Regression kriging analysis for predicting the shallow depth water from Sentinel-2 satellite multi-spectral images, study area: Coastline of Florida, USA

The shallow depth water mapping has become important to the study of morphology and resources management of the coastal area. Moreover, for city or urban planning, it can be used for determining the proper location for seaport or tourist destination areas such as diving spots, coral reef monitoring, etc. However, the acquisition of shallow depth water data in a large area is somehow costly. In this paper, we represent an approach of mapping the high accuracy bathymetry with input from open satellite access data and some point measurement of bathymetry in the field (can be from LiDAR, Fathometer, Single Beam, or Multi-Beam). The study area is located in the Florida coastline, the USA that has satellite data from Sentinel-2 while the bathymetry is from a single beam survey. The method is combining satellite-derived bathymetry (SDB) with the regression kriging analysis, which shows a better depth water prediction compared to the SDB alone or the ordinary kriging method. The statistical result of the bathymetry shows the regression kriging has a better mean value, standard deviation and coefficient correlation compared to the true bathymetry value. Thus, this method can be utilized as an alternative method to map shallow depth water.