A System for Near-Real-Time Monitoring of the Sea State Using SAR Satellites

This article 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 sea state retrieval (SAR-SeaStaR) 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 Tm-2 , 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-m and implemented into SAR-SeaStaR for processing Level-1 (L1) 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 H-s 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 (SSP) software using modular architecture and applied at the DLR ground station (GS) 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.