Geography Reference
In-Depth Information
Table 6.5
Recent and planned satellite missions dedicated to hydrology using radar technology.
Mission/
Launch
Band (GHz)
Revisit Time at
Spatial
Primary Use
Satellite
Year
Equator (Days)
Resolution
for Flood
(m)
Hydrology
ALOS
2006
L (1.3)
46
10-100
Flood mapping
Soil moisture at
small scale
TerraSAR-X
(TerraSAR-X 2
planned)*
2007
X (9.6)
11
1-18
High resolution
flood mapping
RADARSAT-2
(RADARSAT
constellation
mission -
RCM - planned)**
2007
C (5.3)
24
3-100
High resolution
flood mapping
Soil moisture at
small scale
COSMO-SkyMed
2007
X (9.6)
16 (4 to
<
1, with
4 satellites in
constellation)
1-100
High resolution
flood mapping
SMOS
2009
L (1.4) (passive)
2-3
30-50 km
Large scale flood
mapping
Soil moisture at
large scale
Cryosat-2
2010
Ku (13.575)
Interferometric
altimeter
369
∼
250 m imaging
Water level
measurement
Topography
TanDEM-X
2010
X (9.6) (Interfer-
ometry)
11 (in tandem
orbit with
TerraSAR-X)
3 m
Water level
dynamics
Topography
Sentinel-1
2013
C (5.3)
12 (6, with 2
satellites in
constellation)
5-100
High resolution
flood mapping
Soil moisture at
small scale
TanDEM-L
(potential)
2015
L (1-2) (Interfer-
ometry)
8
5-100
Water level
dynamics
Topography
SWOT
2019
Ka (35)
Interferometric
altimeter
22
<
100 m imaging
Water level
measurement
Topography
∗
Source: http://www.infoterra.de
∗∗
Source: http://www.eoportal.org/
will clearly cause a shift from a data-poor to a data-rich
environment in which scientists may need to rethink the
way they handle data and develop models. For instance,
Bates (2004) foresaw that such a data-rich environment
gives a potential for model redesign to take advantage
of new information, for research into optimal ways to
assimilate data into hydraulic models, for investigations
of parameter scaling behaviour, for studies of the physical
meaning of grid-scale effective parameters in models
of different dimensionality or discretisation, and for an
exploration of the ways in which new data sources may
reduce uncertainty in model predictions.
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