Bathymetry, the topography of the sea floor, is in high demand due to the increase in offshore constructions like wind parks. It is also an important dataset for climate change modelling, when sea level rises and changes in circulation currents are to be simulated. The retrieval of accurate bathymetry data is a cost-intensive task usually requiring a survey vessel charting the respective area. However, bathymetry can also be retrieved remotely using data from Earth observation satellites. The main point of this study is the development of a processor that allows the automatic derivation of gridded bathymetry information from spaceborne Synthetic Aperture Radar (SAR) data. Observations of sea state modifications in SAR images are used to derive the bathymetry in shelf areas using the shoaling effect, which causes wavelengths to become shorter when reaching shallower waters. The water depth is derived using the dispersion relation for surface water waves, which requires wavelength and wave period as input parameters. While the wavelength can be directly retrieved from the SAR image, for the peak period additional information and procedures are required, e.g. local measurements or complex SAR data. A method for automatically deriving the wave period for swell waves in SAR images was developed and tested in this paper. It uses depth data from public databases as initial values which are compared to derived depths iterating through possible peak periods along the calculation grid; the peak period resulting in a minimal root-mean-square deviation is then used for bathymetry calculation. The bathymetry derived from a TerraSAR-X acquisition of the Channel Islands is presented; the resulting peak wave period of 11.3 s fits well to nearby in situ measurement data.

Satellite remote sensing acquisitions are usually processed after downlink to a ground station. The satellite travel time to the ground station adds to the total latency, increasing the time until a user can obtain the processing results. Performing the processing and information extraction onboard of the satellite can significantly reduce this time. In this study, synthetic aperture radar (SAR) image formation as well as ship detection and extreme weather detection were implemented in a multiprocessor system on a chip (MPSoC). Processing steps with high computational complexity were ported to run on the programmable logic (PL), achieving significant speed-up by implementing a high degree of parallelization and pipelining as well as efficient memory accesses. Steps with lower complexity run on the processing system (PS), allowing for higher flexibility and reducing the need for resources in the PL. The achieved processing times for an area covering 375 km ² were approximately 4 s for image formation, 16 s for ship detection, and 31 s for extreme weather detection. These developments combined with new downlink concepts for low-rate information data streams show that the provision of satellite remote sensing results to end users in less than 5 min after acquisition is possible using an adequately equipped satellite.

We present an algorithm for automatic detection of the land-water-line from TerraSAR-X images acquired over the Wadden Sea. In this coastal region of the southeastern North Sea, a strip of up to 20 km of seabed falls dry during low tide, revealing mudflats and tidal creeks. The tidal currents transport sediments and can change the coastal shape with erosion rates of several meters per month. This rate can be strongly increased by storm surges which also cause flooding of usually dry areas. Due to the high number of ships traveling through the Wadden Sea to the largest ports of Germany, frequent monitoring of the bathymetry is also an important task for maritime security. For such an extended area and the required short intervals of a few months, only remote sensing methods can perform this task efficiently. Automating the waterline detection in weather-independent radar images provides a fast and reliable way to spot changes in the coastal topography. The presented algorithm first performs smoothing, brightness thresholding, and edge detection. In the second step, edge drawing and flood filling are iteratively performed to determine optimal thresholds for the edge drawing. In the last step, small misdetections are removed.

This paper introduces an improved system for sea state observations for Near Real-Time (NRT) services using satellite-borne synthetic aperture radar (SAR). The empirical algorithm SAR-SeaStaR (SAR Sea State Retrieval) applies a combination of a classical approach using linear regression (LR) with machine learning (ML). SAR-SeaStaR includes a series of filtering and control procedures and a series of LR and ML model functions for different satellites/modes and following integrated sea state parameters: total significant wave height H_s , wave heights of dominant and secondary swells and windsea, mean, first and second moment wave periods T_m2 , and windsea period. SAR scenes are processed in raster format, the output are fields for each parameter showing their spatial distribution. In the scope of this study, the ML models were developed for H_s and T_m2 and implemented into SAR-SeaStaR for processing Level-1 products of X-band TerraSAR-X (TS-X) StripMap (SM) and C-band Sentinel-1 (S1) Interferometric Wide Swath Mode (IW), S1 Extra Wide (EW). The validations are based on processing large worldwide archives with several years of acquisitions. Hindcast data from numerical spectral models and in-situ buoys measurements are used as ground truth. The root mean squared errors of the complete system reached from these archived data for Hs are RMSE=0.35 m for TS-X SM (pixel spacing ca. 1.2-4.5 m pixel), RMSE=0.25 m for S1 Wave Mode (WV, ca. 3.5 m pixels), RMSE=0.42 m for the coarser S1 IW (10 m pixels) and RMSE=0.52 m for S1 EW (40 m pixels). SAR-SeaStaR was implemented in the Sea State Processor software using modular architecture and applied at the DLR Ground Station in Neustrelitz as part of an NRT demonstrator service. S1 IW data acquired over North and Baltic Sea are processed automatically, surface wind and sea state parameters are provided daily.