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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B1. Istanbul 2004
Variable Sensor Spatial Temporal Period of Reference
Resolution Resolution Record
Leaf Area Index AVHRR 8 or 16 km Bi-weekly 1980-Present | Goward
MODIS 500m-1km Bi-weekly 1999-present | Justice
Soil Moisture SSM/I 50km 2/day 1987-present | Lakshmi,1997a
AMSR 50km 2-4/day 2002-present | Njoku, 1999
Surface ASTER 90m On-request 2000-present | Gellipse
Temperature MODIS 500m-1km 2-4/day 2000-present Justice
AVHRR 1-5 km 1-8 day 1980-present | Becker
TOVS [e 2/day 1980-Present Susskind, 1997
AIRS 50km 2/day 2002-Present | Susskind, 2003
Surface Air TOVS ]" 2/day 1980-present | Susskind, 1997
Temperature AIRS 50km 2/day 2002-present Susskind, 2003
Precipitation TRMM 20km Daily 1998-present | Kummerow, 2000
SSM/I 50km Daily 1987-present | Ferraro, 1997
AMSR 50km Daily 2002-present | Wilheit, 2003
Table 1. Remotely sensed data used in land surface hydrology
Precipitation is perhaps the most important variable in
land surface hydrology. In the absence of, or inadequate
number of stream gauges, the discharge at a watershed
outlet is limited by the amount of precipitation on the
watershed. Since precipitation is highly variable in space
(scales of 1-2km), and time (scales of « 30minutes),
accurate spatial and temporal measurements of rainfall
are desired. Most ungauged (discharge) catchments
suffer from a lack of or inadequate number of rain
gauges. Therefore, remote sensing offers an alternative
solution. However, satellite remote sensing yields
products with low spatial (10-20km) resolution and
temporal repeat (1-2 day revisit). However, weather
radars have a much better spatial resolution (1-2km) and
temporal revisit times (15-30 minutes). This would
greatly enhance hydrological forecasting as well as
deckease the prediction uncertainty of ungauged basins.
3.0 Future Challenges and applicability to PUB
We have a wealth of satellite data at various
spatial scales, and different temporal resolutions that can
be used in putting together a complete picture of the land
surface hydrological cycle. Figure 1 represents both the
wealth of data and the dilemma on its usage. It remains
as a challenge to the scientific community to reconcile
these issues and use this data in the most synergistic
methodology possible to help in our endeavor to predict
fluxes in ungauged basins.
The broad scientific objectives of PUB (as
stated in the PUB Science Plan) which can be answered
by satellite remote sensing are-
l. Advance the ability of hydrologists worldwide to
predict the fluxes of water and associated
constituents from ungauged basins, along with
estimates of the uncertainty of predictions;
315
Prediction of fluxes of water by using vegetation,
surface air temperatures as inputs to hydrological models
and surface temperature and soil moisture as validation
variables in the intermediate step to calculation. of
overland flow and stream flow.
2. Advance the knowledge and understanding of
climatic and landscape controls on hydrological
processes to constrain the uncertainty in hydrologic
predictions;
Spatial mapping of land surface areas helps to
identify regions of saturation/high vegetation content
along with surface flow characteristics, viz,
infiltration dominated and/or runoff dominated
3. Demonstrate the value of data for hydrologic
predictions, and provide a rational basis for future
data requirements, by investigating links between
data and predictive uncertainty;
Key progress in the future will be accomplished by
synergism of observational data and modeling.
Specifically, altimetry-based systems that can
observe surface water heights (Alsdorf et al., 2003)
can be used along with stage-discharge curve to
validate stream flow measurements in remote,
ungauged areas and the difference between the two
estimates (model versus satellite observations) can
serve as a basis for data assimilation
4. Advance the scientific foundations of hydrology, and
provide a scientific basis for sustainable river basin
management.
Future estimation of water resources requires an
accurate prediction of sources of surface and subsurface
water, both of which can be mapped in space with the use
of satellite remote sensing. Tracking fresh water
estimates from space is a challenging problem that can be
solved by a combination of satellite sensors (currently
under research and development) and existing gauge
