Toward an Enhanced SMOS Level-2 Ocean Salinity Product

The quality of the Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) measurements has been noticeably improved in the past years. However, for some applications, there are still some limitations in the use of the Level-2 ocean salinity product. First, the SSS measurements are still affected by a latitudinal and seasonal bias. Second, the high standard deviation of the SSS error could significantly degrade part of the SSS signal. Finally, the coverage of the Level-2 salinity measurements is significantly reduced after applying filtering criteria to discard the poor-quality retrievals. In this work, we apply nodal sampling to the SMOS brightness temperatures (TBs), which effectively reduces the standard deviation of the TB error; then, we use debiased non-Bayesian retrieval for the mitigation of systematic biases on SSS and the statistical filtering criteria of the degraded salinity retrievals; and finally, we comprehensively characterize the residual latitudinal and seasonal biases and derive a correction for the retrieved SSS. We generate three years of an enhanced SMOS Level-2 Ocean Salinity product and we compare its performances with the ones corresponding to the European Space Agency SMOS Level-2 Ocean Salinity product (v662).

[1]  M. Martin-Neira,et al.  MIRAS, a two-dimensional aperture synthesis radiometer , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[2]  Yann Kerr,et al.  SMOS: The Challenging Sea Surface Salinity Measurement From Space , 2010, Proceedings of the IEEE.

[3]  Lin Wu,et al.  Impact of Correlator Efficiency Errors on SMOS Land–Sea Contamination , 2015, IEEE Geoscience and Remote Sensing Letters.

[4]  Manuel Martín-Neira,et al.  The MIRAS “all-licef” calibration mode , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[5]  Adriano Camps,et al.  Fast Processing Tool for SMOS Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[6]  Jacqueline Boutin,et al.  Mitigation of systematic errors in SMOS sea surface salinity , 2016 .

[7]  Joaquim Ballabrera-Poy,et al.  Enhancing SMOS brightness temperatures over the ocean using the nodal sampling image reconstruction technique , 2016 .

[8]  Justino Martínez,et al.  Improvements on Calibration and Image Reconstruction of SMOS for Salinity Retrievals in Coastal Regions , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Manuel Martín-Neira,et al.  SMOS: The Payload , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Joaquim Ballabrera-Poy,et al.  Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity , 2017 .

[11]  Antonio Turiel,et al.  Real-time Reconstruction of Surface Velocities from Satellite Observations in the Alboran Sea , 2020, Remote. Sens..

[12]  Antonio Turiel,et al.  Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis , 2018, Remote. Sens..

[13]  Joaquim Ballabrera-Poy,et al.  Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions , 2018, Remote. Sens..

[14]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[15]  Cristina González-Haro,et al.  Global ocean current reconstruction from altimetric and microwave SST measurements , 2014 .

[16]  Rosemary Morrow,et al.  The French contribution to the voluntary observing ships network of sea surface salinity , 2015 .

[17]  Joaquim Ballabrera-Poy,et al.  Improving time and space resolution of SMOS salinity maps using multifractal fusion , 2016 .

[18]  Joaquim Ballabrera-Poy,et al.  Empirical Characterization of the SMOS Brightness Temperature Bias and Uncertainty for Improving Sea Surface Salinity Retrieval , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  J. Boutin,et al.  New SMOS Sea Surface Salinity with reduced systematic errors and improved variability , 2018, Remote Sensing of Environment.

[20]  Manuel Martín-Neira,et al.  SMOS instrument performance and calibration after 6 years in orbit , 2013, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[21]  Adriano Camps,et al.  Nodal Sampling: A New Image Reconstruction Algorithm for SMOS , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Joaquim Ballabrera-Poy,et al.  Retrieval of eddy dynamics from SMOS sea surface salinity measurements in the Algerian Basin (Mediterranean Sea) , 2016 .

[23]  Jacqueline Boutin,et al.  First Assessment of SMOS Data Over Open Ocean: Part II—Sea Surface Salinity , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Philippe Richaume,et al.  SMOS Radio Frequency Interference Scenario: Status and Actions Taken to Improve the RFI Environment in the 1400–1427-MHz Passive Band , 2012, IEEE Transactions on Geoscience and Remote Sensing.