High spatial resolution bathymetric maps of coral reefs can show the details of terrain. However, most satellite-based imagery with a spatial resolution < 10 m has only three visible bands and one near-infrared (NIR) band. When in situ bathymetric data are unavailable, it is difficult to map bathymetry from high spatial resolution imagery with spectral matching or empirical models for clear or turbid waters. In this study, we developed a downscaled bathymetric mapping approach (DBMA) that uses the water depth estimated from multitemporal Landsat-8 data to calibrate the empirical model for high spatial resolution imagery (e.g., Sentinel-2A/B, GaoFen-1/2, ZiYuan-3, and WorldView-2) in the absence of in situ bathymetric data. Our results show that DBMA provides high accuracy for depth ranging from 0 to 12 m for clear waters (0 m to 5 m for turbid waters), with a root mean squared error (RMSE) smaller than 2 m. Relative to empirical models (calibrated with in situ data), DBMA underestimates water depth more for depth >12 m for clear waters (5 m for turbid waters) while slightly overestimates more for depth < 4 m for clear waters (3 m for turbid waters). Nevertheless, DBMA performs better than the empirical models for depth between 4 m and 12 m for clear waters (3 m and 5 m for turbid waters). Furthermore, the good performance of DBMA is also demonstrated by the finding that the scaling effect on the DBMA is limited. DBMA presents a reliable solution to obtain high spatial resolution bathymetric maps without leaving out small regions in the absence of in situ data.

珊瑚礁的高空间分辨率测深图可以显示地形的细节。然而，大多数空间分辨率 < 10 m 的卫星图像只有三个可见波段和一个近红外 (NIR) 波段。当原位测深数据不可用时，很难用光谱匹配或经验模型从高空间分辨率图像中绘制水深图，用于清澈或混浊的水域。在本研究中，我们开发了一种缩小的测深映射方法 (DBMA)，该方法使用从多时相 Landsat-8 数据估计的水深来校准高空间分辨率影像的经验模型（例如，Sentinel-2A/B、GaoFen-1/2 、ZiYuan-3 和 WorldView-2）在没有原位测深数据的情况下。我们的结果表明，DBMA 为清水提供了 0 到 12 m 的深度范围（浑水为 0 m 到 5 m）的高精度，均方根误差 (RMSE) 小于 2 m。相对于经验模型（用原位数据校准），DBMA 低估了水深大于 12 m 的清水（浑水 5 m）的水深，而稍微高估了清水< 4 m 的水深（浑水 3 m） ）。尽管如此，对于清水，DBMA 在 4 m 到 12 m 之间的深度（对于浑水为 3 m 和 5 m）时，其性能优于经验模型。此外，DBMA 的扩展效应有限的发现也证明了 DBMA 的良好性能。DBMA 提供了一种可靠的解决方案，可以在没有原位数据的情况下，在不遗漏小区域的情况下获得高空间分辨率的测深图。