5:15 PM - 6:45 PM
[MIS10-P07] Satellite sea ice motion vector retrieval in the Antarctic Ocean: Application to study of recent Antarctic sea ice reduction
Keywords:sea ice, Antarctic Ocean, GCOM-W, AMSR2
Sea ice extent in the Antarctic Ocean, which reaches yearly minimum and maximum in February and September, respectively, hit record lows in both timings in 2023. Since such drastic sea ice reductions promote air-ocean interaction and damage ecosystems developed on the sea ice cover, satellite monitoring of sea ice variations is in great demand from both physical and biochemical aspects. Unlike the Arctic Ocean, sea ice advance in the Antarctic Ocean is not limited by coastal boundaries. Therefore, Antarctic sea ice variability is expected to be more sensitive to dynamic variability. This study aims twofold: 1) applying a sea ice motion vector algorithm developed by Yoshizawa et al. (2023) for monitoring the Antarctic sea ice motions, and 2) investigating a mechanism of recent Antarctic sea ice reductions.
This algorithm based on particle image velocimetry using satellite images from GCOM-W/AMSR2 observations has the advantage of improving velocity resolution using high-resolution AMSR2 89-GHz channel images. Although a high sensitivity of this channel to atmospheric moisture increases noises in retrieved vector fields, the noises are reduced by incorporating a validation step referring to vector fields from simultaneous 18- and 36-GHz channel observations. In this study, the algorithm is improved further by applying the de-striping technique proposed by Bouali and Ladjal (2011) for reducing noises in 89-GHz images. In the presentation, we will report the impacts of using de-striped images in noise reduction, validation results by comparing them with drifting buoy trajectories, and a relationship between Antarctic sea ice reduction and dynamic movement.
References
Yoshizawa, E., T. Kamoshida, and K. Shimada, Sea Ice Motion Vector Retrievals from AMSR2 89-GHz Data: Validation Algorithm with Simultaneous Multichannel Observations. Journal of Atmospheric and Oceanic Technology, 40, 3-13, 2023, https://doi.org/10.1175/JTECH-D-22-0049.1.
Bouali, M., and S. Ladjal, Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model. IEEE Transactions on Geoscience and Remote Sensing, 49(8), 2924-2935, 2011, https://doi: 10.1109/TGRS.2011.2119399.
This algorithm based on particle image velocimetry using satellite images from GCOM-W/AMSR2 observations has the advantage of improving velocity resolution using high-resolution AMSR2 89-GHz channel images. Although a high sensitivity of this channel to atmospheric moisture increases noises in retrieved vector fields, the noises are reduced by incorporating a validation step referring to vector fields from simultaneous 18- and 36-GHz channel observations. In this study, the algorithm is improved further by applying the de-striping technique proposed by Bouali and Ladjal (2011) for reducing noises in 89-GHz images. In the presentation, we will report the impacts of using de-striped images in noise reduction, validation results by comparing them with drifting buoy trajectories, and a relationship between Antarctic sea ice reduction and dynamic movement.
References
Yoshizawa, E., T. Kamoshida, and K. Shimada, Sea Ice Motion Vector Retrievals from AMSR2 89-GHz Data: Validation Algorithm with Simultaneous Multichannel Observations. Journal of Atmospheric and Oceanic Technology, 40, 3-13, 2023, https://doi.org/10.1175/JTECH-D-22-0049.1.
Bouali, M., and S. Ladjal, Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model. IEEE Transactions on Geoscience and Remote Sensing, 49(8), 2924-2935, 2011, https://doi: 10.1109/TGRS.2011.2119399.