Day 4

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Paper title Analysis of sea surface wind estimated using scatterometer-based GMFs and azimuth cut-off
  1. Ferdinando Nunziata Università Parthenope Speaker
  2. Matteo Alparone Università degli Studi di Napoli Parthenope
  3. Emanuele Ferrentino Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
  4. Maurizio Migliaccio Università degli Studi di Napoli
  5. Giuseppe Grieco CNR - Consiglio Nazionale delle Ricerche
  6. Marcos Portabella Institut de Ciències del Mar (ICM-CSIC)
Form of presentation Poster
  • A8. Ocean
    • A8.13 Remote-sensing of Ocean Winds and Stress
Abstract text Microwave scatterometers play a key role when dealing with operational surface wind measurements. However, their relatively coarse spatial resolution triggered the development of wind retrieval techniques based on synthetic aperture radar (SAR) measurements. Commonly used techniques are based on the normalized radar cross section (NRCS) or radar backscatter and several empirical geophysical model functions (GMFs), originally developed to exploit C-band VV-polarized scatterometer measurements, have been tuned and recalibrated to deal with SAR measurements at different frequencies and polarizations [1]-[3]. The radar backscatter is sensitive to both wind speed and wind direction; hence, the latter must be available to constrain the GMFs [4] when retrieving the wind speed. Such a technique is limited by the fact that errors in the wind direction estimation are propagated into the wind speed estimation [5].
The so-called azimuth cutoff technique, originally proposed by Kerbaol et al. [6] to derive significant wave height (SWH) and sea surface wind speed, does not need neither calibration of the data nor any a priori information on wind direction and, therefore, has recently gained more attention [7].
In this study sea surface wind estimation is addressed using both scatterometer-based GMFs and the azimuth cut-off technique using a data set of Sentinel-1A/B SAR imagery where collocated HY2-A scatterometer wind estimates (on a 25km spatial grid) are available. The proposed rationale aims at proving that SAR NRCS, averaged on a 25km grid, is consistent with the HY2-A NRCS. Two steps will be accomplished: 1) estimating sea surface wind speed using SAR imagery through the scatterometer-based GMFs forced by the HY2-A wind direction and contrasting it with the HY2-A wind speed and with estimates obtained using the azimuth cut-off; 2) estimating the wind direction from the scatterometer-based GMF forced by the azimuth cut-off wind speed and contrasting it with the HY2-A wind direction;

[1] A. A. Mouche et al., “On the use of Doppler shift for sea surface wind retrieval from SAR,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 7, pp. 2901–2909, Jul. 2017.
[2] G. Grieco, F. Nirchio, and M. Migliaccio, “Application of state-of-the- art SAR X-band geophysical model functions (GMFs) for sea surface wind (SSW) speed retrieval to a data set of the Italian satellite mission COSMO-SkyMed,” Int. J. Remote Sens., vol. 36, no. 9, pp. 2296–2312, 2015.
[3] Y. Ren, S. Lehner, S. Brusch, X. Li, and M. He, “An algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data,” Int. J. Remote Sens., vol. 33, no. 23, pp. 7310–7336, 2012.
[4] C. C. Wackerman, C. L. Rufenach, R. A. Shuchman, J. A. Johannessen, and K. L. Davidson, “Wind vector retrieval using ERS-1 synthetic aperture radar imagery,” IEEE Trans. Geosci. Remote Sens., vol. 34, no. 6, pp. 1343–1352, Nov. 1996.
[5] M. Portabella, A. Stoffelen, and J. A. Johannessen, “Toward an optimal inversion method for synthetic aperture radar wind retrieval,” J. Geophys. Res., Oceans, vol. 107, no. C8, pp. 1-1–1-13, 2002.
[6] V. Kerbaol, B. Chapron, and P. W. Vachon, “Analysis of ERS-1/2 syn- thetic aperture radar wave mode imagettes,” J. Geophys. Res., Oceans, vol. 103, no. C4, pp. 7833–7846, 1998.
[7] V. Corcione, G. Grieco, M. Portabella, F. Nunziata and M. Migliaccio, “A novel azi- muth cut-off implementation to retrieve sea surface wind speed from SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 6, pp. 3331-3340, 2018.