Bathymetry Determination from SAR imagery

Figure: (left) Ray tracing for wave propagation obtained from a C-band SAR image showing the refraction effect of the wave front. (right) Wave spectrum in kspace derived from a SAR image at a particular point (Envisat SAR data by ESA).


Coastal areas are exposed and subject to several natural risks, including coastal erosion, flooding and environmental degradation, with consequent losses of habitats and species. Particularly in the recent decades, sea level rise has increased the susceptibility of coastal areas, resulting in loss of territory and heavy damage of natural resources. The majority of the world’s population, infrastructures and economic activities are concentrated in coastal areas, so these are of strategic importance and need to be preserved. Therefore, monitoring the evolution of the coastline and, in particular, the bathymetry of a coastal zone is essential for management purposes, focusing the coastal protection efforts and resources on the most critical areas.

In high energetic sandy coasts, the underwater morphology can change significantly at storm time-scales. These rapid changes cannot be easily measured by traditional sound surveying methodologies, because there are time-consuming and expensive, but also because surveying vessels cannot operate in the wave shoaling and wave breaking regions under storm conditions. Earth Observation from space has instead become a preferred method for the monitoring of extensive coastal areas.

This research application is focused on the bathymetry derivation from SAR satellite data. We aim to adapt algorithms initially developed for TerraSAR-X images (Brusch et al. 2011; Lehner et al. 2012) to ESA Sentinel-1 data. The XWAVE methodology is used to determine water depth directly from the measurement of the ocean swell peak wavelength and wave direction, using a fast Fourier transformation (FFT) and the linear wave dispersion relationship. Implementation of corrections (Bruning et al. 1990; Hasselmann and Hasselmann 1991; Schulz-Stellenfleth et al. 1995) on the linear modulation transfer functions (MTF) approach (Alpers et al 1981; Monteiro 2013) are being considered in the present methodology. Improvements using non-linear wave theories and the correspondent dispersion relationships (Flampouris et al. 2011) are also envisaged.



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Brüning, C., R. Schmidt, and W. Alpers (1994). Estimation of ocean waveradar modulation transfer function from synthetic aperture radar imagery. Journal of Geophysical Research, Vol. 99, No. C5:98039815

Flampouris, S., J. Seemann, C. Senet, and F. Ziemer (2011). The Influence of the Inverted Sea Wave Theories on the Derivation of Coastal Bathymetry. IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 3, May 2011

Hasselmann, K., and S. Hasselmann (1991). On the nonlinear mapping of an ocean wave spectrum into a synthetic aperture radar image spectrum and its inversion. Journal of Geophysical Research, Vol. 96, No. C6:1071310729

Monteiro, F.M. (2013). Advanced Bathymetry Retrieval from Swell Patterns in HighResolution SAR Images. Open Access Theses, Paper 466

SchultzStellenfleth, J., S. Lehner, and D. Hoja (2005). A parametric scheme for the retrieval of twodimensional ocean wave spectra from synthetic aperture radar look cross spectra. Journal of Geophysical Research, Vol. 110, No. C5:417.

Brusch, S., P. Held, S. Lehner, W. Rosenthal & A. Pleskachevsky (2011). Underwater bottom topography in coastal areas from TerraSARX data, International Journal of Remote Sensing, 32:16, 45274543, DOI: 10.1080/01431161.2010.489063 URL:

Lehner, S., A. Pleskachevsky & M. Bruck (2012). High resolution satellite measurements of coastal wind field and sea state, International Journal of Remote Sensing, 33:23, 73377360, DOI: 10.1080/01431161.2012.685975 URL:

Posted on: 2nd June 2016, by : admin