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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 4, pp. 207-220

Sea surface salinity estimation using AMSR2 measurements over warm oceans

E.V. Zabolotskikh 1 , B. Chapron 2, 1 
1 Russian State Hydrometeorological University, Saint Petersburg, Russia
2 Institut Français de Recherche pour l’Exploitation de la Mer, Plouzané, France
Accepted: 14.04.2020
DOI: 10.21046/2070-7401-2020-17-4-207-220
An algorithm is presented to estimate sea surface salinity (SSS) of the upper ocean layer from the measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on GCOM-W1 satellite. The algorithm is based on the results of physical modeling of the brightness temperatures (Tbs) of the ocean-atmosphere system microwave radiation using contemporary models of the ocean emission and absorption in the atmospheric gases and clouds. The simulation was carried out for vertically and horizontally polarized radiation at the frequencies of 6.9 and 10.65 GHz, since radiation at higher frequencies is less sensitive to SSS. ERA-Interim reanalysis data on hydrometeorological parameters and climatological SSS values were used to calculate an array of Tbs. The sensitivity of the low-frequency AMSR2 measurements to salinity was estimated. It was shown that only vertically polarized brightness temperature at a frequency of 6.9 GHz at surface temperatures (Ts) exceeding 22 C had the sensitivity allowing salinity to be retrieved. The inverse problem was solved with a neural network (NN) approach. Tbs for vertically and horizontally polarized radiation at the frequencies of 6.9 and 10.65 GHz were used as input parameters to take into account the influence of surface wind, surface temperature, and atmospheric moisture content on the Tbs. The theoretical error of the algorithm was 1.6 ppm. The results of the algorithm application to the AMSR2 measurements were compared with the SSS satellite product based on the Soil Moisture Active Passive (SMAP) measurements for 2015 over the areas of the World Ocean with Ts > 22 °C. The algorithm error proved to be 1 ppm. The algorithm is characterized by overestimation of low and underestimation of high salinity values as compared with SMAP SSS data.
Keywords: sea surface salinity, satellite microwave radiometers, brightness temperatures, AMSR2, physical modeling, Neural Networks
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