Search Results (1,406)

Global water environment monitoring urgently requires remote sensing data with high temporal resolution and wide spatial coverage. However, current space-borne ocean color spectrometers still face a significant trade-off among spatial resolution, swath width, and system compactness, which limits the large-scale deployment of satellite constellations. To address this challenge, this study developed a lightweight high-resolution spectral imager (LHRSI) with a total mass of less than 25 kg and power consumption below 80 W. The visible (VIS) camera adopts an interleaved dual-field-of-view and detectors splicing fusion design, while the shortwave infrared (SWIR) camera employs a transmission-type focal plane with staggered detector arrays. Through the field-of-view (FOV) optical design, the instrument achieves swath widths of 207.33 km for the VIS bands and 187.8 km for the SWIR bands at an orbital altitude of 500 km, while maintaining spatial resolutions of 12 m and 24 m, respectively. On-orbit imaging results demonstrate that the spectrometer achieves excellent performance in both spatial resolution and swath width. In addition, preliminary analysis using index-based indicators illustrates LHRSI’s potential for observing chlorophyll-related features in water bodies. This research not only provides a high-performance, miniaturized spectrometer solution but also lays an engineering foundation for developing low-cost, high-revisit global ocean and water environment monitoring constellations. Full article

This study analyzed the spatial distribution of large green algae (LGA) in the northern coastal waters of China, including the Yellow Sea and Bohai Sea, using Coastal Zone Imager (CZI) data from the HY-1C/D satellites. An inversion model (coastal zone imager model) of LGA was established, based on which the distribution details of large green algae in the Yellow Sea and Bohai Sea were investigated. The results indicated the following: (1) LGA exhibits a clearly seasonal pattern from May to August. Initially occurrences are detected in May in the southern Yellow Sea (32–34° N), followed by a rapid expansion and intensification from June to mid-July, with peak distribution around 35° N near the Shandong Peninsula. The affected area subsequently decreases in late August. (2) High LGA coverage is mainly concentrated along the Subei Shoal and the Shandong Peninsula in the Yellow Sea, as well as the coastal regions of Yantai, Qinhuangdao, and Yingkou in the Bohai Sea. (3) The LGA-M inversion model demonstrates stable performance in nearshore waters with similar optical characteristics and is applicable to LGA extraction in adjacent coastal seas, highlighting the potential of HY-1C/D satellite data in marine environmental monitoring and protection. Full article

Earth Embedding (EMB) is a method that adapts embedding techniques from Large Language Models (LLMs) to compress the information contained in multiple remote sensing satellite images into feature vectors. This article introduces a new approach to measuring economic activity from EMBs. Using the Google Satellite Embedding Dataset (GSED), we extract a 64-dimensional representation of the Earth’s surface that integrates optical and radar imagery. A neural network maps these embeddings to nighttime light (NTL) intensity, yielding a 32-dimensional “income-aware” feature space aligned with economic variation. We then predict GDP levels and growth rates across countries and compare the results with those of traditional NTL-based models. The Earth-Embedding (EMB) based estimator achieves substantially lower mean squared error in estimating GDP levels. Combining the two sources yields the best overall accuracy. Further analysis shows that EMB performs particularly well in low-statistical-capacity and high-income economies. These results suggest that satellite embeddings can provide a scalable, globally consistent framework for monitoring economic development and validating official statistics. Full article

This research proposes the design and implementation of an adaptive optical system (AOS) for monitoring low-orbit satellites (LOSs) to ensure that they do not deviate from their pre-planned path. This is achieved by designing a telescope with an optical system that contains six mirrors in a regular hexagonal shape; the side length of one mirror is 30 cm, and there is also a spectral analyzer system in the middle to separate the spectra emitted by stars from those reflected from low-orbit satellites. A SwinTrack-Tiny (STT) is used, with modifications using temporal information via insertion. The model incorporates a new purpose-built image update template as a third input to the model and combines the attributes of the new image with the attributes of the primary template via an attention block. To maintain the dimensions of the original model and take advantage of its weights, an attention block with four vertices is used. Full article

The absolute radiometric calibration of a satellite sensor is an essential process that determines the coefficients required to convert the radiometric quantities of satellite images. This procedure is crucial for ensuring the applicability and enhancing the reliability of optical sensors onboard satellites. This study performs the absolute radiometric calibration of the Compact Advanced Satellite 500-1 (CAS500-1) Advanced Earth Imaging Sensor System-C (AEISS-C), a low Earth orbit satellite developed independently by Republic of Korea for precise ground observation. Field campaign using a tarp, an Analytical Spectral Devices FieldSpecIII spectroradiometer, and a MicrotopsII sunphotometer was conducted. Additionally, reflectance-based vicarious calibration was performed using observational data and the MODerate resolution atmospheric TRANsmission model (version 6) radiative transfer model (RTM). Cross-calibration was also performed using data from the Sentinel-2 MultiSpectral Instrument, RadCalNet observations, and MODIS Bidirectional nReflectance Distribution Function (BRDF) products (MCD43A1) to account for differences in spectral response functions, viewing/solar geometry, and atmospheric conditions between the two satellites. From these datasets, two correction factors were derived: the Spectral Band Adjustment Factor and the BRDF Correction Factor. CAS500-1/AEISS-C acquires satellite imagery using two Time Delay Integration (TDI) modes, and the absolute radiometric calibration coefficients were derived considering these TDI modes. The coefficient of determination (R²) ranged from 0.70 to 0.97 for the reflectance-based vicarious calibration and from 0.90 to 0.99 for the cross-calibration. For reflectance-based vicarious calibration, aerosol optical depth was identified as the primary source of uncertainty among atmospheric factors. For cross-calibration, the reference satellite and RTMs were the primary sources of uncertainty. The results of this study will support the monitoring of CAS500-1/AEISS-C, which produces high-resolution imagery with a spatial resolution of 2 m, and can serve as foundational material for absolute radiometric calibration procedures for other CAS500 satellites. Full article

Infrared remote sensing images are often degraded by blur and stripe noise caused by satellite attitude variations, optical distortions, and electronic interference, which significantly compromise image quality and target detection performance. Existing joint deblurring and destriping methods tend to over-smooth image edges and textures, failing to effectively preserve high-frequency details and sometimes misclassifying ringing artifacts as stripes. This paper proposes a variational framework for simultaneous deblurring and destriping of infrared remote sensing images. By leveraging an adaptive structure tensor model, the method exploits the sparsity and directionality of stripe noise, thereby enhancing edge and detail preservation. During blur kernel estimation, a fidelity term orthogonal to the stripe direction is introduced to suppress noise and residual stripes. In the image restoration stage, a WCOB (Non-blind restoration based on Wiener-Cosine composite filtering) model is proposed to effectively mitigate ringing artifacts and visual distortions. The overall optimization problem is efficiently solved using the alternating direction method of multipliers (ADMM). Extensive experiments on real infrared remote sensing datasets demonstrate that the proposed method achieves superior denoising and restoration performance, exhibiting strong robustness and practical applicability. Full article

Spatial monitoring of olive systems in arid regions is essential for understanding agricultural expansion, water pressure, and productive sustainability. This study aimed to map coverage and estimate olive plantation density (Olea europaea L.) in the Atacama Desert, Tacna (Peru) through the integration of UAV-satellite multispectral images and machine learning algorithms (CART, Random Forest, and Gradient Tree Boosting). Forty-eight optical, radar, and topographic covariates were analyzed. Fifteen were selected for coverage classification and 16 for plantation density, using Pearson’s correlation (|r| > 0.75). The classification maps reported an area of 23,059.87 ha (38.21%) of olive groves, followed by 5352.10 ha (8.87%) of oregano cultivation and 725.74 ha (1.20%) of orange cultivation, with respect to the total study area, with overall accuracy (OA) of 86.6% and a Kappa coefficient of 0.81. Meanwhile, the RF and GTB regression models showed R² ≈ 0.89 and RPD > 2.8, demonstrating excellent predictive performance for estimating tree density (between 1 and 8 trees per 100 m²). Furthermore, the highest concentration of olive trees was found in the central and southern zones of the study area, associated with favorable soil and microclimatic conditions. This work constitutes the first comprehensive approach for olive mapping in southern Peru using UAV–satellite fusion, demonstrating the capability of ensemble models to improve agricultural mapping accuracy and support water and productive management in arid ecosystems. Full article

Coastal sandbars play a crucial role in shoreline protection, yet monitoring their dynamics remains challenging due to the cost and limited temporal coverage of traditional surveys. This study assesses the feasibility of using Sentinel-2 multispectral imagery combined with the logarithmic band ratio method to automatically detect submerged sandbar crests along three morphologically distinct beaches on the northwestern Mediterranean coast. Pseudo-bathymetry was derived from log-transformed band ratios of blue-green and blue-red reflectance used to extract the sandbar crest and validated against high-resolution in situ bathymetry. The blue-green band ratio achieved higher accuracy than the blue-red band ratio, which performed slightly better in very shallow waters. Its application across single, single/double, and double shore-parallel bar systems demonstrated the robustness and transferability of the approach. However, the method requires relatively clear or calm water conditions, and breaking-wave foam, sunglint, or cloud cover conditions limit the number of usable satellite images. A temporal analysis at a dissipative beach further revealed coherent bar migration patterns associated with storm events, consistent with observed hydrodynamic forcing. The proposed method is cost-free, computationally efficient, and broadly applicable for large-scale and long-term sandbar monitoring where optical water clarity permits. Its simplicity enables integration into coastal management frameworks, supporting sediment-budget assessment and resilience evaluation in data-limited regions. Full article

In inland waters, Atmospheric Correction (AC), including Adjacency Effect (AE) correction, is a major challenge for water quality retrieval using optical satellite data. This study evaluated three image pre-processing options for turbidity retrieval in the St. Lawrence River using Sentinel-2 (S2) and Landsat 8/9 (L8/9) imagery with the Light Gradient Boosting Machine (LightGBM) model: (1) No pre-processing, i.e., use of Top-of-Atmosphere (TOA) reflectance, (2) AC pre-processing, obtaining water-leaving reflectance (R_w) from AC for the Operational Land Imager lite (ACOLITE)’s Dark Spectrum Fitting (DSF) technique, and (3) AE pre-processing, correcting for the AE using T-Mart before obtaining R_w from DSF. Results demonstrated that AE pre-processing outperformed the other two options. For L8/9, AE pre-processing reduced the Root Mean Square Error (RMSE) and improved the median symmetric accuracy (ε) by 48.8% and 19.0%, respectively, compared with AC pre-processing, and by 48.5% and 50.7%, respectively, compared with No pre-processing. For S2, AE pre-processing performed better than AC pre-processing and also outperformed No pre-processing, reducing RMSE by 28.4% and ε by 50.8%. However, No pre-processing yielded the lowest absolute symmetric signed percentage bias (|β|) among all pre-processing options. Analysis indicated that AE pre-processing yielded superior performance within 0–300 m from shore than other options, where the AE influence is strongest. Turbidity maps generated using AE pre-processing were smoother and less noisy compared to the other pre-processing options, particularly in cloud-adjacent regions. Overall, our findings suggest that incorporating AE correction through T-Mart improves the performance of the LightGBM model for turbidity retrieval from both L8/9 and S2 imagery in the St. Lawrence River, compared to the alternative pre-processing options. Full article

Thermal control of spaceborne micro-hyperspectral imagers (MHIs) operating in inclined low-Earth orbits (LEOs) presents significant challenges due to the complex and dynamically varying external heat flux, which lacks a stable heat dissipation surface. This study proposes a thermal radiation analysis method capable of rapidly deriving accurate numerical solutions for the thermal radiation characteristics of spacecraft in such orbits. A dedicated thermal control system (TCS) was designed, featuring a radiator oriented towards the +z_s plane, which was identified as having stable and low incident heat flux across extreme solar–orbit angle conditions. The system employs efficient thermal pathways, including thermal pads and a flexible graphite thermal ribbon, to transfer heat waste from the imaging module to the radiator, supplemented by electric heaters and multilayer insulation for temperature stability. Steady-state thermal analysis demonstrated excellent temperature uniformity, with gradients below 0.017 °C on critical optics. Subsequent thermo-optical performance analysis revealed that the modulation transfer function (MTF) degradation was maintained below 2% compared to the ideal system. The results confirm the feasibility and effectiveness of the proposed thermal design and analysis methodology in maintaining the stringent thermo-optical performance required for MHIs on inclined-LEO platforms. Full article

In infrared detectors, the readout circuits usually cause horizontal or vertical streak noise, whereas the infrared focal plane arrays experience triangular nonuniform fixed-pattern noise. In addition, imaging devices suffer from optically relevant fixed-pattern noise owing to the temperature. When the infrared camera is in orbit, it is affected by the photon effect, temperature change, and time drift. This makes the nonuniformity correction coefficients pertaining to the ground no longer applicable, resulting in the degradation of the nonuniformity correction effect. The existing methods are not fully applicable to triangular fixed-pattern noise or the fixed-pattern noise caused by detector optics. To address this situation, this paper proposes a nonuniformity correction method, namely infrared image sequences based on the optimization of $L_{2,1}$ group sparsity in the spatiotemporal domain. We established a nonuniformity correction model of differential operators in the spatiotemporal domain for infrared image sequences by applying the time-domain differential operator constraints to the images to denoise the image. This enables the adaptive correction of the nonuniformity of the above types of noise. We demonstrate that the proposed method is effective for triangular nonuniform and optically induced fixed-pattern noises. The proposed method was extensively evaluated using publicly available datasets and datasets containing image sequences of different scenes captured by a high-resolution infrared camera of the Qilu-2 satellite. The method has high robustness and good processing results with effective ghost suppression and significant reduction of nonuniform noise. Full article

To meet the requirements of high resolution, compact size, and ultra-lightweight for micro–nano satellite optoelectronic payloads while ensuring high structural stability during launch and in-orbit operation, mirrors were designed with high surface accuracy. The opto-thermo-mechanical system of the space camera was designed and analyzed accordingly. First, an optical system was designed to achieve high resolution and a compact form factor. A coaxial triple-reflector configuration with multiple refractive paths was adopted, which significantly shortened the optical path and laid the foundation for a lightweight, compact structure. This design also defined the accuracy and tolerance requirements for the primary and secondary mirrors. Subsequently, mathematical models for topology optimization and dimensional optimization were established to optimize the design of the main support structure, primary mirror, and secondary mirror. Two design schemes for the main support structure and primary mirror were compared. Steady-state thermal analysis and thermal control design were carried out for both mirrors. Simulations were then performed on the main system (including the primary/secondary mirror assemblies and the main support structure). Under the combined effects of gravity, a 4 °C temperature increase, and an assembly flatness deviation of 0.01 mm, the surface accuracy of both mirrors, the displacement of the secondary mirror relative to the primary mirror reference, and the tilt angle all met the overall specification requirements. The system’s first-order natural frequency was 156.731 Hz. After precision machining, fabrication, and assembly, wavefront aberration testing was conducted on the main system with the optical axis horizontal. Under gravity, the root mean square (RMS) wavefront error at the center of the field of view was 0.073λ, satisfying the specification of ≤1/14λ. The fundamental frequency measured during vibration testing was 153.09 Hz, which aligned closely with the simulated value and well exceeded the requirement of 100 Hz. Additionally, in-orbit imaging verification was conducted. All results satisfied the technical specifications of the satellite’s overall requirements. Full article

Disparity estimation from high-resolution satellite stereo images is critical for 3D reconstruction but remains challenging due to large disparities, complex structures, and textureless regions. To address this, we propose a Semantic-Aware Optical Flow (SAOF) framework for fine-grained disparity estimation, which enhances optical flow-based via a multi-level optimization incorporating sub-top pyramid re-PatchMatch, scale-adaptive matching windows, and multi-feature cost refinement. For improving the spatial consistency of the resulting disparity map, SAMgeo-Reg is utilized to produce semantic prototypes, which are used to build guidance embeddings for integration into the optical flow estimation process. Experiments on the US3D dataset demonstrate that SAOF outperforms state-of-the-art methods across challenging scenarios. It achieves an average endpoint error (EPE) of 1.317 and a D1 error of 9.09%. Full article

The article presents an approach to designing a low-orbit remote Earth sensing spacecraft. The low operational orbit of the satellite is maintained using a corrective electric propulsion system. The comprises an optical imaging system based on the Richey-Cretien telescope design augmented with an additional swivel reflection mirror. The optical system’s layout was optimized to minimize the spacecraft’s midsection area. This reduction in the frontal cross-sectional area decreases the aerodynamic drag forces exerted by the upper atmosphere, thereby reducing the propellant mass required for orbit maintenance. The article presents a model of constraints imposed by the satellite’s power supply system on the operating modes of the electric propulsion system and the orbit correction modes. Finally, a preliminary design of a low-orbit satellite, derived from the proposed approach, is presented. Full article

This paper addresses the degradation of imaging quality in high-resolution CubeSats caused by micro-vibrations from attitude control flywheels. It proposes a micro-vibration suppression scheme that incorporates multi-disciplinary integrated modeling, dual passive vibration isolation, and multi-level verification. A comprehensive model encompassing flywheel disturbance, optics, attitude control, and structure is developed to elucidate the transmission dynamics of micro-vibrations from the source to the optical payload. A dual suppression system utilizing silicone rubber isolators is engineered for both the disturbance source (flywheel) and the payload (optical camera). By optimizing stiffness matching and damping, it achieves a balance between isolation efficiency and stability in attitude control. A three-tier verification system comprising “numerical simulation–ground microgravity testing–on-orbit imaging” has been established. The findings indicate that the dual isolation system diminishes the pixel offset amplitude of the optical payload to under 0.1 pixels (down to the 0.02 pixel level in the high-frequency band), with an isolation efficiency of 80%. Consistent outcomes from terrestrial and orbital validation affirm the engineering viability of the plan. This research offers theoretical backing for the precise control of micro-vibrations in micro-nano satellites, thereby enhancing their utility in high-resolution remote sensing applications. Full article