Comparison of spaceborne measurements of sea surface salinity and colored detrital matter in the Amazon plume

• Published in: Journal of Geophysical Research: Oceans
• Main Authors: Fournier, Severine, Chapron, Bertrand, Salisbury, J., Vandemark, Douglas, Reul, Nicolas
• Other Authors: Laboratoire d'Océanographie Spatiale (LOS), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2015
• Subjects: [SDU]Sciences of the Universe [physics]; Northwest Atlantic
• Online Access: https://hal.science/hal-04200581; https://doi.org/10.1002/2014JC010109

Large rivers are key hydrologic components in oceanography, particularly regarding air-sea and land-sea exchanges and biogeochemistry. We enter now in a new era of Sea Surface Salinity (SSS) observing system from Space with the recent launches of the ESA Soil Moisture and Ocean Salinity (SMOS) and the NASA Aquarius/Sac-D missions. With these new sensors, we are now in an excellent position to revisit SSS and ocean color investigations in the tropical northwest Atlantic using multi-year remote sensing time series and concurrent in situ observations. The Amazon is the world's largest river in terms of discharge. In its plume, SSS and upper water column optical properties such as the absorption coefficient of colored detrital matter (acdm) are strongly negatively correlated (<-0.7). Local quasi-linear relationships between SSS and acdm are derived for these plume waters over the period of 2010-2013 using new spaceborne SSS and ocean color measurements. Results allow unprecedented spatial and temporal resolution of this coupling. These relationships are then used to estimate SSS in the Amazon plume based on ocean color satellite data. This new product is validated against SMOS and in situ data and compared with previously developed SSS retrieval models. We demonstrate the potential to estimate tropical Atlantic SSS for the extended period from 1998 to 2010, prior to spaceborne SSS data collection.