PRELIMINARY RESULTS OF SEA SURFACE SALINITY RETRIEVAL FROM MICAP COMBINED ACTIVE AND PASSIVE OBSERVATION

W. Wang¹, X. Tong¹, Z. Wang¹, H. Liu¹

¹National Space Science Center, Chinese Academy of Sciences

The Chinese Ocean Salinity Mission (COSM), launched in November 2024, is equipped with two primary payloads. One of these is the Microwave Imager Combined Active and Passive (MICAP), which combines a multi-frequency synthetic-aperture radiometer (operating in the L, C, and K bands) with an L-band digital beamforming scatterometer. This advanced configuration is designed to enable the simultaneous retrieval of sea surface salinity (SSS), sea surface temperature (SST), and sea surface wind speed (WS). The spatial resolution is better than 75 km × 50 km at L-band and better than 15 km × 25 km at C- and K-bands. The noise equivalent delta temperature (NEDT) is better than 0.15 K for the L-band and 0.5 K for the C-and K-bands.

The MICAP Level-2 (L2) operational processing system generates estimates of SSS, SST, and WS from brightness temperatures (TBs) provided by the Level-1C (L1C) gridded data product. The L1C product is geolocated using an equal-area grid system based on the Icosahedral Snyder Equal Area projection (ISEA 4H9), similar to that used in the SMOS mission[1]. The L2 processing system produces two distinct products: the real-time operational product (L2A) and the delayed-time operational product (L2B). In the L2A processing chain, the SSS background field is obtained from the National Marine Environment Forecasting Center (NMEFC), while other ancillary data are sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction models. In contrast, the L2B processing utilizes ancillary data from the HYbrid Coordinate Ocean Model (HYCOM) and ERA5 reanalysis.

The geophysical model function (GMF) plays a critical role in the L2 retrieval process. Currently, SURFEM-Ocean model [2], implemented within the Radiative Transfer code for TOVS (RTTOV) framework, is used for L-band simulations. For the C- and K-bands, the RSS 2012 Radiative Transfer Model (RTM) [3], [4] is employed. Additionally, the Aquarius L-band scatterometer GMF [5] has been adapted and interpolated to match the characteristics of the MICAP scatterometer. The MICAP L2 retrieval employs two main algorithms: (1) The L-band mode retrieval, based on the SMOS L2 Algorithm[6], [7], which uses only L-band radiometer TBs while deriving other parameters—such as SST, WS, total column water vapor, and total cloud liquid water—from ancillary datasets. (2) The Combined Active-Passive (CAP) mode retrieval, which is similar to the Aquarius/SMAP CAP retrieval algorithm [8], [9]. In this mode, all the measurements from MICAP are used to retrieve SSS, SST and WS.

1. Nicolas Reul et al., “Overview of the First SMOS Sea Surface Salinity Products. Part I: Quality Assessment for the Second Half of 2010,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 5, pp. 1636–1647, May 2012, doi: 10.1109/TGRS.2012.2188408.

2. Lise Kilic et al., “Development of the SURface Fast Emissivity Model for Ocean (SURFEM-Ocean) Based on the PARMIO Radiative Transfer Model,” Earth and Space Science, vol. 10, no. 11, p. e2022EA002785, Nov. 2023, doi: 10.1029/2022EA002785.

3. Thomas Meissner and Frank J. Wentz, “The Emissivity of the Ocean Surface Between 6 and 90 GHz Over a Large Range of Wind Speeds and Earth Incidence Angles,” IEEE Trans. Geosci. Remote Sensing, vol. 50, no. 8, pp. 3004–3026, Aug. 2012, doi: 10.1109/tgrs.2011.2179662.

4. Frank J. Wentz and Thomas Meissner, “Atmospheric absorption model for dry air and water vapor at microwave frequencies below 100GHz derived from spaceborne radiometer observations,” Radio Sci., vol. 51, no. 5, pp. 381–391, May 2016, doi: 10.1002/2015rs005858.

5. S. H. Yueh, S. J. Dinardo, A. G. Fore, and F. K. Li, “Passive and Active L-Band Microwave Observations and Modeling of Ocean Surface Winds,” IEEE Trans. Geosci. Remote Sensing, vol. 48, no. 8, pp. 3087–3100, Aug. 2010, doi: 10.1109/TGRS.2010.2045002.

6. Sonia Zine et al., “Overview of the SMOS Sea Surface Salinity Prototype Processor,” IEEE Trans. Geosci. Remote Sensing, vol. 46, no. 3, pp. 621–645, Mar. 2008, doi: 10.1109/TGRS.2008.915543.

7. X. Yin, J. Boutin, and P. Spurgeon, “First Assessment of SMOS Data Over Open Ocean: Part I—Pacific Ocean,” IEEE Trans. Geosci. Remote Sensing, vol. 50, no. 5, pp. 1648–1661, May 2012, doi: 10.1109/TGRS.2012.2188407.

8. Simon Yueh, Wenqing Tang, Alexander Fore, Akiko Hayashi, Yuhe T. Song, and Gary Lagerloef, “Aquarius geophysical model function and combined active passive algorithm for ocean surface salinity and wind retrieval,” JGR Oceans, vol. 119, no. 8, pp. 5360–5379, Aug. 2014, doi: 10.1002/2014JC009939.

9. Alexander G. Fore, Simon H. Yueh, Wenqing Tang, Bryan W. Stiles, and Akiko K. Hayashi, “Combined Active/Passive Retrievals of Ocean Vector Wind and Sea Surface Salinity With SMAP,” IEEE Trans. Geosci. Remote Sensing, vol. 54, no. 12, pp. 7396–7404, Dec. 2016, doi: 10.1109/TGRS.2016.2601486.