标题： 利用部分卷积神经网络从稀疏卫星激光测高数据重建南极海冰厚度 显示英文标题

标题： Reconstruction of Antarctic sea ice thickness from sparse satellite laser altimetry data using a partial convolutional neural network

摘要： 南极海冰厚度（SIT）数据在亚月分辨率下持续缺乏，从根本上限制了对大规模海冰质量平衡过程的定量理解。 本研究基于稀疏的冰云和陆地高程卫星（ICESat：2003-2009）和ICESat-2（2018-2024）沿轨激光测高SIT检索，采用深度学习方法，开发了一套全南极5天和12.5公里分辨率的SIT数据集。 重建的SIT通过独立的向上看声纳（ULS）观测进行了定量验证，并且比其他四个卫星衍生和再分析的南极SIT数据集更准确。 通过ULS和ICESat-2观测进一步验证了重建SIT的时间演变。 这些数据集之间一致的季节性循环和季节内趋势证实了重建的可靠性。 除了增进对南极海冰变化和气候联系的机制理解外，这一重建数据集的近乎实时更新能力对于监测和预测南极海冰状态具有实际操作价值。 显示英文摘要

摘要： The persistent lack of spatially complete Antarctic sea ice thickness (SIT) data at sub-monthly resolution has fundamentally constrained the quantitative understanding of large-scale sea ice mass balance processes. In this study, a pan-Antarctic SIT dataset at 5-day and 12.5 km resolution was developed based on sparse Ice, Cloud and Land Elevation Satellite (ICESat: 2003-2009) and ICESat-2 (2018-2024) along-track laser altimetry SIT retrievals using a deep learning approach. The reconstructed SIT was quantitatively validated against independent upward-looking sonar (ULS) observations and showed higher accuracy than the other four satellite-derived and reanalyzed Antarctic SIT datasets. The temporal evolution of the reconstructed SIT was further validated by ULS and ICESat-2 observations. Consistent seasonal cycles and intra-seasonal tendencies across these datasets confirm the reconstruction's reliability. Beyond advancing the mechanistic understanding of Antarctic sea ice variability and climate linkages, this reconstruction dataset's near-real-time updating capability offers operational value for monitoring and forecasting the Antarctic sea ice state.