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Multiscale Hydrologic Remote Sensing: Perspectives and Applications
Houser (2004) suggested that MODIS observations may still be useful for a 94%
cloud cover threshold.
Other thresholds were investigated when passive microwave remote sensing products were used to classify the land as snow or no snow. Tong et al. (2010) applied
MOD10A2 as ground truth for the assessment of the SSM/I mapping performance
and showed that increasing the threshold from 0 to 12–37 mm increased the overall
mapping accuracy from 50% to 90%.
9.6 CONCLUSIONS
The MODIS instruments were launched in 2000 and 2002. In spite of a design life
of 6 years, MODIS has delivered comprehensive snow cover information for more
than a decade. Numerous studies showed that the MODIS snow cover products are,
overall, in good agreement with other satellite data and ground-based snow data.
The mapping accuracy depends on the region and the season and, very often, is
within a range that makes the data very useful and attractive for hydrologic applications. Obscuration by clouds may limit the application potential of MODIS snow
cover products significantly. Simple cloud impact reduction methods based on data
merging were demonstrated to be remarkably efficient without deteriorating the
snow mapping performance much relative to ground snow observations. The main
strength of the merging approaches lies in their simplicity and robustness. They can
be easily applied in an operational context without much additional data as would
be needed in assimilation schemes. Numerous applications of MODIS snow cover
data in hydrologic studies show that MODIS products provide very attractive information for mapping the spatial and temporal changes in snow cover. The methodology for assimilating MODIS data into hydrologic models needs to account for the
differences between the two snow representations (presence of snow in the case of
MODIS, SWE in the case of the models). Threshold methods are usually used to link
these two representations. Assimilation of MODIS data generally improves the ability of the hydrologic models to simulate snow processes, although the improvement
in terms of simulating runoff is usually smaller. In the near future, more hydrologic
applications of using MODIS data for real-time forecasting, such as flood forecasting
or stream flow forecasting under climate change impact, are expected.
ACKNOWLEDGMENT
We would like to thank the ÖAW project “Predictability of Runoff in a Changing
Environment” for financial support.
REFERENCES
Ackerman, S. A., Strabala, K. I., Menzel, P. W. P., Frey, R. A., Moeller, C. C., and Gumley,
L. E. (1998). Discriminating clear sky from clouds with MODIS. Journal of Geophysical
Research, 103, 32141–32157.
Akyurek, Z., Hall, D. K., Riggs, G. A., and Sensoy, A. (2010). Evaluating the utility of the
ANSA blended snow cover product in the mountains of eastern Turkey. International
Journal of Remote Sensing, 31(14), 3727–3744.
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
Houser (2004) suggested that MODIS observations may still be useful for a 94%
cloud cover threshold.
Other thresholds were investigated when passive microwave remote sensing products were used to classify the land as snow or no snow. Tong et al. (2010) applied
MOD10A2 as ground truth for the assessment of the SSM/I mapping performance
and showed that increasing the threshold from 0 to 12–37 mm increased the overall
mapping accuracy from 50% to 90%.
9.6 CONCLUSIONS
The MODIS instruments were launched in 2000 and 2002. In spite of a design life
of 6 years, MODIS has delivered comprehensive snow cover information for more
than a decade. Numerous studies showed that the MODIS snow cover products are,
overall, in good agreement with other satellite data and ground-based snow data.
The mapping accuracy depends on the region and the season and, very often, is
within a range that makes the data very useful and attractive for hydrologic applications. Obscuration by clouds may limit the application potential of MODIS snow
cover products significantly. Simple cloud impact reduction methods based on data
merging were demonstrated to be remarkably efficient without deteriorating the
snow mapping performance much relative to ground snow observations. The main
strength of the merging approaches lies in their simplicity and robustness. They can
be easily applied in an operational context without much additional data as would
be needed in assimilation schemes. Numerous applications of MODIS snow cover
data in hydrologic studies show that MODIS products provide very attractive information for mapping the spatial and temporal changes in snow cover. The methodology for assimilating MODIS data into hydrologic models needs to account for the
differences between the two snow representations (presence of snow in the case of
MODIS, SWE in the case of the models). Threshold methods are usually used to link
these two representations. Assimilation of MODIS data generally improves the ability of the hydrologic models to simulate snow processes, although the improvement
in terms of simulating runoff is usually smaller. In the near future, more hydrologic
applications of using MODIS data for real-time forecasting, such as flood forecasting
or stream flow forecasting under climate change impact, are expected.
ACKNOWLEDGMENT
We would like to thank the ÖAW project “Predictability of Runoff in a Changing
Environment” for financial support.
REFERENCES
Ackerman, S. A., Strabala, K. I., Menzel, P. W. P., Frey, R. A., Moeller, C. C., and Gumley,
L. E. (1998). Discriminating clear sky from clouds with MODIS. Journal of Geophysical
Research, 103, 32141–32157.
Akyurek, Z., Hall, D. K., Riggs, G. A., and Sensoy, A. (2010). Evaluating the utility of the
ANSA blended snow cover product in the mountains of eastern Turkey. International
Journal of Remote Sensing, 31(14), 3727–3744.