Geostationary Operational Environmental Satellite Imager infrared channel-to-channel co-registration characterization algorithm and its implementation in the ground system

Zhenping Li,Timothy J Schmit,Fangfang Yu,Anthony J Schreiner,Chris Schmidt,James P Nelson,Michael Grotenhuis,Xiangqian Wu,Hyre Bysal

doi:10.1117/1.jrs.8.083530

Abstract

Channel-to-channel co-registration is an important performance metric for the Geo- stationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) can be as large as 1 pixel (or ∼ 4k m) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is pre- sented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transfor- mation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asym- metry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the qual- ity of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to deter- mine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10

Highlights

Co-registration, or the assurance that measurements from multiple channels are from the same geolocation, is an important performance metric for the Imager instrument on the Geostationary Operational Environmental Satellite (GOES)
To demonstrate how the GII4C works with IR channel images from the GOES Imager, we will show the application of the algorithm to the IR channels 2 and 4 images shown in Figs. 3 and 4
The GII4C algorithm presented in this paper provides a systematic approach to characterize the co-registration errors for the GOES Imager IR channels

Summary

Introduction

Co-registration, or the assurance that measurements from multiple channels are from the same geolocation, is an important performance metric for the Imager instrument on the Geostationary Operational Environmental Satellite (GOES). The FFTR algorithm has been shown to be accurate, reversible, and efficient enough for spacecraft image resampling It does not have the discontinuity problem for the correlation function seen with the images being resampled using linear interpolation.[2] Using the fast Fourier transform (FFT) to resample an image for the purpose of co-registration has been investigated in the literature.[6] A common issue with the FFT in subpixel image registration is the aliasing effects that cause the resampled image to be distorted. Because the correlations between images in different channels are driven by features with large temperature gradients, the spatial and spectral gradient approach proposed in Ref. 3 is a very useful representation for highlighting the co-registration error effects.

FFTR Algorithm

GII4C Algorithm

Simulation Results

Algorithm Verification and Validation

Implementation of the GII4C Algorithm in the GOES Ground System

Summary