@article{chen_sea_2019,
	title = {Sea Surface Height Estimation with Multi-{GNSS} and Wavelet De-noising},
	volume = {9},
	issn = {2045-2322},
	url = {https://doi.org/10.1038/s41598-019-51802-9},
	doi = {10.1038/s41598-019-51802-9},
	abstract = {This paper presents a new sea surface height ({SSH}) estimation using {GNSS} reflectometry ({GNSS}-R). It is a cost-effective remote sensing technique and owns long-term stability besides high temporal and spatial resolution. Initial in-situ {SSH} estimates are first produced by using the {SNR} data of {BDS} (L1, L5, L7), {GPS} (L1, L2, L5), and {GLONASS} (L1, L2), of {MAYG} station, which is located in Mayotte, France near the Indian Ocean. The results of observation data over a period of seven days showed that the root mean square error ({RMSE}) of {SSH} estimation is about 32 cm and the correlation coefficient is about 0.83. The tidal waveform is reconstructed based on the initial {SSH} estimates by utilizing the wavelet de-noising technique. By comparing the tide gauge measurements with the reconstructed tidal waveform at {SSH} estimation instants, the {SSH} estimation errors can be obtained. The results demonstrate that the correlation coefficient and {RMSE} of the wavelet de-noising based {SSH} estimation is 0.95 and 19 cm, respectively. Compared with the initial estimation results, the correlation coefficient is improved by about 14.5\%, while the {RMSE} is reduced by 40.6\%.},
	pages = {15181},
	number = {1},
	journaltitle = {Scientific Reports},
	shortjournal = {Scientific Reports},
	author = {Chen, Fade and Liu, Lilong and Guo, Fei},
	date = {2019-10-23}
}

{"_id":"bH4kGbm3gz2qrBq4D","bibbaseid":"chen-liu-guo-seasurfaceheightestimationwithmultignssandwaveletdenoising","authorIDs":[],"author_short":["Chen, F.","Liu, L.","Guo, F."],"bibdata":{"bibtype":"article","type":"article","title":"Sea Surface Height Estimation with Multi-GNSS and Wavelet De-noising","volume":"9","issn":"2045-2322","url":"https://doi.org/10.1038/s41598-019-51802-9","doi":"10.1038/s41598-019-51802-9","abstract":"This paper presents a new sea surface height (SSH) estimation using GNSS reflectometry (GNSS-R). It is a cost-effective remote sensing technique and owns long-term stability besides high temporal and spatial resolution. Initial in-situ SSH estimates are first produced by using the SNR data of BDS (L1, L5, L7), GPS (L1, L2, L5), and GLONASS (L1, L2), of MAYG station, which is located in Mayotte, France near the Indian Ocean. The results of observation data over a period of seven days showed that the root mean square error (RMSE) of SSH estimation is about 32 cm and the correlation coefficient is about 0.83. The tidal waveform is reconstructed based on the initial SSH estimates by utilizing the wavelet de-noising technique. By comparing the tide gauge measurements with the reconstructed tidal waveform at SSH estimation instants, the SSH estimation errors can be obtained. The results demonstrate that the correlation coefficient and RMSE of the wavelet de-noising based SSH estimation is 0.95 and 19 cm, respectively. Compared with the initial estimation results, the correlation coefficient is improved by about 14.5%, while the RMSE is reduced by 40.6%.","pages":"15181","number":"1","journaltitle":"Scientific Reports","shortjournal":"Scientific Reports","author":[{"propositions":[],"lastnames":["Chen"],"firstnames":["Fade"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Lilong"],"suffixes":[]},{"propositions":[],"lastnames":["Guo"],"firstnames":["Fei"],"suffixes":[]}],"date":"2019-10-23","bibtex":"@article{chen_sea_2019,\n\ttitle = {Sea Surface Height Estimation with Multi-{GNSS} and Wavelet De-noising},\n\tvolume = {9},\n\tissn = {2045-2322},\n\turl = {https://doi.org/10.1038/s41598-019-51802-9},\n\tdoi = {10.1038/s41598-019-51802-9},\n\tabstract = {This paper presents a new sea surface height ({SSH}) estimation using {GNSS} reflectometry ({GNSS}-R). It is a cost-effective remote sensing technique and owns long-term stability besides high temporal and spatial resolution. Initial in-situ {SSH} estimates are first produced by using the {SNR} data of {BDS} (L1, L5, L7), {GPS} (L1, L2, L5), and {GLONASS} (L1, L2), of {MAYG} station, which is located in Mayotte, France near the Indian Ocean. The results of observation data over a period of seven days showed that the root mean square error ({RMSE}) of {SSH} estimation is about 32 cm and the correlation coefficient is about 0.83. The tidal waveform is reconstructed based on the initial {SSH} estimates by utilizing the wavelet de-noising technique. By comparing the tide gauge measurements with the reconstructed tidal waveform at {SSH} estimation instants, the {SSH} estimation errors can be obtained. The results demonstrate that the correlation coefficient and {RMSE} of the wavelet de-noising based {SSH} estimation is 0.95 and 19 cm, respectively. Compared with the initial estimation results, the correlation coefficient is improved by about 14.5\\%, while the {RMSE} is reduced by 40.6\\%.},\n\tpages = {15181},\n\tnumber = {1},\n\tjournaltitle = {Scientific Reports},\n\tshortjournal = {Scientific Reports},\n\tauthor = {Chen, Fade and Liu, Lilong and Guo, Fei},\n\tdate = {2019-10-23}\n}\n\n","author_short":["Chen, F.","Liu, L.","Guo, F."],"key":"chen_sea_2019","id":"chen_sea_2019","bibbaseid":"chen-liu-guo-seasurfaceheightestimationwithmultignssandwaveletdenoising","role":"author","urls":{"Paper":"https://doi.org/10.1038/s41598-019-51802-9"},"downloads":0,"html":""},"bibtype":"article","biburl":"https://cloud.ifremer.fr/index.php/s/N5RUrxe0NmktYhq/download","creationDate":"2021-02-02T15:18:06.577Z","downloads":0,"keywords":[],"search_terms":["sea","surface","height","estimation","multi","gnss","wavelet","noising","chen","liu","guo"],"title":"Sea Surface Height Estimation with Multi-GNSS and Wavelet De-noising","year":null,"dataSources":["HRywGBFdy4ALvsWmW"]}