A purely spaceborne open source approach for regional bathymetry mapping: Bahamas Median DEM

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 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 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.

受限于原位校准与验证数据采集的成本与难度，制作大地理范围的及时更新型水深测绘图始终是一项极具挑战的工作。近年来，结合星载ICESat-2激光雷达数据与Landsat/Sentinel-2数据的研究方案已有效缓解了上述难题。然而截至目前，尚无针对模型校准/验证的、可从ICESat-2航迹中自动提取水深光子的方法，也缺乏成熟的开源工作流用于生成区域尺度的水深模型。本研究提供了一套开源方法，用于生成巴哈马安德罗斯岛周边浅水区的水深测绘图。研究展示了一种高效处理224条ICESat-2航迹与221景Landsat-8影像的方案：采用C-SHELPh算法与极端树回归（Extra Trees Regression），实现30米分辨率像元级的水深与标准误差估算。本研究生成的水深图均方根误差（RMSE）为0.32米，百分比均方根误差（RMSE%）为6.7%。本研究的工作流与实验结果证明，仅依靠星载数据即可生成高精度的区域尺度水深测绘图。