Bathymetric information for shallow coastal/lake areas is essential for hydrological engineering applications
such as sedimentary processes and coastal studies. Remotely
sensed imagery is considered a time-effective, low-cost, and
wide-coverage solution for bathymetric measurements. This
study assesses the performance of three proposed empirical
models for bathymetry calculations in three different areas:
Alexandria port, Egypt, as an example of a low-turbidity deep
water area with silt-sand bottom cover and a depth range of
10.5 m; the Lake Nubia entrance zone, Sudan, which is a
highly turbid, unstable, clay bottom area with water depths
to 6 m; and Shiraho, Ishigaki Island, Japan, a coral reef area
with varied depths ranging up to 14 m. The proposed models
are the ensemble regression tree-fitting algorithm using bagging
(BAG), ensemble regression tree-fitting algorithm of least squares boosting (LSB), and support vector regression algorithm (SVR). Data from Landsat 8 and Spot 6 satellite images were used to assess the performance of the proposed models. The three models were used to obtain bathymetric maps using the reflectance of green, red, blue/red, and
green/red band ratios. The results were compared with corresponding
results yielded by two conventional empirical
methods, the neural network (NN) and the Lyzenga generalised
linear model (GLM). Compared with echosounder data,
BAG, LSB, and SVR results demonstrate higher accuracy
ranges from 0.04 to 0.35 m more than Lyzenga GLM. The
BAG algorithm, producing the most accurate results, proved
to be the preferable algorithm for bathymetry calculation.