{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T02:41:42Z","timestamp":1768704102732,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["61931025"],"award-info":[{"award-number":["61931025"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["U2006207"],"award-info":[{"award-number":["U2006207"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["2022003"],"award-info":[{"award-number":["2022003"]}]},{"name":"Key Program of Joint Fund of the National Natural Science Foundation of China and Shandong Province","award":["61931025"],"award-info":[{"award-number":["61931025"]}]},{"name":"Key Program of Joint Fund of the National Natural Science Foundation of China and Shandong Province","award":["U2006207"],"award-info":[{"award-number":["U2006207"]}]},{"name":"Key Program of Joint Fund of the National Natural Science Foundation of China and Shandong Province","award":["2022003"],"award-info":[{"award-number":["2022003"]}]},{"name":"Fund of Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources","award":["61931025"],"award-info":[{"award-number":["61931025"]}]},{"name":"Fund of Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources","award":["U2006207"],"award-info":[{"award-number":["U2006207"]}]},{"name":"Fund of Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources","award":["2022003"],"award-info":[{"award-number":["2022003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Empirical algorithms have become the mainstream of significant wave height (SWH) retrieval from synthetic aperture radar (SAR). But the plentiful features from multi-polarizations make the selection of input for the empirical model a problem. Therefore, the XGBoost models are developed and evaluated for SWH retrieval from polarimetric Gaofen-3 wave mode imagettes using the SAR features of different polarization combinations, and then the importance of each feature on the models is further discussed. The results show that the reliability of SWH retrieval models is independently confirmed based on the collocations of the SAR-buoy and SAR-altimeter. Moreover, the combined-polarization models achieve better performance than single-polarizations. In addition, the importance of different features to the different polarization models for SWH inversion is not the same. For example, the normalized radar cross section (NRCS), cutoff wavelength (\u03bbc), and incident angle (\u03b8) have more decisive contributions to the models than other features, while peak wavelength (\u03bbp) and the peak direction (\u03c6) have almost no contribution. Besides, NRCS of cross-polarization has a more substantial effect, and the \u03bbc of hybrid polarization has a stronger one than other polarization models.<\/jats:p>","DOI":"10.3390\/rs15010149","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:30:27Z","timestamp":1672205427000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Significant Wave Height Retrieval Using XGBoost from Polarimetric Gaofen-3 SAR and Feature Importance Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Tianran","family":"Song","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Qiushuang","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Chenqing","family":"Fan","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]},{"given":"Junmin","family":"Meng","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]},{"given":"Yuqi","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"},{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4529","DOI":"10.1109\/TGRS.2019.2891426","article-title":"On the effect of polarization and incidence angle on the estimation of significant wave height from SAR data","volume":"57","author":"Collins","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1029\/2018JC014638","article-title":"A New Ocean SAR Cross-Spectral Parameter: Definition and Directional Property Using the Global Sentinel-1 Measurements","volume":"124","author":"Li","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1109\/TGRS.1986.289702","article-title":"On the relative importance of motion-related contributions to the SAR imaging mechanism of ocean surface waves","volume":"GE-24","author":"Alpers","year":"1986","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","first-page":"3019","article-title":"An empirical approach for the retrieval of integral ocean wave parameters from synthetic aperture radar data","volume":"112","author":"Koenig","year":"2007","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/TGRS.2010.2052364","article-title":"Ocean wave integral parameter measurements using Envisat ASAR wave mode data","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1002\/2016JC012364","article-title":"Significant wave heights from Sentinel-1 SAR: Validation and applications","volume":"122","author":"Stopa","year":"2017","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5086","DOI":"10.1080\/01431161.2016.1226525","article-title":"Dependency of the Sentinel-1 azimuth wavelength cut-off on significant wave height and wind speed","volume":"37","author":"Grieco","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Song, T., Fan, C., Yan, Q., and Zhang, J. (2022). Dependence of the Azimuth Cutoff from Quad-Polarization Gaofen-3 SAR Image on Significant Wave Height and Wind Speed, IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2299\u20132302.","DOI":"10.1109\/IGARSS46834.2022.9883149"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s13131-015-0769-6","article-title":"Significant wave height estimation using azimuth cutoff of C-band RADARSAT-2 single-polarization SAR images","volume":"34","author":"Ren","year":"2015","journal-title":"Acta Oceanol. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shao, W.Z., Zhang, Z., Li, X.F., and Li, H. (2016). Ocean wave parameters retrieval from Sentinel-1 SAR imagery. Remote Sens., 8.","DOI":"10.3390\/rs8090707"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5234716","DOI":"10.1109\/TGRS.2022.3204409","article-title":"Impact of Polarization Basis on Wind and Wave Parameters Estimation Using the Azimuth Cutoff From GF-3 SAR Imagery","volume":"60","author":"Bao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","first-page":"59","article-title":"A semi-empirical algorithm for SAR wave height retrieval and its validation using Envisat ASAR wave mode data","volume":"31","author":"Wang","year":"2012","journal-title":"Acta Oceanol. Sin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13131-018-1217-1","article-title":"Validation of significant wave height retrieval from co-polarization Chinese Gaofen-3 SAR imagery using an improved algorithm","volume":"37","author":"Sheng","year":"2016","journal-title":"Acta Oceanol. Sin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1080\/07038992.2019.1683444","article-title":"Wave retrieval under typhoon conditions using a machine learning method applied to Gaofen-3 SAR imagery","volume":"45","author":"Shao","year":"2019","journal-title":"Can. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/TGRS.2020.3003839","article-title":"Deep learning for predicting significant wave height from synthetic aperture radar","volume":"59","author":"Quach","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2020JC016946","article-title":"Retrieval of ocean wave heights from spaceborne SAR in the Arctic Ocean with a neural network","volume":"126","author":"Wu","year":"2021","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s13131-018-1203-7","article-title":"Estimating significant wave height from SAR imagery based on an SVM regression model","volume":"37","author":"Gao","year":"2018","journal-title":"Acta Oceanol. Sin."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, J., Yang, J., Ren, L., Zhu, J., and Yuan, X. (2018). Empirical algorithm for significant wave height retrieval from wave mode data provided by the Chinese satellite Gaofen-3. Remote Sens., 10.","DOI":"10.3390\/rs10030363"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112969","DOI":"10.1016\/j.rse.2022.112969","article-title":"Quad-polarimetric SAR sea state retrieval algorithm from Chinese Gaofen-3 wave mode imagettes via deep learning","volume":"273","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fan, C., Song, T., Yan, Q., Meng, J., Wu, Y., and Zhang, J. (2022). Evaluation of Multi-Incidence Angle Polarimetric Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval. Remote Sens., 14.","DOI":"10.3390\/rs14215480"},{"key":"ref_21","unstructured":"von Storch, H., and Zwiers, F. (2002). Statistical Analysis in Climate Research, Cambridge University Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.aap.2019.05.005","article-title":"A feature learning approach based on XGBoost for driving assessment and risk prediction","volume":"129","author":"Shi","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TGRS.20O4.826811","article-title":"Measurement of 2-D sea surface elevation fields using complex synthetic aperture radar data","volume":"42","author":"Lehner","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1080\/01431169508954390","article-title":"A microwave technique to improve the measurement of directional ocean wave spectra","volume":"16","author":"Schuler","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1109\/TGRS.2004.836813","article-title":"Ocean wave spectra from a linear polarimetric SAR","volume":"42","author":"He","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, B., Perrie, W., and He, Y. (2010). Validation of RADARSAT-2 fully polarimetric SAR measurements of ocean surface waves. J. Geophys. Res. Ocean., 115.","DOI":"10.1029\/2009JC005887"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s13131-021-1855-6","article-title":"Ocean wave parameters retrieved directly from compact polarimetric SAR data","volume":"41","author":"Liu","year":"2022","journal-title":"Acta Oceanol. Sin."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1109\/LGRS.2018.2821238","article-title":"A preliminary evaluation of the GaoFen-3 SAR radiation characteristics in land surface and compared with Radarsat-2 and Sentinel-1A","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1109\/JSTARS.2019.2911922","article-title":"Calibration of the copolarized backscattering measurements from Gaofen-3 synthetic aperture radar wave mode imagery","volume":"12","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7833","DOI":"10.1029\/97JC01579","article-title":"Analysis of ERS-1\/2 synthetic aperture radar wave mode imagettes","volume":"103","author":"Kerbaol","year":"1998","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_34","unstructured":"National Data Buoy Center (2009). Handbook of Automated Data Quality Control Checks and Procedures."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1080\/01490419.2014.1000470","article-title":"Assessment of SARAL\/AltiKa wave height measurements relative to buoy, Jason-2, and Cryosat-2 data","volume":"38","author":"Sepulveda","year":"2015","journal-title":"Mar. Geod."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zheng, H., Yuan, J., and Chen, L. (2017). Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies, 10.","DOI":"10.3390\/en10081168"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning, Springer. Springer Series in Statistics.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_38","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees. Wadsworth International Group, Chapman and Hall\/CRC."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ustebay, S., Turgut, Z., and Aydin, M.A. (2018, January 3\u20134). Intrusion detection system with recursive feature elimination by using random forest and deep learning classifier. Proceedings of the 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), Ankara, Turkey.","DOI":"10.1109\/IBIGDELFT.2018.8625318"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"108533","DOI":"10.1016\/j.ecolind.2022.108533","article-title":"Estimating the grade of storm surge disaster loss in coastal areas of China via machine learning algorithms","volume":"136","author":"Zhang","year":"2022","journal-title":"Ecol. Indic."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:52:46Z","timestamp":1760147566000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,27]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010149"],"URL":"https:\/\/doi.org\/10.3390\/rs15010149","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,27]]}}}