Evaluation and Improvement of No-Ground-Truth Dual Band Algorithm for Shallow Water Depth Retrieval: A Case Study of a Coastal Island

Abstract

Conventional bathymetric inversion approaches require bathymetric data as ground truth to obtain shallow water depth from high spatial resolution remote sensing imagery. Thus, bathymetric mapping methods that do not require inputs from in situ measurements are highly desirable. In this paper, we propose a dual-band model improvement method and evaluate the performance of this novel dual-band model approach to obtain the underwater terrain around a coastal island by using four WorldView-2/3 imageries. Then, we validate the results through changing water column properties with the Kd multiple linear regression model simulated by Hydrolight. We multiply the best coefficient and blue–green band value with different substrates on the pixels, which sample along the coastal line and isobath. The results show that the mean bias of inversed depth ranges from 1.73 to 2.96 m in the four imageries. The overall accuracy of root mean square errors (RMSEs) is better for depths shallower than 10 m, and the average relative error is 11.89%. The inversion accuracy of this new model is higher than Lee’s classical Kd model and has a wider range of applications than Chen’s dual-band model. The no-ground-truth dual-band algorithm has higher accuracy than the other log-ratio methods mentioned in this paper.