Enhancing SMOS Salinity Accuracy in Areas Affected by RFI

This research focuses on the impact of Radio Frequency Interference (RFI) on the accuracy of Sea Surface Salinity (SSS) measurements obtained from the Soil Moisture Ocean Salinity (SMOS) mission. RFI can affect the accuracy of SSS measurements in regions that are crucial for understanding ocean dynamics and climate change. The extent of RFI contamination in SMOS SSS data varies based on the location of SSS across the swath. This variability is exploited in our study by comparing SSS fields constructed from different swath locations. We employ Principal Component Analysis (PCA) and regression techniques to correct RFI signatures in SMOS SSS data. The effectiveness of this correction is validated through comparison with independent SSS data derived from an in-situ dataset (global SSS field), and with RFI probability (CESBIO dataset). Our results show that this approach significantly improves the accuracy of SSS data in regions affected by RFI. In particular, the correction procedure is able to restore the SSS variability associated with El Nino Southern Oscillation (ENSO) using a method based solely on the anomalies of the SMOS measurements. We also explore two correction methods: a regional correction (RM) and a pointwise correction (PM). While PM allows for independent correction of RFI contamination at each location without needing prior information about the RFI source or affected area, RM is more effective in areas with high SSS variability. The potential for combining these two methods will be further discussed at the conference.