Heroimage Institut Fuer Dynamik Der Kuestenmeere Quelle

Optical characteristics of coastal and oceanic waters

KOF Startseite neu

The German Bight seen from satellite Sentinel-2 -image: Hereon-

Optical remote sensing data from satellites and airborne platforms provide an excellent spatial overview of processes in the global ocean as well as in coastal environments. These data reveal a strong coupling of physical and biogeochemical processes on many different length scales up to the regional and global scale. These are, for example, the functional types, distribution, and productivity of phytoplankton, the transport of suspended material in the coastal ocean, or submesoscale fronts with their significant influence on the food chain.
Small-scale processes play an important role in many oceanic and coastal processes. A significant development is therefore the recent, extensive improvement in the spatial and spectral resolution of satellite remote sensing data, which now offer insights into coastal environments on a much larger scale and with much finer spatial and temporal resolution.

Image Sentinel-2 Satellite

Sentinel-2 is a is a polar-orbiting, multispectral high-resolution imaging satellite for land monitoring to provide, for example, imagery of vegetation, soil and water cover, inland waterways and coastal areas. -image: ESA-

The European Space Agency (ESA) and EU-Copernicus provide with the Sentinel program an extended (>25 years) satellite remote sensing framework that includes several optical sensors for coastal waters. Recently, three of these satellites (Sentinel-2A, Sentinel-3A, Sentinel-2B) were launched. In particular, Sentinel-3 allows coastal water observations much more frequently now, while Sentinel-2s provides a very high spatial resolution of 10-60 m. At the same time, they complement the NASA satellites MODIS-Aqua and VIIRS-Suomi-NPP with their optical sensors.

Image Ocean Colours

Different colors of the ocean: There is very cloudy and strongly scattering water in estuaries, deep blue Atlantic water, North Sea water with a lot of phytoplankton and cases in which a "color coding" is hardly possible. -image: Hereon/Martin Hieronymi-

To interpret such remote sensing data in the optically very complex coastal waters, the optical-physical properties of natural waters and the single constituents (from pure water itself to phytoplankton, and dissolved and particulate organic matter) must be known with the highest possible accuracy. While interpretation involves optical and radiative transfer modeling, the accuracy of the model results is directly influenced by the accuracy of the optical parameters used as model input.
In order to interpret remote sensing data based on accurate in situ data, Hereon developed regional algorithms for the North and Baltic Seas.

Hereon provides the Baltic Sea algorithm to the Copernicus Marine Environment Monitoring System (CMEMS). The Hereon coastal water algorithms originally developed for the instrument MERIS on ENVISAT were adapted to the instrument OLCI on Sentinel-3 to be used by ESA.
In addition, a new approach was developed that allows the use of remote sensing data in clear, coastal and optically extreme waters (such as highly absorbing and turbid lakes) with only one processor: the OLCI Neural Network Swarm processor (ONNS). Hereon is also taking part in preparing the first hyperspectral satellite mission (Environmental Mapping AND Analysis Program/ ENMAP, Germany) and has investigated hyperspectral optical data over water surfaces (ocean, coastal waters, and lakes) with respect to the differentiation of phytoplankton and suspended matter types.

Image IR Kamera Zeppelin

Zeppelin NT, motorglider Stemme, and the IR camera mounted on the zeppelin. -image: Hereon & Janine Martin-

For observations with even higher resolution, a HySpex hyperspectral camera with 1024 spectral bands and a horizontal resolution of approximately 0.5 m was mounted on a zeppelin, alongside a cooled infrared camera during the Expedition Clockwork Ocean. The infrared camera can also be flown from a Stemme motorized glider aircraft and delivers unprecedented resolution of submesoscale features in the coastal ocean and their temporal evolution over the course of several hours. The observed processes in the Baltic Sea showed a very strong coupling of physical processes (fronts, eddies, and internal waves) with the distribution of cyanobacteria. The interpretation of airborne and satellite optical remote sensing data, however, demands accurate optical property information for all natural water body constituents. Uncertainties in determining these optical properties, such as absorption and scattering coefficients of particles as well as the water itself, are rather large and limit remote sensing approaches – especially in coastal waters.

Hieronymi, M., Müller, D., Doerffer, R: The OLCI Neural Network Swarm (ONNS): A Bio-geo-optical Algorithm for Open Ocean and Coastal Waters. Front. Mar. Sci. 4, 140, 2017. doi:10.3389/fmars.2017.00140

Tan, H., Doerffer, R., Oishi, T., Tanaka, A.: A new approach to measure the volume scattering function. Optics Express, 21: 18697-18711, 2013. doi:10.1364/OE.21.018697.

Tan, H., Oishi, T., Tanaka, A., Doerffer, R.: Accurate estimation of the backscattering coefficient by light scattering at two backward angles, Appl. Opt. 54(25), 7718–7733, 2015, doi:10.1364/AO.54.007718.

Röttgers, R., Heymann, K., Krasemann, H.: Suspended matter concentrations in coastal waters: methodological improvements to quantify individual measurement uncertainty. Estuarine, Coastal and Shelf Science 151, 148-155, 2014, doi:10.1016/j.ecss.2014.10.010.

Röttgers, R., Dupouy, C., Taylor, B.B., Bracher, A., Wozniak, S.B.: Mass-specific light absorption coefficients of natural aquatic particles in the near-infrared spectral region. Limnol. Oceanogr., 59(5), 1449-1460, 2014, doi:10.4319/lo.2014.59.5.1449.


For ground-truthing of remote sensing data with improved precision and accuracy, and to improve bio-optical models, several approaches were taken:

Instrument development

Image PSICAM im Labor.jpg

PSICAM setup in laboratory. -image: Hereon-

A Point-Source Integrating-Cavity Absorption Meter (PSICAM), its flow-through counterpart (FT-PSICAM) , the Quantitative Filter Technique Integrating-Cavity Absorption Meter (QFT-ICAM), and an Imaging Volume Scattering Function Meter (I-VSFM) were developed at Hereon.
All of these instruments provide optical data of high quality and accuracy and are unique developments. Due to its very high sensitivity, the PSICAM in particular is used to measure specific properties of pure water that could not be determined before, contributing to Hereon’s international recognition for high-quality optical data.

Wollschläger, J.; D. Voß; O. Zielinski; W. Petersen, In situ Observations of Biological and Environmental Parameters by Means of Optics—Development of Next-Generation Ocean Sensors With Special Focus on an Integrating Cavity Approach, IEEE Journal of Oceanic Engineering, 41, 1-10, 2016, doi:10.1109/JOE.2016.2557466

Röttgers, R., Doxaran, D., Dupouy, C.: Quantitative filter technique measurements of spectral light absorption by aquatic particles using a portable integrating cavity absorption meter (QFT-ICAM). Optics Express 24(2), A1-A20, 2016, doi:10.1364/OE.24.0000A1.

Improvement of water absorption coefficient

Image Lena Delta.png

Study area and location of sampling stations in the Lena Delta. -image: Örek, H., Doerffer, R., Röttgers, R., Boersma, M., and Wiltshire, K. H.: Contribution to a bio-optical model for remote sensing of Lena River water, Biogeosciences, 10, 7081-7094, https://doi.org/10.5194/bg-10-7081-2013, 2013. CC BY 3.0-

The temperature and salinity dependences of the liquid pure water absorption coefficient are a fundamental optical property. They are difficult to measure, but are required to determine chlorophyll- and mass-specific optical properties for modeling light transfer in coastal waters, to develop optical remote sensing algorithms and to interpret remote sensing data.
Seven ship campaigns and five field campaigns were carried out to collect the most accurate optical properties from a range of different environments, such as the German Bight or the Lena Delta. Using this new method and the aforementioned new instrumentation, mass-specific optical properties of the North Sea and Baltic Sea were determined that are internationally among the most accurate optical data of coastal waters.

Röttgers, R., McKee, D., Utschig, C.: Temperature and salinity correction coefficients for light absorption by water in the visible to infrared spectral region. Optics Express, 22(21), 25093-25108 , 2014b, doi:10.1364/OE.22.025093.

Röttgers, R., Dupouy, C., Taylor, B.B., Bracher, A., Wozniak, S.B.: Mass-specific light absorption coefficients of natural aquatic particles in the near-infrared spectral region. Limnol. Oceanogr., 59(5), 1449-1460, 2014c, doi:10.4319/lo.2014.59.5.1449.

Örek, H., Doerffer, R., Röttgers, R., Boersma, M., Wiltshire, K.H: Contribution to a bio-optical model for remote sensing of Lena River water. Biogeosciences, 10, 7081-7094, 2013. doi:10.5194/bg-10-7081-2013.

Optical differentiation of functional or taxonomic groups of phytoplankton

image phytoplankton absorption spectras.png

Examples of (a) normalized absorption spectra for different phytoplankton groups with (b) corresponding fourth-derivative spectra. -image: Xi, H.; Hieronymi, M.; Röttgers, R.; Krasemann, H.; Qiu, Z. Hyperspectral Differentiation of Phytoplankton Taxonomic Groups: A Comparison between Using Remote Sensing Reflectance and Absorption Spectra. Remote Sens. 2015, 7, 14781-14805. CC BY 4.0-

Satellite or aerial information of phytoplankton group distributions over large areas will provide valuable information about the formation processes and distribution of phytoplankton blooms as well as the identification of harmful algal blooms.
This differentiation from remote sensing data and in situ methods is possible due to differences in pigment composition of various algal groups and the absorption characteristics of these pigments. For this differentiation, Hereon’s unique, large database of light absorption spectra for different phytoplanktonic algae from culture work is used. The main outcomes are that the differentiation of specific groups is possible when directly based on reflectance.
Other substances, such as suspended matter and gelbstoff, do not interfere with the results as long as they are not optically dominant. Inversion approaches for retrieve algal absorption from remote sensing reflectance are not accurate enough to use absorption-based approaches for the group differentiation.
This work is in preparation for the German ENMAP mission, the first full hyperspectral optical satellite mission.

Bracher, A., Bouman, H., Brewin, R.J., Bricaud, A., Brotas, V., Ciotti, A.M., Clementson, L., Devred, E., Di Cicco, A., Dutkiewicz, S., Hardman-Mountford, N., Hickman, A.E., Hieronymi, M., Hirata, T., Losa, S.N., Mouw, C., Organelli, E., Raitsos, D.E., Uitz, J., Vogt, M., Wolanin, A.: Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development, Front. Mar. Sci. 4(55), 2017. doi:10.3389/fmars.2017.00055.

Xi, H., Hieronymi, M., Röttgers, R., Krasemann, H., Qiu, Z.: Hyperspectral Differentiation of Phytoplankton Taxonomic Groups: A Comparison between Using Remote Sensing Reflectance and Absorption Spectra, Remote Sens. 7 (11), 14781-14805, 2015, doi:10.3390/rs71114781.

Xi, H., Hieronymi, M., Krasemann, H., Röttgers, R.: Phytoplankton Group Identification Using Simulated and in situ Hyperspectral Remote Sensing Reflectance. Front. Mar. Sci. 4, 272, 2017, doi:10.3389/fmars.2017.00272.