EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

New Algorithm and Processor for Obtaining Maritime Information from Sentinel-1 Radar Imagery for Near Real Time Services

Andrey Pleskachevsky1, Björn Tings1, Sven Jacobsen1, Egbert Schwarz2, Detmar Krause2, and Holger Daedelow2
Andrey Pleskachevsky et al.
  • 1German Aerospace Center (DLR), Maritime Safety and Security Lab Bremen, Bremen, Germany (
  • 2German Aerospace Center (DLR), German Remote Sensing Data Center, National Ground Segment, Neustrelitz, Germany

The focus of the study is analysing storm peak/center propagation, front movement and arrival of swell using newest remote sensing information, numerical models, in-situ measurements and their combination. For this purposes, a new empirical algorithm for sea state retrieval from satellite borne Sentinel-1 (S1) Synthetic Aperture Radar (SAR) imagery was developed. The algorithm is applied inside a new processor for meteo-marine parameter estimation for Near Real Time (NRT) applications. These NRT-applications include the investigation of geophysical processes using different satellite modes ranging from high resolution modes with small image coverage of ~20km in open ocean to low resolution modes with wide coverage of ~250km in shelf areas.

The quick developments in satellite techniques, processors, algorithms and ground infrastructures provide new possibilities for series of oceanographic applications in the last years. These new techniques allow estimation of a wide range of oceanographic information including properties of surface waves and internal waves, surface wind speed, sub-meso scale fronts and eddies, ice coverage, oil spills, coastal bathymetry, currents and others. Generally, the new high resolution products from different models allow verification of meteo-marine parameters more accurately. Here, a cross validation with different sea state model results using WWIII (NOAA) and CMEMS (COPERNICUS), with in situ buoy measurements and with satellite estimated parameters allowed an significant improvement of the accuracy of the derived sea state and wind fields. 

The new empirical algorithm allows estimation of total integrated sea state parameters including significant wave height Hs, first moment wave period Tm1, second moment period Tm2, mean period Tm and also partial integrated parameters like swell and windsea wave heights and windsea period. The algorithm allows processing of different S1 Synthetic Aperture Radar (SAR) modes into sea state fields: 

  • S1 Wave Mode (WV) acquires multiple vignettes with an extent of ~20km×20km and each displaced by 100 km along satellite tracks in open ocean (global). About 60 tracks around the globe have been acquired per day. The relatively high spatial resolution of ~4 m allows estimating wave height with accuracy of ~35cm. This is comparable with the accuracy of satellite altimetry and a new achievement for SAR based techniques. 
  • S1 Interferometric Wide Swath Mode (IW) covers area-strips of thousand kilometres of earth and ocean surface in coastal areas with a resolution of ~30m by sequences of multiple images with an approximate size of 200km×250km. The accuracy of ~ 70cm (Hs) for this mode is not as so high as for S1-WV, because the short waves are not visible for S1-IW mode and imaged as noise. However, the accuracy is much higher than state-of-the-art methods for this mode. 

The algorithm has been integrated into a prototype processor for Sentinel-1 SAR imagery. The DLR Ground Station Neustrelitz applies this prototype as part of a near real-time demonstrator MSA service. The presented scientific service involves daily provision of surface wind and sea state parameters estimated fully automatically from S1 IW images of North and Baltic Sea in and around German territorial waters.

How to cite: Pleskachevsky, A., Tings, B., Jacobsen, S., Schwarz, E., Krause, D., and Daedelow, H.: New Algorithm and Processor for Obtaining Maritime Information from Sentinel-1 Radar Imagery for Near Real Time Services, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18487,, 2020


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