Wie untersucht man die räumliche Variabilität in Seen?
Wie bilden sich Kalkpartikel in Seen, und wie beeinflussen sie ihre Färbung an der Oberfläche?
Wie lässt sich von der natürlichen, Sonnen-induzierten Fluoreszenz auf das Wachstum von Algen schliessen?
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Resolving biogeochemical processes in lakes using remote sensing
Remote sensing helps foster our understanding of inland water processes allowing a synoptic view of water quality parameters. In the context of global monitoring of inland waters, we demonstrate the benefit of combining in-situ water analysis, hydrodynamic modelling and remote sensing for investigating biogeochemical processes. This methodology has the potential to be used at global scales. We take the example of four Landsat-8 scenes acquired by the OLI sensor and MODIS-Aqua imagery over Lake Geneva (France—Switzerland) from spring to early summer 2014. Remotely sensed data suggest a strong temporal and spatial variability during this period. We show that combining the complementary spatial, spectral and temporal resolutions of these sensors allows for a comprehensive characterization of estuarine, littoral and pelagic near-surface features. Moreover, by combining in-situ measurements, biogeochemical analysis and hydrodynamic modelling with remote sensing data, we can link these features to river intrusion and calcite precipitation processes, which regularly occur in late spring or early summer. In this context, we propose a procedure that can be used to monitor whiting events in temperate lakes worldwide.
Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes
The use of ground sampled water quality information for global studies is limited due to practical
and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide
synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data
processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset
consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and
is based on data from the full ENVISAT MERIS operation period (2002–2012). Existing retrieval methods and
datasets were selected after an extensive algorithm intercomparison exercise. Chlorophyll-a, total suspended
matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating
vegetation maps, as well as several auxiliary data layers, provide a generically specified database that can be
used for assessing a variety of locally relevant ecosystem properties and environmental problems. For validation
and accuracy assessment, we provide matchup comparisons for 24 lakes and a group of reservoirs representing
a wide range of bio-optical conditions. Matchup comparisons for chlorophyll-a concentrations indicate mean
absolute errors and bias in the order of median concentrations for individual lakes, while total suspended matter
and turbidity retrieval achieve significantly better performance metrics across several lake-specific datasets. We
demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific
processes and prominent regime shifts documented in the literature. The Diversity II data are available from
doi.pangaea.de/10.1594/PANGAEA.871462, and Python scripts for their analysis and visualization are
provided at github.com/odermatt/diversity/.
Odermatt, D.; Danne, O.; Philipson, P.; Brockmann, C. (2018) Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes, Earth System Science Data, 10(3), 1527-1549, doi:10.5194/essd-10-1527-2018, Institutional Repository
Are surface temperature and chlorophyll in a large deep lake related? An analysis based on satellite observations in synergy with hydrodynamic modelling and in-situ data
Phytoplankton growth depends on various factors, and primarily on nutrient availability, light and water
temperature, whose distributions are largely controlled by hydrodynamics. Our main objective is to analyse the
link between spatial and temporal variability of surface water temperature and algal concentration in a large
lake by means of remote sensing and hydrodynamic modelling. We compare ten years of satellite images
showing chlorophyll concentrations and surface water temperature of Lake Geneva. Our observations suggest
different correlations depending on the season. Elevated chlorophyll concentrations in spring are correlated with
warmer zones. But, in summer, higher chlorophyll concentrations are observed in colder zones. We show with a
three-dimensional hydrodynamic model that the spatial variability of the surface water temperature reflects the
upwelling and downwelling zones resulting from wind forcing. In springtime, nearshore downwellings induce
locally increased surface temperature and stratification, which are associated with high chlorophyll concentration.
In summertime, colder surface temperature area, often interpreted as transient upwellings, represents
the thermal surface signature of wind-induced basin-scale internal waves, bringing either nutrients or phytoplankton
from deeper layers to the surface. Our study suggests the latter to be the dominant process, with the
basin-scale internal wave activity and associated transient summertime upwellings and downwellings having
little net effects on the algal concentration. This study finally demonstrates the necessity to connect remote
sensing retrievals and three-dimensional hydrodynamic modelling to properly understand the dynamic of the
Bouffard, D.; Kiefer, I.; Wüest, A.; Wunderle, S.; Odermatt, D. (2018) Are surface temperature and chlorophyll in a large deep lake related? An analysis based on satellite observations in synergy with hydrodynamic modelling and in-situ data, Remote Sensing of Environment, 209, 510-523, doi:10.1016/j.rse.2018.02.056, Institutional Repository
MERIS observations of phytoplankton blooms in a stratified eutrophic lake
The use of spaceborne medium resolution imaging spectrometers with neural network algorithms has proven a large potential for application with optically complex inland waters. We make use of this approach to investigate the bio-physical dynamics in a eutrophic lake, applying three different neural networks to a dataset of 16 images acquired in June through August 2011. Concurrent in-situ data are measured by means of automatically deployed instruments from a moored platform, resolving the vertical distribution of various parameters at sub-daily temporal resolution. Phytoplankton blooms occur in different stratification layers, allowing the assessment of their influence on remote sensing estimates. A qualitative synopsis of the biophysical processes in the lake is given, but parameterization with in-situ attenuation profiles and accurate IOP estimates is needed to significantly enhance quantitative matchup comparisons. Recommendations on the combination of in-situ and satellite measurements are therefore given as an outlook.
Odermatt, D.; Pomati, F.; Pitarch, J.; Carpenter, J.; Kawka, M.; Schaepman, M.; Wüest, A. (2012) MERIS observations of phytoplankton blooms in a stratified eutrophic lake, Remote Sensing of Environment, 126, 232-239, doi:10.1016/j.rse.2012.08.031, Institutional Repository