Retrieval of Ocean Wind Speed Using Super-Resolution Delay-Doppler Maps

Wang, Hao Yu; Juang, Jyh‐Ching

doi:10.3390/rs12060916

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…In the field of computer vision, there is a task similar to statistical downscaling, i.e., single-image super-resolution (SISR). SISR is used to recover the high-resolution (HR) image from a low-resolution (LR) image [17]. Simple methods include bilinear or bicubic interpolation of LR images, which easily smooth out images.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme

et al. 2022

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Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. the highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD.

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Section: Introductionmentioning

confidence: 99%

A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme

et al. 2022

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Bistatic radar cross section estimation by using DDM for better spatial resolution of ocean surface wind speed

Yeh,

Tsai,

Lin

2024

Terr Atmos Ocean Sci

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The delay-Doppler map (DDM) is the signal power distribution of the Coarse/Acquisition code (C/A code) of the received Global Navigation Satellite System (GNSS) signal in different code phase delay and Doppler frequency. When the received signal is reflected from ocean surface, the DDM can be used to retrieve the ocean surface wind speed, which is the GNSS-reflectometry (GNSS-R) technique. Due to the signal power distribution in DDM is the correlation results of received and artificial C/A code from receiver in different code phase delay and Doppler frequency, the Woodward ambiguity function (WAF) occurs in the DDM. In the case of DDM, the WAF is the correlation results of two square waves in different code phase delay and Doppler frequency, and is approximated a triangular function and a sinc function in code phase delay and Doppler frequency axes, respectively. It means the correlation results not only show the code phase delay and Doppler frequency of the received signal but also influence the surrounding code phase delay and Doppler frequency values and cause the structure of DDM more complex. Using more bins in DDM in the wind speed retrieval process can reduce the influence of WAF but cause the spatial resolution to worsen. In order to use as less bins as possible in the retrieval process and not reduce the retrieval efficiency too much, a simple method to estimate bistatic radar cross section (BRCS) from DDM is developed in this study. Furthermore, a retrieval process is also developed for ocean surface wind speed retrieving by using less bins in the DDM from Cyclone Global Navigation Satellite System (CYGNSS) mission.

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