Search Results (7,086)

The use of large-aperture scanning mirrors for high-resolution and wide-swath imaging represents a major trend in Earth observation technology. However, to improve dynamic response performance, scanning mirror assemblies are highly lightweighted, resulting in reduced overall stiffness. This makes the mirror surface susceptible to thermal and inertial loads during operation, leading to degraded surface accuracy and poor imaging quality. Moreover, dynamic scanning mirror has the multi-disciplinary coupling effects and non-circular structural characteristics. It poses significant challenges for surface figure analysis. To address these issues, this paper proposes a surface analysis method for high-dynamic scanning mirrors under multi-physics coupling in non-circular domains. First, a finite element model of the mirror assembly is established based on the minimum aperture and angular velocity parameters. Through finite element analysis, the surface response of the scanning mirror assembly under thermal loads, dynamic inertial loads, and their coupled effects is quantitatively investigated. Subsequently, an analytical approach, which combines rigid-body displacement separation and Gram–Schmidt orthogonalization, is developed to construct non-circular Zernike polynomials, enabling high-precision fitting and reconstruction of the mirror’s dynamic surface distortions. Numerical experiments validate the accuracy of the model. Results show that for a scanning mirror with an aperture of 466 mm × 250 mm under the coupled condition of a 5 °C temperature rise and 50 N·mm torque, the surface figure achieves RMS < 2 nm and PV < 22 nm, with a fitting accuracy achieves 10⁻⁶. These results verify the accuracy and reliability of the proposed method. The surface analysis approach presented in this study provides theoretical guidance and a design framework for subsequent image quality evaluation and assurance. Full article

Reusable launch vehicles are a key category of next-gen European launchers, as multiple companies are in advanced stages of study and are shifting towards the development of first demonstrators. A worldwide tendency to reduce the costs associated with satellite insertion into low Earth orbits can be observed, together with the existence of a niche in the future European launcher family for reusable small launch vehicles, known as microlaunchers. Multiple recovery methods exist for space launch vehicles; in this study, a return to launch site (RTLS) vertical-landing approach is being prioritized for the recovery of the first stage of a two-stage LOX/methane microlauncher. In 2023, INCAS, with support from the Romanian Nucleu Program, initiated a large study to address the prospect of developing a partially reusable microlauncher. A multidisciplinary optimization (MDO) environment has been developed, which is used in this paper to assess the implications of stage recovery versus the landing location (return to launch site versus downrange recovery) and state whether an RTLS can be feasible for small launchers. The paper will also present some key results from previous studies, such that a clear solution trade-off can be made, together with the quantitative assessment of how different vertical-landing techniques affect the microlauncher specifications. Full article

The increasing availability of satellite data at different spatial resolutions offers new opportunities for environmental monitoring, highlighting the limitations of medium-resolution products for fine-scale territorial analysis. However, it also raises the need to enhance the resolution of low-quality imagery to enable more detailed spatial assessments. This study investigates the effectiveness of different super-resolution techniques applied to low-resolution (LR) multispectral Sentinel-2 satellite imagery to generate high-resolution (HR) data capable of supporting advanced knowledge extraction. Three main methodologies are compared: traditional bicubic interpolation, a generic Artificial Neural Network (ANN) approach, and a Convolutional Neural Network (CNN) model specifically designed for super-resolution tasks. Model performances are evaluated in terms of their ability to reconstruct fine spatial details, while the implications of these methods for subsequent visualization and environmental analysis are critically discussed. The evaluation protocol relies on RMSE, PSNR, SSIM, and spectral-faithfulness metrics (SAM, ERGAS), showing that the CNN consistently outperforms ANN and bicubic interpolation in reconstructing geometrically coherent structures. The results confirm that super-resolution improves the apparent spatial detail of existing spectral information, thus clarifying both the practical advantages and inherent limitations of learning-based super-resolution in Earth observation workflows. Full article

This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all SDB methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the Iterative Closest Point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error (RMSE). Thus, it is recommended to perform detrending where possible to achieve an error distribution as close to normality as possible for which error metrics are more diagnostic. Full article

Climate change is impacting rivers worldwide, with a reduction in normal flow conditions in temperate regions like Poland. Such changes might have a significant influence on riparian vegetation and channel planform dynamics. To better understand how these changes impact the river morphology, this research focuses on a 250 km-long reach of the Polish Vistula River and investigates variations of monthly maximum discharges and vegetation conditions over the period 1984–2023 by means of Landsat satellite images. These satellite data were handled via Google Earth Engine, looking at a common index such as the Normalized Difference Vegetation Index, considered as a proxy of vegetation coverage variations. Results point out an increase in the median NDVI over the study area from 0.2 in 1984 to 0.3 in 2023, connected with a reduction of monthly discharge from around 920 m³/s to 880 m³/s. This suggests that changes in flow discharge are associated with a process of riparian vegetation growth, leading to a reduction of planform and bars dynamics and closure of secondary channels (i.e., oversimplification). This is particularly evident over the last couple of decades, during which water availability has decreased significantly, as more humid years in the middle of the study period are now no longer existing, with an observed decrease in the maximum monthly discharge during the last 20 years, likely connected with a more severe impact of climate change. This reduction in flooding events allows more time for vegetation to establish on river bars and banks, eventually creating new islands and causing the observed oversimplification of the active channel. In summary, using the Vistula River as an exemplary case study, this research suggests that prolonged dry periods, more common in recent decades due to climate change, might impact large rivers located in temperate climates, favouring the development of vegetation on exposed sandbars, eventually resulting in a less dynamic active channel. Full article

Remote sensing approaches to monitoring dryland ecosystem states and trends have been dominated by the binary distinction between degraded/non-degraded areas, leading to inconsistent results. We propose a different conceptual framework that better reflects the states and pressures of these ecosystems—ecological integrity—that is, the maintenance of ecosystem composition and its capacity to contribute to human needs and adapt to change. We systematically reviewed earth observation techniques for characterizing ecological integrity in trusted databases together with studies identified through expert-guided search. A total of 137 papers were included, and their metadata (i.e., location, year) and data (i.e., aspect of ecological integrity assessed, techniques employed) were analyzed. The results show that remote sensing ecological integrity is becoming an increasingly researched topic, especially in countries with extensive drylands. Vegetation was the most frequently monitored attribute and was often employed as an indicator of other attributes (i.e., soil and water quality) and as a key feature in approaches that aimed for a comprehensive ecosystem assessment. However, most of the literature employed the normalized difference vegetation index (NDVI) as a descriptor of vegetation characteristics (i.e., health, structure, cover), which has been shown not to be a good indicator of the litter/senescent vegetation components that tend to frequently dominate drylands. Methods to overcome this weakness have been identified, although more research is needed to demonstrate their application in ecological integrity monitoring. Specifically, knowledge gaps in the relationship between vegetation cover fractions (i.e., green, non-green, and bare soil), descriptors of ecosystem quality (e.g., soil condition or vegetation structure complexity), and management (i.e., how human intervention affects ecosystem quality) should be addressed. Notable potential has been identified in time series analysis as a means of operationalising remotely sensed vegetation fractional cover. Nevertheless, limitations in benchmarking must also be tackled for effective ecological integrity monitoring. 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

Quantifying forest patch dynamics is essential for understanding how forest patch characteristics vary in relation to ancient tree locations. This study developed a satellite-based framework to analyze the differences among forest patches associated with natural and planted ancient trees across the Sichuan–Chongqing region, China. Using monthly LandTrendr on Google Earth Engine, we analyzed long-term (1990–2024) and high-frequency observations of forest dynamics at a 180 m × 180 m (6 × 6 pixels) spatial scale. Disturbance and recovery events were characterized by their magnitude, rate, timing, and duration. Patches were classified into six categories based on ancient tree type and proximity and further subdivided by land use type. The results show that in natural forests, patches with natural ancient trees are associated with more stable change signatures, whereas in planted forests, patches containing planted ancient trees are associated with stronger recovery-related change patterns. Over 60% of detected changes were short-lived (≤5 years), indicating that most disturbances and recovery processes were transient rather than persistent. These findings show that the presence and spatial context of ancient trees are associated with differences in patch change patterns. The proposed workflow provides a scalable approach for integrating multi-temporal remote sensing into large-scale monitoring and management of ancient trees and their associated forest patches. Full article

The degradation of vegetation cover and the vulnerability of urban market gardening systems to climate risks are a major challenge for food security in peri-urban areas. This study analyzes the spatio-temporal dynamics of vegetation using the NDVI and assesses its correspondence with producers’ perceptions of hydroclimatic impacts. NDVIs were extracted from the MODIS MOD13Q1v6.1 product via Google Earth Engine, with a spatial resolution of 250 m × 250 m and a temporal resolution of 16 days, then processed in Python v3.14.0 using the xarray library. Additionally, 369 producers in Grand Nokoué were surveyed about the risks of flooding, drought, and heat waves, as well as the adaptation strategies they implement. The results reveal a decline in areas with a moderate to high NDVI (between 0.41 and 0.81) and an expansion of areas with a low or very low NDVI (below 0.41), reflecting increased fragmentation and degradation of vegetation cover. Producers’ perceptions confirm this vulnerability and reveal different strategies depending on the type of crop and risk, including irrigation, temporary abandonment of plots, agroforestry, and the adoption of resilient crops. These observations highlight the need to implement targeted policies and appropriate agroecological practices in order to strengthen the resilience of urban market gardening systems to extreme climate risks. Full article

This study presents a novel experimental methodology enabling the synchronous observation of strain and stress evolution in granular backfill subjected to active earth pressure. A physical model of plane deformation was used in which a rigid retaining wall was gradually moved away from the ground while simultaneously recording, at each step, both displacement-based images for digital image correlation (DIC) and photoelastic pictures of the force-chain rearrangements. The results show that active failure develops gradually through narrow shear bands, initiated near the wall base and propagating towards the ground surface. A consistent inverse relationship between shear-strain location and photoelastic stress concentration was identified: low-strain zones within the shear wedge in the shear and volumetric strain images correspond to strong force-chain development, whereas high-strain zones (strain localization) correspond to local stress release. These findings provide new experimental evidence regarding the micromechanics of active pressure and offer comparative data for calibrating DEM (discrete element method) models and interpreting the reduced active pressures reported in confined granular backfills. Full article

Dynamic task planning for heterogeneous platforms across land, sea, air, and space is essential for achieving integrated situational awareness, yet current systems suffer from limited spatiotemporal coverage and inefficient resource scheduling. To address these challenges, we propose a novel mission planning method that integrates spatiotemporal segmentation with Deep Reinforcement Learning (DRL). The approach establishes a multidimensional spatiotemporal decomposition model to break down complex observation scenarios into manageable subtasks, while incorporating a unified accessibility–visibility computation framework that accounts for Earth curvature, platform dynamics, and sensor constraints. Using a Spatio-Temporal Adaptive Scheduling Network (STAS-Net) algorithm optimized with a multi-objective reward function covering mission completion rate, temporal coordination, and residual detection capacity, the method enables intelligent coordination of heterogeneous platforms. Experimental results across small-, medium-, and large-scale scenarios demonstrate that the proposed framework consistently achieves high target coverage (up to 98.4% in small-scale and 89.7% in large-scale tasks), with a reduction in coverage loss that is only about half of that exhibited by greedy and genetic algorithms as task scale expands. Moreover, STAS-Net maintains low planning time (as low as 9.5 s in small-scale and only 18.3 s in large-scale scenarios) and high resource utilization (reaching 86.8% under large-scale settings), substantially outperforming both baseline methods in scalability and scheduling efficiency. The framework not only establishes a solid theoretical foundation but also provides a practical and feasible solution for enhancing the overall performance of multi-platform cooperative observation systems. Full article

We have developed a magnetic holographic memory using transparent bismuth-substituted rare-earth iron garnet as a next-generation optical memory. To realize this, a magnetic garnet with a large Faraday rotation angle and a moderately small extinction coefficient is required. In this study, we investigated the effect of Al or Ga substitution for the iron site of bismuth-substituted yttrium iron garnet (Bi/YIG) films on their magneto-optical properties. The Faraday rotation angle decreased with the amount of substitution, x, increase, for both Al- and Ga-substituted Bi/YIG, and a reversal of sign of rotation angle was only observed for Ga-substituted Bi/YIG, indicating a compensation composition. In the Al-substituted sample, due to small squareness, the residual Faraday rotation angle at zero magnetic field, |θ_R,res|, gradually decreased above x = 0.5, whereas in the Ga-substituted sample, the squareness ratio increased with increasing substitution up to x = 2.0, and thus showed a peak at x = 1.5. The Curie temperature and extinction coefficient were reduced with increasing substitution amount. As a result of a decrease in extinction coefficient, k, the high figure of merit, (|θ_R,res|/2πk) · λ was obtained around x = 1.5~1.9 for Ga and x = 2.1 for Al, while it was smaller than that of Bi/RIG we usually used. Full article

Accurate and timely crop yield prediction is crucial for global food security and modern agricultural management. Traditional methods often lack the scalability and granularity required for precision farming. This paper introduces FARM (Fine-tuning Agricultural Regression Models), a deep learning framework designed for high-resolution, intra-field canola yield prediction. FARM leverages a pre-trained, large-scale geospatial foundation model (Prithvi-EO-2.0-600M) and adapts it for a continuous regression task, transforming multi-temporal satellite imagery into dense, pixel-level (30 m) yield maps. Evaluated on a comprehensive dataset from the Canadian Prairies, FARM achieves a Root Mean Squared Error (RMSE) of $0.44$ and an $R^2$ of $0.81$. Using an independent high-resolution yield monitor dataset, we further show that fine-tuning FARM on limited ground-truth labels outperforms training the same architecture from scratch, confirming the benefit of pre-training on large, upsampled county-level data for data-scarce precision agriculture. These results represent improvement over baseline architectures like 3D-CNN and DeepYield, which highlight the effectiveness of fine-tuning foundation models for specialized agricultural applications. By providing a continuous, high-resolution output, FARM offers a more actionable tool for precision agriculture than conventional classification or county-level aggregation methods. This work validates a novel approach that bridges the gap between large-scale Earth observation and on-farm decision-making, offering a scalable solution for detailed agricultural monitoring. Full article

This study analyzes the establishment of Local Agri-Food Systems (LAFSs) in the triple-border region between the states of Minas Gerais, Rio de Janeiro, and São Paulo, by identifying and mapping potential areas of primary peasant agri-food production. An integrated analysis of data sources was treated, processed, and integrated into a common spatial support. Land use and land cover data were used from demographic and agricultural censuses, from the Rural Environmental Registry, agrarian reform settlement projects and conservation units. Our study revealed that 23.73% of the regional area has potential for peasant production, identifying four regions that stand out in terms of this potential. The area presented livestock and animal husbandry as the main agri-food chain, with potential for processing within the territory itself, in addition to extractive activities in the Atlantic Forest biome. The results indicate that there are possibilities for the establishment of LAFSs as a local development strategy associated with social inclusion and environmental responsibility, although there is a need to expand and strengthen the transportation and marketing channels for products from these short chains. The cartographies produced aim to contribute as auxiliary instruments to land use planning and management, seeking to strengthen LAFSs at different scales of governance. Full article

In January 2025, multiple wildfires erupted across the Los Angeles region, fueled by prolonged dry conditions and intense Santa Ana winds. Southern California has faced increasingly frequent and severe wildfires in recent years, driven by prolonged drought, high temperatures, and the expanding wildland–urban interface. These fires have caused major loss of life, extensive property damage, mass evacuations, and severe air-quality decline in this densely populated, high-risk region. This study integrates passive and active satellite observations to characterize the spatiotemporal and vertical distribution of wildfire emissions and assesses their impact on air quality. TROPOMI (Sentinel-5P) and the recently launched TEMPO geostationary instrument provide hourly high temporal-resolution mapping of trace gases, including nitrogen dioxide (NO₂), carbon monoxide (CO), formaldehyde (HCHO), and aerosols. Vertical column densities of NO₂ and HCHO reached 40 and 25 Pmolec/cm², respectively, representing more than a 250% increase compared to background climatological levels in fire-affected zones. TEMPO’s unique high-frequency observations captured strong diurnal variability and secondary photochemical production, offering unprecedented insights into plume evolution on sub-daily scales. ATLID (EarthCARE) lidar profiling identified smoke layers concentrated between 1 and 3 km altitude, with optical properties characteristic of fresh biomass burning and depolarization ratios indicating mixed particle morphology. Vertical profiling capability was critical for distinguishing transported smoke from boundary-layer pollution and assessing radiative impacts. These findings highlight the value of combined passive–active satellite measurements in capturing wildfire plumes and the need for integrated monitoring as wildfire risk grows under climate change. Full article