Abstract
As an emerging remote sensing technology, GNSS reflectometry (GNSS-R) has been widely investigated for retrieving ocean parameters including ocean significant wave height (SWH). Ocean SWH consists of contribution from swell and wind waves, which are commonly modeled separately in the field of marine science and engineering to facilitate practical application. In this study, we present a deep convolutional neural network (DCNN) model for retrieving swell and wind wave SWHs. The DCNN model makes use of auxiliary data and effective DDM features extracted in the convolution layer, and it is trained by using the ERA5 data and CYGNSS observations. The proposed DCNN model and seven existing models [i.e., random forest, extremely randomized trees, bagging tree (BT), decision tree, support vector machine (SVM), artificial neural network, and convolutional neural network] were extensively tested using the ERA5 and WaveWatch III (WW3) data. The results show that when ERA5 is used as reference data, the proposed DCNN model performs best among the eight models, with the root mean square errors (RMSEs) of retrieving swell and wind wave SWH being better than 0.394 m and 0.397 m, respectively, and the correlation coefficient (R) being 0.90. Compared with the SVM model, RMSEs are improved by 28.82% and 31.92%, respectively. When WW3 is employed as reference, the RMSEs of retrieving swell and wind wave SWH are better than 0.497 m and 0.502 m, respectively, with R of 0.89 and 0.90. Compared with the BT model, RMSEs are improved by 26.74% and 27.41%, respectively. The research also found that the auxiliary variables are important for swell and wind wave SWH retrieval. Furthermore, the retrieval of SWH for swells and wind waves using spaceborne GNSS-R technology is affected by rainfall, resulting in about 6% increase in RMSE. This method provides a new idea for studying global ocean swell and wind waves using CYGNSS data.
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Data availability
The CYGNSS data used for this study can be downloaded from the Web site (https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_V3.0); the ERA5 swell SWH and wind waves SWH data are available at https://cds.climate.copernicus.eu/cdsapp#!/home; the Wavewatch III (WW3) swell SWH and wind waves SWH data are available at https://pae-paha.pacioos.hawaii.edu/thredds/catalog/ww3_global/catalog.html?dataset=ww3_global/WaveWatch_III_Global_Wave_Model_best.ncd; the IMERG rainfall data can be downloaded from the Web site (https://gpm.nasa.gov/data/directory).
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Acknowledgements
We would like to express my gratitude to NASA for providing CYGNSS data, the European Center for Medium-Range Weather Forecasts (ECMWF) for providing swell and wind wave SWHs data, the developers of WaveWatch III (WW3) product for providing the swell and wind wave SWHs data freely available to public, and the makers of IMERG products for making the rainfall data publicly available. The authors also want to express their gratitude to the anonymous reviewers for their thorough critiques and useful ideas, which have greatly aided in the improvement of this research. This research was supported in part by the National Natural Science Foundation of China under Grant 42174022 and Grant 41574031, in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX20_2003, in part by the Future Scientists Program of China University of Mining and Technology under Grant 2020WLKXJ049, in part by the Independent Innovation Project of ‘‘Double-First Class’’ Construction of the China University of Mining and Technology under Grant 2018ZZCX08, in part by the Jiangsu Dual Creative Teams Programme Project Awarded in 2017 under Grant CUMT07180005, and in part by the China Scholarship Council (CSC) through a State Scholarship Fund (No.202106420009).
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All authors have made significant contributions to this manuscript. JB partly designed the improved method, analyzed the data, wrote the initial version of paper, and validated the improved method; KY conceived the improved method; WH wrote the revised version of the paper and provided supervision; JN checked and revised this paper. All authors have read and agreed to the published version of the manuscript.
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Bu, J., Yu, K., Ni, J. et al. Combining ERA5 data and CYGNSS observations for the joint retrieval of global significant wave height of ocean swell and wind wave: a deep convolutional neural network approach. J Geod 97, 81 (2023). https://doi.org/10.1007/s00190-023-01768-4
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DOI: https://doi.org/10.1007/s00190-023-01768-4