{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:16:15Z","timestamp":1784301375231,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42274119"],"award-info":[{"award-number":["42274119"]}]},{"name":"National Natural Science Foundation of China","award":["XLYC2002082"],"award-info":[{"award-number":["XLYC2002082"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFF1400500"],"award-info":[{"award-number":["2022YFF1400500"]}]},{"name":"Liaoning Revitalization Talents Program","award":["42274119"],"award-info":[{"award-number":["42274119"]}]},{"name":"Liaoning Revitalization Talents Program","award":["XLYC2002082"],"award-info":[{"award-number":["XLYC2002082"]}]},{"name":"Liaoning Revitalization Talents Program","award":["2022YFF1400500"],"award-info":[{"award-number":["2022YFF1400500"]}]},{"name":"National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration","award":["42274119"],"award-info":[{"award-number":["42274119"]}]},{"name":"National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration","award":["XLYC2002082"],"award-info":[{"award-number":["XLYC2002082"]}]},{"name":"National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration","award":["2022YFF1400500"],"award-info":[{"award-number":["2022YFF1400500"]}]},{"name":"Key Project of Science and Technology Commission of the Central Military Commission","award":["42274119"],"award-info":[{"award-number":["42274119"]}]},{"name":"Key Project of Science and Technology Commission of the Central Military Commission","award":["XLYC2002082"],"award-info":[{"award-number":["XLYC2002082"]}]},{"name":"Key Project of Science and Technology Commission of the Central Military Commission","award":["2022YFF1400500"],"award-info":[{"award-number":["2022YFF1400500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Traditional spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) sea surface height (SSH) retrieval methods have the disadvantages of complicated error models, low retrieval accuracy, and difficulty using full DDM information. To compensate for these deficiencies while considering the heterogeneity of the input data, this paper proposes an end-to-end Modified Residual Multimodal Deep Learning (MRMDL) method that can utilize the entire range of DDM information. First, the MRMDL method is constructed based on the modified Residual Net (MResNet) and Multi-Hidden layer neural network (MHL-NN). The MResNet applicable to DDM structures is used to adaptively capture productive features of the full DDM and to convert the two-dimensional DDM data into one-dimensional numerical form. Then, the extracted features and auxiliary parameters are fused as the input data for MHL-NN to retrieve the SSH. Second, the reliability of the model is verified using SSH with tide-corrected DTU Sea Surface Height 18 (DTU18) and spaceborne radar altimeters (Jason3, HY-2C, HY-2B). Compared to the SSH provided by the DTU18 validation model and the spaceborne radar altimeter, the Pearson correlation coefficients (PCC) are 0.98 and 0.97, respectively. However, the CYGNSS satellite is not primarily employed for ocean altimetry, and the mean absolute differences (MAD) are 3.92 m and 4.32 m, respectively. Finally, the retrieval accuracy of the MRMDL method and the HALF retracking approach are compared and analyzed. Finally, this study also implements the HALF retracking algorithm to derive the SSH, and the results are compared with those computed by the MRMDL method. The MRMDL method is more accurate than the HALF retracking approach according to MAD, Root-Mean-Square Error (RMSE), and PCC, with an improvement of 35.21%, 17.25%, and 2.08%, respectively. The MRMDL method will contribute a new theoretical and methodological reference for future GNSS-R altimetry satellites with high spatiotemporal SSH retrieval.<\/jats:p>","DOI":"10.3390\/rs15061481","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T01:58:22Z","timestamp":1678240702000},"page":"1481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Information Fusion for Spaceborne GNSS-R Sea Surface Height Retrieval Using Modified Residual Multimodal Deep Learning Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Qiang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"},{"name":"China Academy of Aerospace Science and Innovation, Beijing 100094, China"},{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"},{"name":"China Academy of Aerospace Science and Innovation, Beijing 100094, China"},{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8605-6536","authenticated-orcid":false,"given":"Huizhong","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aigong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yelong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4088","DOI":"10.1109\/TGRS.2018.2823316","article-title":"Global ocean altimetry with GNSS reflections from TechDemoSat-1","volume":"56","author":"Mashburn","year":"2018","journal-title":"IEEE Trans. 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