The Infrared Hyperspectral Atmospheric Sounder II (HIRAS-II) is the key equipment on FengYun-3E (FY-3E) satellite, which can realize vertical atmospheric detection, featuring hyper spectral, high sensitivity and high precision. To ensure its accuracy of detection, it is necessary to correlate their thermal models to in-orbit data. In this work, an investigation of intelligent correlation method named Intelligent Correlation Platform for Thermal Model (ICP-TM) was established, the advanced Kriging surrogate model and efficient adaptive region optimization algorithm were introduced. After the correlation with this method for FY-3E/HIRAS-II, the results indicate that compared with the data in orbit, the error of the thermal model has decreased from 5 K to within ±1 K in cold case (10 ℃). Then, the correlated model is validated in hot case (20 ℃), and the correlated model exhibits good universality. This correlation precision is also much superiors to the general ones like 3 K in other similar literature. Furthermore, the process is finished in 8 days using ICP-TM, the efficiency is much better than 3 months based on manual. The results show that the proposed approach significantly enhances the accuracy and efficiency of thermal model, this contributes to the precise thermal control of subsequent infrared optical payloads.

The energy received in the mid-infrared (MIR) band at the sensor's aperture includes both reflected solar energy and the emitted energy from Earth's surface. Typically, the reflected solar energy in this band is weak. However, under certain conditions, such as in sun glint regions on the sea surface, the reflected solar energy detected by the MIR channel can be substantial. Currently, the application of sun glints physical models in the MIR band is not yet-clear. This study investigates the accuracy of applying different visible light and shortwave infrared sun glint models in the MIR band to evaluate their applicability. The paper selects three models,namely Breon-Henriot, Ebuchi-Kizu, to first Wu, and evaluate the sensitivity of each sun glint model. Subsequently, four selected MODIS sun glint images as data sources, and to evaluate their applicability ERA5 reanalysis data matched with satellite data wal usel to calculate atmospheric parameters. The solar radiation intensity reflected by the sea surface is computed using the three models. The accuracy of each model is then further validated with an MIR radiation transfer model. The results show that the Breon-Henriot model generally performs best in terms of correlation coefficient and root-mean-square error compared to MODIS measurements. These findings not only extend the application range of sun glint models in the MIR band but also enhance the MIR forward modeling system, providing new theoretical support for MIR radiation transfer and improving the effectiveness and accuracy of MIR remote sensing products in climate change monitoring and sea surface temperature dynamic analysis.

To address the issues of low detection rate and high false alarm rate caused by complex background during sub-pixel aerial aircraft detection in hyperspectral remote sensing image, an aerial aircraft detection method was proposed based on contrails cloud proposal. Firstly, a hyperspectral semantic segmentation model was used to search for the contrails cloud, and regions of interest(ROIs) of aircraft were proposed to reduce invalid search ranges and suppress false alarms based on the contrails cloud. Secondly, an endmember extraction algorithm based on dictionary learning and semi-blind non-negative matrix factorization was proposed to improve the accuracy of aircraft endmember extraction for hyperspectral subpixels. Finally, verification experiments were carried out on the hyperspectral remote sensing image dataset of Gaofen-5 satellite. The results demonstrated that the algorithm proposed in this paper can effectively suppress false alarms in complex scenes, and significantly improve the detection rate and detection accuracy of sub-pixel aerial vehicles.

Urban tree species provide various essential ecosystem services in cities, such as regulating urban temperatures, reducing noise, capturing carbon, and mitigating the urban heat island effect. The quality of these services is influenced by species diversity, tree health, and the distribution and composition of trees. Traditionally, data on urban trees has been collected through field surveys and manual interpretation of remote sensing images. In this study, we evaluated the effectiveness of multispectral airborne laser scanning (ALS) data in classifying 24 common urban roadside tree species in Espoo, Finland. Tree crown structure information, intensity features, and spectral data were used for classification. Eight different machine learning algorithms were tested, with the extra trees (ET) algorithm performing the best, achieving an overall accuracy of 71.7% using multispectral LiDAR data. This result highlights that integrating structural and spectral information within a single framework can improve classification accuracy. Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.

When extracting coastal zone tidal flats using remote sensing transient images, the influence of tides greatly limits the accuracy of tidal flat spatial distribution extraction. With the purpose of weakening the influence of tides, a method of extracting coastal zone tidal flats by combining time-series Sentinel-2 images and tidal flat index was proposed. First, based on the Sentinel-2 time-series image data, we us the quantize synthesis method to generate high- and low-tide images, and then analyz the spectral reluctance characteristics of different land classes on the high- and low-tide images. A NIR-band tidal flat extraction index that excludes the interference of the tidal transient was constructed. Secondly, the image spectral information and the tidal flat extraction index were input into a machine learning algorithm to realize fast and efficient extraction of the tidal flat. In addition, the study discussed the separability of the tidal flats index and the generalizability of the methodology. The results show that the tidal flat's extraction index constructed in this research had a good separability for tidal flats, the overall accuracy of tidal flats extraction was 93.02%, the Kappa coefficient was 0.86, and the proposed method had good applicability to remote sensing images containing near-infrared bands. This method can realize automatic and rapid tidal flat extraction, and provide data support for the sustainable management and protection of coastal zone resources.

The presence of water in lunar materials can significantly impact the evolution of lunar geology and environment, as well as provide necessary conditions for the utilization of lunar resources. However, due to the limitations of lunar remote sensing methods, it is challenging to obtain direct evidence of water or determine its form of occurrence. Laser Raman spectroscopy, on the other hand, can provide valuable information on the type, distribution, and content of water in lunar materials without the need for illumination, sample pretreatment, or destructive measures. In this study, we utilized Raman spectroscopy to detect and quantify the water-containing characteristics of typical lunar rocks and minerals, including adsorbed water, ice, crystalline water, and hydroxyl-structured water. First, we used a 532 nm laser micro-Raman spectroscopy to identify and analyze the water-containing signals of various forms of water in lunar soil simulants. We then examined and analyzed the detection limits of adsorbed water, crystalline water, and hydroxyl-structured water in these simulants, as well as the relationship between their content and signal intensity. Finally, we employed linear regression (LR), ridge regression (RR), and partial least squares regression (PLSR) to quantitatively analyze the contents of these three forms of water in the lunar soil simulants. Our results demonstrate that the characteristic spectral peaks of the four forms of water in the lunar soil simulants can be clearly identified, with peak distribution regions located at 100-1 700 cm-1 and 2 600-3 900 cm-1 for the lunar soil components and water bodies, respectively. The spectral peaks of water are a combination of broad envelope peaks of hydrogen-bonded OH and sharp peaks of non-hydrogen-bonded OH stretching vibrations in varying proportions. The detection limits for adsorbed water, crystalline water (MgSO4·7H2O), and hydroxyl water (Al2Si2O5(OH)4) in the lunar soil simulants are 1.3 wt%, 0.8 wt%, and 0.3 wt%, respectively. There is a linear relationship between the intensity of water-containing peaks and the water content in the lunar soil simulants, with root mean square errors of 1.75 wt%, 1.16 wt%, and 1.19 wt% obtained through LR, RR, and PLSR.

In sub nanometer carbon nanotubes, water exhibits unique dynamic characteristics, and in the high-frequency region of the infrared spectrum, where the stretching vibrations of the internal oxygen-hydrogen (O-H) bonds are closely related to the hydrogen bonds (H-bonds) network between water molecules. Therefore, it is crucial to analyze the relationship between these two aspects. In this paper, the infrared spectrum and motion characteristics of the stretching vibrations of the O-H bonds in one-dimensional confined water (1DCW) and bulk water (BW) in (6, 6) single-walled carbon nanotubes (SWNT) are studied by molecular dynamics simulations. The results show that the stretching vibrations of the two O-H bonds in 1DCW exhibit different frequencies in the infrared spectrum, while the O-H bonds in BW display two identical main frequency peaks. Further analysis using the spring oscillator model reveals that the difference in the stretching amplitude of the O-H bonds is the main factor causing the change in vibration frequency, where an increase in stretching amplitude leads to a decrease in spring stiffness and, consequently, a lower vibration frequency. A more in-depth study found that the interaction of H-bonds between water molecules is the fundamental cause of the increased stretching amplitude and decreased vibration frequency of the O-H bonds. Finally, by analyzing the motion trajectory of the H atoms, the dynamic differences between 1DCW and BW are clearly revealed. These findings provide a new perspective for understanding the behavior of water molecules at the nanoscale and are of significant importance in advancing the development of infrared spectroscopy detection technology.

The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction. In satellite laser communication systems, the use of short-wave infrared wavelengths as beacon light can reduce atmospheric absorption and signal attenuation. However, there are strong non-uniformity and blind pixels in the short-wave infrared image, which makes the image distorted and leads to the decrease of spot centroid positioning accuracy. Therefore, the high-precision localization of the spot centroid of the short-wave infrared images is of great research significance. A high-precision spot centroid positioning model for short-wave infrared is proposed to correct for non-uniformity and blind pixels in short-wave infrared images and quantify the localization errors caused by the two, further model-based localization error simulations are performed, and a novel spot centroid positioning payload for satellite laser communications has been designed using the latest 640×512 planar array InGaAs shortwave infrared detector. The experimental results show that the non-uniformity of the corrected image is reduced from 7% to 0.6%, the blind pixels rejection rate reaches 100%, the frame rate can be up to 2000 Hz, and the spot centroid localization accuracy is as high as 0.1 pixel point, which realizes high-precision spot centroid localization of high-frame-frequency short-wave infrared images.