The HY-1C Satellite Calibration Spectrometer (SCS) is designed for high-accuracy and high-frequency cross-calibration for sensors mounted on the HY-1C satellite; thus, its onboard calibration consistency and stability are crucial for application. Most onboard calibration errors can be corrected via observation physical models and the prelaunch calibration process. However, the practical SCS calibration coefficient still retains some regularity, which indicates the existence of residual calibration errors. Therefore, in this study, a time series analysis-based method is proposed to eliminate this residual error. First, the SCS onboard calibration method and coefficients are described; second, a seasonal–trend decomposition based on the Loess (STL) method is used to model the SCS calibration coefficient; third, the calibration coefficient is validated, corrected and predicted using the constructed STL model; and finally, a long short-term memory (LSTM) neural network method is also used to model and forecast the calibration coefficient. The analysis results show that: 1. the STL method can effectively model, interpret and correct the SCS calibration coefficient error; and 2. the LSTM method can also fit and forecast the calibration coefficients, while its accuracy and interpretability are poor. The proposed methods provide a data analysis-based perspective to monitor remote sensors and help improve the calibration accuracy.

A greenhouse gas sensor has been developed to simultaneously detect multiple gas species within a hollow-core photonic bandgap fiber (HC-PBF) structure entirely composed of fibers. To enhance sensitivity, the gas cell consists of HC-PBF enclosed between two single-mode fibers fused with a reflective end surface to double the absorption length. The incorporation of side holes for gas diffusion allows for analysis of the relationship between gas diffusion speed, number of drilled side holes, and energy loss. As the number of drilled holes increases, the response time decreases to less than 3 min at the expense of energy loss. Gas experiments demonstrated detection limits of 0.1 ppm for methane and 2 ppm for carbon dioxide, with an average time of 50 s. In-situ testing conducted in rice fields validates the effectiveness of the developed gas detection system using HC-PBF cells, establishing all-fiber sensors with high sensitivity and rapid response.

Accurately detecting atmospheric carbon dioxide is a vital part of responding to the global greenhouse effect. Conventional off-axis integral cavity detection systems are computationally intensive and susceptible to environmental factors. This study deploys an Extreme Learning Machine model incorporating a cascaded integrator comb (CIC) filter into the off-axis integrating cavity. It is shown that appropriate parameters can effectively improve the performance of the instrument in terms of lower detection limit, accuracy, and root mean square deviation. The proposed method is incorporated successfully into a monitoring station situated near an industrial area for detecting atmospheric carbon dioxide (CO₂) concentration daily.

Reliability is the most critical characteristic of space missions, for example in capturing and tracking moving targets. To this end, two methods are designed to track sunlight using solar remote-sensing instruments (SRSIs). The primary method is to use the offset angles of the guide mirror for closed-loop tracking, while the alternative method is to use the sunlight angles, calculated from the satellite attitude, solar vector, and mechanical installation correction parameters, for open-loop tracking. By comprehensively analyzing the error and rectifying the calibration of the solar tracking system, we demonstrate that the absolute value of the azimuth tracking precision is less than 0.0121° and the pitch is less than 0.0037° with the primary method. Correspondingly, they are 0.0992° and 0.0960° with the alternative method. These precisions meet the requirements of SRSIs. In addition, recalibration due to mechanical vibration during the satellite’s launch may invalidate the above methods, leading to the failure of SRSIs. Hence, we propose a dedicated injection parameter strategy to rectify the sunlight angles to capture and track the sunlight successfully. The stable and effective results in the ultraviolet to near-infrared spectrum validate the SRSI’s high-precision sunlight tracking performance. Furthermore, the above methods can also be applied to all orbital inclinations and may provide a solution for capturing and tracking moving targets.

Conventional mirror support structures often fail to meet optical system tolerances due to inadequate mitigation of the installation stresses caused by machining-induced surface irregularities and inclinations. This study proposes a novel single-point central support using an arc-shaped flexible hinge. Materials for the mirror and support structure are carefully selected based on their properties, and the optimal dimensions are determined through stiffness analysis. The new flexible support structure is detailed in this work. Simulations show that the mirror assembly achieves a first-order frequency of 296.66 Hz. Under separate triaxial gravitational and thermal loads at −40 °C and 50 °C, the mirror’s surface figure accuracy RMS remains better than λ/45 (λ = 632.8 nm). With a 0.008 mm forced displacement applied to the installation surface, the RMS change in the mirror’s surface figure is less than λ/200, meeting structural design specifications. In practical assembly tests, even when surface irregularities exceed tolerances by more than double, the RMS change in the mirror’s surface figure is only 7 nm. This design effectively ensures high-precision surface quality while significantly reducing assembly difficulty and installation surface precision requirements, achieving a large-tolerance assembly. It provides a valuable reference for mirror support design in imaging optical systems.

Simultaneous observation of aerosols, water vapor, and clouds is conducive to the analysis of their interactions, and the consistency of observation equipment, instrument performance, and observation time is crucial. Molecular oxygen A-band (758–778 nm) and water vapor absorption band (758–880 nm) are two bands with similar wavelengths, and the hyperspectral remote sensing information of these two bands can be exploited to invert the vertical profile of aerosol and water vapor. In this paper, a double-grating spectrometer system (DGSS) was developed. DGSS uses a telescope system and fiber to introduce multi-angle, double-band sunlight, and it splits light synchronously (non-sequentially) to different positions of the detector through a slit plate and two gratings. The DGSS was calibrated in the laboratory and observed in the external field. The results indicated that the spectral resolution reached 0.06 nm (molecular oxygen A-band, 758–778 nm) and 0.24 nm (water vapor absorption band, 758–880 nm). Meanwhile, the spectra of the two bands (three angles in each band) are not aliased on the detector. Besides, the multi-angle simultaneous observation of the high-resolution spectra of the two bands is realized, which proves the effectiveness of this method. This study will provide a scientific basis for the observation of aerosol, water vapor, and cloud ground-based networks.