Timely and up-to-date bathymetry maps over large geographical areas have been difficult to create, due to the cost and difficulty of collecting in situ calibration and validation data. Recently, combinations of spaceborne Ice, Cloud, and Elevation Satellite-2 (ICESat-2) lidar data and Landsat/sentinel-2 data have reduced these obstacles. However, to date, there have been no means of automatically extracting bathymetry photons from ICESat-2 tracks for model calibration/validation and no well-established open source workflows for generating regional scale bathymetric models. Here we provide an open source approach for generating bathymetry maps for the shallow water region around the island of Andros, Bahamas. We demonstrate an efficient means of processing 224 ICESat-2 tracks and 221 Landsat-8 scenes, using the classification of subaquatic height extracted photons (C-SHELPh) algorithm and Extra Trees Regression to provide 30 m pixel estimates of per-pixel depth and standard error. We map bathymetry with an RMSE of 0.32 m and RMSE% of 6.7%. Our workflow and results demonstrate a means of achieving accurate regional–scale bathymetry maps from purely spaceborne data.

中文翻译：

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用于区域测深测绘的纯星载开源方法


由于收集现场校准和验证数据的成本和困难，很难在大的地理区域创建及时、最新的测深图。最近，星载冰、云和高程卫星 2 (ICESat-2) 激光雷达数据与 Landsat/sentinel-2 数据的结合减少了这些障碍。然而，迄今为止，还没有办法从 ICESat-2 轨迹中自动提取测深光子以进行模型校准/验证，也没有用于生成区域尺度测深模型的完善的开源工作流程。在这里，我们提供了一种开源方法，用于生成巴哈马安德罗斯岛周围浅水区域的测深图。我们展示了处理 224 个 ICESat-2 轨道和 221 个 Landsat-8 场景的有效方法，使用水下高度提取光子分类 (C-SHELPh) 算法和额外树回归来提供每像素深度和标准的 30 m 像素估计错误。我们绘制的测深图的 RMSE 为 0.32 m，RMSE% 为 6.7%。我们的工作流程和结果展示了一种从纯星载数据获取准确的区域尺度测深图的方法。