Element Evaluation and Selection for Multi-Column Redundant Long-Linear-Array Detectors Using a Modified Z-Score

New-generation geostationary meteorological satellite radiometric imagers widely employ multi-column redundant long-linear-array detectors, for which the Best Detector Selection (BDS) strategy is crucial for enhancing the quality of remote sensing data. Addressing the limitation of current BDS methods that often rely on a single metric and thus fail to fully exploit the detector’s comprehensive performance, this paper proposes a detector evaluation method based on a modified Z-score. This method systematically categorizes detector metrics into three types: positive, negative, and uniformity. It introduces, for the first time, spectral response deviation (SRD) as an effective quantitative measure for the Spectral Response Function (SRF) and employs a robust normalization strategy using the Interquartile Range (IQR) instead of standard deviation, enabling multi-dimensional detector evaluation and selection. Validation using laboratory data from the FY-4C/AGRI long-wave infrared band demonstrates that, compared to traditional single-metric optimization strategies, the best detectors selected by our method show significant improvement across multiple performance indicators, markedly enhancing both data quality and overall system performance. The proposed method features low computational complexity and strong adaptability, supporting on-orbit real-time detector optimization and dynamic updates, thereby providing reliable technical support for high-quality processing of remote sensing data from geostationary meteorological satellites.