Sea Surface Salinity Remote Sensing

• Main Authors: Yin, Xiaobin, Dinnat, Emmanuel P
• Format: eBook
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2019; MDPI
• Subjects: alboran sea; Aquarius; Aquarius satellite; Aquarius Validation Data System (AVDS); Arctic Gateways; Arctic Ocean; Arctic rivers; Argo; bias characteristics; Book Industry Communication; brightness temperature (TB); calibration; coastal; combined active/passive SSS retrieval algorithm; data processing; different instrument configurations; forward model; Gulf of Maine; Gulf of Mexico; interferometric microwave imager (IMI); mediterranean sea; MICAP; microwave radiometry; microwave remote sensing; n/a; ocean salinity; ocean surface roughness; one-dimensional (1D) aperture synthesis radiometer; Q1-390; quality assessment; Reference, information & interdisciplinary subjects; remote sensing; Research & information: general; retrieval algorithm; retrieval errors; retroflections; river discharge; salinity; salt transport; satellite salinity; Scotian Shelf; sea ice; sea surface salinity; sea surface salinity (SSS); sea surface temperature; SMAP; smos; surface velocity; upwelling; validation; Water Cycle Observation Mission (WCOM); water transport
• ISBN: 3039210777; 9783039210770; 3039210769; 9783039210763
• DOI: 10.3390/books978-3-03921-077-0

This Special Issue gathers papers reporting research on various aspects of remote sensing of Sea Surface Salinity (SSS) and the use of satellite SSS in oceanography. It includes contributions presenting improvements in empirical or theoretical radiative transfer models; mitigation techniques of external interference such as RFI and land contamination; comparisons and validation of remote sensing products with in situ observations; retrieval techniques for improved coastal SSS monitoring, high latitude SSS and the assessment of ocean interactions with the cryosphere; and data fusion techniques combining SSS with sea surface temperature (SST). New instrument technology for the future of SSS remote sensing is also presented.