Search Results (1,366)

Since its launch in 2015, the Sentinel-2 mission has become a cornerstone of moderate-resolution vegetation monitoring, enabling spatially explicit and temporally dense observations of terrestrial ecosystems. Its combination of 10–20 m spatial resolution, a revisit interval of less than five days, and a spectral configuration including red-edge and Short-Wave Infrared (SWIR) bands has transformed optical vegetation monitoring beyond coarse-resolution greenness products. This review synthesises the use of Sentinel-2 for vegetation monitoring, with emphasis on phenology and growth dynamics, biomass and carbon estimation, vegetation stress detection, and associated methodological developments. A systematic Scopus search identified 1700 publications, of which 1097 studies were retained following thematic and methodological screening. The results reveal rapid growth in Sentinel-2-based research after 2018, reflecting its transition into a widely adopted data source supported by cloud-based processing platforms and harmonised data products. Research output is concentrated in a limited number of journals and regions, with Europe and Asia dominating contributions, while other regions remain underrepresented. Phenology and growth monitoring, biomass and carbon assessment, and vegetation stress analysis emerged as the principal application domains. Across these themes, methodological development has shifted from vegetation indices towards machine learning, hybrid radiative-transfer modelling, and multi-sensor data fusion. The reviewed evidence indicates that no single methodological approach consistently outperforms others; rather, performance depends on the target variable, ecosystem characteristics, and the treatment of observational uncertainty. Sentinel-2 has transformed vegetation monitoring by enabling spatially explicit assessment of vegetation phenology, biomass, carbon dynamics, and stress across ecosystems. However, important challenges remain, including uncertainty propagation, limited sensitivity to early physiological stress, the absence of thermal observations, and uneven validation across ecosystem types. Future progress will depend on uncertainty-aware retrieval frameworks, physically informed hybrid models, multi-sensor integration, and expanded calibration and validation across underrepresented ecosystems. Full article

The white peach scale, Pseudaulacaspis pentagona (Targioni-Tozzetti) (Hemiptera: Diaspididae), is a highly polyphagous insect pest of global phytosanitary relevance affecting fruit trees, perennial crops, and ornamental plants in temperate and subtropical regions. Its invasive success is largely driven by high thermal tolerance, a wide host range, and strong survival capacity under postharvest handling and cold-chain storage, which greatly enhances its potential for long-distance dispersal through international trade of fresh fruit and plant material. A systematic review and meta-synthesis of 105 scientific studies published between 1958 and 2026 identified key research areas, including geographic distribution and invasion pathways, host plants and varietal susceptibility, temperature-dependent life history, agricultural impact and quarantine risk, chemical and biological control strategies, and the development of phenology-based monitoring and prediction tools using thermal models and pheromones. Although recent official reports do not confirm the presence of P. pentagona in Ecuador, climate suitability modeling and evidence of long-term survival during cold storage indicate a high risk of introduction and establishment, particularly in inter-Andean valleys. Consequently, preventive phytosanitary surveillance and integrated pest management strategies tailored to Ecuadorian Andean fruit production are proposed, emphasizing phenological monitoring, nursery and agro-urban inspections, and the conservation of natural enemies as a foundation for sustainable management of this emerging pest. Full article

Ginkgo biloba is a dioecious species valued for landscaping and medicine, but rapid sex identification outside the flowering and fruiting stages remains challenging. Hyperspectral imaging offers a potential solution, though whether spectral sex markers are stable across phenological stages and can support a single year-round model is unclear. Here, leaf hyperspectral reflectance (400–1000 nm) was acquired from mature G. biloba at flowering (n = 360), green-leaf (n = 1395), and yellow-leaf (n = 243) stages. Sex identification models were built using multiple machine learning classifiers, with leaf flavonoid content as biochemical validation. Stage-specific models achieved optimal test accuracies of 96.67% (flowering, MSC + PLS-DA), 95.77% (green-leaf, raw spectra + LDA), and 97.12% (yellow-leaf, MSC + LDA). However, discriminative bands shifted from the visible (520–690 nm) at the green-leaf stage to the near-infrared (700–1000 nm) at the yellow-leaf stage, with almost no bands shared across all stages. Furthermore, cross-stage prediction accuracy dropped to near-chance levels (~50%). As t-SNE analysis revealed, the universal model had learned phenological rather than sex-specific information. In addition, flavonoid measurements revealed a highly significant sex × stage interaction (p < 0.001) and a reversal of the sex difference between green-leaf (male > female) and yellow-leaf (female > male) stages. Thus, the spectral and biochemical sex markers examined in this study varied substantially with phenology, and stage-specific models are required for practical sex identification in G. biloba. Full article

Leaf nitrogen content (LNC) is an important physiological indicator for evaluating citrus nutritional status, photosynthetic capacity, and fertilization demand. However, conventional LNC determination mainly relies on field sampling and laboratory chemical analysis, which are destructive, time-consuming, labor-intensive, and limited in spatial continuity, making them unsuitable for large-scale real-time nitrogen monitoring in complex orchard environments. To achieve rapid and non-destructive estimation of citrus LNC, this study developed a UAV multispectral inversion framework integrating object-based canopy extraction and machine learning models. Field experiments were conducted in a citrus orchard in western Hubei Province, China. Multi-temporal UAV multispectral images were collected from April to October 2025, and ground measurements of citrus LNC were collected simultaneously. First, minimum distance classification (MDC), maximum likelihood classification (MLC), and object-based image analysis (OBIA) were used for land-cover classification of citrus orchard images, and their canopy extraction performance under complex orchard backgrounds was compared. Subsequently, multiple vegetation indices were calculated from the extracted citrus canopy spectra, and sensitive spectral features were selected through correlation analysis. Finally, seven models, including simple linear regression, quadratic regression, partial least squares regression (PLS), back propagation neural network (BP), extreme learning machine (ELM), particle swarm optimization-extreme learning machine (PSO-ELM), and particle swarm optimization-back propagation neural network (PSO-BP), were constructed to systematically evaluate the inversion performance of citrus LNC across the entire growth period. The results showed that: (1) OBIA achieved higher classification accuracy and temporal stability in citrus orchard land-cover classification, with overall accuracy ranging from 68.86% to 85.65% and Kappa coefficients ranging from 0.56 to 0.72, outperforming MDC and MLC. This indicates that OBIA can effectively reduce the interference of bare soil, grass, shadows, and other non-target objects on canopy spectral extraction. (2) The correlations between vegetation indices and LNC varied markedly among different growth stages, suggesting that the spectral response of citrus LNC has strong phenological dependence and that a single vegetation index is insufficient to stably characterize LNC variation across the whole growth period. (3) At the whole-growth-period scale, multi-index fusion models outperformed single-index models, among which EVI, TVI, and MTVI showed relatively strong cross-stage sensitivity. (4) Optimized machine learning models generally outperformed traditional regression models and unoptimized machine learning models. Among them, PSO-BP achieved the best performance, with a validation R² of 0.68 and an RMSE of 1.54 g kg⁻¹, representing an increase in R² of 23.64% compared with the PLS model and 25.93% over the baseline BP model in terms of R². Overall, this study demonstrates that OBIA-based canopy spectral quality improvement combined with PSO-optimized machine learning can effectively improve the stability and reliability of UAV multispectral estimation of citrus LNC under complex orchard backgrounds. The proposed framework provides technical support for citrus nitrogen diagnosis, precision fertilization, and intelligent orchard management. Full article

Quantifying dust aerosol radiative impacts on crop growth in arid regions is challenging due to sparse ground observation networks for photosynthetically active radiation (PAR). Conventional meteorological stations only provide regional-averaged solar radiation and fail to capture fine spatial heterogeneity and instantaneous attenuation caused by dust storms. To address this gap, this study developed a coupled framework integrating WRF-Chem, LibRadtran, multi-source remote sensing, and interpretable machine learning. We combined field sampling data, remote sensing products, and atmospheric simulations to explore how dust aerosol direct radiative forcing alters cotton gross primary productivity (GPP) and yield across the Weigan River Basin, Xinjiang, China. Results revealed significant PAR reduction induced by dust in 87% of cotton fields (p < 0.05). Dust presented a dual effect: it reduced photosynthetic productivity via radiation attenuation, while alleviating heat stress above 35 °C. SHAP analysis demonstrated that cotton GPP and yield declined nonlinearly when daily PAR loss exceeded 20 W·m⁻², with the flowering-to-boll stage (July–August) identified as the most sensitive phenological window. This study verifies the necessity of combining atmospheric models and remote sensing for fine-scale assessment of dust radiative effects in data-scarce regions. The identified threshold provides practical references for targeted field management in arid cotton areas. Full article

Accurate and efficient soybean yield prediction is essential for ensuring food security and optimizing agricultural management. Remote-sensing approaches based on a single phenological stage or sensor may not fully capture the physiological and structural dynamics associated with soybean yield formation, thereby constraining prediction accuracy under field-level spatial heterogeneity. This study therefore developed and evaluated an integrated multi-source and multi-temporal UAV-based framework for plot-scale soybean yield prediction under production-field conditions. A UAV platform equipped with multispectral, RGB, and LiDAR sensors was used to extract multidimensional remote-sensing features at four key growth stages: beginning pod, full seed, beginning maturity, and full maturity. By integrating spectral indices, texture metrics, and three-dimensional canopy structural parameters, seven machine-learning algorithms—Ridge, LASSO, Random Forest, MLP, LightGBM, XGBoost, and CatBoost—were evaluated under single-temporal and multi-temporal scenarios. Nonlinear tree-based ensemble models generally achieved higher predictive accuracy than the linear models. On the independent test set, CatBoost performed best under the full multi-temporal, three-sensor fusion scenario, with an R² of 0.816 and an RMSE of 245 kg ha⁻¹. Among individual growth stages, the full seed stage (R6) produced the highest single-stage prediction accuracy. Three-sensor fusion improved prediction relative to the single-source multispectral scheme, and integrating features across phenological stages further improved performance by representing cumulative crop-growth dynamics. The proposed framework provides methodological support for UAV-based precision management and data-driven decision-making in soybean production. Full article

Representatives of the genus Dendrolimus are major conifer defoliators whose variable developmental duration complicates phenological forecasting, outbreak prediction, and pest-management timing. This review synthesizes evidence on polymorphism in ontogenetic duration in Dendrolimus and frames it at three levels: intrapopulation, interspecific, and spatial–geographical. We distinguish biological generation time, the interval from oviposition to adult emergence, from calendar span, the number of calendar years occupied by one generation; this distinction is essential for interpreting two- and three-calendar-year cycles. The evidence indicates that diapause is the main physiological mechanism generating alternative developmental trajectories, but its expression is shaped by interacting photoperiodic, thermal, trophic, density-dependent, and physiological factors. The strongest evidence for natural intrapopulation splitting is available for the Siberian moth in which fast and delayed trajectories appear associated with facultative summer diapause, or a diapause-like state. Key gaps remain concerning the induction cues, physiological status, molecular regulation, and field frequency of this state. We argue that models should incorporate delayed fractions, fast/delayed cohort ratios, and adult-emergence overlap to improve monitoring, forecasting, and control of Dendrolimus pests, especially under climate warming and during the early stages of outbreaks. Full article

Climate change poses an increasing threat to viticulture sustainability, particularly in traditional wine regions, where varietal regulations restrict the adoption of new cultivars. Elevated temperature and water deficit may alter anthocyanin accumulation and the broader phenolic profile of ripening berries, directly affecting wine colour, structure, and quality. In this context, exploring intra-varietal diversity may help to identify clones with a favourable phenolic profile that could contribute to adaptation strategies within existing varietal regulations. We report the first comprehensive monomeric phenolic profiling, by UHPLC-QqQ-MS/MS, of grape berries from eleven Vitis vinifera L. ‘Tempranillo Tinto’ clones (two seasons) and seven ‘Graciano’ clones (three seasons), from old vineyards in La Rioja (Spain). Six phenolic classes were quantified: hydroxycinnamic acids, hydroxybenzoic acids, flavonols, flavanols, stilbenes, and anthocyanins. Multivariate analysis of variance, linear mixed-effects models, principal component analysis, and hierarchical cluster analysis were used to characterize clonal diversity. Significant clone effects were detected for all six phenolic classes in ‘Graciano’ and four in ‘Tempranillo Tinto’. In ‘Graciano’, clone GR_1250 was distinguished by elevated flavonol and anthocyanin levels, whereas GR_1265 showed the highest flavanol and hydroxybenzoic acid content. In ‘Tempranillo Tinto’, clone TT_1041 stood out for its higher flavanol and hydroxybenzoic acid concentrations, whereas TT_767 had elevated anthocyanin levels. Two to four distinct phenotypic clusters were identified within each variety. These results confirm substantial intra-varietal phenolic diversity in both cultivars and identify clonal selections with differential phenolic profiles that may be useful for quality-oriented clonal selection, particularly when integrated with agronomic, phenological, sanitary and wine-level information. Full article

Shifting climatic conditions challenge simplified, yield-oriented agriculture, particularly in fragile mountain regions. In the Ecuadorian Andes, agriculture functions as a socio-ecological system shaped by biocultural relationships integrating production, agrobiodiversity, and farmer decision-making. This study examines how climate variability interacts with crop phenology and management practices within ancestral associated cropping systems (chakras). The analysis focuses on maize, common bean, faba bean, and potato during the 2024–2025 agricultural cycle in Imantag, Ecuador. A total of 30 native, introduced, and improved varieties were sown in traditional rows (wachos) and monitored within a single 420.8 m² Andean chakra. Using multiple ordinary least squares (OLS) regression (n = 30; R² = 0.440, adj. R² = 0.351, F (4,25) = 4.914, p = 0.005), results show that maize significantly outperformed the faba bean reference group (β = 20.34, p = 0.017), while common bean showed intermediate performance (β = 15.31, p = 0.048). Seed mass at planting was positively associated with relative yield (β = 6.71, p = 0.069), highlighting early-stage management decisions. Elevated maximum temperatures during maturation negatively affected yield (r = −0.42, p = 0.020), while accumulated precipitation had a positive effect (r = +0.45, p = 0.014). Model quality criteria, including VIF diagnostics (max. VIF = 3.37), Shapiro–Wilk residual normality test (W = 0.983, p = 0.896), and cross-validation using Ridge and Lasso regression, confirm the robustness of the statistical findings. These findings demonstrate that chakras function as adaptive socioecological systems that enhance productivity, agrobiodiversity conservation, and resilience under climate variability. Full article

Understanding vegetation phenology is critical for assessing the impacts of climate change, particularly in regions vulnerable to environmental fluctuations. This study investigates the temporal trends and spatial variability of the start of photosynthetic activity (SOP) in natural vegetation across Central Asia over the past four decades (1982–2022) using satellite-derived Normalized Difference Vegetation Index (NDVI) data. The research emphasizes the critical role of vegetation phenology in understanding responses to climate change. To extract spring phenological data, various smoothing techniques were applied, including filter-based methods, asymmetrical Gaussian fitting, and three nonlinear and piecewise linear methods, ensuring accurate representation from continuous time series data. NDVI time series were smoothed using a phenological extraction package. The results indicate an average advance in SOP of 1.26 days per decade, with forest ecosystems exhibiting the greatest shift at 3.05 days per decade. A spring temperature threshold near 0 °C was identified as a reliable predictor for dormancy break. Ensemble Empirical Mode Decomposition (EEMD) was utilized to differentiate between cumulative and instantaneous trends, revealing dynamic phenological responses. A notable shift around 2005 was observed, with approximately 76.28% of pixels showing a change in SOP trend, while 23.60% displayed a stable, monotonic trend. Vegetation at elevations between 1500 and 3000 m experienced a significant SOP advance of 2.27 days per decade, whereas vegetation above 3000 m showed no significant change over the study period. Spatially, SOP trends exhibited a latitudinal gradient, with delays observed in the southern regions and advancements north of 45° N. These findings underscore the importance of developing region-specific phenological models to inform environmental management and climate adaptation strategies, particularly in arid and semi-arid ecosystems. Full article

Highly perishable strawberries demand strict post-harvest time management, making accurate short-term yield prediction central to optimizing modern greenhouse production and supply chain scheduling. However, existing models that rely excessively on isolated environmental factors exhibit delayed responsiveness to actual crop physiological dynamics and struggle with integrating multimodal data. To overcome these limitations, we propose a short-term method for predicting greenhouse strawberry yield that integrates visual phenology with meteorological sequences. The proposed method was validated using a multimodal dataset acquired from 150 tracked greenhouse strawberry plants over a 72-day monitoring period (11 December 2025, to 20 February 2026), incorporating continuous microclimate records and an image repository of 784 original images annotated into five distinct phenological classes (flower, green, white, pink, and red). First, using our improved YOLO11-SC model, we effectively resolve challenges of complex illumination and dense foliage occlusion, achieving high-precision automated extraction of five consecutive strawberry phenological stages. Second, by fusing these visual markers with meteorological time series (e.g., temperature, humidity, and light intensity), we construct a multimodal spatiotemporal feature matrix. To accommodate diverse smart agriculture application scenarios, we designed two distinct prediction architectures: on servers with ample computing power, a Bidirectional Temporal Convolutional Network with self-attention (BiTCN-SA) to achieve highly accurate predictions; and for resource-constrained IoT edge nodes, a lightweight machine learning ensemble (Stack-LGR). Experimental results demonstrate that, in predicting the cumulative mature fruit yield within the next harvesting cycle, BiTCN-SA achieves strong performance with a coefficient of determination ($R^2$) of 0.958 and a root mean square error (RMSE) of 3.154. Simultaneously, the edge-deployed Stack-LGR ensemble maintains stable prediction accuracy ($R^2$ = 0.892) while ensuring acceptable inference latency. This study mitigates the latency limitations of single-environment-driven models. It provides a solution for precise crop yield prediction and tiered computational deployment, with good predictive performance, deployment adaptability, and methodological reference value. Full article

Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development. Full article

Accurate crop mapping in middle-to-high latitude agricultural basins is essential for food security, agricultural management, and sustainable land-use planning. However, crop classification in these regions remains challenging because fragmented field patterns, mixed pixels, and overlapping phenological stages often lead to severe spectral confusion among major dryland crops. To address this issue, this study developed a Phenology–Spectral Dual-Constrained Strategy (PS-DCS) by integrating agronomic knowledge with physically constrained spectral features. The proposed framework identified August as the optimal observation window based on crop phenological divergence. Wheat was first extracted using a spectral fingerprint combining the Chlorophyll Index Red Edge (CI_RE) and Redness index. Subsequently, maize and soybean were separated within the non-wheat mask using the B6 red-edge band selected through feature separability analysis. Validation based on Sentinel-2 time-series imagery and 1056 independent field samples collected in 2025 yielded an Overall Accuracy of 95.36% with a Kappa coefficient of 0.928. Compared with RF, XGBoost, and CNN models, PS-DCS maintained competitive classification performance while substantially reducing dependence on large training datasets and complex parameter tuning. Cross-year validation during 2022–2024 further demonstrated stable spatial transferability without threshold recalibration. These results indicate that translating agronomic mechanisms into physically interpretable remote sensing rules provides an effective and transparent framework for high-precision crop mapping and long-term agricultural monitoring in complex agricultural landscapes. Full article

In coastal wetlands, Tamarix chinensis often occurs as patches intermixed with Phragmites australis, Suaeda salsa, and saline–alkaline bare soil. This mixed distribution makes tamarisk prone to omission in medium-resolution remote sensing classification, while the recall of the target species is often masked by a relatively high overall accuracy. In this study, we focused on the Yellow River Delta National Nature Reserve and developed a multi-source feature set using summer 2025 Sentinel-2, Sentinel-1, and UAV/GPS data, comprising spectral, SAR, phenological, and red-edge-oriented features. To enhance the separability between tamarisk and co-occurring herbaceous vegetation, we introduced a red-edge–vegetation-cover coupling feature (RE_cov) based on their contrasting responses in the red-edge region. Within an XGBoost framework, we evaluated the marginal contribution of this feature using feature ablation, replacement, and spatial block cross-validation. The full feature set achieved an AUC of 0.8042, a recall of 0.9340, and an overall accuracy of 0.8194 on an independent test set. Ablation and replacement experiments showed that the red-edge-oriented features contributed to both model separability and tamarisk recall, and this contribution remained evident under spatial block validation. We further converted the pixel-level extraction results into local tamarisk density grades, revealing a pattern of a few clustered cores embedded within a broad low-density background. These results suggest that target-species-oriented red-edge–vegetation-cover coupling features can improve tamarisk recall while maintaining acceptable overall accuracy, providing a spatial product to support zoned patrol and management in protected coastal wetlands. Full article

Apolygus lucorum is a major piercing–sucking pest in viticulture, yet its seasonal dynamics and outbreak risk in semi-arid wine-grape regions remain insufficiently understood. This study was conducted in a semi-arid wine-grape region of northwestern China during the 2024–2025 growing seasons. Adult density was monitored at 150 fixed sampling points across five landscape units. LOWESS-based phenological staging, stage-specific spatial interpolation, and an XGBoost-SHAP framework integrating meteorological, topographic, and grape phenological predictors were used to characterize spatiotemporal patterns and key predictors. A. lucorum density remained low in May, increased from June to July, peaked during August-September, and remained relatively high in October, with higher overall abundance in 2025 than in 2024. Spatial analyses revealed marked heterogeneity among landscape units, with high-density patches shifting across years and phenological phases. The XGBoost model showed good predictive performance, with an R² of 0.878 on the independent test set and a mean GroupKFold cross-validation R² of 0.869 ± 0.014. SHAP analysis identified grape phenology, elevation, relative humidity, sunshine duration, and temperature as the leading predictors of model-predicted density. PDP and ICE analyses showed higher predicted counts during later phenological periods, at lower elevations, and under higher relative humidity, particularly around 55%. Two-dimensional PDPs further indicated that high predicted densities mainly occurred under combinations of higher relative humidity, later phenological timing, moderate-to-high temperature, longer sunshine duration, and lower elevation. These findings provide a scientific basis for implementing precision-integrated pest management strategies in semi-arid viticultural regions, where monitoring relative humidity during critical phenological windows can serve as an early warning indicator for impending outbreaks. Full article