1 Introduction
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tially very powerful tool for advancing hydrologic sciences. Each of these characteristics are discussed below:
Measuring System States. Thermal infrared and microwave remote sensing,
because of their unique responses to surface properties important to hydrology,
such as surface temperature, soil moisture and snow water content, have the capability to measure these system states directly. However, using system-state data
will require new models to incorporate the new data types. Such models would
structurally resemble contemporary simulation models but would be more capable
of accounting for spatial variability and changes. Also, the subprocess algorithms
(infiltration, evapotranspiration, etc.) would be designed to use remote sensing
data as well as the more traditional inputs.
Area versus Point Data. The use of data representing an area in which the spatial
variability of specific parameters of the area have been integrated may help provide one of the keys to understanding scaling and scale interdependence in hydrologic systems. The capability to aggregate up in scale or dis aggregate down in
scale by electronic means may provide a perspective of scaling that may instill
new insight to answering the scale questions that dominate scientific hydrology.
Temporal Data. Remote sensing data from a satellite platform can provide unique
time series data for hydrologic use. The actual frequency of observation can vary
from continuous to once every two weeks or so, depending upon the sensors and
type of orbit. This approach is appealing because it may be a very cost-effective
method to monitor various hydrologic states over very large areas as well as
monitor the dynamic properties in hydrology. Temporal data may provide a means
for imparting a hydrologic interpretation to certain observations. For example,
observing the time changes in soil moisture may provide information on soil types
and even hydraulic properties such as hydraulic conductivity. In fact the interpretation of soil properties as a remote sensing signature could be extremely useful
for hydrology because it would represent an areal value rather than a point value
determined in a laboratory or with a field measurement.
New Data Forms. Entirely new data types may be formed by merging several
data sets of different wavelengths, polarizations, look angles, etc. to provide entirely new hydrologic parameters that are developed from the unique characteristics of remote sensing. New data forms could also be considered to be combinations of remote sensing data combined with other spatial data (such as soil maps)
and even point data through a data assimilation scheme or sophisticated GIS
(Geographical Information System).
Weather radar is a good example of a new remote sensing data form that combines an areal signature and a temporal signature. The weather radars produce a
nearly continuous picture of the space-time changes of rainfall rates over the radar's operational area.
These and other ideas need to be explored through research that combines remote sensing and hydrologic modeling. Each presents a unique opportunity for
hydrologists and water managers to apply remote sensing in ways other than sim-
5
tially very powerful tool for advancing hydrologic sciences. Each of these characteristics are discussed below:
Measuring System States. Thermal infrared and microwave remote sensing,
because of their unique responses to surface properties important to hydrology,
such as surface temperature, soil moisture and snow water content, have the capability to measure these system states directly. However, using system-state data
will require new models to incorporate the new data types. Such models would
structurally resemble contemporary simulation models but would be more capable
of accounting for spatial variability and changes. Also, the subprocess algorithms
(infiltration, evapotranspiration, etc.) would be designed to use remote sensing
data as well as the more traditional inputs.
Area versus Point Data. The use of data representing an area in which the spatial
variability of specific parameters of the area have been integrated may help provide one of the keys to understanding scaling and scale interdependence in hydrologic systems. The capability to aggregate up in scale or dis aggregate down in
scale by electronic means may provide a perspective of scaling that may instill
new insight to answering the scale questions that dominate scientific hydrology.
Temporal Data. Remote sensing data from a satellite platform can provide unique
time series data for hydrologic use. The actual frequency of observation can vary
from continuous to once every two weeks or so, depending upon the sensors and
type of orbit. This approach is appealing because it may be a very cost-effective
method to monitor various hydrologic states over very large areas as well as
monitor the dynamic properties in hydrology. Temporal data may provide a means
for imparting a hydrologic interpretation to certain observations. For example,
observing the time changes in soil moisture may provide information on soil types
and even hydraulic properties such as hydraulic conductivity. In fact the interpretation of soil properties as a remote sensing signature could be extremely useful
for hydrology because it would represent an areal value rather than a point value
determined in a laboratory or with a field measurement.
New Data Forms. Entirely new data types may be formed by merging several
data sets of different wavelengths, polarizations, look angles, etc. to provide entirely new hydrologic parameters that are developed from the unique characteristics of remote sensing. New data forms could also be considered to be combinations of remote sensing data combined with other spatial data (such as soil maps)
and even point data through a data assimilation scheme or sophisticated GIS
(Geographical Information System).
Weather radar is a good example of a new remote sensing data form that combines an areal signature and a temporal signature. The weather radars produce a
nearly continuous picture of the space-time changes of rainfall rates over the radar's operational area.
These and other ideas need to be explored through research that combines remote sensing and hydrologic modeling. Each presents a unique opportunity for
hydrologists and water managers to apply remote sensing in ways other than sim-