Abstract's details
Time series over the Brahmaputra and Ganges rivers from Envisat, CryoSat-2 and SARAL/AltiKa altimetry
Event: 2014 Ocean Surface Topography Science Team Meeting
Session: Science Results from Satellite Altimetry: Inland waters (multi-mission and long-term monitoring)
Presentation type: Poster
This study presents a comparison of river level time series obtained from Envisat, CryoSat-2 and SARAL/AltiKa altimetry in the Brahmaputra and Ganges river basin.
A key concern of the CryoSat-2 orbit has been its long repeat period of 369 days, which is usually undesirable for river and lake monitoring. However, the results of the method presented in this study show that the CryoSat-2 data can indeed be used for such monitoring by utilizing the high spatial coverage and the sub-cycle period of 30 days.
The CryoSat-2 river levels are evaluated by comparing with Envisat data from the period in which the two missions overlapped (2010-2012). In addition, the CryoSat-2 time series are compared with recent results from the French-Indian mission SARAL/AltiKa, which is an innovating Ka-band altimeter system, dedicated to accurate measurement of ocean surface topography and flies the same orbit as Envisat, providing readily available time series from virtual stations.
A key concern of the CryoSat-2 orbit has been its long repeat period of 369 days, which is usually undesirable for river and lake monitoring. However, the results of the method presented in this study show that the CryoSat-2 data can indeed be used for such monitoring by utilizing the high spatial coverage and the sub-cycle period of 30 days.
The CryoSat-2 river levels are evaluated by comparing with Envisat data from the period in which the two missions overlapped (2010-2012). In addition, the CryoSat-2 time series are compared with recent results from the French-Indian mission SARAL/AltiKa, which is an innovating Ka-band altimeter system, dedicated to accurate measurement of ocean surface topography and flies the same orbit as Envisat, providing readily available time series from virtual stations.
Contribution: timeseries_poster.compressed.pdf (pdf, 3285 ko)
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