Abstract
Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016–2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0–5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.
Highlights
The knowledge of ocean surface wave is important for various scientific and operational studies
Regarding fully developed sea state, which corresponds to unlimited wind fetch wind speed retrievals, which is in agreement with independent studies (e.g., root mean square error (RMSE) of 1.10 and wave age of approximately 1.2, the wind–wave connection could be modelled by m/s in [37])
Our analysis indicates that Significant wave height (SWH) and Advanced Scatterometer (ASCAT) retrieved U10 error are related
Summary
The knowledge of ocean surface wave is important for various scientific and operational studies. The main mechanism for this is that the presence of large-scale waves changes the wind stress [13,14], and impact small-scale roughness, changes the radar backscattering over the ocean In this context, they are typical ocean wind sensors, theoretically, estimating sea state information is feasible from the wide-swath scatterometers. The technology of artificial neural network has dramatically progressed and been widely used for geophysical retrieving ocean wind/waves from microwave sensors, for example, estimating SWH [6] and ocean winds [19,20] from C-band SARs. Especially, as the growing availability of big Earth data, deep learning techniques (by employing larger and deeper neural networks) have emerged and been increasingly applied in remote sensing field [21,22,23]. Conclusions and perspectives are given in the last section
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