RSS SMAP Level 2C Sea Surface Salinity NRT V6.0 Validated Dataset

The SMAP-SSS level 2C near real-time (NRT) V6.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. SMAP, launched on January 31, 2015, was initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles, and has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle.



The dataset includes derived SMAP SSS, SSS uncertainty using the NRT SMAP Salinity Retrieval Algorithm, top of atmosphere brightness temperature (TB), wind speed and direction data for extreme winds, and other all necessary ancillary data and the results of all intermediate steps. The observations are global, provided on a 0.25° fixed Earth grid with an approximate spatial resolution of 70 km. The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. Each data file covers one 98-minute orbit (15 files per day), is available in netCDF-4 file format with about 5 hours l
atency.



This RSS SMAP-SSS V6.0 NRT dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature with its 1000km swath, it achieves global coverage in approximately three days, enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth’s hydrological cycle, ocean circulation, and climate change.