Search Results (294)

In contrast to the well-established vicarious methods for satellite radiometric calibration, studies on polarimetric calibration remain relatively limited. This study presented a sun glint based in-flight polarization calibration method in which measurements of the degree of linear polarization (DOLP) at the top of atmosphere (TOA) are directly compared with theoretical simulations generated by an atmosphere-ocean coupled vector radiative transfer model. A well calibrated reference band is introduced to retrieve the instantaneous sea surface wind speed (WS), thereby improving the accuracy of simulated TOA DOLP. The dependencies of WS retrieval error on reference band wavelength, actual WS, and solar-viewing geometry are analyzed. The results show that the WS retrieval error generally decreases with increasing reference band wavelength and increases with WS and sun glint angle. A reference band with a wavelength longer than 670 nm and the highest radiometric calibration accuracy is recommended for WS retrieval. Under typical conditions, a WS retrieval error of approximately ±0.33 m/s is predicted, which is substantially lower than the ±2 m/s uncertainty commonly associated with meteorological reanalysis WS data. Theoretical TOA DOLP simulation errors are then quantified by considering typical uncertainties in atmospheric and oceanic input parameters across spectral bands from blue to SWIR under various solar-viewing geometries. Among the investigated factors, aerosol optical depth and aerosol model are the dominant sources to DOLP calibration uncertainty, accounting for more than 80% of the total error budget in most spectral bands. Chlorophyll concentration mainly affects the short visible bands. The WS effect can be reduced by using reference band retrievals instead of meteorological reanalysis data. The wind direction effect is negligible near the center of glint spots and under small solar zenith angle conditions, but it increases considerably with the sun glint angle under oblique illumination geometry condition. The contributions from ozone, and water vapor are negligible. Overall, the total TOA DOLP error increases with solar zenith angle, viewing zenith angle (i.e., longer atmospheric optical paths), and sun glint angle (i.e., weaker sun glint brightness). The errors exhibit weak wavelength dependence, with relative lower values in the short visible and SWIR bands and slightly higher values in the NIR bands. The typical total DOLP calibration error ranges from 0.0091 to 0.0125 across blue to SWIR spectral bands (according to the center of sun glint region with solar zenith angle of 30°). This study presents theoretical guidance for satellite in-flight polarization calibration using sun glint across blue to SWIR bands. As budgeted, this method can be effective for the validation of polarimeters with moderate DOLP accuracy (e.g., 0.01–0.02). Nevertheless, it may not be ideally suited to act as an absolute reference for high-accuracy polarimeters (e.g., 0.002). Full article

Cross-event fire-severity mapping requires a model to delineate the burned footprint and grade severity within it across wildfires whose spectral expression varies with vegetation and observation conditions. Nominal multiclass models treat the classes as parallel alternatives and leave the order implicit. We introduce Co-Burn, a bi-temporal Siamese model that adds a pre-to-post dNBR channel to the post-fire branch as an NIR-SWIR change anchor and uses a conditional ordinal head to estimate burn presence before high-severity assignment. Ten methods were compared across 14 New South Wales wildfires against a Sentinel-2 FESM-derived three-class target, with 4 complete fires held out from model development and selection. Co-Burn ranked first under both pixel-pooled and event-mean aggregation, reaching 0.520 ± 0.017 and 0.524 ± 0.028 external burned mIoU. Event-level factorial contrasts showed that burned-mIoU effects varied among fires, while dNBR anchoring reduced false-burn rate on all four external events. At the fixed operating point, Co-Burn assigned 19.9% of reference-unburned pixels to burned classes, against 28.9% for the reflectance-only nominal variant. On the hardest held-out fire, limited false-burn expansion coexisted with a downward shift across the ordered severity classes. Co-Burn supports ordered three-class mapping of previously unseen forest fires before target-fire labels become available. Full article

In complex agroforestry mosaics, similarities in canopy structure and spectral response among tree species constrain medium-resolution peach orchard identification. Using 2023 multi-temporal Sentinel-2 imagery from Ganyu District, Lianyungang, Jiangsu, we assessed NIR–SWIR features, vegetation indices, and phenological parameters for classifying peach orchards, poplar, pine, and willow. First, spectral curves, Jeffries–Matusita (JM) distances, and bootstrap confidence intervals compared separability across leaf-off, early-spring, and leaf-on periods. Subsequently, HANTS reconstructed the time series from which SOS and EOS were extracted; multi-temporal bands, MSI, NDVI, EVI, and NDRE were used in Random Forest schemes. Furthermore, McNemar tests, repeated random splits, and permutation feature importance assessed differences, stability, and feature contributions; the 2023 model was transferred to 2025 imagery. Results showed phenological dependence, with leaf-off and early-spring performance exceeding leaf-on. MSI and NDII showed similar separability and performance, indicating effects from shared B8 and B11 information rather than index formulation. The optimal scheme yielded OA = 94.545%, Kappa = 0.924, peach orchard PA = 97.619%, and UA = 91.111%. Ten splits gave OA = 92.83% ± 2.08% and peach orchard F1-score = 94.34% ± 2.27%. At fixed validation locations, cross-year transfer achieved OA = 90.184% and Kappa = 0.863, indicating temporal transfer potential within the forestland and orchard mask. Full article

Calligonum polygonoides is a rare desert shrub that is of high ecological importance in Egypt, yet its habitat requirements and physiological responses to environmental variability remain poorly understood, particularly under increasing climatic and anthropogenic pressures. To address this knowledge gap, this study integrates GIS-based habitat suitability modeling with hyperspectral leaf spectroscopy to evaluate the environmental factors associated with species distribution and leaf optical responses across two ecologically contrasting wadis (Wadi El-Galala and Wadi El-Assiuty). Environmental layers (DEM, EC, TSS, pH, temperature, rainfall, humidity, evaporation) were integrated using a multi-criteria evaluation framework, while plant cover, density, and spectral measurements were collected from 14 field plots. The results show that C. polygonoides favors moderately elevated zones characterized by low salinity, slightly alkaline soils, and intermediate climatic conditions. Approximately 24.3% of the landscape was classified as highly suitable, primarily along channel belts and alluvial fans with improved drainage and reduced salt accumulation. Spectral signatures showed descriptive differences between the two wadis. Plants from Wadi El-Galala showed relatively higher NIR reflectance and more pronounced SWIR water-absorption features compared with Wadi El-Assiuty, which may reflect differences in leaf structure and water status under contrasting habitat conditions. These spectral patterns were broadly consistent with the spatial suitability outputs and provide complementary descriptive information on leaf optical properties. Habitat suitability modeling showed that 91.7% of the recorded field occurrence points (33 out of 36 shrubs) were located within high-suitability zones, while no occurrences were recorded in low-suitability areas. This pattern indicates spatial agreement between observed occurrences and predicted suitability classes, but it should not be interpreted as formal model validation because absence data and independent validation records were not available. Overall, the findings provide preliminary spatial and spectral information that may support future field verification and site-specific conservation planning for C. polygonoides in the investigated wadis. Full article

Multispectral remote sensing classification benefits from the complementary information carried by visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) Sentinel-2 bands, yet most deep models process them as a single stacked tensor without explicitly preserving their inter-group relationships. We propose Relation-Consistency Group Contrastive Learning (Group-CL-RC), a robustness-oriented framework combining group-level contrastive alignment with a relation-consistency regularizer defined over a compact VIS–NIR–SWIR similarity descriptor. The method is evaluated on EuroSAT All Bands using four backbones under radiometric drift, spatial masking, K-drop band removal, and compound spectral–spatial corruption (CS2C), and externally validated on Sentinel-2-only SEN12MS under standard and seasonal-shift protocols. Group-CL-RC preserves strong clean performance and yields statistically supported robustness gains over the multispectral-only baseline, with the largest improvements under K-drop and CS2C. SEN12MS supports the transfer of these robustness trends beyond EuroSAT, while showing that gains over standard Group-CL remain perturbation-dependent. Ablation studies further indicate that relation consistency is an effective robustness mechanism, particularly when spectral-group availability is degraded. Relation-deformation diagnostics show that Group-CL-RC primarily reduces decision-level sensitivity to relational distortions rather than uniformly minimizing raw deformation. Overall, inter-group relational geometry provides an interpretable and effective robustness target under controlled structured spectral and spectral–spatial degradation. Full article

Photosynthetic phenotypic parameters of winter wheat are important indicators of canopy physiological status, photosynthetic function, and crop growth. However, canopy-scale hyperspectral estimation of these parameters remains affected by canopy structural heterogeneity, environmental variation, and mixed spectral signals. This study evaluated the contribution of visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) regions and their combinations to estimating photosynthetic phenotypic parameters of winter wheat. Field experiments were conducted under three nitrogen application levels and 65 winter wheat genotypes, and a total of 507 valid canopy-level samples were used for model development and validation. Competitive adaptive reweighted sampling (CARS) was used to select characteristic wavelengths, and partial least squares regression (PLSR), Bayesian ridge regression (BR), and backpropagation neural network (BPNN) were applied to construct estimation models. Model performance was assessed using R², RMSE, and RPD. Results showed that NIR-based models achieved the best overall performance, with the highest validation R² of 0.828 for photosynthetic rate. The VIS + NIR combination showed stable predictive ability across multiple parameters, whereas SWIR-only models showed limited performance, with R² values below 0.5 for most parameters. Photosynthetic rate, intercellular CO₂ concentration, performance index on an absorption basis, and chlorophyll a content were predicted more accurately than the other traits. These findings indicate that canopy hyperspectral data can support quantitative monitoring of photosynthetic phenotypic parameters, and that NIR-related structural and scattering information plays a key role in winter wheat canopy phenotyping. Full article

Pastures are the main feed source for beef cattle production, a sector in which Brazil plays a prominent global role. This study aimed to develop predictive models for dry matter yield (DM yield, kg ha⁻¹), crude protein (CP), and neutral detergent fiber (NDF) in Urochloa brizantha tropical pastures using laboratory VIS-NIR-SWIR spectroscopy, and to identify spectral patterns associated with these variables. Samples were collected from a commercial pasture area of approximately 200 ha, subdivided into 19 paddocks cultivated with Urochloa brizantha cv. Marandu and managed under rotational grazing during 2023. Forage samples were oven-dried, ground, and spectrally measured using a FieldSpec spectroradiometer (350–2500 nm). Partial least squares regression (PLSR) models were calibrated and evaluated using cross-validation, and informative wavelengths were identified using Variable Importance in Projection (VIP) scores. DM variability was mainly associated with near-infrared regions, CP with visible and near-infrared regions, and NDF with the visible region. Models calibrated with VIP-selected wavelengths achieved acceptable performance for CP (R²CV = 0.74) and NDF (R²CV = 0.72), whereas the general full-spectrum models showed moderate performance for CP (R²CV = 0.57) and acceptable performance for NDF (R²CV = 0.75). Temporal transferability varied among sampling periods, with greater robustness for CP and NDF than for DM. Overall, DM prediction remained limited and showed poor temporal transferability. Full article

This study evaluated the potential of VIS–NIR–SWIR leaf spectroscopy to predict N, P, and K contents in sugarcane, considering spectral variability across two growing seasons (2023/2024 and 2024/2025), phenological stages, and five varieties. Spectral signatures (350–2500 nm) were acquired using a FieldSpec 3 spectroradiometer and processed with MSC, smoothing, and the first Savitzky–Golay derivative. Diagnostic bands were identified by Spearman correlation, and prediction was performed using partial least squares regression (PLSR). PCA showed that spectral variability was driven mainly by seasonal and phenological factors, whereas varietal effects were secondary. In 2023/2024, greater spectral homogeneity was associated with lower predictive performance. In 2024/2025, greater spectral heterogeneity was associated with improved prediction for N (R² = 0.83; RMSE = 0.78 g kg⁻¹) and P (R² = 0.80; RMSE = 0.10 g kg⁻¹). Potassium remained the most challenging nutrient to predict (maximum R² = 0.25), mainly due to its ionic nature and the resulting lack of significant correlation with specific VIS, NIR, and SWIR spectral features. These results indicate strong potential for predicting N and P in fresh sugarcane leaves, although model robustness depends on the extent of spectral variability in the dataset. Full article

Accurate and up-to-date mapping of built-up areas is of great importance for sustainable urban planning, disaster management, and the monitoring of environmental changes. In this study, a residual U-Net-based deep learning architecture named FiveBandTTA is proposed for built-up area segmentation from Sentinel-2 multispectral satellite imagery. The proposed model aims to simultaneously learn spatial and spectral features by jointly processing RGB, NIR (B8), and SWIR (B11) bands within the same encoder–decoder structure. The model incorporates standard residual blocks following the conventional residual learning principle, multi-level skip connection mechanisms, and TTA-based inference strategies. Within the scope of the study, a multi-temporal built-up area dataset was constructed from Sentinel-2 imagery acquired over Kocaeli Province. The performance of the proposed model was comparatively evaluated against RGB Baseline, FiveBand Single, DeepLabV3+, and SegFormer models. Experimental results demonstrated that the proposed model achieved the highest segmentation performance among all compared approaches, obtaining 0.8447 IoU, 0.9124 Dice, and 0.9249 Precision scores. It was observed that the use of multispectral bands together with the residual encoder–decoder structure may contribute to improved representation of small-scale built-up regions and complex boundary structures. Furthermore, the comparative experiments indicated that the NIR and SWIR bands provide complementary spectral information for distinguishing built-up areas, while the TTA-based inference strategy may contribute to improved segmentation stability and prediction consistency. Overall, the obtained results demonstrate that the proposed approach is an effective and robust method for built-up area segmentation from medium-resolution Sentinel-2 imagery. Full article

Accurate land use/land cover (LULC) mapping from remotely sensed data remains challenging in arid regions, particularly for spatial and seasonal generalization. This work proposes a novel exclude-one-city-out (EOCO) framework based on machine learning (ML) to achieve LULC generalization across summer and winter in arid environments. Four cities in Saudi Arabia witnessing rapid urban growth were selected: Riyadh, Madinah, Jeddah, and Dammam. The ML models were trained on three cities and tested on the unseen city. Sentinel-2 surface reflectance data for the visible (Blue, Green, and Red) and near-infrared bands (NIR, SWIR1, and SWIR2) were used. Spectral indices, texture features, and topographical data were used to form five feature sets, which were utilized as inputs for four ML algorithms: random forest, support vector machine, classification and regression trees, and K-nearest neighbors. Statistical tests (Friedman, Kendall’s W, and Wilcoxon signed rank) were conducted to assess differences across ML models, feature sets, and seasons. The random forest model consistently outperformed other models across the five feature sets, while the spectral texture and combined feature sets outperformed other feature combinations. Significant differences in feature importance were observed across cities and seasons for spectral texture during summer and winter (p-values: 1.25 × 10⁻⁴ and 9.2 × 10⁻⁵, respectively), with strong agreement (Kendall’s W = 0.9212 and 0.9424). The findings can support urban environmental monitoring in arid regions, contributing to sustainable urban development. Full article

Rapid urbanization in semi-arid cities intensifies heat exposure, air pollution, and land-surface degradation, yet these stressors are often assessed separately. This study develops a scale-aware Urban Environmental Stress (UES) framework for Jaipur, India, using multi-sensor Earth observation data. The framework explicitly addresses indicator redundancy, weighting bias, short time-series interpretation, and temporal comparability. The final primary UES surface uses twelve retained stress-oriented indicators on a 500 m common analysis grid, excludes NDBI because it is algebraically redundant with NDMI when both are computed from the same NIR/SWIR bands, and applies equal weights so that built fraction does not dominate the composite. Entropy weighting is reported only as a sensitivity diagnostic. The resulting UES map identifies high relative stress in Jaipur’s dense urban core and transport-industrial corridors, with lower stress along the Aravalli flank and peri-urban green or water-adjacent areas. The framework is presented as a relative spatial prioritization tool rather than an absolute physical time series; temporal claims are limited to independently reported land-cover and individual-indicator trajectories unless fixed multi-year normalization and fixed weights are applied. Full article

The field of infrared (IR) photodetection is undergoing rapid development through the emergence of solution-processable nanoparticle (NP)-based materials and fabrication strategies. This review critically examines recent advances in fabrication approaches for NP-based IR detectors, emphasizing the relationship between synthesis, surface engineering, deposition processes, and device architecture in determining detector performance. Representative material platforms are discussed, including colloidal quantum dots (CQDs) such as PbS and HgTe, which enable tunable operation from the near-infrared (NIR) and short-wave infrared (SWIR) to selected mid-wave (MWIR), long-wave (LWIR), and emerging very-long-wave infrared (VLWIR) regimes depending on material composition and operating conditions. Further platforms including plasmonic metal NPs, black phosphorus, and topological nanomaterials are evaluated for their unique mechanisms of optical enhancement and broadband response. Fabrication approaches including continuous-flow synthesis, ligand exchange, blade coating, inkjet printing, electrophoretic deposition, and other scalable solution-processing methods are analyzed with respect to their influence on film quality, charge transport, interface engineering, and integration compatibility. The review further compares major device architectures, including photoconductors, photodiodes, plasmonic absorbers, and phototransistors, using key performance metrics such as specific detectivity (D*), responsivity (R), response speed, and operating temperature, while emphasizing the importance of measurement conditions in cross-platform comparisons. Critical challenges including dark-current generation, 1/f noise, transport limitations associated with ligand chemistry, environmental instability of narrow-bandgap materials, manufacturability constraints, and toxicity considerations are also discussed. Emerging directions such as neuromorphic sensing, CMOS-compatible integration, and sustainable lead-free nanomaterials are highlighted. By linking nanoscale material design and fabrication processes to device-level performance, this review provides a framework for advancing NP-based IR technologies toward scalable and application-relevant sensing systems. Full article

Accurate field-scale wheat yield estimation is essential for precision agriculture, farm-level decision-making, and food security planning. However, operational studies conducted under real commercial farming conditions in the eastern Mediterranean remain limited. This study investigated whether multi-temporal Sentinel-2 imagery could support reliable wheat yield estimation across nine commercial wheat fields near Ptolemaida, Greece, during the 2023–2024 growing season. Both durum and common wheat fields were included, and combine-harvester yield maps were used as ground-truth observations. Six regression algorithms—the Random Forest (RF), Support Vector Regression (SVR), k-nearest neighbors (KNN), Decision Tree (DT), LASSO regression, and Gaussian Process Regression (GPR) algorithms—were evaluated using three feature configurations: raw Sentinel-2 spectral bands only (Sentinel-only (SO)), spectral bands combined with vegetation indices (Sentinel+Indices, SI), and vegetation indices only (Indices-only, IO). Model generalization was assessed through a strict Leave-One-Field-Out (LOFO) cross-validation protocol, and the method of SHapley Additive exPlanations (SHAP) was used to interpret model behavior and identify the most influential spectral regions and phenological stages. RF achieved the highest predictive accuracy, with a MAPE of 7.90% and an RMSE of 45.15 kg decare⁻¹ under the SO configuration, demonstrating a statistically significant improvement over DT and KNN models ($p<0.05$). SHAP analysis indicated that model predictions were mainly driven by SWIR-1, NIR-narrow, and red-edge bands acquired during late grain filling and maturity, while vegetation indices contributed limited additional information. These findings suggest that raw multi-temporal Sentinel-2 spectral bands are highly effective for field-scale wheat yield estimation within the scope of this study, although further validation across diverse growing seasons and geographic regions is required to confirm broad operational sufficiency. Full article

Monitoring boreal above-ground biomass (AGB) change requires approaches that are both measurement-based and spatially explicit. We integrated permanent sample plots from Quebec and Ontario with Landsat-7 spectral trajectories (1999–2023) to quantify non-fire-related AGB change after excluding wildfire-affected intervals and to evaluate whether annualized AGB change can be predicted from spectral change at the plot-interval scale. Tree height was estimated using a multilayer perceptron model (R² = 0.83) and combined with species-specific allometry to derive plot-level AGB and interval ΔAGB. These estimates were aggregated to ecodistricts using effective sample sizes and confidence intervals. Across well-sampled ecodistricts, mean annualized ΔAGB ranged from −0.82 to +3.54 t ha⁻¹ yr⁻¹, with lower or negative changes mainly occurring in eastern regions. Spectral indices derived from NIR–SWIR bands showed relatively stronger associations with ΔAGB than greenness-based indices, consistent with the sensitivity of moisture- and disturbance-related metrics to canopy stress, including defoliation. An XGBoost ensemble correctly predicted the direction of change in 77% of intervals. These results provide a measurement-constrained and scalable framework for monitoring non-fire-related biomass change and supporting greenhouse-gas reporting across boreal forest landscapes. Full article

Geographical origin identification of fragrant pears is crucial for ensuring fruit quality, protecting regional brand value, and maintaining market order. However, pears from different origins often exhibit highly similar appearance and physicochemical properties, making rapid and nondestructive identification challenging for traditional methods. This study proposes a hyperspectral origin identification method based on an enhanced one-dimensional convolutional neural network (ECNN-1D) incorporating an Efficient Channel Attention (ECA) mechanism, using visible–near-infrared (Vis–NIR) and short-wave infrared (SWIR) spectral data. To address the technical challenges of highly similar spectra, redundant features, and complex information distribution, ECNN-1D enhances discriminative spectral feature representation, overcoming limitations of conventional machine learning and standard deep learning models in feature extraction and classification stability. Systematic comparisons with machine learning models (LDA, RF, KNN, SVM) and deep learning models (VGG-1D, ResNet-1D, CNN-1D) showed that while all models performed well on Vis–NIR spectra, ECNN-1D achieved the highest test accuracy of 98.94% and F1 score of 98.95% on the more challenging SWIR spectra, outperforming other approaches. These results indicate that ECNN-1D enables high-precision, nondestructive origin identification of fragrant pears, with potential cost advantages, providing a reliable technical solution for fruit traceability and quality supervision. Full article