Remotely Sensed Empirical Modeling of Bathymetry in the Southeastern Caspian Sea

Abstract

Remotely sensed imagery is proving to be a useful tool in estimating water depths in coastal zones. On the other hand, many coastal zone studies in the southern part of the Caspian Sea are only concerned with areas of shallow water and would benefit from easily updated bathymetric estimates. In this study, we tested three different methods for extracting bathymetry information from Landsat 5 data in the southeastern Caspian Sea, Iran. The first method used was a single band algorithm (SBA), utilizing either blue or red bands. The second method was principal components analysis (PCA), and the third method was the multi-layer perceptron (back propagation) neural network between visible bands and one output neuron (bathymetry). This latter MLP-ANNs method produced the best depth estimates (r = 0.94). The single band algorithm utilizing a red band also produced reasonably accurate results (r = 0.66), while the blue band algorithm and PCA did not perform (correlation between the estimated and measured depths 0.49 and 0.21, respectively). Furthermore, the shallow waters have negative influences on the accuracy of bathymetric modeling, thus the correction of data in these shallow waters is challenged by the presence of continental aerosols, bottom reflectance, and adjacency of land.

Keywords

• bathymetry
• estimation
• satellite imagery
• Landsat
• artificial intelligence
• Southeast Caspian Sea