Abstract's details
Sea-ice and snow facies classifications from Altika data over the Polar Regions
CoAuthors
Event: 2015 Ocean Surface Topography Science Team Meeting
Session: Others (poster only)
Presentation type: Type Poster
Contribution: PDF file
Abstract:
Two sets of classification algorithms have been computed for Altika mission following developments performed previously for the Envisat altimetry mission. There is one specific algorithm for each polar region within each set. They take advantage of having both passive and active microwave sensors on the same platform with co-registered measurements.
The first set of algorithms concerns sea-ice. They detect sea-ice corrupted sea surface height data for oceanography applications, but also provide sea-ice type (i.e. first-year ice, multi-year ice, ambiguous ice observed during summer and mixture of types) for cryosphere studies. Their performances have been evaluated based on collocations between the along-track Altika data with daily grids of sea ice type from the Ocean and Sea Ice Satellite Application Facility (OSI SAF). Results show better performances for the present approach for recognition of sea-ice corrupted data vs. ice-free ocean data when one compares with those observed with the operational algorithm. Concerning the sea-ice extent monitoring, we obtain a good continuity with the Envisat time-series and a good agreement with other mission estimations.
The second algorithm aims to separate different snow regions within the polar ice sheets based on measured microwave signatures. Our approach broadens the description of the snow pack by taking into account characteristics such as surface roughness, grain size, stratification, and snow melt effects, whereas this latter has often been solely considered in most previous works. This difference in snow morphology is due to variable conditions in local climate which is governed by local topography. Such partition of the ice sheet might help to better understand relationships between microwave signatures and snow morphology and might represent a useful and simple tool for tracking the effects of climate change. Comparison with past Envisat results has been performed.
All these results come from the CNES PEACHI project.
The first set of algorithms concerns sea-ice. They detect sea-ice corrupted sea surface height data for oceanography applications, but also provide sea-ice type (i.e. first-year ice, multi-year ice, ambiguous ice observed during summer and mixture of types) for cryosphere studies. Their performances have been evaluated based on collocations between the along-track Altika data with daily grids of sea ice type from the Ocean and Sea Ice Satellite Application Facility (OSI SAF). Results show better performances for the present approach for recognition of sea-ice corrupted data vs. ice-free ocean data when one compares with those observed with the operational algorithm. Concerning the sea-ice extent monitoring, we obtain a good continuity with the Envisat time-series and a good agreement with other mission estimations.
The second algorithm aims to separate different snow regions within the polar ice sheets based on measured microwave signatures. Our approach broadens the description of the snow pack by taking into account characteristics such as surface roughness, grain size, stratification, and snow melt effects, whereas this latter has often been solely considered in most previous works. This difference in snow morphology is due to variable conditions in local climate which is governed by local topography. Such partition of the ice sheet might help to better understand relationships between microwave signatures and snow morphology and might represent a useful and simple tool for tracking the effects of climate change. Comparison with past Envisat results has been performed.
All these results come from the CNES PEACHI project.