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Information - CR100 Remote sensing of snow cover and sea ice (co-listed in HS)
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Event Information |
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Remote sensing represents an efficient tool for monitoring and mapping the cryosphere over large areas where, otherwise, it would be difficult if not impossible. This session will bring together recent advances in remote sensing of seasonal components of the cryosphere such as frozen soil, snow, sea ice, etc. including results from recent field experiments, development and improvement of electromagnetic models, evaluation/development of techniques for the retrieval of geophysical parameters, data assimilation and data fusion.
Recently, several measurements have been carried out from ground-, aircraft- and satellite-based instruments, also to improve our understanding of scaling issues related to remote sensing and to support future satellite missions. Other experiments are planned for the near future. Important advances have been recently made in the development of electromagnetic models and in the coupling between electromagnetic and hydrological models. Also, inversion techniques for the retrieval of geophysical and hydrological parameters (e.g. snow water equivalent, grain size, sea ice concentration, snow impurities) have been proposed and improved in view of new satellites launched recently (e.g. Envisat, Terra, Aqua, ICESat).
Contributions regarding the following topics are solicited: experimental data from experimental campaigns and plans for next campaigns; electromagnetic modeling of snow and sea/ice and merging of hydrological and electromagnetic models; retrieval of parameters of hydrological and climatological interest; data fusion of multisensoral remote sensing data; assimilation of radiances/products in Land Surface or Climate Models; combination of different remote sensing techniques for studying snow/sea ice; missions in orbit/planned.
Other contributions regarding remote sensing of seasonal components of the cryosphere are encouraged.
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Preliminary List of Solicited Speakers |
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Back to Session Programme
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