An Empirical Algorithm for Mitigating the Sea Ice Effect in SMAP Radiometer for Sea Surface Salinity Retrieval in the Arctic Seas

The L-band radiometer onboard the Soil MoistureActive Passive (SMAP) mission is used to retrieve sea surfacesalinity (SSS) over global ocean. In the Arctic seas, one of themajor challenges of SSS remote sensing is the presence of sea ice.This paper proposes a data-driven ice correction (IC) algorithmwhich extracts emission from the water portion of measuredbrightness temperature (TB) in scenes mixed with water and ice.Emission of the ice portion was removed based on estimationaccording to the ice fraction (fice) in the satellite footprint and icesignature derived from surrounding pixels. The IC algorithm isapplied to SMAP TB data to obtain TB with ice correction (TBIC),which are used for SSS retrieval using the standard JPL SMAPCAP processing system. We show that the algorithm is mosteffective near the ice edge, thereby increasing the fice threshold forpossible SSS retrieval to 15% from the current 3% without IC.SMAP SSS are validated using in situ salinity collected duringNASA’s Ocean Melting Greenland (OMG) mission from 2016 to2020 along the Greenland coast. The number of collocationsbetween OMG and SMAP daily gridded salinity increased bymore than 30% with IC. The statistical analysis shows a similarretrieval accuracy with or without IC, with the standard deviationof the difference between OMG and SMAP of 1.41 psu (with IC)and 1.42 psu (without IC). The bias adjusted SMAP SSS depictssalinity patterns and gradients around Greenland consistent withOMG measurements.