Enhancing Information Content in the Satellite-Derived Daytime Infrared Sea Surface Temperature Dataset Using a Transformative Approach

Abstract

The regression-based operational daytime Sea Surface Temperature (SST) retrieval from remote sensing Infrared (IR) measurements does not conventionally use Mid-Wave IR (MWIR) channels due to solar contamination. However, MWIR channels are desirable to obtain unambiguous surface information. A transformative approach, Physical Deterministic Sea Surface Temperature (PDSST) retrieval scheme, including MWIR channels, to enhance the information content in satellite-derived SST data, is presented here. This paper mainly emphasizes on the quality and availability of swath-processed daytime SSTs from MODIS-AQUA radiances using the PDSST suite, including MWIR channels, for coastal and near-coastal areas, which are needed the most. The focus areas of this study are the California coast in the Pacific Ocean, which is a highly dynamic oceanographic region, the Bay of Bengal in the Indian Ocean, which has a sparse population of in situ, and the Chesapeake Bay in the Atlantic Ocean, which is best known for seafood production. Apart from in situ validation using iQuam (NOAA), indirect validation, by comparing different SST products, is also performed. The results of PDSST-suite are compared with the currently operational MODIS-AQUA SSTs, obtained from the NASA website, and microwave SSTs from AMSR2, obtained from RSS website. Both the operational products are based on the regression method. It is found that the PDSST suite including MWIR channels can extract 3−5 times as much information as the currently operational NASA-produced regression-based daytime SSTs from MODIS-AQUA radiances for coastal areas. Oceanic fronts’ study is also included by using the increased information content of satellite-derived SSTs from PDSST.