A Bispectral Approach for Destriping and Denoising the Sea Surface Temperature from SGLI Thermal Infrared Data

Abstract

Abstract Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation. Significance Statement This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement.