3
Toward Multiscale Hydrologic Remote Sensing
interactions in time and space. These latter challenges are critical elements in the
context of human- and climate-driven changes in the water cycle, as the emerging hydrologic structural changes induced new connectivity, and cross-scale
inter action patterns have no historical precedence. In addition, optimizing the
synergistic effects of sensors and sensor networks in order to provide decision
makers and stakeholders with timely decision support tools is deemed as a critical challenge (National Center for Atmospheric Research 2002; National Science
Foundation 2003; Chang et al. 2009, 2010, 2012). With the recent development of
the sensors and sensor networks, however, accessing the large amount of dynamic
sensor observations for specific times and locations has also become a challenging task (Chen et al. 2007).
In order to advance the science of hydrologic prediction under environmental
and human-induced changes, it is essential that an integrated as well as quantitative
method of remote sensing at the system science level is applied for investigating the
dynamics of coupled natural systems and the built environment. Recent advances in
hydrologic remote sensing with the aid of various data assimilation, machine learning, data mining, and image processing techniques have provided us with a reliable
means to explore the changing hydrologic variations via a temporally and spatially
sensitive approach. With this foundation, designing an effective hydrologic observatory to facilitate essential research and education by developing new knowledge of
hydrologic processes becomes possible.
These hydrologic observatories can tell us about what is happening in the unique
water cycle, and the hydrologic observations oftentimes concentrate on investigating
the dynamics between systems (such as feedback mechanisms or couplings) of water,
how scale affects processes and our understanding of them, and the implications
for prediction (Pacific Northwest Hydrologic Observatory 2011). To achieve these
goals, relevant sensor platforms are used to collect a wealth of data sets that are more
spatially and temporally comprehensive. With these different types of sensors and
sensor networks, the relationships among the components of the hydrologic cycle at
different scales may be linked with each other in concert with various earth systems
models. It is even possible that these data sets may be stored in a data center for end
users providing data and sensor planning service (Chen et al. 2007). With abundant
data collected from these mission-oriented hydrologic observatories, the identification of potential information via several machine learning and image processing
techniques may be applied to potentially retrieve useful information and discover
knowledge (Zilioli and Brivio 1997; Volpe et al. 2007; Chang et al. 2009, 2010,
2012).
1.3 FEATURED AREAS
All of the endeavors mentioned above can be geared toward achieving a suite of
multi scale hydrologic remote sensing tasks for advancing the hydrologic science. The
spectrum of our book’s chapters includes all components in the hydrologic cycle with
a range of space and time scales. They include, but are not limited to, precipitation,
soil moisture, evapotranspiration (ET), water vapor embedded in the cloud, terrestrial water storage, river discharge, snow pack, and improved monitoring of glaciers
Toward Multiscale Hydrologic Remote Sensing
interactions in time and space. These latter challenges are critical elements in the
context of human- and climate-driven changes in the water cycle, as the emerging hydrologic structural changes induced new connectivity, and cross-scale
inter action patterns have no historical precedence. In addition, optimizing the
synergistic effects of sensors and sensor networks in order to provide decision
makers and stakeholders with timely decision support tools is deemed as a critical challenge (National Center for Atmospheric Research 2002; National Science
Foundation 2003; Chang et al. 2009, 2010, 2012). With the recent development of
the sensors and sensor networks, however, accessing the large amount of dynamic
sensor observations for specific times and locations has also become a challenging task (Chen et al. 2007).
In order to advance the science of hydrologic prediction under environmental
and human-induced changes, it is essential that an integrated as well as quantitative
method of remote sensing at the system science level is applied for investigating the
dynamics of coupled natural systems and the built environment. Recent advances in
hydrologic remote sensing with the aid of various data assimilation, machine learning, data mining, and image processing techniques have provided us with a reliable
means to explore the changing hydrologic variations via a temporally and spatially
sensitive approach. With this foundation, designing an effective hydrologic observatory to facilitate essential research and education by developing new knowledge of
hydrologic processes becomes possible.
These hydrologic observatories can tell us about what is happening in the unique
water cycle, and the hydrologic observations oftentimes concentrate on investigating
the dynamics between systems (such as feedback mechanisms or couplings) of water,
how scale affects processes and our understanding of them, and the implications
for prediction (Pacific Northwest Hydrologic Observatory 2011). To achieve these
goals, relevant sensor platforms are used to collect a wealth of data sets that are more
spatially and temporally comprehensive. With these different types of sensors and
sensor networks, the relationships among the components of the hydrologic cycle at
different scales may be linked with each other in concert with various earth systems
models. It is even possible that these data sets may be stored in a data center for end
users providing data and sensor planning service (Chen et al. 2007). With abundant
data collected from these mission-oriented hydrologic observatories, the identification of potential information via several machine learning and image processing
techniques may be applied to potentially retrieve useful information and discover
knowledge (Zilioli and Brivio 1997; Volpe et al. 2007; Chang et al. 2009, 2010,
2012).
1.3 FEATURED AREAS
All of the endeavors mentioned above can be geared toward achieving a suite of
multi scale hydrologic remote sensing tasks for advancing the hydrologic science. The
spectrum of our book’s chapters includes all components in the hydrologic cycle with
a range of space and time scales. They include, but are not limited to, precipitation,
soil moisture, evapotranspiration (ET), water vapor embedded in the cloud, terrestrial water storage, river discharge, snow pack, and improved monitoring of glaciers