First Assessment of CyGNSS-Incorporated SMAP Sea Surface Salinity Retrieval Over Pan-Tropical Ocean

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 12163 - 12173
• Main Authors: Liu, Baojian, Wan, Wei, Guo, Zhizhou, Ji, Rui, Wang, Tsechun, Tang, Guoqiang, Cui, Yaokui, Hong, Yang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Buoys; Cyclone global navigation satellite system (CyGNSS); Emissivity; Empirical analysis; Feasibility studies; Geophysics; Global navigation satellite system; GNSS-reflectometry; Mathematical models; microwave radiometry; Navigation; Navigational satellites; ocean salinity; Ocean temperature; ocean wind; Oceans; Parameters; Radiometers; Radiometry; Retrieval; Salinity; Salinity (geophysical); Salinity effects; Scatterometers; Sea measurements; Sea surface; Sea surface temperature; Significant wave height; Soil moisture; soil moisture active and passive (SMAP); Statistical analysis; Statistical methods; Surface salinity; Temperature measurement; Tropical climate; Wave height; Wind; Wind effects; Wind speed
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3128553

In sea surface salinity (SSS) retrieval using L-band passive radiometry, radiometer-independent ocean wind speed is needed as auxiliary data. Wind speed data from scatterometer and weather models are commonly used as auxiliary data in satellite SSS missions. This article's overarching goal is to explore the feasibility of incorporating the cyclone global navigation satellite system (CyGNSS) data into the SSS retrieval algorithm of the soil moisture active and passive (SMAP) mission over tropical and subtropical oceans. As a proof-of-concept study, empirical geophysical model functions in the retrieval algorithm are developed using the statistics of collocated SMAP, CyGNSS, and referenced buoys measurements. The SSS accuracy of CyGNSS-incorporated salinity retrieval is investigated against the SMAP SSS data product. Comparisons show that the proposed CyGNSS-incorporated retrieval algorithm improves the SSS accuracy by 0.1∼0.2 psu at low wind speed (<2 m/s). To some extent, it proves that spaceborne global navigation satellite system-reflectometry (GNSS-R) could be a new and helpful data source to understand wind-induced emissivity over a smooth ocean. The dependencies of emissivity on different geophysical parameters (i.e., sea surface temperature, significant wave height, and precipitation) are analyzed, and the spatial and seasonal variabilities of SSS errors are shown and linked to these geophysical parameters. The findings of this research provide valuable insights for future development and operation of the radiometer-based SSS retrieval algorithm using wind speed data from spaceborne GNSS-R.