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bathymetric mapping emphasised in this chapter. One
obvious extension is quantitative mapping of different
bottom types on the basis of their reflectance properties.
Image-derived depth estimates, along with information
on optical properties, could be used to account for
the influence of the water column, estimate bottom
reflectance, and thus delineate various substrate types
(e.g., Dierssen et al., 2003). For example, one could
potentially distinguish among various grain size patches
(e.g., gravel vs. sand vs. fine sediments) or different
species of submerged aquatic vegetation or algae; quan-
titative estimates of biomass might be possible as well.
A similar approach could be used to identify particular
features of interest, such as bank failure, which could be
expressed as a distinct spectral signal of water flowing
over blocks of grassy sod. More sophisticated techniques,
such as spectral mixture analysis or lookup-table based
methods similar to those employed by Lesser and Mobley
(2007) would facilitate the development of such novel
applications. Additional prospects worthy of attention
include improved characterisation of various aspects of
water quality, such as concentrations of suspended sed-
iment, chlorophyll, and dissolved organic matter, and
inference of flow hydraulics from remote observations of
surface-reflected radiance.
We anticipate a number of future challenges and
research needs that must be addressed in order to advance
remote sensing of rivers. For example, we know little
about the spectral variability of different substrate and
streambank materials, and this dearth of information jus-
tifies a concerted effort to build representative spectral
libraries from a range of fluvial environments. Similarly,
additional data on optical cross-sections are needed to
improve parametrisation of water column optical prop-
erties in radiative transfer models. Sun glint is pervasive
in many river images and is present to some extent in
almost all cases, but effective techniques for detecting
and removing surface-reflected radiance have yet to be
developed. This problem is important because sun glint
can preclude inference of channel attributes of interest.
Given the relatively small amounts of radiance that leave
river channels and the increasing availability of high spa-
tial resolution satellite image data, improved, practical
methods of atmospheric correction are also necessary.
Finally, before remote sensing of rivers can become a
viable, operational tool for monitoring and management,
significant effort must be invested in the development
of efficient, readily available software tools that facilitate
practical implementation of various image processing
techniques.
The very existence of this volume is testament to the
current utility and future potential for remote sensing of
rivers. In order to fully realise this considerable potential,
we as a community of river-oriented managers and scien-
tists must first develop confidence in image-derived river
information. This confidence must be justified. Along
with the capabilities afforded by remote sensing tech-
nology, we must bear in mind the inherent limitations
associated with these data. To reiterate the premise of this
chapter, an understanding of the physical processes that
both enable and limit the application of remote sensing
to rivers is critical to the effective use of this powerful
tool. Improving our knowledge of these processes will
allow us to identify appropriate uses of remote sensing
techniques and to define realistic expectations of what
can and cannot be achieved using these methods. Such
a critical perspective, informed by an appreciation of
the underlying physics, will enable us to more efficiently
derive river information from remotely sensed data and
more effectively use this information in a range of fluvial
applications.
3.6 Notation
λ =
Wavelength
ν =
Frequency
c
=
Speed of light
q
=
Radiant energy of a single photon
Planck's constant
Q
=
Total radiant energy in a beam of light
n
i
=
h
=
Number of photons at a particular
wavelength
Φ =
Radiant flux
E
=
Irradiance
θ =
Angle of incidence
l
=
Arc length
r
=
Radius of circle of sphere; distance
Ω =
Solid angle
A
=
Surface area on sphere used to compute a
solid angle; constant in Equation (3.16)
L
=
Radiance
Irradiance reflectance
E
u
(
λ
)
=
Upwelling spectral irradiance
E
d
(
R
(
λ
)
=
λ
)
=
Downwelling spectral irradiance
z
=
Vertical position (depth) within the water
column
z
b
=
Vertical position (depth) at bottom of water
column
θ
v
=
Viewing angle
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