Search Results (869)

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

Mangrove ecosystems are recognized for their exceptional carbon sequestration potential and crucial contribution to coastal ecological balance. However, the sharp decline in mangrove area necessitates efficient monitoring via remote sensing. While Deep Neural Networks (DNNs) have excelled in segmentation tasks, their robustness remains inadequate. This limitation stems from the lack of theoretical guarantees regarding the continuity of layer-by-layer discrete transformations, rendering models susceptible not only to man-made adversarial attacks but also to natural degradations. To address these vulnerabilities, this paper leverages Neural Ordinary Differential Equations (NODEs) to enhance the robustness of mangrove segmentation. We designed and integrated various NODE architectures, including a novel NODE-SE-Block inspired by adaptive feature recalibration, to achieve more stable feature representations. Crucially, our findings reveal that by employing an adversarial training framework based on known attacks, the NODE-integrated network demonstrates superior cross-domain robustness. It not only defends against malicious exploits but also exhibits significantly enhanced resilience toward natural degradations, such as Gaussian noise and sensor-induced artifacts. Experimental results on mangrove datasets verify that the proposed methodology provides a reliable and interference-resistant foundation for ecological management in mission-critical scenarios. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for remote dimensional inspection in transportation monitoring and infrastructure control. In such applications, measurement results are often interpreted relative to regulatory thresholds, making the reliability of inspection decisions strongly dependent on measurement uncertainty. This study presents a probabilistic framework for evaluating measurement uncertainty and decision risk in UAV-based dimensional inspection tasks. A measurement model describing uncertainty scaling with observation geometry is formulated, and the probability of exceedance relative to a regulatory limit is derived. The framework integrates probabilistic measurement modeling with a risk-based decision formulation that accounts for false-positive and false-negative inspection outcomes. The resulting integral inspection risk is analyzed for representative sensing modalities commonly used in UAV platforms, including vision-based systems, LiDAR, and radar sensors. The results demonstrate that uncertainty scaling with flight altitude significantly influences exceedance probability and decision reliability. Sensors with lower intrinsic dispersion maintain sharper threshold transitions and therefore provide more stable regulatory decisions. Sensitivity analysis further confirms that moderate variations in measurement uncertainty can substantially affect inspection risk. The proposed framework provides a quantitative tool for evaluating sensing technologies in UAV-based inspection missions and supports the design of reliable drone-assisted dimensional compliance monitoring systems. Full article

Recent advances in satellite missions, particularly the Landsat, Sentinel, and Gaofen series, have led to the rapid accumulation of high-quality remote sensing data with frequent revisits. As these data have become more widely available, Satellite Image Time Series (SITS) have become an important tool for monitoring Earth surface dynamics. SITS now supports a wide range of applications, including precision agriculture, Land Use/Cover Change (LULCC) monitoring, environmental management, and disaster response. This growth has also promoted the development of advanced SITS classification datasets. However, existing reviews have mainly focused on SITS classification algorithms or specific applications, while systematic comparisons of public SITS benchmark datasets remain limited. This lack of synthesis makes it difficult for researchers to navigate fragmented resources and select datasets that match specific scientific or operational tasks. To address this gap, this paper provides a comprehensive review and analysis of 29 publicly available medium-to-high-resolution SITS classification benchmark datasets released between 2017 and 2025. These datasets are intended for training, testing, and validating land-cover classification algorithms, rather than for direct use as operational map products. We conduct a detailed statistical and comparative analysis of these datasets, focusing on their key characteristics across spectral, temporal, and spatial dimensions, as well as their labeling systems. In addition, this review summarizes the SITS classification algorithms that have been developed and benchmarked using these datasets. Finally, we identify the main challenges in constructing and applying SITS classification datasets and discuss future research directions, particularly in data reconstruction, multimodal fusion, change analysis, and advanced model architectures. This survey provides the research community with a systematic overview of SITS classification benchmark datasets and aims to support continued progress in this rapidly developing field. Full article

Unmanned aerial vehicles (UAVs) provide a highly flexible platform for synthetic aperture radar (SAR), enabling efficient, high-quality imaging in remote sensing applications. In realistic imaging missions, regions of interest (ROIs) usually have different sizes and spatial distributions. While deploying SAR-UAVs with heterogeneous flight and imaging capabilities can improve mission time efficiency, realizing this improvement depends critically on task assignment and path planning. In this paper, the joint task assignment and path planning problem for heterogeneous SAR-UAVs in multi-region imaging missions is addressed. First, flight and imaging models of SAR-UAVs are established, and a constrained optimization problem is formulated to minimize the mission completion time. Then, an improved clustering strategy based on area-density and cost prediction (ADCP) is proposed to align ROI-dependent imaging workloads with heterogeneous SAR-UAV capabilities, thereby leveraging capability advantages and reducing the mission completion time. Finally, a discrete secretary bird optimization algorithm (DSBOA) is developed to generate feasible, high-quality paths. To accelerate convergence, UAV paths are encoded as waypoint sequences, and a mutation-based operator is introduced to update the population. Extensive Monte Carlo simulations show that the proposed approach consistently outperforms the baselines in mission completion time, demonstrating its effectiveness in improving time efficiency for multi-region SAR imaging missions. Ablation experiments further confirm the independent contributions of the proposed ADCP method and DSBOA algorithm. Full article

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges. Full article

The initial development of the Cyclone Global Navigation Satellite System (CYGNSS) Ocean Surface Heat Flux Product, shortly after the satellite mission began, quickly became a valuable tool for analyzing and monitoring latent and sensible heat fluxes over tropical and subtropical oceans. It helps improve understanding of their influence on tropical and extratropical cyclones, tropical convection, atmospheric rivers, and more. Since its first release, the product has been updated with new ancillary input data (such as temperature and humidity), algorithm adjustments to incorporate equivalent neutral winds from CYGNSS, and the addition of local solar time to support diurnal analysis. As a mature mission and data product, CYGNSS provides important climatological and long-term insights into the tropical and subtropical oceans, filling gaps where in situ observations and data from other remote sensing instruments are limited. This paper outlines the updates and changes made to the CYGNSS Fluxes since its inception, compares the current dataset with in situ data, and discusses CYGNSS’s long-term observations of ocean surface heat fluxes in the tropical and subtropical regions. Full article

Methane (CH₄) is a potent greenhouse gas, and accurately estimating its global budget is essential for climate change mitigation. This review provides a comparative synthesis of top-down, bottom-up, and integrated approaches for quantifying methane emissions and sinks, with a particular focus on the role of remote sensing. Top-down methods, leveraging satellite observations from instruments like GOSAT and TROPOMI within atmospheric inversion frameworks (Bayesian, 4D-Var), provide observationally constrained, spatially integrated fluxes, reducing global budget uncertainty to ±5–10%. However, they face challenges in source attribution and rely heavily on transport model accuracy. Conversely, bottom-up approaches, including process-based models (e.g., CLM, DNDC) and emission inventories (e.g., EDGAR), offer detailed, sector-specific insights but are prone to underestimating emissions from super-emitters and diffuse sources like wetlands, with uncertainties often exceeding ±20–40% for individual sectors. Key persistent discrepancies between the two approaches are largest for natural sources (e.g., a 20–40 Tg yr⁻¹ gap for tropical wetlands). Integrated approaches, which synergize top-down atmospheric constraints with bottom-up inventory data, are emerging as the most robust methodology, effectively narrowing the global budget gap and improving confidence. Recent advancements in satellite missions (e.g., MethaneSAT), machine learning algorithms for plume detection, and high-resolution inversion models are transforming monitoring capabilities. However, challenges remain in harmonizing datasets, representing complex microbial processes in models, and expanding observational coverage in data-scarce tropical regions. This review concludes by outlining a future path centered on hybrid inversion frameworks, AI-driven source attribution, and cross-disciplinary collaboration to deliver the actionable methane budgets needed for effective climate policy. Full article

Accurate absolute radiometric calibration is critical for ensuring the data quality of spaceborne Synthetic Aperture Radar (SAR) systems and supporting quantitative remote sensing applications. Absolute radiometric calibration generally relies on ground reference targets with known radar cross-section (RCS) deployed at dedicated calibration sites. Such ground-based calibration methods are costly and time-consuming, and calibration frequency is constrained by the distribution of calibration sites and the satellite revisit cycles. Additionally, for specialized SAR missions, such as deep space exploration, deploying calibration equipment on the observed extraterrestrial surface is infeasible. This study proposes a space-based absolute calibration concept using a small calibration satellite carrying a well-characterized reference (e.g., a passive reflector or an active transponder) and flying in formation with the SAR satellite. The relative motion ensures a side-looking acquisition geometry, enabling the SAR to image the accompanying target and derive calibration factors. The overall calibration process is divided into two stages: determination of an in-orbit calibration factor using the calibration satellite, followed by its transformation to accommodate ground imaging conditions. This method effectively isolates the radar system gain to characterize the intrinsic hardware response. Furthermore, by operating entirely in space, it avoids atmospheric and ground-clutter distortions, ensuring a fully space-based, end-to-end calibration process dominated primarily by sensor systematic errors. Moreover, it allows for more frequent and flexible calibration, eliminating reliance on ground calibration sites and infrastructure. The feasibility and advantages of the proposed concept are demonstrated through comprehensive simulations, covering orbit analysis, echo simulation, and image processing. Full article

The increasing availability of microwave bistatic remote sensing data highlights the need for reliable and computationally efficient scattering models to support data interpretation, system design, and mission planning. This is particularly relevant in urban and maritime environments, where the electromagnetic (EM) interaction between buildings and ships with the surrounding environment significantly affects the observed bistatic signatures. This paper presents a fully analytical model for EM bistatic scattering from a canonical target, represented as a parallelepiped with smooth dielectric faces located over a lossy random rough surface. The formulation is developed within the framework of the Kirchhoff Approximation and accounts for both single- and multiple-bounce scattering mechanisms arising from the mutual interaction between the target and the underlying surface. Reflections from the target walls are modeled using the Geometrical Optics solution, while scattering from the rough surface is described through the zeroth-order Physical Optics approximation. The resulting closed-form expressions provide both coherent and incoherent components of the scattered field as explicit functions of system and scene parameters. The proposed closed-form model enables fast and reliable evaluation of bistatic scattering from parallelepiped-like structures, such as buildings and large ships interacting with surrounding rough surfaces. This capability is particularly beneficial for the design and optimization of bistatic remote sensing missions in urban and maritime contexts as well as the development and assessment of inversion methods and large-scale analyses. Validation against numerical simulations and experimental results available in the literature demonstrates the effectiveness of the proposed approach across different operating conditions. Full article

Artificial intelligence (AI), cloud computing, robotics, automation, and remote sensing technologies are all contributing to digital transformation in forestry. Improving on low-accuracy Global Navigation Satellite Systems (GNSS) positioning affected by multipath error and interception under forest canopies is critical for integrating smart and digital technologies into equipment in forest operations. In an era where lidar-derived individual tree locations are now increasingly available in digital forest inventories, a possible alternative approach to positioning resources such as people or equipment accurately could be to match locally-measured tree positions and attributes in the forest with an existing global reference map based on prior remote sensing missions, effectively using the trees themselves as satellites to circumvent the need for GNSS-based positioning. We evaluated a lidar-based alternative to GNSS positioning using predicted tree positions from local terrestrial laser scanning (TLS) matched with a global stem map derived from prior airborne laser scanning (ALS), a methodology we refer to as TreePS. The horizontal error of the TreePS system was estimated using 154 permanent single-tree inventory plots on the University of Idaho Experimental Forest with two different workflows based on two common R packages (lidR v. 4.3.0, FORTLS v. 1.6.2) using either spatial coordinates or spatial plus stem DBH predicted using one or both segmentation routines and a custom matching algorithm. Mean TreePS error using lidR for below and above-canopy segmentation had mean error of 1.04 and 2.04 m with 93.5% and 91.6% of plots with viable match solutions on spatial and spatial plus DBH matching. The second workflow with both FORTLS (TLS point cloud) and lidR (ALS point cloud) had errors of 1.09 and 2.67 m but only 57.9% and 54.2% of plots with solutions using spatial and spatial plus DBH, respectively. There is room for improvement in the matching algorithm but the TreePS methodology and similar feature-matching solutions may be useful for below-canopy positioning of equipment, people or other resources under dense forests and other GNSS-degraded environments to help advance smart and digital forestry. Full article

Satellite constellations are increasingly employed in regional remote sensing applications such as environmental monitoring and disaster management. However, achieving efficient and timely coverage for discontinuous regions with high revisit frequency remains a significant challenge. This study first compares low Earth circular and elliptical orbit constellations in terms of coverage performance, economic efficiency, and orbital lifetime. Based on this comparison, a dedicated design methodology for elliptical orbit constellations aimed at discontinuous regional coverage is developed. A critical Sun-synchronous repeating elliptical orbit is selected as the baseline configuration, and its key orbital parameters including the semi-major axis, eccentricity, and argument of perigee are analytically derived. Furthermore, a flexible constellation configuration model is proposed, introducing a modified Walker-inspired $kn/kn/k$ pattern. This model establishes direct mathematical relationships between the constellation’s repetition factor, phasing parameters, and temporal coverage metrics to systematically guide the overall design process. A case study on wildfire monitoring in China’s Qinling Mountains demonstrates the feasibility and effectiveness of the proposed approach, achieving a one-hour revisit time over the target region with a 24-satellite constellation. The results indicate that the proposed methodology provides a cost-effective and adaptable framework for satellite constellation design in remote sensing applications, particularly suited to dynamic environmental monitoring and emergency response missions. Full article

This review provides a comprehensive bibliometric analysis of the literature on the integration of remote sensing data and machine learning or deep learning algorithms in precision agriculture. The analysis covers 1056 publications, included in the Web of Science Core Collection, and identifies the temporal patterns of research, the most frequently used algorithms, the prominent remote sensing technologies, and the geographical distribution of research output. Increased research output during the period of 2013–2025 is attributed to the availability of high-level computing, satellites, and UAV imagery. The earlier studies in machine learning primarily involved the use of the Random Forest and Support Vector Machine algorithms, whereas in the past few years, deep learning, and especially Convolutional Neural Networks, have become more dominant. The most widely used data sources in remote sensing are the imagery from UAVs and the Sentinel satellite missions. The evaluation revealed that most of the geographical research activity was centered in the United States and China, but there is a trend of increasing research activity in most of the other developed countries. Research in Africa and South America remains particularly underdeveloped. Considering the rapid development of research, data fusion of optical and radar satellite imagery, UAV imagery, weather and soil datasets are expected to further improve the representation of agricultural systems. Full article

The increasing volume, temporal density, and diversity of satellite Earth observation (EO) data have fundamentally transformed quantitative vegetation remote sensing. Dense multi-sensor time series and computationally intensive modelling have rendered traditional download-and-process workflows increasingly impractical. Cloud-native computing—where data access, storage, and computation are co-located and analyses are executed in data-proximate environments—has therefore emerged as a key paradigm for scalable and reproducible vegetation EO analysis. This review provides a science-oriented synthesis of cloud-native EO for quantitative vegetation research. We examine architectural principles, data models, and compute patterns that shape how vegetation analyses are implemented, scaled, and scientifically interpreted. Particular attention is given to machine learning as a system component, including model lifecycle management, domain shift, and evaluation integrity in distributed environments. We analyse how cloud-native data abstractions influence algorithmic assumptions, validation design, and long-term product consistency, highlighting trade-offs between analytical complexity, computational cost, latency, and scientific robustness. We provide a forward-looking perspective on emerging imaging spectroscopy missions and the growing system-level requirements for reproducible, scalable, and uncertainty-aware vegetation analytics at continental-to-global scales. We also outline how cloud-native EO infrastructures are driving new scientific paradigms based on continuous monitoring, systematic reprocessing, and AI-driven modelling. Full article

Onboard GNSS-based relative baseline determination has emerged as a primary solution for formation-flying satellites dedicated to mapping and remote sensing missions. For ambiguity resolution (AR), the double-difference (DD) method is widely adopted in relative baseline determination. However, this method entails relatively complex satellite pairing, which not only increases computational load and complicates the processing workflow but also imposes higher requirements on onboard embedded computing and storage resources, thereby introducing potential risks to engineering implementation. To address these issues, this paper proposes incremental refinements to the single-difference (SD) model by introducing the combined GPS/BDS uncombined SD method for closely spaced formation satellites. By leveraging the enhanced satellite visibility of the combined GPS/BDS constellation and adopting a purely geometric approach, high-precision real-time relative baseline determination results are achieved. Validation using onboard observation data from the Lutan-1 satellite mission of China demonstrates that centimeter-level relative baseline determination accuracy can be attained. Full article