Search Results (1,800)

Atmospheric carbon dioxide in mole fraction (XCO₂) is one of the key parameters in estimating CO₂ fluxes at the air–sea interface. Satellite-derived column-averaged XCO₂ has been widely used in the estimates of air–sea CO₂ fluxes, yet the uncertainties induced by using column-averaged XCO₂ instead of atmospheric XCO₂ in the ocean boundary layer have been generally unknown. In this study, based on an extensive dataset of atmospheric XCO₂ measured in the ocean boundary layer from global ocean mooring arrays (N = 945,243) and historical cruises (N = 170,000) between 2002 and 2024, for the first time, we quantitatively evaluated the performance of four satellites, including the Greenhouse gases Observing SATellite (GOSAT and GOSAT-2), the Orbiting Carbon Observatory-2 (OCO-2), and the Atmospheric InfraRed Sounder (AIRS), in monitoring the atmospheric XCO₂ over oceanic regions. The atmospheric XCO₂ has been increasing from 375 ppm in 2002 to 417 ppm in 2024 based on the longest data record from AIRS. We found that the column-averaged atmospheric XCO₂ can serve as a good proxy for atmospheric XCO₂ in the ocean boundary layer, with associated uncertainties of 2.48 ppm (0.46%) for GOSAT, 1.01 ppm (0.24%) for GOSAT-2, 2.45 ppm (0.45%) for OCO-2, and 4.22 ppm (0.83%) for AIRS. We also investigated the consistency of these satellites in monitoring the growth rates of atmospheric XCO₂ in the global ocean basins. Based on the longest data record from AIRS, the atmospheric XCO₂ has been increasing at a rate of 1.87–1.97 ppm year⁻¹ over oceanic regions in the past two decades. These findings contribute to improving the reliability of satellite-derived column-averaged XCO₂ observations in the estimates of air–sea CO₂ fluxes and support future efforts in monitoring ocean carbon dynamics through satellite remote sensing. Full article

Terraces are the main engineering of soil erosion control on the Loess Plateau, offering measures for sediment reduction and water conservation, as well as the potential for increasing agricultural productivity. Over the years, large-scale terrace construction has been undertaken; however, the management has been inadequate, especially in terms of long-term monitoring and mapping. Moreover, the sediment reduction effect of terrace construction is not yet fully understood. Therefore, this study utilizes Landsat series data, integrating remote sensing imaging principles with machine learning techniques to achieve long–term temporal sequence mapping of terraces at a 30 m spatial resolution on the Loess Plateau. The sediment reduction effect brought about by terrace construction on the Loess Plateau is quantified using a sediment reduction formula. The results show that Elevation (Ele.), red band (R), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Near-infrared Reflectance of Vegetation (NIRv) are key parameters for remote sensing identification of terraces. These five remote sensing variables explain 88% of the terrace recognition variance. Coupling the Random Forest classification model with the LandTrendr algorithm allows for rapid time-series mapping of terrace spatial distribution characteristics on the Loess Plateau. The producer’s accuracy of terrace identification is 93.49%, the user’s accuracy is 93.81%, the overall accuracy is 88.61%, and the Kappa coefficient is 0.87. The LandTrendr algorithm effectively removes terraces affected by human activities. Terraces are mainly distributed in the southeastern Loess areas, including provinces such as Gansu, Shaanxi, and Ningxia. Over the past 30 years, the terrace area on the Loess Plateau has increased from 0.9790 million hectares in 1990 to 9.8981 million hectares in 2020. The sediment reduction effect is particularly notable, with an average reduction of 49.75% in soil erosion across the region. This indicates that terraces are a key measure for soil erosion control in the region and a critical strategy for improving farmland productivity. The data from this study provides scientific evidence for soil erosion control on the Loess Plateau and enhances the precision of terrace management. Full article

Soil moisture (SM) is a key variable regulating land–atmosphere energy exchange, hydrological processes, and ecosystem functioning. Though important, there are still unresolved problems in accurate SM monitoring and the practical application and validation of existing methods. In this review, we integrate mechanistic classification and applicability and constraint discussions to develop a coherent understanding of current SM monitoring approaches. Within this framework, in situ measurements, optical and thermal infrared methods, active and passive microwave remote sensing (RS) techniques, and model-based simulations are compared, and publicly accessible SM dataset products are comparatively analyzed in terms of product characteristics and application limitations. Different from other published reviews, this study covers a large scope of SM monitoring methods varying from in situ observation to RS inversion, and classifies them based on their mechanisms, thereby constructing a complete comparative framework for SM research. Moreover, three types of open-access SM dataset products are investigated, optical and microwave RS products, model simulation and data fusion products, and reanalysis dataset products, and evaluated according to their resolution, depth, applicability, advantages, and limitations. By doing so, it is concluded that in situ observations remain essential for calibration and validation but are spatially limited. Optical and thermal infrared methods are restricted by atmospheric conditions and a shallow penetration depth, while microwave techniques exhibit varying performances under different vegetation and soil conditions. Existing datasets differ significantly in resolution, consistency, and coverage, making no single product universally applicable. Future research should focus on multi-source and spatiotemporal data fusions, the integration of machine learning with physical mechanisms, enhancement for cross-sensor consistency, the establishment of standardized uncertainty evaluation frameworks, and the refinement of high-order RTMs and parameterization. Full article

This study introduces a controlled laboratory setup for hyperspectral longwave infrared (LWIR) imaging of soils, designed to bridge the gap between laboratory measurements and remote sensing observations. A Fourier-transform hyperspectral LWIR imaging spectrometer (Telops Hyper-Cam LW) was employed, together with a specialized heating plate, rigorous calibration protocols, and a Spatial Averaging Before Blackbody Fitting (SABBF) method to enable accurate LWIR indoor measurements. Unlike established laboratory techniques that measure reflectance and calculate emissivity indirectly, this setup enables direct passive measurement of soil emissivity, replicating airborne and spaceborne LWIR measurements of the surface. The emissivity spectra of 12 variable soil samples obtained with the lab setup were compared and validated based on LWIR Hyper-Cam LW spectra acquired under outdoor conditions, then were subsequently analyzed to determine the mineral composition of each sample. Spectral features and indices were used to estimate the relative content of quartz, clay minerals, and carbonates, from the most to least abundant. The results demonstrate that the laboratory-based setup preserves spectral fidelity while offering improved repeatability, scheduling flexibility, and reduced dependence on weather. Beyond replicating outdoor measurements, this controlled setup is easy to install and provides a reproducible framework for LWIR soil spectroscopy that could be considered for standard laboratory protocols, enabling reliable mineral identification, calibration/validation of airborne and satellite LWIR data, and broader applications in soil monitoring and environmental remote sensing. Full article

The cereal leaf beetle (CLB; Oulema melanopus L., Coleoptera: Chrysomelidae) is a serious pest of wheat, capable of causing yield losses of up to 40% through photosynthetic impairment. Early detection and severity assessment are essential for effective and sustainable pest management. This study evaluates the potential of hyperspectral remote sensing (RS) combined with machine learning (ML) for non-invasive detection of CLB-induced stress in winter wheat. Spectral reflectance was measured using a full-range spectroradiometer (350–2500 nm) from flag leaves categorized into four damage levels (healthy, slightly, moderately, and severely damaged). Three input datasets were used for ML classification: full spectral reflectance, a set of 13 vegetation indices (VIs), and outputs of dimensionality reduction technique. CLB stress increased reflectance in the visible range (400–700 nm) and reduced it in the near-infrared (700–1400 nm), consistent with chlorophyll degradation and mesophyll damage. Several VIs, including RIGreen, NDVI750, GNDVI, and NDVI, correlated strongly with damage severity (τ = 0.78–0.81). Among the six ML models tested, Support Vector Machine (SVM) achieved the highest classification accuracy of 90.0% (precision = 0.90, recall = 0.90, F1 = 0.90) across the four severity classes, and achieved 91.9% accuracy at the early-detection threshold. As far as the currently available literature indicates, this study provides one of the earliest quantitative assessments of CLB damage severity based on full-spectrum leaf-level hyperspectral reflectance integrated with ML classification. These findings were obtained under controlled, leaf-level measurement conditions and therefore represent a proof-of-concept; future validation using UAV and satellite platforms is needed to assess performance under operational field variability. Overall, our findings highlight the potential of hyperspectral RS and ML for precision pest monitoring, supporting threshold-based decision-making and more sustainable insecticide use. Full article

Assessing the extent and magnitude of wildlife impact on forest regeneration (e.g., % browsed seedlings or reduction in regeneration density) remains a central challenge. This study explores the potential of unmanned aircraft systems (UAS) to quantify wildlife impact through the integration of drone-based thermal surveys and vegetation assessments. Specifically, it evaluates whether UAS-derived wildlife density estimates can be linked to browsing intensity and regeneration structure, thereby enabling an indirect assessment of silviculturally relevant forest dynamics. By combining remotely sensed wildlife data with field-based vegetation inventories, the study aims to identify measurable relationships between structural forest characteristics and browsing effects. This approach contributes to the development of spatially efficient, objective, and reproducible monitoring methods at the forest–wildlife interface. Ultimately, the study provides a novel framework for integrating modern remote sensing technologies into wildlife–ecological monitoring and for improving adaptive, evidence-based management in forest ecosystems increasingly affected by high ungulate densities and climate-related stressors. Two silviculturally contrasting study areas were selected: a broadleaf-dominated mixed forest in Hesse, where high ungulate densities were expected, and a pine-dominated site in Brandenburg, anticipated to experience lower browsing pressure. Thermal surveys were conducted using a DJI Matrice 30T drone equipped with a high-resolution infrared camera to detect and geolocate wildlife. In parallel, browsing impact was assessed using a modified circular transect method (“Neuzeller method”). Regeneration was recorded by tree species, height class, and browsing intensity. Statistical analyses and GIS-based spatial visualizations were used to examine the relationship between estimated ungulate densities and browsing levels. Results revealed clear differences in wildlife abundance and browsing intensity between the two sites. In the Heppenheim forest, roe deer densities exceeded 40 individuals per 100 ha, correlating with high browsing pressure—particularly on ecologically and silviculturally valuable species such as sycamore maple and sessile oak. In contrast, the Rochauer Heide exhibited lower densities and a comparatively moderate browsing impact, although certain tree species still showed signs of selective pressure. This study demonstrates that drone-based wildlife monitoring offers an innovative, non-invasive means to indirectly evaluate forest structural conditions in regeneration layers. The findings highlight the relevance of UAV-supported methods for evidence-based wildlife management and the adaptive planning of silvicultural measures. The method enhances transparency and spatial resolution in forest–wildlife management and supports evidence-based decision-making in times of ecological and climatic change. Full article

The Leaf Area Index (LAI) is a key vegetation biophysical variable extensively studied using various remote sensing platforms and applications. Most studies focused on retrieving LAI using remote sensing data have primarily applied visible to shortwave infrared (0.3–2.5 µm) data. While we have previously retrieved LAI using thermal infrared (TIR 2.5–14 µm) hyperspectral data under controlled laboratory conditions, this study aims to evaluate the reliability of our earlier findings using in situ and airborne TIR hyperspectral data. In this study, 36 plots, each 30 × 30 m in size, were randomly selected in the Bavarian Forest National Park in southeastern Germany. The EUFAR-TIR flight campaign, conducted on 6 July 2017, aligned with field data collection using an AISA Owl TIR hyperspectral sensor at 3 m spatial resolution. Statistical univariate and multivariate approaches have been applied to predict LAI using emissivity data. The LAI was derived using six narrowband indices, computed from all possible combinations of wavebands between 8 µm and 12.3 µm, via partial least squares regression (PLSR) and artificial neural network (ANN) models, applying the Levenberg–Marquardt and Scaled Conjugate Gradient algorithms. The results indicated that compared to LAI estimation under controlled conditions, TIR narrowband indices demonstrated poor performance in estimating in situ LAI (R² = 0.28 and RMSE_CV = 0.02). Instead, it was observed that the PLSR model unexpectedly achieved higher prediction accuracy (R² = 0.86 and RMSE_CV = 0.36) in retrieving LAI compared to the ANN approach using the Levenberg–Marquardt algorithm (R² = 0.56, RMSE_CV = 0.71); however, it was outperformed by the Scaled Conjugate Gradient algorithm (R² = 0.83, RMSE_CV = 0.18). The results revealed that wavebands located at 8.1 µm, 9.1 µm, 9.85–9.95 µm, and 9.99–10.27 µm are equally effective in predicting LAI, regardless of sensor or measurement/environmental conditions. Our findings have important implications for upscaling LAI predictions, as the identified wavebands are effective across varying conditions and align with the capabilities of upcoming thermal satellite missions such as Landsat Next and Copernicus LSTM. Full article

Accurate prediction of land surface temperature (LST) is critical for remote sensing applications, yet remains hindered by in situ data scarcity, limited input variables, and regional variability. To address these limitations, we introduce a three-stage strategic fine-tuning-based transfer learning (SFTL) framework that integrates a large simulated dataset (430 K samples), in situ measurements from the Heihe and Huailai regions in China, and high-resolution imagery from the GF5-02 Visible and Infrared Multispectral Imager (VIMI). The key novelty of this study is the combination of large-scale simulation, an engineered humidity-sensitive feature, and multiple parameter-efficient tuning strategies—full, head, gradual, adapter, and low-rank adaptation (LoRA)—within a unified transfer-learning framework for cross-site LST estimation. In Stage 1, pre-training with 5-fold cross-validation on the simulated dataset produced strong baseline models, including Random Forest (RF), Light Gradient Boosting Machine (LGBM), Deep Neural Network (DNN), Transformer (TrF), and Convolutional Neural Network (CNN). In Stage 2, strategic fine-tuning was conducted under two cross-regional scenarios—Heihe-to-Huailai and Huailai-to-Heihe—and model transfer for tree-based learners. Fine-tuning achieved competitive in-domain performance while materially improving cross-site transfer. When trained on Huailai and tested on Heihe, DNN-gradual attained RMSE 2.89 K (R² ≈ 0.96); when trained on Heihe and tested on Huailai, TrF-head achieved RMSE 3.34 K (R² ≈ 0.94). In Stage 3, sensitivity analyses confirmed stability across IQR multipliers of 1.0–1.5, with <1% RMSE variation across models and sites, indicating robustness against outliers. Additionally, application to real GF5-02 VIMI imagery demonstrated that the best SFTL configurations aligned with spatiotemporal in situ observations at both sites, capturing the expected spatial gradients. Overall, the proposed SFTL framework—anchored in cross-validation, strategic fine-tuning, and large-scale simulation—outperforms the widely used Split-Window (SW) algorithm (Huailai: RMSE = 3.64 K; Heihe: RMSE = 4.22 K) as well as direct-training Machine Learning (ML) models, underscoring their limitations in modeling complex regional variability. Full article

Infrared–visible-oriented object detection aims to combine the strengths of both infrared and visible images, overcoming the limitations of a single imaging modality to achieve more robust detection with oriented bounding boxes under diverse environmental conditions. However, current methods often suffer from two issues: (1) modality misalignment caused by hardware and annotation errors, leading to inaccurate feature fusion that degrades downstream task performance; and (2) insufficient directional priors in square convolutional kernels, impeding robust object detection with diverse directions, especially in densely packed scenes. To tackle these challenges, in this paper, we propose a novel method, Fine-Grained Multispectral Fusion (FGMF), for oriented object detection in the paired aerial images. Specifically, we design a dual-enhancement and fusion module (DEFM) to obtain the calibrated and complementary features through weighted addition and subtraction-based attention mechanisms. Furthermore, we propose an orientation aggregation module (OAM) that employs large rotated strip convolutions to capture directional context and long-range dependencies. Extensive experiments on the DroneVehicle and VEDAI datasets demonstrate the effectiveness of our proposed method, yielding impressive results with accuracies of 80.2% and 66.3%, respectively. These results highlight the effectiveness of FGMF in oriented object detection within complex remote sensing scenarios. Full article

This study addresses the observation of floating plastic debris in freshwater environments using an Unmanned Aerial Vehicle (UAV) multi-sensor strategy. An experimental campaign is described where an heterogeneous plastic assemblage, namely a plastic target, and a naturally occurring leaf-litter mat are observed by a UAV platform in the Lake Calore (Avellino, Southern Italy) within the framework of the “multi-layEr approaCh to detect and analyze cOastal aggregation of MAcRo-plastic littEr” (ECOMARE) Italian Ministry of Research (MUR)-funded project. Three UAV platforms, equipped with optical, multispectral, and thermal sensors, are adopted, which overpass the two targets with the objective of analyzing the sensitivity of optical radiation to plastic and the possibility of discriminating the plastic target from the natural one. Georeferenced orthomosaics are generated across the visible, multispectral (Green, Red, Red Edge, Near-Infrared—NIR), and thermal bands. Two novel indices, the Plastic Detection Index (PDI) and the Heterogeneity Plastic Index (HPI), are proposed to discriminate between the detection of plastic litter and natural targets. The experimental results highlight that plastics exhibit heterogeneous spectral and thermal responses, whereas natural debris showed more homogeneous signatures. Green and Red bands outperform NIR for plastic detection under freshwater conditions, while thermal imagery reveals distinct emissivity variations among plastic items. This outcome is mainly explained by the strong NIR absorption of water, the wetting of plastic surfaces, and the lower sensitivity of the Mavic 3′s NIR sensor under high-irradiance conditions. The integration of optical, multispectral, and thermal data demonstrate the robustness of UAV-based approaches for distinguishing anthropogenic litter from natural materials. Overall, the findings underscore the potential of UAV-mounted remote sensing as a cost-effective and scalable tool for the high-resolution monitoring of plastic pollution over inland waters. Full article

Land surface temperature (LST) is a critical variable for understanding energy exchanges and water balance at the Earth’s surface, as well as for calculating turbulent heat flux and long-wave radiation at the surface–atmosphere interface. Remote sensing techniques, particularly using satellite platforms like Landsat 8 OLI/TIRS and Sentinel-2A, have facilitated detailed LST mapping. Sentinel-2 offers high spatial and temporal resolution multispectral data, but it lacks thermal infrared bands, which Landsat 8 can provide a 30 m resolution with less frequent revisits compared to Sentinel-2. This study employs Sentinel-2 spectral indices as independent variables and Landsat 8-derived LST data as the target variable within a machine-learning framework, enabling LST prediction at a 10 m resolution. This method applies grid search-based hyperparameter-tuned machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and k-Nearest Neighbours (kNN)—to model complex nonlinear relationships between the spectral indices (NDVI, NDWI, NDBI, and BSI) and LST. Grid search, combined with cross-validation, enhanced the model’s prediction accuracy for both pre- and post-monsoon seasons. This approach surpasses earlier methods that either employed untuned models or failed to integrate Sentinel-2 data. This study demonstrates that capturing urban thermal dynamics at fine spatial and temporal scales, combined with tuned machine learning models, can enhance the capability of urban heat island monitoring, climate adaptation planning, and sustainable environmental management models. Full article

Far-infrared imaging is a powerful tool in night vision and temperature measurement, with broad applications in military, astronomy, meteorology, industrial, and medical fields. However, conventional imaging lenses face challenges such as large size, heavy weight, and difficulties in miniaturization, which hinder their integration and use in applications with strict requirements for mass and volume, such as drone-based observation and imaging. To address these limitations, we designed a dual-plane diffractive optical lens optimized for the 10.9–11.1 μm wavelength band with a 0.2 μm bandwidth. By optimizing parameters including focal length, spot size, and field of view, we derived the phase distribution of the lens and converted it into the surface sag. To enhance diffraction efficiency and minimize energy loss, the lens was fabricated using a continuous phase surface on single-crystal Germanium. Finally, an imaging system was constructed to achieve clear imaging of various samples, demonstrating the feasibility of both the device and the system. This approach shows great potential for applications requiring lightweight and miniaturized solutions, such as infrared imaging, machine vision, remote sensing, biological imaging, and materials science. Full article

Cloud Top Height (CTH), defined as the altitude of the highest cloud layer above mean sea level, is a crucial geophysical parameter for quantifying cloud radiative effects, analyzing severe weather systems, and improving climate models. To enhance the accuracy of CTH retrieval from Fengyun-4A (FY-4A) satellite data, this study proposes a multi-stage deep learning framework that progressively refines cloud parameter estimation. The method utilizes cloud information from the FY-4A/AGRI (Advanced Geosynchronous Radiation Imager) Level 1 calibrated scanning imager radiance data product to construct a multi-source data fusion neural network model. The model inputs combine multi-channel radiance data with cloud parameters, including Cloud Top Temperature (CTT) and Cloud Top Pressure (CTP). We used the CTH measurement data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite as a reference to verify the model output. Results demonstrate that the proposed multi-stage model significantly improves retrieval accuracy. Compared to the official FY-4A CTH product, the Mean Absolute Error (MAE) was reduced by 49.12% to 2.03 km, and the Pearson Correlation Coefficient (PCC) reached 0.85. To test the applicability of the model under complex weather conditions, we applied it to the CTH inversion of the double typhoon event on 10 August 2019. The model successfully characterized the spatial distribution of CTH within the typhoon regions. The results are consistent with the National Satellite Meteorological Centre (NSMC) reports and clearly reveal the different intensity evolutions of the two typhoons. This research provides an effective solution for high-precision retrieval of high-level cloud CTH at a large scale, using geostationary meteorological satellite remote sensing data. Full article

Owing to their high vertical resolution, remote sensing data from meteorological satellite hyperspectral infrared sounders are well-suited for the identification, monitoring, and early warning of high-impact weather events. The effective utilization of full field-of-view (FOV) observations from satellite infrared sounders in high-impact weather applications remains a major research focus and technical challenge worldwide. This study proposes a generalized variational retrieval framework to estimate full FOV cloud fraction and precipitable water vapor (PWV) from observations of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A (FY-4A) satellite. Based on this method, experiments are performed using high-frequency FY-4A/GIIRS observations during the landfall periods of Typhoon Lekima (2019) and Typhoon Higos (2020). A three-step channel selection strategy based on information entropy is first designed for FY-4A/GIIRS. A constrained generalized variational retrieval method coupled with a cloud cost function is then established. Cloud parameters, including effective cloud fraction and cloud-top pressure, are initially retrieved using the Minimum Residual Method (MRM) and used as initial cloud information. These parameters are iteratively optimized through cost-function minimization, yielding full FOV cloud fields and atmospheric profiles. Full FOV brightness temperature simulations are conducted over cloudy regions to quantitatively evaluate the retrieved cloud fractions, and the derived PWV is further applied to the identification and analysis of hazardous weather events. Experimental results demonstrate that incorporating cloud parameters as auxiliary inputs to the radiative transfer model improves the simulation of FY-4A/GIIRS brightness temperature in cloud-covered areas and reduces brightness temperature biases. Compared with ERA5 Total Column Water Vapour (TCWV) data, the PWV derived from full FOV profiles containing cloud parameter information shows closer agreement and, at certain FOVs, more effectively indicates the occurrence of high-impact weather events. The simplified methodology proposed in this study provides a robust basis for the future assimilation and operational utilization of infrared data over cloud-affected regions in numerical weather prediction models. Full article

Infrared–visible image matching is a prerequisite for environmental monitoring, military reconnaissance, and multisource geospatial analysis. However, pronounced texture disparities, intensity drift, and complex non-linear radiometric distortions in such cross-modal pairs mean that existing frameworks such as SuperPoint + SuperGlue (SP + SG) and LoFTR cannot reliably establish correspondences. To address this issue, we propose a dual-path architecture, the Environment-Adaptive Wavelet Enhancement and Radiation Priors Aided Matcher (EWAM). EWAM incorporates two synergistic branches: (1) an Environment-Adaptive Radiation Feature Extractor, which first classifies the scene according to radiation-intensity variations and then incorporates a physical radiation model into a learnable gating mechanism for selective feature propagation; (2) a Wavelet-Transform High-Frequency Enhancement Module, which recovers blurred edge structures by boosting wavelet coefficients under directional perceptual losses. The two branches collectively increase the number of tie points (reliable correspondences) and refine their spatial localization. A coarse-to-fine matcher subsequently refines the cross-modal correspondences. We benchmarked EWAM against SIFT, AKAZE, D2-Net, SP + SG, and LoFTR on a newly compiled dataset that fuses GF-7, Landsat-8, and Five-Billion-Pixels imagery. Across desert, mountain, gobi, urban and farmland scenes, EWAM reduced the average RMSE to 1.85 pixels and outperformed the best competing method by 2.7%, 2.6%, 2.0%, 2.3% and 1.8% in accuracy, respectively. These findings demonstrate that EWAM yields a robust and scalable framework for large-scale multi-sensor remote-sensing data fusion. Full article