The payload Geostationary High-speed Imager (GHI) onboard the Fengyun-4B satellite employs the technology of the long linear array detectors. The long-wave infrared channel of GHI exhibits significant non-uniformity, manifested as brightness temperature discontinuities at the edges of adjacent swaths and abnormal signal value drops at the edges of the full-disk targets. To address this issue, ground tests were conducted on the GHI backup unit, identifying channel crosstalk within the infrared bands, which further impacted the radiometric calibration coefficients. Based on these ground tests, a linear crosstalk model was established along with a solution method. Subsequently, a crosstalk parameter solving algorithm suitable for long linear array detectors was proposed. This algorithm can solve the crosstalk coefficients using data from the edge regions of the full-disk in orbit and use them for correction. After applying this algorithm, the crosstalk phenomenon was well corrected, reducing non-uniformity by more than 90%. Furthermore, a fixed-scene processing method based on the generalized inverse matrix of least squares was proposed, enabling automated operation of the crosstalk correction algorithm. Currently, this algorithm has been deployed in the Data Acquisition & Tele-command System (DTS) of the Fengyun satellite, where it is used for real-time correction of GHI’s data.

Striping noise is a common problem in multi-detector scanning radiometers on remote sensing satellites, typically caused by response inconsistency among detector elements. For payloads with a multi-column redundant architecture, this paper proposes a detector-selection framework that jointly considers sensitivity and uniformity from the perspective of detector-element selection to mitigate striping noise. First, the degree of detector consistency is quantified using the Inter-Row Brightness Temperature Difference (IRBTD). Then, a dynamic programming approach based on the Viterbi algorithm is employed to select detector elements row by row with linear time complexity, optimizing the process through a weighted cost function that integrates sensitivity and consistency. Experiments on raw data from the FY-4B Geostationary High-speed Imager (GHI) show that the method reduces inconsistency by 10–40% while increasing the noise-equivalent temperature difference (NEdT) by only 1–4% (≤4 mK). The average IRBTD decreases by approximately 20–100 mK, and high-frequency striping energy is significantly suppressed (reduction of 50–90%). The algorithm exhibits linear time complexity and low computational overhead, making it suitable for real-time on-board processing. Its weighting parameter enables flexible trade-offs between sensitivity and uniformity. By suppressing striping noise directly during the detector-selection stage without introducing data distortion or requiring calibration adjustments, the proposed method can be widely applied to scanning radiometers that employ multi-column long-linear-arrays.

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.