both the appearance and intensification of peak around 700 nm and the shift of
reflectance peak wavelength position from 694 to 706 nm are noticeable.
However, for spectral inversion procedures, a hyperspectral dataset could facilitate
a physics-based modelling approach to quantitatively retrieve multiple constituents of
interest (e.g. PC, chl-a, SPM, CDOM, phytoplankton functional types and benthic
composition) ([51] and references therein). In fact, hyperspectral remote sensing
provides essential data to de-convolve the remotely sensed signal and, thence, to
detect the water components that could be missed by multispectral instruments, e.g. if
the dataset contains many narrow spectral bands, then hyperspectral measurements
can make the direct detection of the PC pigment easier ([52] and references therein).
Modern space-borne hyperspectral sensors (e.g. Hyperion, Hyperspectral Imager
for the Coastal Ocean (HICO)) showed increasing capabilities in water quality [53–
55], but they still present some inaccuracies in monitoring environments that are
highly variable in space, e.g. especially in inland and near coastal waters. O ¨ zesmi
and Bauer [56] observed how spatial resolution is one of the primary limiting factors
in the application of satellite remote sensing to freshwater ecosystems. High spatial
and spectral resolution data are essential attributes to provide accurate retrieval of
water quality in both optically deep and shallow waters (e.g. [22]). The signal-tonoise ratio of the sensor is also critical in making accurate measurements of water
quality parameters but, for the sake of brevity, will be not discussed in this chapter. In
this context, high-resolution airborne hyperspectral sensors (e.g. Airborne Imaging
Spectrometer for Applications (AISA), MIVIS, Airborne Visible/InfraRed Imaging
Spectrometer (AVIRIS), Compact Airborne Spectrographic Imager (CASI),
HYperspectral MAPper (HYMAP)) represent enhanced mapping tools (e.g. [16,
38, 39]) and also preliminary tests to design satellite-based systems (e.g. PRecursore
IperSpettrale della Missione Applicativa (PRISMA), Environmental Mapping and
Analysis Programme (EnMAP), Hyperspectral Infrared Imager (HyspIRI)). For
example, in previous years, Koponen et al. [57] and Giardino et al. [58] used airborne
AISA and MIVIS images, respectively, for simulating MERIS data on lakes.
4 MIVIS Applications in Italian Inland Waters
Generally inland waters include freshwater bodies as lakes, streams, rivers, reservoirs and ponds. In this study some Italian natural lakes, one fluvial lake and one
river were considered. Italy has the highest number of lakes among Mediterranean
countries; its most important lacustrine region is located in Northern Italy and
includes deep subalpine lakes and some small–medium lakes. These lakes represent
more than 90 % of the total Italian freshwater volume [59, 60]. The largest Italian
subalpine lakes have morphometric characteristics in common: they are narrow,
with north–south elongated shapes, and their floors lie below sea level.
The Italian subalpine lakes have high ecological and environmental value and are
valuable resources of water within densely inhabited areas. Management and conservation of water quality and maintenance of biodiversity currently represent topics of
major importance because of the need for technical support and scientific data for
planning needed interventions. Within such a frame, imaging spectrometry definitely
66
C. Giardino et al.
reflectance peak wavelength position from 694 to 706 nm are noticeable.
However, for spectral inversion procedures, a hyperspectral dataset could facilitate
a physics-based modelling approach to quantitatively retrieve multiple constituents of
interest (e.g. PC, chl-a, SPM, CDOM, phytoplankton functional types and benthic
composition) ([51] and references therein). In fact, hyperspectral remote sensing
provides essential data to de-convolve the remotely sensed signal and, thence, to
detect the water components that could be missed by multispectral instruments, e.g. if
the dataset contains many narrow spectral bands, then hyperspectral measurements
can make the direct detection of the PC pigment easier ([52] and references therein).
Modern space-borne hyperspectral sensors (e.g. Hyperion, Hyperspectral Imager
for the Coastal Ocean (HICO)) showed increasing capabilities in water quality [53–
55], but they still present some inaccuracies in monitoring environments that are
highly variable in space, e.g. especially in inland and near coastal waters. O ¨ zesmi
and Bauer [56] observed how spatial resolution is one of the primary limiting factors
in the application of satellite remote sensing to freshwater ecosystems. High spatial
and spectral resolution data are essential attributes to provide accurate retrieval of
water quality in both optically deep and shallow waters (e.g. [22]). The signal-tonoise ratio of the sensor is also critical in making accurate measurements of water
quality parameters but, for the sake of brevity, will be not discussed in this chapter. In
this context, high-resolution airborne hyperspectral sensors (e.g. Airborne Imaging
Spectrometer for Applications (AISA), MIVIS, Airborne Visible/InfraRed Imaging
Spectrometer (AVIRIS), Compact Airborne Spectrographic Imager (CASI),
HYperspectral MAPper (HYMAP)) represent enhanced mapping tools (e.g. [16,
38, 39]) and also preliminary tests to design satellite-based systems (e.g. PRecursore
IperSpettrale della Missione Applicativa (PRISMA), Environmental Mapping and
Analysis Programme (EnMAP), Hyperspectral Infrared Imager (HyspIRI)). For
example, in previous years, Koponen et al. [57] and Giardino et al. [58] used airborne
AISA and MIVIS images, respectively, for simulating MERIS data on lakes.
4 MIVIS Applications in Italian Inland Waters
Generally inland waters include freshwater bodies as lakes, streams, rivers, reservoirs and ponds. In this study some Italian natural lakes, one fluvial lake and one
river were considered. Italy has the highest number of lakes among Mediterranean
countries; its most important lacustrine region is located in Northern Italy and
includes deep subalpine lakes and some small–medium lakes. These lakes represent
more than 90 % of the total Italian freshwater volume [59, 60]. The largest Italian
subalpine lakes have morphometric characteristics in common: they are narrow,
with north–south elongated shapes, and their floors lie below sea level.
The Italian subalpine lakes have high ecological and environmental value and are
valuable resources of water within densely inhabited areas. Management and conservation of water quality and maintenance of biodiversity currently represent topics of
major importance because of the need for technical support and scientific data for
planning needed interventions. Within such a frame, imaging spectrometry definitely
66
C. Giardino et al.