Platform jitter in stereo mapping satellites introduces periodic stripe artifacts into digital surface models (DSMs), degrading geometric quality, while existing detection methods usually depend on disparity maps or high-frequency attitude data. This study proposes a DSM-based frequency-domain framework for detecting and removing jitter-induced stripes in GF-7 DSMs. First, 2D Fourier narrow-band notch analysis of detrended high-pass DSMs estimates stripe orientation and dominant period from directional and radial spectral peak prominence, and a Gaussian narrow-band stop filter is constructed for global suppression. Then, under these spectral priors, a profile-template method uses 1D median profiles, IIR notch filtering, and a global stripe template with a slowly varying amplitude field to model and remove the stripe component in the spatial domain. Multi-temporal co-registered DSM differencing provides reference stripe characteristics for evaluation. Experiments on three GF-7 DSMs show a stripe normal direction aligned with the subsatellite ground track, a stable dominant stripe period of about 90 m, period errors not exceeding 0.34 m, and consistency above 99.7% between the two methods. The spectral notch filtering suppresses more than 92% of stripe-band energy while preserving the overall spectral shape. The proposed framework therefore enables accurate jitter characterization and effective destriping without attitude or disparity information.

Spaceborne laser has been widely used for global forest canopy height retrieval, carbon stock estimation, and other forestry surveys, owing to its excellent vertical detection capability. However, its effectiveness in mountainous forest regions is highly dependent on the geolocation accuracy of laser footprints. In such regions, footprints are prone to significant geolocation errors caused by the long-term thermal effects of the laser, satellite jitter, and terrain-vegetation characteristics. Existing studies have struggled to achieve high-accuracy corrections under such complex conditions. To address this challenge, we propose a method to correct laser footprint geolocation errors in mountainous forests by minimizing the sum of spaceborne-airborne collaborative distances. First, a weighting parameter of the order of 10(-3) is introduced to weight the horizontal coordinates of both spaceborne laser footprints and airborne LiDAR-derived terrain data. Second, using these weighted coordinates, spaceborne-airborne collaborative distances between footprints and terrain grids are computed and summed. The optimal target grid is identified by minimizing this total distance. Finally, within the target grid, a residual function is constructed from the summed spaceborne-airborne distances. The optimal footprint position is determined by minimizing this function using a least-squares approach. Experiments were conducted using global ecosystem dynamics investigation (GEDI) and Gaofen-7 (GF-7) laser footprints across four typical terrain-vegetation regions. The results indicate that the proposed method significantly improves both geolocation and elevation accuracy. For GEDI footprints, elevation accuracy improved from 3.14 to 2.18 m. For GF-7 dual-beam laser footprints, average geolocation accuracy improved from 6.79 to 3.13 m, a 53.9% gain. Beam 1 improved from 6.76 to 2.68 m (a 60.4% improvement), and Beam 2 from 6.82 to 3.58 m (a 47.5% improvement). In addition, the proposed method effectively extrapolates GEDI error corrections across beams, greatly reducing reliance on high-precision terrain data.

Platform jitter of stereo mapping satellites induces strip‑like periodic elevation artifacts in digital surface models (DSMs), which severely degrades the geometric quality of stereo mapping products. Existing jitter detection methods mostly rely on disparity fields or high‑frequency attitude data. When disparity maps are unavailable or the jitter frequency is higher than the sampling rate of the attitude sensors, these methods become difficult to apply. Focusing on jitter in GF‑7 DSMs, this paper proposes a frequency‑domain jitter detection and stripe removal framework based solely on DSM data. The framework consists of two main components. (1) In the 2D spectral narrow‑band notch destriping method, the 2D Fourier power spectrum of a high‑pass DSM (after removing the low‑frequency terrain trend) is computed. Within a fixed annular range of periods, the prominence of peaks in the directional energy spectrum and radial period spectrum is analyzed to robustly estimate the dominant stripe orientation and period. A smooth Gaussian narrow‑band stop filter is then constructed in the full‑image frequency domain to concentrate suppression on the narrow‑band energy around the jitter fundamental frequency. (2) In the profile‑template destriping method, 1D median profiles in the direction perpendicular to the stripes are used as observations. Under the prior constraints of stripe direction and period obtained from the 2D spectral method, an infinite impulse response (IIR) notch filter is designed to extract candidate stripe components. A 2D stripe model is then constructed by combining a global 1D stripe template with a slowly varying 2D amplitude field, and the stripe component is explicitly modeled and removed in the spatial domain. Experiments on three GF‑7 DSM scenes over the plains near Shangqiu, Henan Province, show that the 2D spectral narrow‑band notch method robustly estimates a stripe orientation of northeast–southwest, which is highly consistent with the subsatellite ground‑track direction. The dominant period is about 90 m. The profile‑template method yields a spatial period of about 90 m as well, and the consistency of the dominant period between the two methods exceeds 99.7%. Using the resulting narrow‑band stop filter and stripe template, the energy suppression ratio η_PSD of the stripe narrow‑band in the power spectrum exceeds 92% for all DSMs, while the overall spectral shape of the DSM remains nearly unchanged, and the striping artifacts are significantly reduced or eliminated. The results demonstrate that the proposed DSM frequency‑domain jitter detection and stripe modeling approach achieves high parameter stability and destriping performance even in the absence of attitude and disparity information.

Light detection and ranging (LiDAR) onboard satellites are used not only for global height measurement but also to actively obtain global surface reflectance, which helps in separating surface targets on the basis of their reflectivity properties. In this paper, methods for obtaining surface reflectance with the LiDAR onboard the Terrestrial Ecosystem Carbon Inventory Satellite, which is nicknamed Goumang, are studied. The radiation model for LiDAR to obtain surface reflectance is constructed on the basis of the transmission path of the laser. A method for obtaining the coefficients of the radiometric calibration, which are key parameters for calculating surface reflectance in the radiation model, is designed. These coefficients are derived from automated data collected at the Chinese radiometric calibration site in Dunhuang. The uncertainty of this radiometric calibration is evaluated, yielding a value of approximately 5%. Additionally, a retrieval method for obtaining global surface reflectance based on global aerosol products from other satellites is introduced. The results are compared with ground-measured values, revealing a relative deviation of approximately 10%. This research provides a feasible pathway for retrieving global surface reflectance by LiDAR.

Capturing hierarchical relationships among land-cover classes is crucial for accurate semantic segmentation of remote sensing images. Traditional object-based methods face inherent limitations in modeling these complex relationships. To overcome these limitations, we proposed a novel object-based Markov random field (OMRF) model for hierarchical semantic segmentation. The objective of our model is to address two key challenges: (i) the representation of hierarchical semantic features, and (ii) the edge preservation of segmentation results. To address the first challenge, we developed hierarchical semantic representations of images for two distinct land-cover class sets and incorporated a transition probability matrix into OMRF to capture the interaction between these two semantic layers. For the second challenge, we devised an innovative spatial energy function that effectively enforces hierarchical predictions and dynamically regulates boundary smoothness by evaluating spectral dissimilarities among neighboring objects. Furthermore, a generative cross-layer inference strategy was introduced to iteratively exchange and update information across semantic layers for improved prediction. Experimental results on 11 remote sensing images demonstrate the robustness and accuracy of the proposed method, achieving an average Kappa coefficient exceeding 0.96. In comparison to 15 state-of-the-art methods, our model achieved optimal performance in 9 instances and suboptimal performance in 2 instances.

Jitter is a critical factor affecting the geometric accuracy of Earth observation satellites. After a satellite is launched, it is essential to detect and mitigate platform jitter to minimize its impact. The Ziyuan-3 (ZY3) series, China's first civilian stereo mapping satellite constellation, meets the 1:50000 scale mapping accuracy requirement without ground control points. Detecting and suppressing jitter during the satellite's orbit is a key technology for ensuring mapping accuracy. This letter investigates the causes of platform jitter across three satellites in the ZY3 series by analyzing gyroscope data using fast Fourier transform (FFT) at different stages of the satellite lifecycle. It compares the frequency and amplitude of jitter across different platforms and time periods. The results show that ZY3-01 and ZY3-02 exhibit consistent jitter patterns. They have significant amplitude in the 0.6 Hz region early in orbit and less later on, correlating with the satellites' lifespans. In contrast, the 0.2 Hz region remains relatively stable. The ZY3-03 satellite, equipped with a highly stable platform, shows no significant jitter, evidencing the effectiveness of such platforms in jitter suppression.

Light detection and ranging (LiDAR) onboard satellites are used not only for global height measurement but also to actively obtain global surface reflectance, which helps in separating surface targets on the basis of their reflectivity properties. In this paper, methods for obtaining surface reflectance with the LiDAR onboard the Terrestrial Ecosystem Carbon Inventory Satellite, which is nicknamed Goumang, are studied. The radiation model for LiDAR to obtain surface reflectance is constructed on the basis of the transmission path of the laser. A method for obtaining the coefficients of the radiometric calibration, which are key parameters for calculating surface reflectance in the radiation model, is designed. These coefficients are derived from automated data collected at the Chinese radiometric calibration site in Dunhuang. The uncertainty of this radiometric calibration is evaluated, yielding a value of approximately 5%. Additionally, a retrieval method for obtaining global surface reflectance based on global aerosol products from other satellites is introduced. The results are compared with ground-measured values, revealing a relative deviation of approximately 10%. This research provides a feasible pathway for retrieving global surface reflectance by LiDAR.

In this study, a shallow water inversion method based on the Fourier hybrid neural bathymetry network is proposed to address the weak representation of spatial context in active-passive fusion bathymetry. The method uses Ice, Cloud, and land Elevation Satellite-2 laser bathymetry as the reference ground truth and combines Gaofen-2 submeter pansharpened multispectral data. Spatial features (geographic coordinates) are nonlinearly mapped into a high-dimensional space through random Fourier feature mapping. A multilayer perceptron processes the high-dimensional features dimension by dimension, while a convolutional neural network extracts local spectral features, together enabling depth prediction at a submeter resolution. The experiments were conducted at Discovery Reef and Yongxing Island. Same-track validation shows strong accuracy. The root-mean-square error (RMSE) is less than 1.20 m, and the mean absolute error (MAE) is less than 0.50 m. The coefficient of determination (R2) is greater than 0.94, and the Pearson correlation coefficient (PCC) is greater than 0.97. At Discovery Reef, cross-track validation also remained accurate (RMSE = 0.31 m, MAE = 0.24 m, R2=0.90, and PCC = 0.95). Compared with the multilayer perceptron using raw coordinates and the traditional log-ratio model, our method improves accuracy in both same-track and cross-track tests.

Star identification is the most important part of satellite attitude determination. Existing star image identification algorithms show lower robustness with an increase in the number of stars. This study proposes a method for star identification based on local magnitude fitting. First, the similarity of neighboring star images is used for denoising. Then, the Gaussian distribution is used to determine the star point range and calculate the real grayscale cumulative value (RGCV). Finally, the star magnitude fitting range is obtained using the star tracker parameters and the fitting parameters between the RGCV and the star magnitude are determined. This method is used to optimize the rotation invariant additive vector sequence algorithm in this article. The results show that this method can reduce the storage capacity by 96%, enhance the efficiency of the algorithm and achieve a recognition rate of above 98% in real-situations. Furthermore, this method can also be applied to other star identification algorithms.

The correction of spaceborne laser footprint positioning errors in forested areas is the primary task required to improve the reliability of laser data and ensure the accuracy of large-scale Forestry surveys based on laser data. Currently, this correction largely requires high-precision terrain data, which are difficult to obtain over mountainous forests. Therefore, a method for correcting spaceborne laser footprint positioning errors in mountainous forest areas by minimizing the sum of satellite-Earth ranging residuals is proposed. In this method, a satellite digital surface model (DSM) is used to calculate satellite-Earth laser ranges at different ground locations, and the laser footprint positioning error is corrected based on the principle of minimizing the sum of the ranging residual between the satellite-Earth laser ranging and the actual ranging. The proposed method was subsequently tested on the GF7-01 satellite laser, and the corrected laser footprint positions were compared with the true positions of the laser footprints captured by laser detectors. The results indicated that the laser footprint positioning accuracy was 4.16 m for beam 1 of the GF7-01 satellite, while beam 2 achieved an accuracy greater than 3.96 m. Overall, these outcomes clearly demonstrate the effectiveness of the proposed method.

Laser waveform data that contain rich three-dimensional structural object information hold significant value in forest resource monitoring. However, traditional waveform decomposition algorithms are often constrained by complex waveform structures and depend on the initial parameter selections, which affect the accuracy and robustness of the results. To address the issues of the strong dependence on initial parameters, susceptibility to local optima, and difficulty in detecting hidden peaks during waveform overlap in the traditional satellite laser waveform decomposition algorithms, this study proposes a waveform decomposition method that combines hidden peak detection and an adaptive genetic algorithm (HAGA). This method uses hidden peak detection algorithms to improve the accurate extraction of the Gaussian components from the original waveform and provides the initial parameters. The high-precision extraction of waveform parameters is achieved through the adaptive genetic algorithm (AGA) combined with Levenberg–Marquardt (LM) optimization. In the experimental validation, the proposed method outperformed the traditional methods in both waveform decomposition fitting accuracy and tree height extraction. The average waveform decomposition accuracy Rmean2 for more than 2000 laser spots reaches 0.955, whereas the RMSE of the tree height extractions is better than 2 m, demonstrating strong robustness and applicability.

Geometric calibration, as a crucial method for ensuring the precision of spaceborne single-photon laser point cloud data, has garnered significant attention. Nonetheless, prevailing geometric calibration methods are generally limited by inadequate precision or are unable to accommodate spaceborne lasers equipped with multiple payloads on a single platform. To overcome these limitations, a novel geometric calibration method for spaceborne single-photon lasers that integrates laser detectors with corner cube retroreflectors (CCRs) is introduced in this study. The core concept of this method involves the use of triggered detectors to identify the laser footprint centerline (LFC). The geometric relationships between the triggered CCRs and the LFC are subsequently analyzed, and CCR data are incorporated to determine the coordinates of the nearest laser footprint centroids. These laser footprint centroids are then utilized as ground control points to perform the geometric calibration of the spaceborne single-photon laser. Finally, ATLAS observational data are used to simulate the geometric calibration process with detectors and CCRs, followed by conducting geometric calibration experiments with the gt2l and gt2r beams. The results demonstrate that the accuracy of the calibrated laser pointing angle is approximately 1 arcsec, and the ranging precision is better than 2.1 cm, which verifies the superiority and reliability of the proposed method. Furthermore, deployment strategies for detectors and CCRs are explored to provide feasible implementation plans for practical calibration. Notably, as this method only requires the positioning of laser footprint centroids using ground equipment for calibration, it provides exceptional calibration accuracy and is applicable to single-photon lasers across various satellite platforms.

Inland lakes and reservoirs are critical components of global freshwater resources. However, traditional water level monitoring stations are costly to establish and maintain, particularly in remote areas. As an alternative, satellite altimetry has become a key tool for lake water level monitoring. Nevertheless, conventional radar altimetry techniques face accuracy limitations when monitoring small water bodies. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with a single-photon counting lidar system, offers enhanced precision and a smaller ground footprint, making it more suitable for small-scale water body monitoring. However, the water level data obtained from the ICESat-2 ATL13 inland water surface height product are limited in quantity, while the lake water level accuracy derived from the ATL08 product is relatively low. To overcome these challenges, this study proposes a Spatial Distribution-Based Hierarchical Clustering for Photon-Counting Laser altimeter (SD-HCPLA) for enhanced water level extraction, validated through experiments conducted at the Danjiangkou Reservoir. The proposed method first employs Landsat 8/9 imagery and the Normalized Difference Water Index (NDWI) to generate a water mask, which is then used to filter ATL03 photon data within the water body boundaries. Subsequently, a Minimum Spanning Tree (MST) is constructed by traversing all photon points, where the vertical distance between adjacent photons replaces the traditional Euclidean distance as the edge length, thereby facilitating the clustering and denoising of the point cloud data. The SD-HCPLA algorithm successfully obtained 41 days of valid water level data for the Danjiangkou Reservoir, achieving a correlation coefficient of 0.99 and an average error of 0.14 m. Compared with ATL08 and ATL13, the SD-HCPLA method yields higher data availability and improved accuracy in water level estimation. Furthermore, the proposed algorithm was applied to extract water level data for five lakes and reservoirs in Hubei Province from 2018 to 2023. The temporal variations and inter-correlations of water levels were analyzed, providing valuable insights for regional ecological environment monitoring and water resource management.

The successful launch of the GaoFen-7 (GF-7) satellite has made it possible to achieve sub-meter-level three-dimensional mapping. However, most current studies focus on accuracy evaluating of GF-7 stereo mapping in bare land, and less on its performance in mountainous forest areas. This study aims to explore the potential of ground control points (GCPs) from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to enhance the accuracy of GF-7 digital surface models (DSM) in forested areas, and further evaluate the performance of GF-7 stereo mapping in mountainous forest areas. First, the GCPs was extracted from ATL03 based on the direction adaptive OPTICS ground surface detection method. Then, with the assistance of Google Earth (GE), fine screening was applied to extract bare GCPs in forested areas, which were called ATLGE GCPs. Finally, the ATLGE GCPs were used as elevation control points, combined with GF-7 stereo images to generate the forest area DSM, which was called GF-7 DSM_ATLGE. To compare and analyze the potential of ATLGE GCPs in improving the accuracy of GF-7 forest DSM, SRTM and SLA03 were employed as elevation controls data to generate GF-7 DSM_SRTM and GF-7 DSM_SLA03, respectively. The elevation accuracy of the three DSMs was quantitatively evaluated using the DSM obtained from field-surveyed plots. The results showed that while GF-7 DSM_SRTM, GF-7 DSM_SLA03, and GF-7 DSM_ATLGE all effectively represented forest features, their accuracy varied significantly. Specifically, the elevation RMSE of GF-7 DSM_SRTM was 11.87 m, GF-7 DSM_SLA03 reached 4.78 m, and GF-7 DSM_ATLGE reached 2.99 m, showing a significant improvement. These results not only demonstrate the effectiveness of ATLGE GCPs in enhancing the elevation accuracy of GF-7 DSMs in forest areas, but also highlight the stereo mapping capability of GF-7 stereo images under controlled conditions in complex mountainous forest areas.

The dual-head star tracker (DHST) is affected by the space environment during its orbit, and its optical parameters (i.e., principal point, focal length, and distortion) and the relative installation between the heads undergo continuous changes, which affect the attitude determination accuracy. The traditional static calibration method replaces the actual value with the estimated optimal solution of the parameters, which cannot express the real-time changes of the parameters. This article proposes a dynamic post-calibration method based on the extended Kalman filter (EKF). First, star points are selected using the convex hull area percentage, and the optical parameters are calculated using the least-squares (LS) method. Then, the relative installation between the optical heads is expressed in Euler angles, and the EKF method is employed for calibration. Finally, the star image downloaded from the GF-7 star tracker is utilized as the original data for the experiment. The experimental results demonstrate that this method can significantly improve the accuracy of attitude determination, with an accuracy that is superior to that of the LS method by 0.3 arcseconds.

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations.

To tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density clustering, which utilizes numerical simulations to achieve self-adaptation of key parameters (neighborhood radius EpsEps and minimum number of points MinPtsMinPts). First, taking the directional adaptive ellipse DBSCAN (DAE-DBSCAN) as an example, photons with different background photon count rates (bckgrd_ratebckgrd_rate) are used to traverse EpsEps and MinPtsMinPts to calculate their optimal values (EpsEps and MinPtsMinPts with the highest denoising accuracy). Then, a mathematical prediction model of bckgrd_ratebckgrd_rate, EpsEps and MinPtsMinPts was established. The actual background photon count rates were introduced into the key parameter prediction model to obtain the optimal EpsEps and MinPtsMinPts. Finally, a denoising experiment was conducted using the simulated photons and the ATLAS data. The results show that the proposed method had higher accuracy than the constant parameter denoising method, with an F >0.95. Even for photons of complex mountainous terrain with a high background photon count rate, the denoising accuracy was still higher than 0.9. The proposed method improves the denoising accuracy of photons with different noise densities by adapting density clustering parameters.

A full-link simulation of satellite single-photon LiDAR systems is important in laser parameter design, laser performance, and index analysis. Therefore, in this letter, a full-link simulation method for satellite single-photon LiDAR systems is proposed to simulate the whole process of satellite single-photon laser emission, transmission, and the generation of signal and noise photon events. The proposed method initially employs the LiDAR equation to simulate the energy and photon count of single-photon laser echoes, and it rapidly generates photon events using the Monte Carlo method. With the Advanced Topographic Laser Altimeter System (ATLAS) as the experimental object, echo photon events for three ideal terrains and complex natural surfaces are simulated. The simulated photon point cloud of the ideal terrain is consistent with the real terrain. Additionally, the signal photons observed by ATLAS (ATL03 data) are used to validate the accuracy of the simulated photon point cloud. The results indicate that the root mean square error (RMSE) of the distance between the ATL03 photons and the simulated 3-D photon point clouds is approximately 2.0 m, and the Chamfer distance (CD) is approximately 1.5 m. The coefficient of determination between the simulated photon elevation and ATL03 photon is 1.0. These results provide substantial evidence of the high similarity between the simulated and actual photon point clouds.