Day 4

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Paper title Sea surface wind estimation by multi-frequency SAR imagery
  1. Ferdinando Nunziata Università Parthenope Speaker
  2. Maurizio Migliaccio Università degli Studi di Napoli
  3. Emanuele Ferrentino Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
  4. Matteo Alparone Università degli Studi di Napoli Parthenope
  5. Andrea Buono University of Naples
  6. Stefano Zecchetto CNR - Consiglio Nazionale delle Ricerche
  7. Andrea Zanchetta Institute of Polar Sciences, National Research Council of Italy (ISP-CNR)
  8. Marcos Portabella Institut de Ciències del Mar (ICM-CSIC)
  9. Giuseppe Grieco CNR - Consiglio Nazionale delle Ricerche
Form of presentation Poster
  • A8. Ocean
    • A8.13 Remote-sensing of Ocean Winds and Stress
Abstract text Ocean surface wind vector is of paramount importance in a broad range of applications including wave forecasting, weather forecasting, and storm surge [R1-R5].
The primary remote sensing instruments for wind field retrieval from space is the microwave scatterometer. Although the latter calls for a spatial sampling adequate for several climatological and meso-scale applications, severe limitations to the use of scatterometer products arise when dealing with regional-scale applications. In contrast, the Synthetic Aperture Radar (SAR) achieves a finer spatial resolution and therefore has the potential to provide wind field information with much more spatial details. This can be important in several applications, such as in semi enclosed seas, in straits, along marginal ice zones, and in coastal regions, where scatterometer measurements are contaminated by backscatter from land and ice and the wind vector fields are often recognized to be highly variable. In such regions, wind field estimates retrieved from SAR images would be very desirable.
In this study, the main outcomes related to the Italian Space Agency (ASI) funded project APPLICAVEMARS, whose goal is estimating the ocean surface wind vector using L-, C- and X-band SAR imagery, are presented. The wind processor developed to estimate sea surface wind field from L-band SAOCOM, C-band Sentinel-1A/B and X-band CSK/CSG SAR imagery is described through some thought showcases where:
a) the scatterometer-based Geophysical Model Function is forced using both external (SCAT/ECMWF) and SAR-based wind directions, the latter evaluated by the developed methodologies based on the 2D Continuous Wavelet Transform [6] and Convolutional Neural Network [7] at high spatial resolution (1 km);
b) the wind field is estimated over collocated L-, C- and X-band SAR imagery to study both the aspects related to the GMFs and those dependent on the capacity of the different SAR frequencies to reveal the wind spatial structures.

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[6] Zecchetto, S., Wind Direction Extraction from SAR in Coastal Areas, Remote Sensing,10(2), 261, 2018 (doi:10.3390/rs10020261)
[7] Zanchetta, A. and S. Zecchetto, Wind direction retrieval from Sentinel-1 SAR images using ResNet, Remote Sensing of Environment, 253, 2021 (