Signal-to-noise ratio model in Python']Python for high-resolution space-borne electro-optic imagers

We present a signal-to-noise ratio (SNR) simulator design for high-resolution Earth observation imagers with time-delay and integration (TDI) imaging sensors. SNR is one of the space-borne electro-optic imagers' driving design and performance evaluation parameters. TDI line imaging sensors are used to meet these imagers' SNR requirements by increasing effective exposure time during imaging. Determining the optimum number of TDI stages to meet the SNR requirements of the mission is critical for the imaging sensor selection, optical design of the telescope, laboratory radiometric tests, and imaging in orbit. The SNR simulator is based on an end-to-end image chain calculation following the light pathway from the sun to the imager. The simulator comprises radiance, telescope, sensor, and image modules written in the open-access Python environment. The radiance module uses PcModWin/MODerate resolution atmospheric TRANsmission output data of the atmospheric radiance calculation input for the simulator. The simulator computes the SNR for the given telescope and imaging sensor model and simulates associated images to show the radiometric performance. A high-resolution electro-optic imager model is used to demonstrate the performance and versatility of the simulator for the case studies. Experimental simulator validation is demonstrated for the images acquired by the Gokturk-2 space-borne imager.