Bayesian-based image reconstruction and its effect on the radiometric accuracy of infrared imaging systems

Abstract

In this paper, a new image reconstruction scheme is devised for estimating a high resolution temperature map of the top of the earth's atmosphere using the GOES-8 (Geostationary Operational Environmental Satellite) imager infrared channels. By simultaneously interpolating the image while estimating temperature, the proposed algorithm achieves a more accurate estimate of the sub-pixel temperatures than could be obtained by performing these operations independently of one another. The proposed algorithm differs from other Bayesian-based image interpolation schemes in that it estimates brightness temperature as opposed to image intensity and incorporates a detailed optical model of the GOES multi-channel imaging system. In order to test the effectiveness of the proposed technique, high resolution estimates of cloud top temperatures using a single GOES infrared channel are compared to temperature estimates obtained from the AVHRR (Advanced Very-High Resolution Radiometer). This test is achieved by examining sets of infrared data taken simultaneously by the GOES and AVHRR systems over the same geographic area. The AVHRR system collects long-wave infrared data with a spatial resolution of 1 kilometer, which is higher than the 4-kilometer spatial resolution the GOES system achieves. In some cases the estimated temperature differences between these systems are as high as 10 degrees Kelvin. It is shown in this paper that the proposed algorithm consistently improves the consistency between the cloud top temperatures estimated with the GOES and AVHRR systems by allowing the GOES system to achieve substantially higher spatial resolution.