Determination of bathymetric information is key
element for near off shore activities and hydrological studies such as coastal engineering applications, sedimentary processes and hydrographic surveying. Remotely sensed imagery has provided a wide coverage, low cost and time-effective solution
for bathymetric measurements. In this paper a methodology is introduced using Ensemble Learning (EL) fitting algorithm of Least Squares Boosting (LSB) for bathymetric maps calculation
in shallow lakes from high resolution satellite images and water depth measurement samples using Eco-sounder. This
methodology considered the cleverest sequential ensemble that assigns higher weights as Boosting for those training sets that
are difficult to fit. The LSB ensemble using reflectance of Green and Red bands and their logarithms from Spot-4 satellite image
was compared with two conventional methods; the Principal
Component Analysis (PCA) and Generalized Linear Model (GLM). The retrieved bathymetric information from the three
methods was evaluated using Echo Sounder data. The LSB fitting ensemble resulted in RMSE of 0.15m where the PCA and
GLM yielded RMSE of 0.19m and 0.18m respectively over shallow water depths less than 2m. The application of the proposed approach demonstrated better performance and accuracy compared with the conventional methods.