Signal-to-Noise Ratio (SNR) Analysis across Seasons among Diverse Feature Targets using Multitemporal Resourcesat-2A LISS-3 Imagery

Signal-to-Noise Ratio (SNR) is vital in optical remote sensing imagery, measuring data quality and reliability. SNR represents the ratio of useful signal strength to background noise, where the signal denotes valuable surface information, and noise includes interference from sensor electronics, atmospheric effects, and ambient light. High SNR values enhance data interpretation, improve image quality assessment, and inform environmental monitoring and land management decision-making. The paper details the SNR computation methodology adopted to derive SNR from the multi-temporal Resourcesat-2A LISS-3 imagery. The experimental analysis was conducted at various spectral features, including snow, water, vegetation, and urban across different seasons. It is found that sensor specifications, solar angles, and environmental factors significantly influence SNR, affecting the balance between signal and noise levels.