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Total significant wave height is now provided for the Sentinel-1 Wave Mode acquisitions

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Since the beginning of operations of the Sentinel-1 mission, Wave Mode OCN products contain the significant wave height of the swell for each wave partition observed by the SAR instrument.

Since 2022-06-07, the Wave Mode OCN products contain as well the "Total" significant wave height (not only from the swell component) as extracted using the Neural Network algorithm described in [Quach et al 2020] and trained on altimeter measurements.

Two new variables are provided: oswTotalHs (significant wave height) and oswTotalHsStdev (standard deviation of significant wave height). These variables are qualified as "total" to avoid any confusion with the oswHs variable that is already used for the significant wave height of the swell for each wave partition in the products.

The first operational validation of this new measurement illustrates good match with reference mean Hs from CMEMS, as can be seen by the scatter plot and metrics provided in the figure below.

Since the beginning of operations of the Sentinel-1 mission, Wave Mode OCN products contain the significant wave height of the swell for each wave partition observed by the SAR instrument.

Since 2022-06-07, the Wave Mode OCN products contain as well the "Total" significant wave height (not only from the swell component) as extracted using the Neural Network algorithm described in [Quach et al 2020] and trained on altimeter measurements.

Two new variables are provided: oswTotalHs (significant wave height) and oswTotalHsStdev (standard deviation of significant wave height). These variables are qualified as "total" to avoid any confusion with the oswHs variable that is already used for the significant wave height of the swell for each wave partition in the products.

The first operational validation of this new measurement illustrates good match with reference mean Hs from CMEMS, as can be seen by the scatter plot and metrics provided in the figure below.

 

References

[Quach et al 2020] Quach, B., Glaser, Y., Stopa, J. E., Mouche, A. A., & Sadowski, P. (2020). Deep learning for predicting significant wave height from synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 59(3), 1859-1867.


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