Search Results (117)

Husk tomato (Physalis ixocarpa Brot.) is a crop of major economic, cultural, and nutritional importance in Mexico and exhibits substantial genetic and morphological diversity. Characterizing this variability is essential for both germplasm conservation and breeding programs. During the spring–summer 2024 growing season, 28 husk tomato populations were evaluated at the Bajío Experimental Station (INIFAP), Guanajuato, Mexico, using a completely randomized design with 12 replications. Forty-one traits were assessed following UPOV and IPGRI descriptors. Cluster analysis, canonical discriminant analysis, and the ESIM selection index were applied. A total of 77 morphotypes were identified, exhibiting variation in 33 of the 41 evaluated traits, mainly related to growth habit, leaf morphology, fruit traits, and calyx attributes. Correspondence analysis revealed a close relationship between vegetative growth and fruit size. Cluster analysis clustered the morphotypes into six clusters with no clear geographic structure, suggesting extensive gene flow. Canonical discriminant analysis explained 94.65% of the total variation, identifying seed size, leaf dimensions, and number of anthers as key discriminant traits. The ESIM index highlighted six morphotypes with favorable agronomic and morphological combinations. These results provide a practical basis for the selection of parental materials in husk tomato breeding programs under diverse agroecological conditions. Full article

Operational agricultural monitoring in the Central European lowlands requires timely parcel boundaries; however, unmarked field edges produce minimal spectral contrast in single-date imagery. Previous works demonstrated that harmonic NDVI encoding enables zero-shot field delineation using foundational models, but the influence of the spectral index choice on temporal boundaries remained unquantified. This study systematically evaluates eleven vegetation indices—NDVI, GNDVI, NDRE, EVI, EVI2, SAVI, MSAVI, NDWI, CIg, CIre, and NDYVI—within a fixed harmonic phenology encoding pipeline. A one-year PlanetScope time series (15 × 15 km, Slavonija, Croatia) was decomposed via annual sinusoidal regression to extract per-pixel phase, amplitude, and mean parameters. These harmonic descriptors were mapped to HSV colour channels and segmented using the Segment Anything Model without fine-tuning. Official agricultural parcels (PAAFRD, 2025) provided ground truth for pixel-wise, object-wise, and size-stratified evaluation. Performance stratified into three tiers based on object-wise metrics. Soil-adjusted and enhanced-greenness indices (MSAVI, EVI, EVI2, and SAVI) achieved F1 = 0.51–0.52, and mIoU = 0.70–0.71, statistically outperforming standard ratio formulations (NDVI: F1 = 0.49) and chlorophyll indices (CIg, CIre: F1 = 0.45–0.47). Pixel-wise scores remained compressed (F1 > 0.88 across all indices), indicating consistent interior coverage but index-dependent boundary precision. Error analysis revealed scale-dependent patterns: merging dominated small parcels (<10,000 m²), while fragmentation increased with parcel size. Results demonstrate that spectral formulation is a systematic design factor in phenology-based delineation, with soil background correction and dynamic range compression improving seasonal trajectory separability. The harmonic parameters generated by this framework provide feature-ready input for crop classification, suggesting that integrated boundary extraction and crop mapping workflows merit further investigation. Full article

This research investigates the relationships between the parameters of the GR4J hydrological model and a set of morphometric descriptors, climatic indices, land-cover characteristics, and soil properties across the Caquetá River Basin (Colombia). Twelve limnimetric–limnographic gauges with consistent records for the period 2001–2022 were selected for model calibration and validation. The corresponding sub-watersheds were delineated and characterized in terms of geomorphometry, vegetation cover, and soil permeability. According to that, the morphometric assessment focused on estimating key geomorphometric parameters, while land-cover descriptions utilized NDVI data. Soil type identification was based on the average approximate permeability across each analyzed sub-watershed. Model calibration was performed using the Differential Evolution Markov Chain (DE-MC) algorithm with 8000 simulations, forced by CHIRPS satellite precipitation and ERA5 potential evaporation data. Relationships between GR4J parameters and watershed attributes were assessed using Spearman’s rank correlation and curve-fitting analyses. The results reveal strong and consistent relationships between GR4J parameters (X1–X4) and key morphometric variables, including basin perimeter, circularity ratio, main channel length, and channel slope. Coefficients of determination ranged from 0.80 to 0.98, highlighting the potential for parameter regionalization based on physiographic and environmental descriptors. Full article

Global Digital Elevation Models (DEMs) exhibit systematic biases constrained by acquisition geometry and surface penetration. This study aims to evaluate whether the increasing complexity of geometric deep learning (e.g., Graph Neural Networks, GNNs) is justified by performance gains over established feature engineering paradigms (e.g., XGBoost) under the constraints of sparse altimetry supervision. We established a rigorous comparative framework across four mainstream products—ALOS World 3D, Copernicus DEM, SRTM GL1, and TanDEM-X—using Sichuan Province, China, as a representative natural laboratory. Our results reveal a fundamental scale mismatch (where the ~485 m average spacing of sampled altimetry footprints dwarfs the local terrain resolution): despite their topological complexity, Hybrid GNN models fail to establish a statistically significant accuracy advantage over the systematically optimized XGBoost baseline, demonstrating RMSE parity. Mechanistically, we uncover a critical divergence in decision logic: XGBoost relies on a stable “Physics Skeleton” consistently dominated by deterministic features (terrain aspect and vegetation density), whereas GNNs exhibit severe “Attribution Stochasticity” ($\rho \approx$ 0.63–0.77). The GNN component acts as a residual-dependent latent feature learner rather than discovering universal topological laws. We conclude that for geospatial regression tasks relying on sparse supervision, “Physics Trumps Geometry.” A “Feature-First” paradigm that prioritizes robust, domain-knowledge-based physical descriptors outweighs the indeterminate complexity of “Black Box” architectures. This study underscores the imperative of prioritizing explanatory stability over marginal accuracy gains to foster trusted Geo-AI. Full article

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article

Intraspecific phenotypic diversity in clonally propagated crops is frequently constrained by narrow domestication histories and the widespread use of a limited number of elite cultivars. In Stevia rebaudiana, commercial production has largely centred on cv. ‘Morita II’, raising concerns about reduced diversity and adaptive potential. This study characterised and structured phenotypic diversity within a segregating population derived from ‘Morita II’ under tropical field conditions. Eighty-six progeny-derived genotypes (clonally propagated) plus the commercial control (87 genotypes total) were evaluated using 25 agromorphological descriptors (qualitative and quantitative). Quantitative traits showed broad variation, including plant height (28.26–119.50 cm) and dry yield rate (0.94–28.55 g plant⁻¹). Multivariate analyses of mixed descriptors (PCA and hierarchical clustering based on Gower distance) identified plant architecture, vegetative growth, and phenology as the main sources of differentiation. The first two principal components explained 19.65% and 12.58% of total phenotypic variance, respectively (32.23% cumulative). Hierarchical clustering (UPGMA; dissimilarity cut-off = 0.25) resolved four phenotypic groups (GI–GIV) with sizes n = 3, 1, 66, and 17, respectively, enabling the definition of contrasting ideotype candidates based on recurrent trait combinations. These results provide a quantitative baseline for phenotypic structuring, prioritization of materials for further evaluation, and management of clonal stevia collections in tropical production systems. These ideotype candidates should be considered preliminary until validated across environments and linked to chemical quality traits. Full article

Vegetation indices are used to analyze vegetation dynamics and primary productivity. However, most studies rely on short time series and peak or integral metrics, which limit the understanding of long-term vegetation dynamics in heterogeneous areas. This study aimed to classify a subarea of northeastern Mexico using a 25-year EVI time series and to characterize the resulting groups using growth parameters derived from temporal analysis. MODIS EVI mosaics from 2000 to 2024 were averaged and classified using the ISODATA algorithm, resulting in 16 groups. Smoothed EVI time series were analyzed with TIMESAT to extract growth parameters, which were compared among groups using Discriminant Function Analysis with cross-validation. Minimum primary productivity expressed as EVI base value (BVAL) explained most of the observed variance among groups (70.7%). The classification exhibited robust statistical separability, achieving a cross-validated accuracy of 75.1% (κ = 0.73), and showed mesoscale spatial structure (~12.5 km). The groups had moderate but significant associations (Cramer’s V = 0.33) with existing vegetation and climate cartography. The results suggest that long-term BVAL is a stable and ecologically meaningful descriptor of landscape functioning. Overall, the proposed classification captures gradients and transition zones not represented in static cartographic products, revealing vegetation dynamics across heterogeneous landscapes. Full article

Combustion duration is a fire behaviour feature relevant for both the effects and management of fire. We burned small-scale laboratory fuel beds (n = 135) of eight fuel types and developed empirical models to describe variation in flame residence and burn-out times, and fuel mass fraction loss rates during flaming and non-flaming combustion; each fuel sample was ignited at once and burned as a pile. Surface area-to-mass ratio of the fuel particles, by itself, allowed accurate prediction of all combustion properties with better performance than surface area-to-volume ratio. Fuel bed structure was also shown to have an influence, fuel load being the variable that further improved all predictions. This work provides evidence that surface area-to-mass ratio is an adequate descriptor of the combustion characteristics of forest fuel beds. Our expectation is that this approach will assist future modelling efforts to obtain simple empirical models to predict the combustion features of free-spreading fires in a wide range of vegetation types. Full article

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM₁₀ (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM₁₀ surface estimation. This study is an assessment of a national-scale, daily PM₁₀ estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM₁₀ observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), ozone (O₃), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R²) of 0.739 in validation and R² = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m³, and root mean square error (RMSE) was 3.98 µg/m³. The results indicate a slight tendency to overestimate PM₁₀ concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM₁₀ prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM₁₀ concentrations across monitoring stations, with R² ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection. Full article

This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies. 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

Vegetable consumption in childhood remains below recommendations worldwide, particularly in disadvantaged socioeconomic groups. Building on prior work showing no socioeconomic status (SES) differences in children’s liking of familiar vegetables, this study examined whether their sensory and emotional descriptions vary by SES and how these relate to liking beyond hedonic ratings. A total of 363 Chilean fourth graders (9–10 years) from five SES groups evaluated eight vegetables at school. For each sample, children rated overall liking (7-point facial hedonic scale) and completed two CATA (Check-All-That-Apply) tasks: a child-derived sensory list (13 terms) and a validated emoji-based emotion list (33 items). Data were analyzed using Cochran’s Q tests, correspondence analyses, and mean-impact analyses. The use and diversity of sensory and emotional descriptors differed significantly between socioeconomic groups (p < 0.05): children from higher SES levels employed a broader and more differentiated vocabulary, while those from lower SES backgrounds used fewer significant terms. Across the sample, juicy, fresh, and mild flavors increased liking, whereas strong aroma decreased it (p < 0.05); positive emojis increased liking, whereas negative and neutral ones had no effect. These findings reveal that perceptual and affective representations are socially patterned, underscoring the need to foster sensory–affective literacy in lower-SES contexts. Full article

Persistent cloud cover during the growing season and mosaic cropping patterns introduce temporal gaps and mixed pixels, undermining the reliability of large-scale crop identification and acreage statistics. To address these issues, we develop a high spatiotemporal-resolution remote-sensing approach tailored to heterogeneous farmlands. First, an improved Spatiotemporal Adaptive Reflectance Fusion Model (STARFM) is used to fuse Landsat, Sentinel-2, and MODIS observations, reconstructing a continuous Normalized Difference Vegetation Index (NDVI) time series at 30 m spatial and 8-day temporal resolution. Second, at the field scale, we derive phenological descriptors from the reconstructed series—key phenophase timing, amplitude, temporal trend, and growth rate—and use a Random Forest (RF) classifier for detailed crop discrimination. We further integrate SHapley Additive exPlanations (SHAP) to quantify each feature’s class-discriminative contribution and signed effect, thereby guiding feature-set optimization and threshold refinement. Finally, we generate a 2024 crop distribution map and conduct comparative evaluations. Relative to baselines without fusion or without phenological variables, the fused series mitigates single-sensor limitations under frequent cloud/rain and irregular acquisitions, enhances NDVI continuity and robustness, and reveals inter-crop temporal phase shifts that, when jointly exploited, reduce early-season confusion and improve identification accuracy. Independent validation yields an overall accuracy (OA) of 90.78% and a Cohen’s kappa(κ) coefficient of 0.882. Coupling dense NDVI reconstruction with phenology-aware constraints and SHAP-based interpretability demonstrably improves the accuracy and reliability of cropping-structure extraction in complex agricultural regions and provides a reusable pathway for regional-scale precision agricultural monitoring. Full article

High-resolution lithological mapping of outcrops is fundamental for reservoir characterization and petroleum geology, yet distinguishing lithologies with subtle petrophysical contrasts remains a major challenge. This study proposes MC-H-Geo, a multi-scale contextual hierarchical framework for automated lithology classification from terrestrial laser scanning (TLS) point clouds. The framework integrates three modules: (i) a multi-scale contextual feature engine that extracts spectral, geometric, and textural descriptors across local and stratigraphic contexts, enhanced by cross-scale differentials to capture stratigraphic variability; (ii) a gated expert classifier with task-adaptive feature subsets for hierarchical vegetation–rock and intra-rock discrimination; and (iii) a two-step geological post-processing procedure that enforces stratigraphic continuity through Z-axis correction and neighborhood smoothing. Experiments on the Qianwangjiahe outcrop (Ordos Basin, China) demonstrate state-of-the-art performance (OA = 94.3%, Macro F1 = 0.944), outperforming PointNet++ (77.1%), SG-RFGeo (74.2%), and XGBoost (61.7%). Error analysis reveals that residual sandstone–vegetation confusion results from feature aliasing in weathered zones, highlighting the intrinsic limitations of TLS-only data. Overall, MC-H-Geo establishes an advanced framework for fine-grained lithological mapping and identifies multi-sensor data fusion as a promising pathway toward robust, geologically consistent outcrop interpretation. Full article

Corylus avellana L. is a rare and endangered species in Kazakhstan, included in the national Red Book. The results of morphological and genetic characterization of the sole known natural population of C. avellana in the Western Kazakhstan region are presented in this study. Sixty wild accessions were evaluated based on tree and leaf morphological traits using standard descriptors in accordance with Bioversity International guidelines. Genetic diversity was assessed using ten nuclear simple sequence repeat (SSR) markers. A total of 120 alleles were detected across the nuclear loci, with the number of alleles per locus ranging from 9 to 16 and an average of 12. The mean effective number of alleles (Ne) per locus was 3.862. A high level of intraspecific polymorphism was observed, with an average observed heterozygosity (Ho) of 0.70. The population showed considerable genetic diversity, as highlighted by a mean Shannon’s diversity index of 1.526. STRUCTURE, PCoA, and phylogenetic analyses confirmed strong differentiation between the wild Kazakh population and the cultivated hazelnut germplasm. Due to the lack of viable seeds, in vitro conservation was initiated using vegetative shoots. A two-step disinfection protocol, involving Plant Preservative Mixture and mercuric chloride, significantly improved explant survival, enabling successful establishment of an aseptic in vitro collection. These findings highlight the urgent need for targeted conservation strategies and show the potential of biotechnological approaches for safeguarding Kazakhstan’s only natural C. avellana population. Full article