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

Jia Liu,Lizhe Wang,Yongjian Sun,Kaijun Ren,Yanlai Zhao,Kefeng Deng

doi:10.3390/rs14030769

Abstract

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.

Highlights

In Section 4.4.1, we present the downscaling results on the synthetic LR-HR data, i.e., the original sea surface wind (SSW) are used as HR ground truth to validate our proposed method, and the spatial resolution of downscaling SSW corresponds to the original SSW of 0.25◦
Our proposed method achieves the highest accuracy for the 16× SSW downscaling, and the specific scatter plots are shown in Figure 9. due to the lack of higher-resolution SSW ground truth for 8×
A novel spatial downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as an example, is presented

Summary

Introduction

As one of the major sources of momentum for the Ocean, sea surface wind (SSW), called ocean surface wind, is a key parameter for a variety of studies such as ocean waves, ocean circulation, and air–sea interaction [1]. Accurate and timely observation of SSW is valuable for a wide range of applications such as numerical weather prediction (NWP), marine environmental monitoring and transportation, search and rescue missions for natural and manmade maritime hazards, and wind energy assessment [2,3]. SSW can be obtained in situ from ships, buoys, and monitoring stations. These measurements are considered accurate, with limited spatial coverage, Remote Sens.

Methods

Results

Discussion

Conclusion