Retrieval of sea surface salinity from SMAP L‐band radiometer: A novel approach for wind speed correction. (5th September 2019) - Sharma, Neerja
Abstract: The present article introduces a new sea surface salinity (SSS) retrieval technique from the Soil Moisture Active Passive (SMAP) L–band (1.4 GHz) microwave radiometer. The ocean surface wind speed (WS) correction, which is very critical in any of the SSS retrieval techniques, is accounted for with a different approach. A model function between ΔSSS (the difference between SSS of flat and rough ocean surfaces) and ocean surface WS is developed and used to correct the impact of ocean surface roughness due to the winds on SSS. In addition to WS correction, the retrieval technique appropriately accounts for the effects of atmospheric attenuation on L‐band frequency due to water vapour and oxygen, and ocean surface temperature on SSS. The performance of the estimations is statistically evaluated by comparing the estimations with the in situ Argo, buoy SSS, and SMAP version 3, 70 km resolution SSS observations in global oceans during 2016. The retrieved salinity well captured the global distribution patterns of SSS, including low and high values. Comparison of retrieved salinity with Argo and SMAP products show a root‐mean‐square error (RMSE) of 0.32 and 0.39 psu, a Pearson correlation coefficient of 0.94 and 0.93, and a scatter index of 0.0093 and 0.011 respectively on a monthly time‐scale. In addition, retrieved SSS promisingly captured day‐to‐day variability in SSS observed by tropical moored buoy SSS with RMSE of 0.36 psu and a Pearson correlation coefficient of 0.94. The validation confirms the potential ability of the new retrieval technique in capturing SSS changes on daily and monthly time‐scales. Abstract : A novel approach of SSS retrieval from the Soil Moisture Active Passive L‐band microwave radiometer is proposed. A new geophysical model function (GMF) between ocean surface wind speed and difference in SSS of rough and flat ocean surfaces is developed. This GMF considers the influence of ocean surface wind speed on SSS and improves its retrieval accuracy. Estimations well captured global distribution patterns of SSS. Validation of estimations with in situ Argo observations has an RMSE of 0.32 psu on a monthly time‐scale.