A bi-spectral approach for destriping and denoising the sea surface temperature from SGLI thermal infrared data

Abstract

Stripe noise is a common issue in Sea Surface Temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multi-detector radiometers. We developed a Bi-Spectral 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 onboard 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.