instruments. The nature and magnitude of these changes are controlled by the total
amount of absorption and scattering occurring into the water volume that may be
attributable to each optically significant organic and inorganic, suspended and
dissolved, living and non-living component contained in the natural waterbody.
At present, a variety of water quality parameters have been identified in literature as
detectable by modern passive sensors onboard satellites: e.g. chlorophyll-a (chl-a)
[5, 6] and phycocyanin (PC) [7, 8], SPM [9, 10], coloured dissolved organic matter
(CDOM or yellow substances) [11, 12] or the diffused attenuation coefficient as
measure for water transparency [13].
Floating materials such as oils (e.g. [14]), cyanobacteria scum (e.g. [15]), pollen
or vegetation (e.g. [16]) can also be detected from remotely sensed imagery. The
capacity of remote sensing in detecting those materials is relevant, because whatever is floating on the water surface might be the consequence of unusual events
(e.g. oil spill, massive blooms of cyanobacteria) and might have relevant impacts on
the landscape (e.g. [17]). Furthermore, when water transparency allows light to
reach the bottom, the backward signal reaching the sensor also includes spectral
information about the substrate. In such cases, the spectral signature of the bottom
and the water depth are detectable from remote sensors. From these measures, the
next ecological relevant information may be derived: total area of plant coverage,
broadness of the littoral zone covered by macrophytes, growth cycle of the macrophytes or species compositions (e.g. [18–22]).
2 Methods: An Overview
Conceptually, remote sensing of water quality is simple: sunlight, whose spectral
properties are known, enters a natural waterbody. The sunlight’s spectral character
is then altered, contingent upon the absorption and scattering properties of the
waterbody (which, of course, depends on type and concentration of the various
constituents composing that particular waterbody). Part of the altered sunlight
eventually makes its way back out of the water and can be detected from a sensor
aboard an aircraft or satellite. Knowing how different substances spectrally alter
sunlight, for example, by wavelength-dependent absorption/scattering or by fluorescence, makes it possible to assess which substances are present in the water and in
what concentration.
When spectral characteristics of the parameters of interest are known, semiempirical methods are generally used. This knowledge is included in the statistical
analysis by focusing on well-chosen spectral areas and appropriate wavebands used
as correlates. Recently, Odermatt et al. [23] provided a review of the remote sensing
algorithms, currently adopted to retrieve water quality parameters. They distinguished semi-empirical [24, 25] from spectral inversion procedures, the latter built
on matching spectral measurements with bio-optical forward model-derived signatures by means of inversion techniques.
Imaging Spectrometry of Inland Water Quality in Italy Using MIVIS: An Overview
63
amount of absorption and scattering occurring into the water volume that may be
attributable to each optically significant organic and inorganic, suspended and
dissolved, living and non-living component contained in the natural waterbody.
At present, a variety of water quality parameters have been identified in literature as
detectable by modern passive sensors onboard satellites: e.g. chlorophyll-a (chl-a)
[5, 6] and phycocyanin (PC) [7, 8], SPM [9, 10], coloured dissolved organic matter
(CDOM or yellow substances) [11, 12] or the diffused attenuation coefficient as
measure for water transparency [13].
Floating materials such as oils (e.g. [14]), cyanobacteria scum (e.g. [15]), pollen
or vegetation (e.g. [16]) can also be detected from remotely sensed imagery. The
capacity of remote sensing in detecting those materials is relevant, because whatever is floating on the water surface might be the consequence of unusual events
(e.g. oil spill, massive blooms of cyanobacteria) and might have relevant impacts on
the landscape (e.g. [17]). Furthermore, when water transparency allows light to
reach the bottom, the backward signal reaching the sensor also includes spectral
information about the substrate. In such cases, the spectral signature of the bottom
and the water depth are detectable from remote sensors. From these measures, the
next ecological relevant information may be derived: total area of plant coverage,
broadness of the littoral zone covered by macrophytes, growth cycle of the macrophytes or species compositions (e.g. [18–22]).
2 Methods: An Overview
Conceptually, remote sensing of water quality is simple: sunlight, whose spectral
properties are known, enters a natural waterbody. The sunlight’s spectral character
is then altered, contingent upon the absorption and scattering properties of the
waterbody (which, of course, depends on type and concentration of the various
constituents composing that particular waterbody). Part of the altered sunlight
eventually makes its way back out of the water and can be detected from a sensor
aboard an aircraft or satellite. Knowing how different substances spectrally alter
sunlight, for example, by wavelength-dependent absorption/scattering or by fluorescence, makes it possible to assess which substances are present in the water and in
what concentration.
When spectral characteristics of the parameters of interest are known, semiempirical methods are generally used. This knowledge is included in the statistical
analysis by focusing on well-chosen spectral areas and appropriate wavebands used
as correlates. Recently, Odermatt et al. [23] provided a review of the remote sensing
algorithms, currently adopted to retrieve water quality parameters. They distinguished semi-empirical [24, 25] from spectral inversion procedures, the latter built
on matching spectral measurements with bio-optical forward model-derived signatures by means of inversion techniques.
Imaging Spectrometry of Inland Water Quality in Italy Using MIVIS: An Overview
63