After 9 years of operation, the ESA’s SMOS (Soil Moisture and Ocean Salinity) mission continues providing good quality full polarimetric Brightness Temperature (TB) data to generate frequent and global maps of soil moisture over landmasses and surface salinity over the ocean thanks to its unique payload, MIRAS (Microwave Imaging Radiometer using Aperture Synthesis). At its operating frequency (1.41 GHz), there is a non-negligible effect that must be compensated, called Faraday Rotation which rotates the electromagnetic field components coming from the Earth microwave radiation when propagating through the ionosphere. The Faraday rotation angle (FRA) magnitude depends on the frequency, geomagnetic data, and the total electron content on the ionosphere [1] [2]. Currently, the FRA is theoretically estimated in SMOS by using a formulation that depends on two external sources where the first one provides geomagnetic field data and the second one, ionosphere data read from a global VTEC database with an interval of two hours. In order to get improved geophysical parameter retrievals, the FRA must be directly recovered from the SMOS full-pol TB data in a continuous way. Latest advances in image reconstruction led to improving third and fourth Stokes parameters [3] making possible the instantaneous retrieval of the FRA with SMOS full-pol brightness temperature [4]. However, due to the large thermal noise, spatial bias, and image reconstruction artifacts, FRA retrievals for a single snapshot present high errors. A previous work showed that FRA could be directly retrieved at boresight from SMOS full-pol TB with good accuracy by using a smart spatio-temporal filtering strategy [5]. However, averaged boresight FRA estimations are not representative across the complete SMOS field of view (FoV), that is, if the averaged boresight FRA is assigned to all pixels in the FoV, a large systematic bias appears across the FoV [6]. In this work, a new methodology is presented in order to retrieve FRA from SMOS radiometric data in the SMOS overpass using most of the extended alias-free FoV. The method is based on deriving SMOS VTEC maps to use them in the FRA correction instead of the ones from the VTEC database. To make a robust VTEC retrieval despite all the factors that make it challenging, a temporal and spatial filtering strategy has to be applied. The size of the filters has to be thoroughly analyzed and chosen taken into account the tradeoff between the mitigation of the noise effect and the accuracy of the VTEC retrieval. We developed in [6] an end-to-end simulator to assess the performance of the different tested approaches by using simulated TB images. By obtaining the VTEC on the SMOS overpass, the FRA can be retrieved because both variables are directly proportional. This methodology is allowing the recovery of VTEC maps directly from full-pol SMOS data giving promising results. We are currently working on the validation of these retrieved SMOS VTEC maps by comparing them to combined GPS files [7] and with the TEC values provided in the L2OS product [8]. Next step will be focused on correcting the TB by using the retrieved FRA from SMOS following the proposed methodology and evaluating the impact on the quality of the TB images.

REFERENCES [1] D. M. Le Vine and S. Abraham, “The effect of the ionosphere on remote sensing of sea surface salinity from space: Absorption and emission at L band,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 4, pp. 771–782, April 2002. [2] S. H. Yueh, “Estimates of Faraday rotation with passive Microwave polarimetry for microwave remote sensing of earth surfaces,” IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 5, pp. 2434–2438, September 2000. [3] L. Wu et al. , “Radiometric performance of SMOS full polarimetric imaging,” IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 6, pp. 1454–1458, November 2013. [4] S. H. Yueh, “Estimates of Faraday rotation with passive Microwave polarimetry for microwave remote sensing of earth surfaces,” IEEE Transactions on Geoscience and Remote. [5] I. Corbella, L. Wu, F. Torres, N. Duffo and M. Martin-Neira. “Faraday Rotation Retrieval Using SMOS Radiometric Data”. IEEE Geoscience & Remote Sensing Letters, Vol.12, iss. 3, pp. 458- 461. 2015. [6] R. Rubino et al., "Direct faraday rotation angle retrieval in SMOS field of view," 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 697-698, 2017, doi: 10.1109/IGARSS.2017.8127047. [7] Schaer, S., Gurtner, W., & Feltens, J. (1998, February). IONEX: The ionosphere map exchange format version 1. In Proceedings of the IGS AC workshop, Darmstadt, Germany (Vol. 9, No. 11). [8] Vergely, J.-L., P. Waldteufel, J. Boutin, X. Yin, P. Spurgeon, and S. Delwart (2014), New total electron content retrieval improves SMOS sea surface salinity, J. Geophys. Res. Oceans, 119, 7295–7307, doi:10.1002/2014JC010150

European Space Agency’s 2019 Living Planet Symposium, 13-17 May 2019, Milan, Italy

Peer reviewed