The optical imagery has the potential for extraction of spatially and temporally explicit bathymetric information in inland/coastal waters. Lyzenga’s model and optimal band ratio analysis (OBRA) are main bathymetric models which both provide linear relations with water depths. The former model is sensitive and the latter is quite robust to substrate variability. The simple regression is the widely used approach for calibration of bathymetric models either Lyzenga’s model or OBRA model. In this research, a multiple regression is examined for empirical calibration of the models in order to take the advantage of all spectral channels of the imagery. This method is applied on both Lyzenga’s model and OBRA model for the bathymetry of a shallow Alpine river in Italy, using WorldView-2 (WV-2) and GeoEye images. Insitu depths are recorded using RTK GPS in two reaches. One-half of the data is used for calibration of models and the remaining half as independent check-points for accuracy assessment. In addition, radiative transfer model is used to simulate a set of spectra in a range of depths, substrate types, and water column properties. The simulated spectra are convolved to the sensors’ spectral bands for further bathymetric analysis. Investigating the simulated spectra, it is concluded that the multiple regression improves the robustness of the Lyzenga’s model with respect to the substrate variability. The improvements of multiple regression approach are much more pronounced for the Lyzenga’s model rather than the OBRA model. This is in line with findings from real imagery; for instance, the multiple regression applied for calibration of Lyzenga’s and OBRA models demonstrated, respectively, 22% and 9% higher determination coefficients (R2) as well as 3 cm and 1 cm better RMSEs compared to the simple regression using the WV-2 image.
Milad Niroumand-Jadidi and Alfonso Vitti, "Improving the accuracies of bathymetric models based on multiple regression for calibration (case study: Sarca River, Italy)," Proc. SPIE 9999, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2016, 99990Q (Presented at SPIE Remote Sensing: September 27, 2016; Published: 19 October 2016); https://doi.org/10.1117/12.2242083.
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