用2米分辨率重采样冰卫星数据，提升海冰分类与厚度计算精度。
Scalable Higher Resolution Polar Sea Ice Classification and Freeboard Calculation from ICESat-2 ATL03 Data
- 用2米窗口重采样原始数据，结合深度学习分类厚冰、薄冰和开水区。
- 通过哨兵2号自动标注训练数据，实现9倍加载加速和16.25倍处理提速。
- 基于局部海平面计算自由度，精度达96.56%，显著优于原有产品。
NASA的ICESat-2（IS2）卫星通过高分辨率地表高程测量，提供海冰信息。现有ATL07和ATL10产品使用10米至200米段落，聚合150个光子信号，可能导致局部海面高估，从而低估自由度（海冰高于海面高度）。为获得更高分辨率的海面高程与自由度信息，本文采用2米窗口对原始ATL03（地理定位光子）数据进行重采样。利用长短期记忆网络（LSTM）与多层感知机（MLP）模型，对2米段落进行厚冰、薄冰和开水分类。训练数据通过时空匹配的哨兵2号（Sentinel-2, S2）多光谱影像自动标注，并在冰水过渡区及云区进行人工修正。采用PySpark实现并行自动标注，实现9倍数据加载加速和16.25倍map-reduce加速。模型训练基于Horovod分布式框架，在8卡DGX A100集群上获得7.25倍加速。随后基于开水域段落计算局部海平面，最终实现自由度计算的8.54倍加载加速与15.7倍map-reduce加速。相比ATL07（局部海平面）和ATL10（自由度）产品，本方法分辨率更高、精度达96.56%。
原文摘要 · Abstract (English)
ICESat-2 (IS2) by NASA is an Earth-observing satellite that measures high-resolution surface elevation. The IS2's ATL07 and ATL10 sea ice elevation and freeboard products of 10m-200m segments which aggregated 150 signal photons from the raw ATL03 (geolocated photon) data. These aggregated products can potentially overestimate local sea surface height, thus underestimating the calculations of freeboard (sea ice height above sea surface). To achieve a higher resolution of sea surface height and freeboard information, in this work we utilize a 2m window to resample the ATL03 data. Then, we classify these 2m segments into thick sea ice, thin ice, and open water using deep learning methods (Long short-term memory and Multi-layer perceptron models). To obtain labeled training data for our deep learning models, we use segmented Sentinel-2 (S2) multi-spectral imagery overlapping with IS2 tracks in space and time to auto-label IS2 data, followed by some manual corrections in the regions of transition between different ice/water types or cloudy regions. We employ a parallel workflow for this auto-labeling using PySpark to scale, and we achieve 9-fold data loading and 16.25-fold map-reduce speedup. To train our models, we employ a Horovod-based distributed deep-learning workflow on a DGX A100 8 GPU cluster, achieving a 7.25-fold speedup. Next, we calculate the local sea surface heights based on the open water segments. Finally, we scale the freeboard calculation using the derived local sea level and achieve 8.54-fold data loading and 15.7-fold map-reduce speedup. Compared with the ATL07 (local sea level) and ATL10 (freeboard) data products, our results show higher resolutions and accuracy (96.56%).
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