Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R² (p < 0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.

中文翻译：

---

将测站深度测量与图像档案相结合，实现基于光谱的河流测深遥感


遥感可以成为绘制河流测深图的有效工具，但需要直接测量来校准图像得出的深度估计，这阻碍了这种方法的更广泛应用。避免专门进行校准的现场活动的一种方法是利用现有数据。在本研究中，我们介绍了使用量具记录和图像数据库 (BaMGRID) 进行测深测绘的框架。该工作流程包括检索在测量站现场访问期间进行的深度测量，下载存档的多光谱图像，然后组合这两个数据集以建立深度和反射率之间的关系。我们开发了一个处理链，其中涉及使用应用程序编程接口来获取现场访问期间进行的深度测量和以量具为中心的图像，然后通过针对小样本量修改的最佳带比分析 (OBRA) 算法将深度与反射率联系起来。将此工作流程应用于两个河流流域内的选定测量仪表明，从多光谱卫星图像进行深度检索可能非常准确，但在给定地点从一张图像到下一张图像的结果各不相同。高分辨率航空摄影不太有利于在所考虑的盆地之一进行测深测绘。在我们评估的深度检索性能的四个预测因子（深度、宽度和水体清晰度指数的平均值和标准差）中，只有宽度始终与 OBRA R ² 显着相关（p < 0.026）。目前，BaMGRID 最适合进行逐站点分析，以支持覆盖范围内的实际应用；河流测深的连续、全流域测绘将需要更多的研究。