IEEE Geoscience and Remote Sensing Magazine - September 2022 - 18

SATELLITE-DERIVED BATHYMETRY
Until now, numerous studies have proposed different
satellite-derived bathymetric methods, which are mainly
grouped into four categories [136], [137]: 1) photogrammetry
and mapping, 2) passive remote sensing, 3) active
remote sensing, and 4) active-passive remote sensing.
Photogrammetry and mapping were promising methods
during early studies, and they can be used to generate accurate
digital bathymetry models of shallow water bodies,
based on stereoscopic images from satellite imagery
[136], [138]. The advantage of this method is that it does
not rely on in situ depth measurements for bathymetry
derivation, preventing potential biases resulting from
training data [139]. However, the approach has not been
widely used because present multisatellite sensors are
unable to collect enough high-resolution stereoscopic
imagery [140], [141].
Passive remote sensing methods are based on the assumption
that bottom depth is primarily related to the optical
properties of water and underwater reflectance, which
can help to establish models, from basic linear functions
to nonlinear inversion models, to derive bathymetry [142]-
[144]. This method is broadly applicable to shallow water
bodies through the different reflectance of wave bands
[145]. However, passive remote sensing is limited by water
depth and optical properties [47]. This technique currently
is not suitable for operational SWS calculation for large and
deeper water bodies [146].
Active remote sensing for bathymetry is generally represented
by SAR, radar altimeters, and satellite lidar, which
emit artificial radiation to study underwater features [140].
The advantages of active remote sensing depend on the ability
to acquire measurements in all-weather conditions and
precise surveys up to 70 m in depth [48], [147], although
the technique has spatial and temporal constraints [148].
Even for the latest Ice, Cloud, and Land Elevation Satellite 2
(ICESat-2) lidar, which has six laser beams, the density of
lidar points is insufficient to directly generate a bathymetric
map [149].
To overcome the disadvantages of existing bathymetry
derivation techniques, several studies either integrate waterlines
obtained from a series of images with a spatial interpolation
of geocoded heights [150]-[152] or combine
multisatellite observations for deriving bathymetry (e.g.,
optical imagery, radar, and laser altimetry) [153]-[155]. For
example, Abileah and Vignudelli [156] proposed the combination
of radar altimetry and multitemporal Landsat data
to calculate bathymetry in Lake Nasser. Armon et al. [157]
successfully acquired lake bathymetries using ICESat-2 data
in combination with Landsat-based water occurrence maps.
More recently, Li et al. [148], [158] explored the potential of
generating high-resolution bathymetry of global reservoirs
using ICESat-2 data and Landsat Imagery. With advances of
satellite technology, the combined methods of multisensors
would be a natural evolution for satellite-derived bathymetry
[137], [159].
18
SATELLITE-DERIVED CHANGES
OF SURFACE WATER STORAGE
More and more efforts have been undertaken to quantify
changes of SWS by using satellite observations [34],
[35], [160], mainly including water balance analysis, a
surface water parameter-based approach, and a satellite
geodetic technique.
WATER BALANCE ANALYSIS METHOD
Water balance analysis is an established approach for estimating
changes in basin-scale SWS, and it has a simple
principle. Generally, changes in SWS (dS/dt) over a time
period (t) are dependent on the water balance between inputs
(including precipitation, surface runoff, and seepage)
and outputs (including evaporation, groundwater outflow,
and water discharge) [108] (see Figure 1), which can be expressed
as [161]
dt
dS
=- -- + f ,
PETQ Qsg
and Qg
(2)
where P is the precipitation over the surface water (m3 h−1);
ET is the evapotranspiration (m3 h−1); Qs
are
the surface runoff and groundwater runoff, respectively
(m3 h−1); and f represents the accumulated errors from all
the components. As can be seen from (2), changes of SWS
are residuals of the four components, and these components
can be obtained with various satellite-based products
[162], [163].
In a practical application, however, it is a challenging
task to use this method. The approach depends on accurate
and continuous measurements of each of the four flux
variables [108], [161], [164]. Currently, significant uncertainties
remain in measuring precipitation and evapotranspiration
by using remote sensing [165], [166]. Surface
water interactions with groundwater are also difficult to
measure and can be acquired only through the use of seepage
piezometers and modeling groundwater flow [167]. Regardless,
water balance analysis is clear in principle. It may
become practical once the satellite retrieval approaches of
the relevant four components mature enough to have the
desired accuracy [168].
SURFACE WATER PARAMETER-BASED METHOD
Variations of SWS correspond to the difference of surface
water levels integrated over the inundated surface [110],
[135]. Temporal variations of the SWS (
TV) are linked to
the expansion/shrinkage of the surface water area (TA) and
the rise/fall of the water level (TH), which can be traced
with the observations of different satellite sensors (Figure 4).
Therefore, combining time series of water area and water
level observations makes it possible to model changes of
SWS directly. The following three equations are generally
used to monitor these changes, depending on data choices
and different situations, and typically suited to retrieving
SWS changes in lakes and reservoirs:
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2022
IEEE Geoscience and Remote Sensing Magazine - September 2022

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