No. 52 (2)

237-257

The Maximum Chlorophyll Index (MCI), developed for the MERIS sensor processing scheme, is used to investigate the seasonal dynamics, spatial distribution, and coverage of cyanobacterial blooms over Lake Peipsi (Estonia/Russia) and Lake Vo~rtsjärv (Estonia). In these optically complex waters, the amounts of suspended matter and dissolved organic matter vary greatly and independently of the phytoplankton biomass. We demonstrate that MCI is a useful, new tool for detecting and estimating cyanobacterial biomass (R2 = 0.73), phytoplankton biomass (R2 = 0.70) and chlorophyll a concentration (R2 = 0.64). The MCI-derived results are consistent with known patterns of phytoplankton dynamics in these lakes, whose optical properties are in the same range as in many coastal regions of the Baltic Sea.