5
Toward Multiscale Hydrologic Remote Sensing
5. Topical area V: continental- and global-scale hydrologic remote sensing. Expanding upon the predictive capabilities, hydrologic models require
comparing observations across field sites. Sometimes such work requires
scaling observations to regional and global extent. Global soil moisture,
ET, precipitation, terrestrial water storage, vegetation cover, and drought
conditions are systematically discussed in Part IV. A few applications and
case studies from Chapters 16 to 21 with sound description of current stateof-the-art platforms applied for contemporary research may demonstrate a
contemporary coverage of these issues.
REFERENCES
Chang, N. B., Daranpob, A., Yang, J., and Jin, K. R. (2009). A comparative data mining analysis for information retrieval of MODIS images: Monitoring lake turbidity changes at
Lake Okeechobee, Florida. Journal of Applied Remote Sensing, 3, 033549.
Chang, N. B., Han, M., Yao, W., Xu, S. G., and Chen, L. C. (2010). Change detection of
land use and land cover in a fast growing urban region with SPOT-5 images and partial
Lanczos extreme learning machine. Journal of Applied Remote Sensing, 4, 043551.
Chang, N. B., Yang, J., Daranpob, A., Jin, K. R., and James, T. (2012). Spatiotemporal pattern
validation of chlorophyll-a concentrations in Lake Okeechobee, Florida using a comparative MODIS image mining approach. International Journal of Remote Sensing, 33,
doi:10.1080/01431161.2011.608089.
Chen, N., Di, L., Yu, G., Gong, J., and Wei, Y. (2007). Use of ebRIM-based CSW with sensor observation services for registry and discovery of remote-sensing observations.
Computing Geoscience, 35(2), 360–372.
Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). (2011).
http: //www.cuahsi.org/hos.html, accessed May 2011.
Intergovernmental Panel on Climate Change (IPCC). (2010). Climate Change 2010: IPCC
Fifth Assessment Report (AR5).
Krajewski, W. F., Anderson, M., Eichinger, W., Entekhabi, D., Hornbuckle, B., Houser, P.,
Katul, G., Kustas, W., Norman, J., Parlange, M., Peters-Lidard, C., and Wood, E. (2004).
A Remote Sensing Observatory for Hydrologic Sciences: A Genesis for Scaling to
Continental Hydrology. CUASHI Publications, Washington, DC, USA.
Kumar, P. (2011). Typology of hydrologic predictability. Water Resource Research, 47,
W00H05, doi:10.1029/2010WR009769.
National Center for Atmospheric Research (NCAR). (2002). Cyberinfrastructure for Environmental Research and Education. NCAR, Boulder, CO, USA.
National Science Foundation (NSF). (2003). Complex environmental systems: Synthesis for
earth, life, and society in the 21st century. NSF Environmental Cyberinfrastructure
Report, Washington, DC, USA.
Pacific Northwest Hydrologic Observatory (PNWHO). (2011). http://pnwho.forestry.oregonstate
.edu/background/index.php, accessed May 2011.
Volpe, G., Santoleri, R., Vellucci, V., d’Alcalà, M. R., Marullo, S., and D’Ortenzio, F. (2007).
The colour of the Mediterranean Sea: Global versus regional bio-optical algorithms
evaluation and implication for satellite chlorophyll estimates. Remote Sensing of Environment, 107, 625–638.
Zilioli, E. and Brivio, P. A. (1997). The satellite derived optical information for the comparative assessment of lacustrine water quality. Science of the Total Environment, 196,
229–245.
Toward Multiscale Hydrologic Remote Sensing
5. Topical area V: continental- and global-scale hydrologic remote sensing. Expanding upon the predictive capabilities, hydrologic models require
comparing observations across field sites. Sometimes such work requires
scaling observations to regional and global extent. Global soil moisture,
ET, precipitation, terrestrial water storage, vegetation cover, and drought
conditions are systematically discussed in Part IV. A few applications and
case studies from Chapters 16 to 21 with sound description of current stateof-the-art platforms applied for contemporary research may demonstrate a
contemporary coverage of these issues.
REFERENCES
Chang, N. B., Daranpob, A., Yang, J., and Jin, K. R. (2009). A comparative data mining analysis for information retrieval of MODIS images: Monitoring lake turbidity changes at
Lake Okeechobee, Florida. Journal of Applied Remote Sensing, 3, 033549.
Chang, N. B., Han, M., Yao, W., Xu, S. G., and Chen, L. C. (2010). Change detection of
land use and land cover in a fast growing urban region with SPOT-5 images and partial
Lanczos extreme learning machine. Journal of Applied Remote Sensing, 4, 043551.
Chang, N. B., Yang, J., Daranpob, A., Jin, K. R., and James, T. (2012). Spatiotemporal pattern
validation of chlorophyll-a concentrations in Lake Okeechobee, Florida using a comparative MODIS image mining approach. International Journal of Remote Sensing, 33,
doi:10.1080/01431161.2011.608089.
Chen, N., Di, L., Yu, G., Gong, J., and Wei, Y. (2007). Use of ebRIM-based CSW with sensor observation services for registry and discovery of remote-sensing observations.
Computing Geoscience, 35(2), 360–372.
Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). (2011).
http: //www.cuahsi.org/hos.html, accessed May 2011.
Intergovernmental Panel on Climate Change (IPCC). (2010). Climate Change 2010: IPCC
Fifth Assessment Report (AR5).
Krajewski, W. F., Anderson, M., Eichinger, W., Entekhabi, D., Hornbuckle, B., Houser, P.,
Katul, G., Kustas, W., Norman, J., Parlange, M., Peters-Lidard, C., and Wood, E. (2004).
A Remote Sensing Observatory for Hydrologic Sciences: A Genesis for Scaling to
Continental Hydrology. CUASHI Publications, Washington, DC, USA.
Kumar, P. (2011). Typology of hydrologic predictability. Water Resource Research, 47,
W00H05, doi:10.1029/2010WR009769.
National Center for Atmospheric Research (NCAR). (2002). Cyberinfrastructure for Environmental Research and Education. NCAR, Boulder, CO, USA.
National Science Foundation (NSF). (2003). Complex environmental systems: Synthesis for
earth, life, and society in the 21st century. NSF Environmental Cyberinfrastructure
Report, Washington, DC, USA.
Pacific Northwest Hydrologic Observatory (PNWHO). (2011). http://pnwho.forestry.oregonstate
.edu/background/index.php, accessed May 2011.
Volpe, G., Santoleri, R., Vellucci, V., d’Alcalà, M. R., Marullo, S., and D’Ortenzio, F. (2007).
The colour of the Mediterranean Sea: Global versus regional bio-optical algorithms
evaluation and implication for satellite chlorophyll estimates. Remote Sensing of Environment, 107, 625–638.
Zilioli, E. and Brivio, P. A. (1997). The satellite derived optical information for the comparative assessment of lacustrine water quality. Science of the Total Environment, 196,
229–245.