This type should be primarily used, as the name suggests, in coastal areas. It contains a classification with a maximum of 16 classes and represents a wide range of coastal water conditions. These conditions are not unique to any particular region. The clusters are representations of averaged conditions governed by the optical properties of the water column. The last 8 classes are combined into one during the processing, therefore the result has only 9 different classes.
This type needs the following wavelengths: 410, 443, 490, 510, 555

This type is intended for the use with rivers and lakes, but it also contains in situ coastal data as input. The maximum number of clusters is 7. The clusters are not unique to any particular lake, region, or a result of differences between freshwater and marine waters. Marine and freshwater stations can be found in the same clusters. The OWTs show a pattern of increasing absorption in the blue/green for low red/NIR features (classes 1 through 3), followed by increasing peak magnitude at 555 nm (classes 4 through 7). OWTs 1 through 5 show increasing chlorophyll-a concentrations, while OWTs 6 and 7 have lower mean chlorophyll-a values.
This type needs the following wavelengths: 412, 443, 490, 510, 560, 620, 665, 680, 709, 754

This type is intended for the use with lakes and was developed in the frame of the EU-GLaSS project. The in situ reflectance used to train the fuzzy classifier come exclusively from lake waters, which marks the distance with the INLAND option. The water composition of the lakes has a higher mix and includes many CDOM samples from Finnish lakes. Only 5 clusters are considered as the optimal number of classes. The classes are sorted purely on remote sensing reflectance distribution and are representations of optical conditions governed by the total absorption and scattering properties (IOPs). The wavelength dependency gives clues for the interpretation: from chlorophyll_a absorbing dominant waters to higher turbid waters due to the increase in the sediment load.
This type needs the following wavelengths: 442.6, 489.9, 509.8, 559.7, 619.6, 664.6, 680.8, 708.3, 753.4

This type is intended for the use with lakes and was developed in the frame of the EU-GLaSS project. The in situ reflectance used to train the fuzzy classifier come exclusively from lake waters, which marks the distance with the INLAND option. The water composition of the lakes has a higher mix and includes many CDOM samples from Finnish lakes. Here 6 clusters are considered as the optimal number of classes. The classes are sorted purely on remote sensing reflectance distribution and are representations of optical conditions governed by the total absorption and scattering properties (IOPs). The wavelength dependency gives clues for the interpretation: from chlorophyll_a absorbing dominant waters to higher turbid waters due to the increase in the sediment load.
This type needs the following wavelengths: 442.6, 489.9, 509.8, 559.7, 619.6, 664.6, 680.8, 708.3, 753.4

This type is intended for the use with lakes and was developed in the frame of the EU-GLaSS project. Thein situ reflectance used to train the fuzzy classifier come exclusively from lake waters, which marks the distance with the INLAND option. The water composition of the lakes has a higher mix and includes many CDOM samples from Finnish lakes. Here 6 clusters are considered as the optimal number of classes. The classes are sorted purely on remote sensing reflectance distribution and are representations of optical conditions governed by the total absorption and scattering properties (IOPs). The wavelength dependency gives clues for the interpretation: from chlorophyll_a absorbing dominant waters to higher turbid waters due to the increase in the sediment load. The normalised classification shows different results than the 6C because the reflectance values were normalised integrating the area under the curve. This removes magnitude effects and focuses more on the spectral shape.
This type needs the following wavelengths: 442.6, 489.9, 509.8, 559.7, 619.6, 664.6, 680.8, 708.3, 753.4