Intertidal Bathymetry Extraction with Multispectral Images: A Logistic Regression Approach - Isabel Bué; João Catalão; Álvaro Semedo (Remote Sensing, 2020)
In this study, a methodology to estimate the intertidal bathymetry from multispectral remote sensing images is presented. The technique is based on the temporal variability of the water and the intertidal zone reflectance and their correlation with the tidal height. The water spectral behavior is characterized by high absorption at the infrared (IR) band or radiation with higher wavelengths. Due to tidal cycles, pixels on the intertidal zone have higher temporal variability on the near IR spectral reflectance. The variability of IR reflectivity in time is modeled through a sigmoid function of three parameters, where the inflection parameter corresponds to the pixel elevation. The methodology was tested at the Tagus river estuary in Lisbon, Portugal, and at the Bijagós archipelago, in the West African nation of Guinea-Bissau. Multispectral images from Sentinel-2 satellites were used, after atmospheric corrections from ACOLITE processor and the derived bathymetric model validated with in situ data. The presented method does not require additional depth data for calibration, and the output can generate intertidal digital elevation models at 10 m spatial resolution, without any manual editing by the operator. The results show a standard deviation of 0.34 m at the Tagus tidal zone, with −0.50 m bias, performing better than the Stumpf ratio transform algorithm, also applied to the test areas to derive intertidal bathymetry. This methodology can be used to update intertidal elevation models with clear benefits to monitoring of intertidal dynamics, morphodynamic modeling, and cartographic update.