52
oceans. Therefore, the model is mainly valid for case-1
waters:
a
A
a
ph
B
λ
λ
λ
( ) = ( )∗
− ( )
chl
1
where a ph refers to phytoplankton absorption, chl a is the
chlorophyll a concentrations, A and B wavelength dependent
parameters. Retrieving results from this bio-optical model,
the chlorophyll concentration is needed. Therefore, the
model is highly dependent on accurate measurement results
of chlorophyll, which can be complicated in case-2 waters
due to the possible presence of additional water constituents
such as CDOM.
Alongside the relations between optical properties and
biogeochemical constituents and the spectral expression of
IOPs, bio-optical models can also relate different IOPs to
each other. For instance, the degradation products of phytoplankton have similar optical properties to CDOM and the
amount increases with increasing chlorophyll a (e.g., Bricaud
et al. 2012). Thus, there is a natural correlation of both
absorption coefficients. Based on data of the North Sea
merged from Nechad et al. (2015), a bio-optical model relating CDOM and a ph 440 yields for 440 nm.
cdom
nm
nm
440
0 24
440
0 43
(
)=
∗ (
)
.
.
a ph
There are also bio-optical models related to scattering of
non-algal particles of phytoplankton. However, estimating or
measuring scattering coefficients is more difficult than
absorption coefficients due to a high dependence on the
viewing angle and the anisotropic behavior of the scattering
phase function constraining the direction of the scattered
light (Petzold 1972).
Pure water absorption and scattering coefficients have
been measured and analyzed in laboratory experiments and,
for instance, are provided by Pope and Fry (1997). Biooptical models are a highly important part in modeling of
water bodies and the simulations and prediction can differ
significantly due to the choice of the models. There are a
wide range of bio-optical models that can be found in literature due to the difficulty in measurement (e.g., measurement
technique and site selection) and various empirical and statistical relations and concepts.
Conclusions and Outlook
Ocean color is an index of ecosystem health. Changes in
ocean color indicate changes in its optical constituents that
contribute to ocean color. Regular monitoring of these
changes is important as it allows the health of ecosystems to
be kept in check. Advances in optical methodologies have
greatly improved our understanding of the oceanic environment. However, the ever increasing effects of anthropogenic
influence and climate change, repeated spatial and temporal
coverage is of utmost relevance. Satellite remote sensing,
therefore, plays an important role by providing opportunities
of global monitoring of the vast and dynamic oceanic ecosystem. Being recognized as an essential climate variable
(ECV) ocean color is monitored as part of the climate change
initiative (CCI) project of the European Space Agency (ESA)
in the global climate observing system (GCOS).
However, accurate interpretation of the remotely sensed
signal is challenging and requires good estimation of atmospheric corrections. Furthermore, the complexities are
amplified in the complex case-2 waters owing to the contribution of non-varying optical components like CDOM and
inorganic suspended sediments. Development of region specific algorithms therefore becomes necessary. Hence, in situ
observations still continue to play an important role in biooptical algorithm development and validation purposes.
Moreover, satellite observations of the surface ocean in combination with bio-optical algorithms (derived from in situ
autonomous profiling systems, e.g., buoys, floats) are being
incorporated into the development of 3D bio-optical ocean
models with potential applications in physics and biogeochemistry of the dynamic environment at a number of relevant scales.
For further reading we recommend ‘Ocean optics web
book’ (http://www.oceanopticsbook.info/) and the ‘IOCCG
Report Series’ (http://ioccg.org/what-we-do/ioccg-publications/ioccg-reports/).
Acknowledgments Authors are extremely thankful to the reviewer for
providing helpful comments, which greatly improved the chapter. The
chapter contains modified Copernicus Sentinel data (2017) and Ocean
Biology Processing Group (OBPG) data products and images.
Appendix
This article is related to the YOUMARES 8 conference session no. 7: “Ocean Optics and Ocean Color Remote Sensing”.
The original Call for Abstracts and the abstracts of the presentations within this session can be found in the appendix
“Conference Sessions and Abstracts”, chapter “3 Ocean
Optics and Ocean Color Remote Sensing”, of this book.
References
Aksnes DL, Nejstgaard J, Sædberg E et al (2004) Optical control of
fish and zooplankton populations. Limnol Oceanogr 49:233–238.
https://doi.org/10.4319/lo.2004.49.1.0233
Aksnes DL, Dupont N, Staby A et al (2009) Coastal water darkening
and implications for mesopelagic regime shifts in Norwegian fjords.
Mar Ecol Prog Ser 384:39–49
V. Mascarenhas and T. Keck
oceans. Therefore, the model is mainly valid for case-1
waters:
a
A
a
ph
B
λ
λ
λ
( ) = ( )∗
− ( )
chl
1
where a ph refers to phytoplankton absorption, chl a is the
chlorophyll a concentrations, A and B wavelength dependent
parameters. Retrieving results from this bio-optical model,
the chlorophyll concentration is needed. Therefore, the
model is highly dependent on accurate measurement results
of chlorophyll, which can be complicated in case-2 waters
due to the possible presence of additional water constituents
such as CDOM.
Alongside the relations between optical properties and
biogeochemical constituents and the spectral expression of
IOPs, bio-optical models can also relate different IOPs to
each other. For instance, the degradation products of phytoplankton have similar optical properties to CDOM and the
amount increases with increasing chlorophyll a (e.g., Bricaud
et al. 2012). Thus, there is a natural correlation of both
absorption coefficients. Based on data of the North Sea
merged from Nechad et al. (2015), a bio-optical model relating CDOM and a ph 440 yields for 440 nm.
cdom
nm
nm
440
0 24
440
0 43
(
)=
∗ (
)
.
.
a ph
There are also bio-optical models related to scattering of
non-algal particles of phytoplankton. However, estimating or
measuring scattering coefficients is more difficult than
absorption coefficients due to a high dependence on the
viewing angle and the anisotropic behavior of the scattering
phase function constraining the direction of the scattered
light (Petzold 1972).
Pure water absorption and scattering coefficients have
been measured and analyzed in laboratory experiments and,
for instance, are provided by Pope and Fry (1997). Biooptical models are a highly important part in modeling of
water bodies and the simulations and prediction can differ
significantly due to the choice of the models. There are a
wide range of bio-optical models that can be found in literature due to the difficulty in measurement (e.g., measurement
technique and site selection) and various empirical and statistical relations and concepts.
Conclusions and Outlook
Ocean color is an index of ecosystem health. Changes in
ocean color indicate changes in its optical constituents that
contribute to ocean color. Regular monitoring of these
changes is important as it allows the health of ecosystems to
be kept in check. Advances in optical methodologies have
greatly improved our understanding of the oceanic environment. However, the ever increasing effects of anthropogenic
influence and climate change, repeated spatial and temporal
coverage is of utmost relevance. Satellite remote sensing,
therefore, plays an important role by providing opportunities
of global monitoring of the vast and dynamic oceanic ecosystem. Being recognized as an essential climate variable
(ECV) ocean color is monitored as part of the climate change
initiative (CCI) project of the European Space Agency (ESA)
in the global climate observing system (GCOS).
However, accurate interpretation of the remotely sensed
signal is challenging and requires good estimation of atmospheric corrections. Furthermore, the complexities are
amplified in the complex case-2 waters owing to the contribution of non-varying optical components like CDOM and
inorganic suspended sediments. Development of region specific algorithms therefore becomes necessary. Hence, in situ
observations still continue to play an important role in biooptical algorithm development and validation purposes.
Moreover, satellite observations of the surface ocean in combination with bio-optical algorithms (derived from in situ
autonomous profiling systems, e.g., buoys, floats) are being
incorporated into the development of 3D bio-optical ocean
models with potential applications in physics and biogeochemistry of the dynamic environment at a number of relevant scales.
For further reading we recommend ‘Ocean optics web
book’ (http://www.oceanopticsbook.info/) and the ‘IOCCG
Report Series’ (http://ioccg.org/what-we-do/ioccg-publications/ioccg-reports/).
Acknowledgments Authors are extremely thankful to the reviewer for
providing helpful comments, which greatly improved the chapter. The
chapter contains modified Copernicus Sentinel data (2017) and Ocean
Biology Processing Group (OBPG) data products and images.
Appendix
This article is related to the YOUMARES 8 conference session no. 7: “Ocean Optics and Ocean Color Remote Sensing”.
The original Call for Abstracts and the abstracts of the presentations within this session can be found in the appendix
“Conference Sessions and Abstracts”, chapter “3 Ocean
Optics and Ocean Color Remote Sensing”, of this book.
References
Aksnes DL, Nejstgaard J, Sædberg E et al (2004) Optical control of
fish and zooplankton populations. Limnol Oceanogr 49:233–238.
https://doi.org/10.4319/lo.2004.49.1.0233
Aksnes DL, Dupont N, Staby A et al (2009) Coastal water darkening
and implications for mesopelagic regime shifts in Norwegian fjords.
Mar Ecol Prog Ser 384:39–49
V. Mascarenhas and T. Keck