1. Abdelmalik, K.: Role of statistical remote sensing for inland water quality parameters prediction. Egypt. J. Rem. Sens. Space Sci.21(2), 193–200 (2018)

   Google Scholar 

2. Agarwal, N., Sharma, R., Basu, S., Agarwal, V.K.: Derivation of salinity profiles in the indian ocean from satellite surface observations. IEEE Geosci. Remote Sens. Lett.4(2) (2007)

   Google Scholar 

3. Alvera-Azcrate, A., Barth, A., Parard, G., Beckers, J.M.: Analysis of smos sea surface salinity data using dineof. Rem. Sens. Environ. 180 (2016)

   Google Scholar 

4. Ansari, M., Akhoondzadeh, M.: Mapping water salinity using landsat-8 oli satellite images (case study: Karun basin located in iran). Adv. Space Res.65(5), 1490–1502 (2020)

   Article  Google Scholar 

5. Bayati, M., Danesh-Yazdi, M.: Mapping the spatiotemporal variability of salinity in the hypersaline lake urmia using sentinel-2 and landsat-8 imagery. J. Hydrol.595, 126032 (2021)

   Article  Google Scholar 

6. Biguino, B., Olmedo, E., Ferreira, A., Zacarias, N., et al.: Evaluation of smos l4 sea surface salinity product in the western iberian coast. Remote Sens.14(2) (2022)

   Google Scholar 

7. Buongiorno Nardelli, B., Droghei, R., Santoleri, R.: Multi-dimensional interpolation of smos sea surface salinity with surface temperature and in situ salinity data. Remote Sens. Environ.180, 392–402 (2016)

   Article  Google Scholar 

8. Casagrande, G., Stephan, Y., Warn Varnas, A.C., Folegot, T.: A novel empirical orthogonal function (eof)-based methodology to study the internal wave effects on acoustic propagation. IEEE J. Oceanic Eng.36(4), 745–759 (2011)

   Article  Google Scholar 

9. Daniels, A., Koutsougeras, C.: Predicting Water Quality Parameters in Lake Pontchartrain Using Machine Learning: A Comparison on K-Nearest Neighbors, Decision Trees, and Neural Networks to Predict Water Quality. ACM (2021)

   Google Scholar 

10. Das, N.N., Entekhabi, D., Njoku, E.G.: An algorithm for merging smap radiometer and radar data for high-resolution soil-moisture retrieval. IEEE Trans. Geosci. Remote Sens.49(5), 1504–1512 (2011)

    Article  Google Scholar 

11. De Rosnay, P., Calvet, J.C., Kerr, Y., et al.: Smosrex: a long term field campaign experiment for soil moisture and land surface processes remote sensing. Remote Sens. Environ. 102(3-4) (2006)

    Google Scholar 

12. Devaraj, C., Shah, C.A.: Automated geometric correction of landsat mss l1g imagery. IEEE Geosci. Remote Sens. Lett.11(1), 347–351 (2014)

    Article  Google Scholar 

13. Ferdous, J., Rahman, M.T.U.: Developing an empirical model from landsat data series for monitoring water salinity in coastal bangladesh. J. Environ. Manage.255, 109861 (2020)

    Article  Google Scholar 

14. Jin, Q., Tian, Y., Sang, Q., Liu, S., et al.: A deep learning model for joint prediction of three-dimensional ocean temperature, salinity and flow fields. In: 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE), pp. 573–577 (2021)

    Google Scholar 

15. Kerr, Y., Philippe, W., Wigneron, J.P., et al.: The smos mission: new tool for monitoring key elements of the global water cycle. Proc. IEEE98 (2010)

    Google Scholar 

16. Lagerloef, G.S., Swift, C.T., Le Vine, D.M.: Sea surface salinity: the next remote sensing challenge. Oceanography8(2), 44–50 (1995)

    Article  Google Scholar 

17. Liu, M., Liu, X., Jiang, J., Xia, X.: Artificial neural network and random forest approaches for modeling of sea surface salinity. Int. J. Remote Sens. Appl.3 (2013)

    Google Scholar 

18. Liu, M., Liu, X., Liu, D., Ding, C., Jiang, J.: Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm. Comput. Geosci.75 (2015)

    Google Scholar 

19. Maliki, A.A., Chabuk, A., Sultan, M.A., et al.: Estimation of total dissolved solids in water bodies by spectral indices case study: Shatt al-arab river. Water Air Soil Pollut.231(9) (2020)

    Google Scholar 

20. Markham, B.L., Storey, J.C., Williams, D.L., Irons, J.R.: Landsat sensor performance: history and current status. IEEE Trans. Geosci. Remote Sens.42(12), 2691–2694 (2004)

    Article  Google Scholar 

21. Matsuoka, A., Babin, M., Devred, E.C.: A new algorithm for discriminating water sources from space: a case study for the southern beaufort sea using modis ocean color and smos salinity data. Remote Sens. Environ.184, 124–138 (2016)

    Article  Google Scholar 

22. Melesse, A.M., Khosravi, K., Tiefenbacher, J.P., et al.: River water salinity prediction using hybrid machine learning models. Water12(10) (2020)

    Google Scholar 

23. Meng, L., Yan, C., Zhuang, W., et al.: Reconstructing high-resolution ocean subsurface and interior temperature and salinity anomalies from satellite observations. IEEE Trans. Geosci. Remote Sens.60, 1–14 (2022)

    Google Scholar 

24. Mueller, N., Lewis, A., Roberts, D., Ring, S., et al.: Water observations from space: Mapping surface water from 25 years of landsat imagery across australia. Remote Sens. Environ.174 (2016)

    Google Scholar 

25. Nguyen, P.T., Koedsin, W., McNeil, D., Van, T.P.: Remote sensing techniques to predict salinity intrusion: application for a data-poor area of the coastal mekong delta, vietnam. Int. J. Rem. Sens.39(20), 6676–6691 (2018)

    Article  Google Scholar 

26. Olmedo, E., Martnez, J., Umbert, M., Hoareau, N., et al.: Improving time and space resolution of smos salinity maps using multifractal fusion. Remote Sens. Environ.180, 246–263 (2016)

    Article  Google Scholar 

27. Ouyang, Y., Zhang, Y., Chi, J., Sun, Q., Du, Y.: Regional difference of sea surface salinity variations in the western tropical pacific. J. Oceanogr.77, 647–657 (2021)

    Article  Google Scholar 

28. Qi, J., Zhang, L., Qu, T., et al.: Salinity variability in the tropical pacific during the central-pacific and eastern-pacific el nio events. J. Mar. Syst.199, 103225 (2019)

    Article  Google Scholar 

29. Ranhotra, S.S.: Detection of Salinity of Sea Water Using Image Processing Techniques, pp. 76–81 (2014)

    Google Scholar 

30. Tang, W., Fore, A., Yueh, S., Lee, T., Hayashi, A., Sanchez-Franks, A., Baranowski, D.: Validating smap sss with in situ measurements. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2561–2564 (2017)

    Google Scholar 

31. Tang, W., Fore, A., Yueh, S., Lee, T., et al.: Validating smap sss with in situ measurements. Remote Sens. Environ.200, 326–340 (2017)

    Article  Google Scholar 

32. Wang, F., Xu, Y.J.: Development and application of a remote sensing-based salinity prediction model for a large estuarine lake in the us gulf of mexico coast. J. Hydrol.360(1–4), 184–194 (2008)

    Article  Google Scholar 

33. Yang, T., Chen, Z.Z., He, Y.: A new method to retrieve salinity profiles from sea surface salinity observed by smos satellite. Acta Oceanologica Sinica 34 (2015)

    Google Scholar 

34. Zhao, J., Temimi, M.: An empirical algorithm for retreiving salinity in the arabian gulf: application to landsat-8 data. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4645–4648. IEEE (2016)

    Google Scholar 

Download references