References

• Ai, B., Wen, Z., Jiang, Y., Gao, S., and Lv, G.N. 2019. “Sea surface temperature inversion model for infrared remote sensing images based on deep neural network.” Infrared Physics & Technology, Vol. 99: pp. 231–239. doi:10.1016/j.infrared.2019.04.022.
   Web of Science ®Google Scholar
• Alshari, H., Saleh, A.Y., and Odabaş, A. 2021. “Comparison of gradient boosting decision tree algorithms for CPU performance.” Journal of Institue of Science and Technology, Vol. 37 (No. 1): pp. 157–168.
   Google Scholar
• Alvera-Azcárate, A., Barth, A., Parard, G., and Beckers, J.-M. 2016. “Analysis of SMOS sea surface salinity data using DINEOF.” Remote Sensing of Environment, Vol. 180: pp. 137–145. doi:10.1016/j.rse.2016.02.044.
   Web of Science ®Google Scholar
• Athira, U.N., Abhilash, S., and Sabeerali, C.T. 2023. “Paradigm shift in the onset phase of the Indian Summer Monsoon since 2000 and its potential connection to South Indian Ocean.” Atmospheric Research, Vol. 296: pp. 107050. doi:10.1016/j.atmosres.2023.107050.
   Web of Science ®Google Scholar
• Bai, Y., Zhao, T. j., Jia, L., Cosh, M.H., Shi, J.C., Peng, Z.Q., Li, X.J., and Wigneron, J.P. 2022. “A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data.” Remote Sensing of Environment, Vol. 280: pp. 113190. doi:10.1016/j.rse.2022.113190.
   Web of Science ®Google Scholar
• Balmaseda, M., Anderson, D., and Vidard, A. 2007. “Impact of Argo on analyses of the global ocean.” Geophysical Research Letters, Vol. 34 (No. 16): pp. L16605. doi:10.1029/2007GL030452.
   Web of Science ®Google Scholar
• Bao, S.L., Zhang, R., Wang, H.Z., Yan, H.Q., Chen, J., and Wang, Y.J. 2023. “Correction of satellite sea surface salinity products using ensemble learning method.” IEEE Access., Vol. 11: pp. 17870–17881. doi:10.1109/ACCESS.2021.3057886.
   Web of Science ®Google Scholar
• Busalacchi, A.J., Hackert, E.C., Alory, G., Arkin, P.A., Ballabrera, J., Delcroix, T.C., Janowiak, J., Ren, L., Murtugudde, R.G., and Zhang, R. 2011. “Spatio-temporal variability and error structure of SSS in the tropics.” Fall Meeting Abstracts, Vol. 1: pp. 1627.
   Google Scholar
• Cao, K.X., Sun, W.F., Meng, J.M., and Zhang, J. 2019. “Assessment and comparison of sea surface salinity data derived from SMAP and SMOS based on Argo measurements.” Advances in Marinee Science, Vol. 37 (No. 4): pp. 574–587.
   Google Scholar
• Chen, J.Z., Shi, X.H., and Wen, M. 2023. “Applicability of ERA5 surface wind speed data in the region of “two oceans and one sea.” Meteor Mon, Vol. 49 (No. 1): pp. 39–51. doi:10.7519/j.issn.1000-0526.2022.072301.
   Google Scholar
• Chen, Z. w., Li, Q.X., and Li, Y. 2018. “Comparison and analysis of sea surface salinity measurement method and data between SMOS and aquarius.” Aerospace Shanghai, Vol. 35 (No. 2): pp. 37–48. doi:10.19328/j.cnki.1006-1630.2018.02.005.
   Google Scholar
• Furue, R., Takatama, K., Sasaki, H., Schneider, N., Nonaka, M., and Taguchi, B. 2018. “Impacts of sea-surface salinity in an eddy-resolving semi-global OGCM.” Ocean Modelling, Vol. 122: pp. 36–56. doi:10.1016/j.ocemod.2017.11.004.
   Web of Science ®Google Scholar
• Gao, M., Huang, X.Y., Wang, F., Zhang, H.L., Zhao, H.X., and Gao, X.Y. 2022. “Sea surface salinity inversion based on DNN model.” Advances in Marine Science, Vol. 40 (No. 3): pp. 496–504. doi:10.12362/j.issn.1671-6647.20210409001.
   Google Scholar
• Guo, F.H., Zhang, C.M., and Jiao, W.J. 2016. “Research progress on mesh parameterization.” Ruan Jian Xue Bao/Journal of Software, Vol. 27 (No. 1): pp. 112–135. doi:10.13328/j.cnki.jos.004919.
   Google Scholar
• He, B.Y., and Wu, J. 2005. “Optimization on circular thinned array for two-dimensional synthetic aperture microwave radiometer.” Acta Electronica Sinica, Vol. 33 (No. 9): pp. 1607.
   Google Scholar
• Huang, Y., and Wu, J. 2002. “Study on image theory of high resolution two-dimensional synthetic aperture microwave radiometer.” Acta Electonica Sinica, Vol. 30 (No. 5): pp. 697.
   Google Scholar
• Huo, C.G., Huo, F.F., and Dong, K. 2021. “Scientific topic prediction | The popularity prediction of scientific topics based on LSTM.” Library and Information Knowledge, Vol. 38 (No. 2): pp. 25–34. doi:10.13366/j.dik.2021.02.025.
   Google Scholar
• Jang, E., Kim, Y.J., Im, J., Park, Y.G., and Sung, T. 2022. “Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learning.” Remote Sensing of Environment, Vol. 273: pp. 112980. doi:10.1016/j.rse.2022.112980.
   Web of Science ®Google Scholar
• Jiang, J.S., Zhang, Y. H., and Dong, X.L. 2000. “The discussion of up to date technologies in microwave remote sensing and the new generation of space remote sensing method.” Engineering Science, Vol. 08: pp. 76–82.
   Google Scholar
• Jiang, Q., Li, W., Fan, Z., He, X., Sun, W., Chen, S., Wen, J., Gao, J., and Wang, J. 2021. “Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland.” Journal of Hydrology, Vol. 595: pp. 125660. doi:10.1016/j.jhydrol.2020.125660.
   Web of Science ®Google Scholar
• Jiao, L.C., Yang, S.Y., Liu, F., Wang, S.G., and Feng, Z.X. 2016. “Seventy years beyond neural networks: Retrospect and prospect.” Chinese Journal of Computers, Vol. 39 (No. 8): pp. 1697–1716. doi:10.11897/SP.J.1016.2016.01697.
   Google Scholar
• Li, C.J., Zhao, Q.H., and Zhao, H. 2018. “Retrieve salinity using BP Neural Networks model based on SMOS data.” Periodical of Ocean University of China, Vol. 13 (No. 1): pp. 125–134. doi:10.16441/j.cnki.hdxb.20150261.
   Google Scholar
• Lin, A.L., Li, T., and Fu, Xh. 2009. “Impact of air-sea interactions over the Indian Ocean on the climatological state of tropical atmospheric circulation in boreal summer.” Chinese Journal of Atmospheric Sciences, Vol. 33 (No. 6): pp. 1123–1136.
   Google Scholar
• Lin, Y., Zhao, H., and Ding, H. 2017. “Solution of inverse kinematics for general robot manipulators based on multiple population genetic algorithm.” Journal of Mechanical Engineering, Vol. 53 (No. 3): pp. 1–8. doi:10.3901/JME.2017.03.001.
   Google Scholar
• Liu, Q.Q., Zhang, Y.S., Xu, M., Li, H.P., and Liu, H.X. 2022. “SMAP satellite sea surface inversion model based on machine learning.” Advance in Marine Science, Vol. 40 (No. 1): pp. 56–65.
   Google Scholar
• Liu, T.S., Zheng, M.P., and Guo, Z.T. 1998. “Initiation and evolution of the asian monsoon system timely coupled with the ice-sheet growth and the tectonic movements in Asia.” Quaternary, Vol. 3 (No. 194): pp. 3.
   Google Scholar
• Liu, Y., Wang, Y., and Zhang, J. 2012. “New machine learning algorithm: Random forest.” Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, Proceedings 3. Berlin Heidelberg: Springer. pp. 246–252. doi:10.1007/978-3-642-34062-8_32.
   Google Scholar
• Ma, W.T., Du, Y.L., Liu, G.H., Yu, Y., Yang, X.F., Yang, J., and Chen, K.S. 2021. “Study on direction dependence of the fully polarimetric wind-induced ocean emissivity at L-band using a semi-theoretical approach for Aquarius and SMAP observations.” Remote Sensing of Environment, Vol. 265: pp. 112661. doi:10.1016/j.rse.2021.112661.
   Web of Science ®Google Scholar
• Mecklenburg, S. 2014. “ESA's Soil Moisture and Ocean Salinity Mission-An overview on the mission’s performance and scientific results.” EGU General Assembly Conference Abstracts.
   Google Scholar
• Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., et al. 2021. “ERA5-Land: A state-of-the-art global reanalysis dataset for land applications.” Earth System Science Data, Vol. 13 (No. 9): pp. 4349–4383. doi:10.5194/essd-13-4349-2021.
   Web of Science ®Google Scholar
• Olmedo, E., Martínez, J., Turiel, A., Ballabrera-Poy, J., and Portabella, M. 2017. “Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity.” Remote Sensing of Environment, Vol. 193: pp. 103–126. doi:10.1016/j.rse.2017.02.023.
   Web of Science ®Google Scholar
• Peng, T., Zhao, L., Zhang, A.J., Yang, X.N., Zhou, Z., and Chang, X.H. 2023. “UAV hyperspectral response characteristics and estimation model construction of soil total nitrogen.” Transactions of the Chinese Society of Agricultural Engineering, Vol. 39 (No. No. 4): pp. 92–101. doi:10.11975/j.issn.1002-6819.202211021.
   Google Scholar
• Reul, N., Grodsky, S.A., Arias, M., Boutin, J., Catany, R., Chapron, B., D'Amico, F., et al. 2020. “Sea surface salinity estimates from spaceborne L-band radiometers: An overview of the first decade of observation (2010–2019).” Remote Sensing of Environment, Vol. 242: pp. 111769. doi:10.1016/j.rse.2020.111769.
   Web of Science ®Google Scholar
• Roemmich, D., and Gilson, J. 2019. “The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program.” Progress in Oceanography, Vol. 82 (No. 2): pp. 81–100. doi:10.1016/j.pocean.2009.03.004.
   Google Scholar
• Sang, Y.M., and Wook, H.K. 1998. “Genetic-based fuzzy control for half car active suspension systems.” International Journal of Systems Science, Vol. 29 (No. 7): pp. 699–710. doi:10.1080/00207729808929564.
   Web of Science ®Google Scholar
• Scotese, C.R., Boucot, A.J., and McKerrow, W.S. 1999. “Gondwanan palaeogeography and pal˦ oclimatology.” Journal of African Earth Sciences, Vol. 28 (No. 1): pp. 99–114. Vol(NO doi:10.1016/S0899-5362(98)00084-0.
   Web of Science ®Google Scholar
• Tangdamrongsub, N., Han, S.C., Yeo, I.Y., Dong, J.Z., Steele-Dunne, S.C., Willgoose, G., and Walker, J.P. 2020. “Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates.” Advances in Water Resources, Vol. 135 pp. 103477. doi:10.1016/j.advwatres.2019.103477.
   Web of Science ®Google Scholar
• Tian, T., Cheng, L.J., Wang, G.J., Abraham, J., Wei, W.X., Ren, S.H., Zhu, J., Song, J.Q., and Leng, H.Z. 2022. “Reconstructing ocean subsurface salinity at high resolution using a machine learning approach.” Earth System Science Data, Vol. 14 (No. 11): pp. 5037–5060. doi:10.5194/essd-14-5037-2022.
   Web of Science ®Google Scholar
• Wang, A.P., Wan, G.W., Cheng, Z.Q., and Li, S.K. 2011. “Incremental learning extremely random forest classifier for online learning.” Journal of Software, Vol. 22 (No. 9): pp. 2059–2074. doi:10.3724/sp.j.1001.2011.03827.
   Google Scholar
• Wang, H., Jiang, Y.N., Zhang, X., Zhong, H.R., Chen, Q.X., and Gao, S.C. 2021. “Lithology identification method based on gradient boosting algorithm.” Journal of Jilin University, Vol. 51 (No. 3): pp. 940–950. doi:10.13278/j.cnki.jjuese.20200081.
   Google Scholar
• Wang, H., Yan, J.Y., Fu, G.M., and Wang, X. 2020. “Current status and application prospect of deep learning in geophysics.” Progress in Geophysics, Vol. 35 (No. No. 2): pp. 0642–0655. doi:10.6038/pg2020CC0476.
   Google Scholar
• Wang, X.X., Wang, X., Han, Z., and Yang, J.H. 2015. “Radio frequency interference detection and characteristic analysis based on the l band stokes parameters remote sensing data.” Journal of Electronics & Information Technology, Vol. 37 (No. 10): pp. 2342–2348. doi:10.11999/JEIT141577.
   Google Scholar
• Wang, X.X., Yang, J.H., Zhao, D.Z., Wang, X., and Sun, G.L. 2013. “SMOS satellite salinity data accuracy assessment in the China coastal areas.” Acta Oceanologica Sinica, Vol. 35 (No. 5): pp. 169–176. doi:10.3969/j.issn.0253-4193.2013.05.019.
   Google Scholar
• Wen, Y.B., Wan, Y.Y., Hu, J.F., Fu, Z.W., Duan, Y.K., and Hu, Y.L. 2000. “The effection of sampling precision on imaging in cross-hole tomography.” Journal of Yunnan University, Vol. 22 (No. S1): pp. 49–53.
   Google Scholar
• Wu, F.F., Fu, Z.Y., Hu, L.S., Zhang, F., Du, Z.H., and Liu, R.Y. 2021. “Retrieval of sea surface salinity in the Gulf of Mexico based on random forest method.” Haiyang Xuebao, Vol. 43 (No. 9): pp. 126–136. doi:10.12284/hyxb2021146.
   Google Scholar
• Xu, J. P. 2002. Argo Global Ocean Observation Exploration. Beijing: China Ocean Press.
   Google Scholar
• Yang, S.L., Zhou, S.F., Zhou, W.F., Wu, Y.M., and Zhang, B.B. 2016. “The relationship between skipjack Katsuwonus pelam is catch and water temperature and surface salinity in the west-central Pacific Ocean based on Argo data.” Journal of Dalian Fisheries University, Vol. 25 (No. 1): pp. 34–40. doi:10.3969/j.issn.1000-9957.2010.01.007.
   Google Scholar
• Yang, T., and Niu, G.S. 2021. “Seasonal and interannual variation characteristics of the tropical Indian ocean’s intertropical convergence zone.” Climate Change Research Letters, Vol. 10 (No. 06): pp. 584–597. doi:10.1007/s00382-020-05195-5.
   Google Scholar
• Yang, X.J., and Yang, X.H. 2022. “Reflections on the application of machine learning in the development of medical data.” Advances in Applied Mathematics, Vol. 11: pp. 3496. doi:10.12677/aam.2022.116372.
   Google Scholar
• Zhang, L.J., Zhang, Y.F., and Yin, X.B. 2023. “Aquarius sea surface salinity retrieval in coastal regions based on deep neural networks.” Remote Sensing of Environment, Vol. 284: pp. 113357. doi:10.1016/j.rse.2022.113357.
   Web of Science ®Google Scholar
• Zhang, Y. 2020. Research on Hyperparameter Optimization for Deep Learning models. Beijing: Capital University of Economics and Business. doi:10.27338/d.cnki.gsjmu.2020.000828.
   Google Scholar
• Zhao, H., and Wang, C.J. 2016. “Study on the sea surface salinity model based on SMOS data.” Journal of Ocean Technology, Vol. 35 (No. 01): pp. 15–22. doi:10.3969/j.issn.1003-2029.2016.01.002.
   Google Scholar
• Zhao, W.J., Li, H.P., and Liu, H.X. 2022. “Remote sensing retrieval of sea surface salinity based on RBF neural network from SMAP satellite.” Advances in Marine Science, Vol. 40 (No. 3): pp. 513–522. doi:10.12362/j.issn.1671-6647.20210702001.
   Google Scholar
• Zhou, S., Yu, B., and Zhang, Y. 2023. “Global concurrent climate extremes exacerbated by anthropogenic climate change.” Science Advances, Vol. 9 (No. 10): pp. eabo1638. doi:10.1126/sciadv.abo1638.
   PubMed Web of Science ®Google Scholar