Search Results (1,545)

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

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

Climate variability represents a major threat to agricultural systems, particularly in arid and semi-arid regions such as the Beni-Amir irrigated perimeter, located in the Tadla plain in central Morocco. In this perimeter, olive trees are exposed to multiple environmental and management-related factors that are associated with variations in phenology and vegetation vigor, such as successive drought episodes. This study represents a spatio-temporal assessment of drought impact on olive using satellite- derived vegetation indices from Sentinel-2 imagery and precipitation satellite data from CHIRPS over the period 2015–2024. The Standardized Precipitation Index (SPI-12) was used to identify wet and dry phases over this period. The results indicate an alternation of dry and wet periods between 2015 and 2021, followed by a predominance of dry conditions from September 2021. Over the same period, the time series of the Normalized Difference Vegetation Index (NDVI) and the other vegetation indices reveals marked interannual variability and a progressive degradation of olive tree phenological cycles. A land cover map derived from a supervised support vector machine (SVM) under three classification scenarios achieved high overall accuracies exceeding 94%. Post-classification change detection highlights a substantial reduction in mapped olive-growing areas between 2016 and 2024, with an estimated 72% loss of the initial area. The findings reported in this study indicate that the succession of drought episodes may have contributed to olive grove degradation, including disruptions in phenological cycles and a decline in maximum NDVI values. Even the most resilient olive groves appeared affected following the severe drought period after 2021. The study underscores the usefulness of satellite-derived vegetation indices and drought indicators for the effective monitoring of drought-related stress and supporting improved management practices under climate change. Full article

Adequate fertilization is essential for optimizing soybean productivity and seed quality, while supplementary fertilization plays a key role in correcting nutrient deficiencies and supporting plant performance under varying environmental conditions. This study evaluated the effects of the foliar application of macro- and micronutrients enriched with free amino acids and Ascophyllum nodosum extract applied at two phenological stages—six fully developed trifoliate leaves (V6) and beginning flowering (R1)—on soybean grain yield, protein content and oil content. Experiments were conducted over two growing seasons at ARDS Turda, Romania, using a randomized complete block design with three replications to compare three fertilization treatments: basic mineral fertilization (control), mineral fertilization supplemented with foliar application at the V6 vegetative stage, and mineral fertilization supplemented with foliar application at the R1 reproductive stage. Basic fertilization was performed before sowing using granulated nitroclacium at a rate of 100 kg ha⁻¹. Foliar fertilization included Naturamin WSP applied at a rate of 0.5 kg ha⁻¹ and Pleniflor and Naturfruit at a rate of 2 L ha⁻¹. Foliar fertilization at the V6 stage significantly improved grain yield in most cultivars, with increases ranging from 1% to 18%, particularly in the 000 and 00 maturity groups (MGs). Across the maturity groups, the highest average yields were recorded in MG 0 cultivars (up to 2845 kg ha⁻¹). In contrast, foliar application at the R1 stage was more effective in increasing seed protein content, with improvements of up to 11% in early maturity cultivars. Oil content showed only minor and inconsistent responses, with maximum increases of 4% depending on genotype. Linear Discriminant Analysis (LDA) revealed that environmental conditions and genotype explained most of the observed variation (Axis 1 = 89.91%, Axis 2 = 7.79%), indicating that cultivar response to foliar fertilization was strongly influenced by genotype × environment interactions. The effectiveness of foliar fertilization depends on application timing, cultivar maturity group and environmental conditions. 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

Machine-learning landslide-susceptibility maps are almost always judged by inventory-split skill (the area under the receiver-operating-characteristic curve, AUC, and Cohen’s κ), not by model-independent physical observation of where an event caused ground disturbance. For the May 2023 Emilia-Romagna event (>80,000 landslides; RER2023 inventory), we confront an open-data, event-conditioned susceptibility model (trigger rainfall is among its predictors) with a co-event disturbance footprint built from two satellites: phenology-matched Sentinel-2 change in the Normalized Difference Vegetation Index (ΔNDVI) and Normalized Burn Ratio (ΔNBR), and a 12-day Sentinel-1A C-band coherence-and-backscatter layer used as a cloud-independent coverage check (C-band 12-day decorrelation is the a priori expectation in this setting); neither enters the model. Apparent geomorphic plausibility depends critically on how the independent footprint is constructed. Against a raw co-event footprint (contaminated by flooding and agriculture), a model with AUC ≈ 0.945 is indistinguishable from a random mask. On a landslide-relevant footprint, the same model captures optically detectable disturbance at roughly twice the chance (bootstrap median 1.94×, 95% CI 1.25–2.72, excluding 1; ≈1.46× above a vegetation-phenology null), but the aggregate is driven by an upper-tail minority of 10 km blocks—the majority of blocks have per-block median lift 0.67× (below chance). The map is therefore a regional, not a per-place, statement. Results are a within-event consistency test; cross-event transferability is not claimed. Footprint construction is decisive and currently neglected. The open-source pipeline is released upon acceptance. Full article

Urbanization alters the local growth environment of vegetation, including local thermal regimes, moisture availability, and human activity intensity. Yet, the gradient-dependent changes in vegetation phenology and the factors responsible for these changes are still not fully clarified. Taking Beijing as the study area, this study used MOD13Q1 EVI records from 2005 to 2024 to reconstruct annual vegetation trajectories with Savitzky–Golay filtering. The start of the growing season (SOS) and the end of the growing season (EOS) were then identified based on a dynamic threshold criterion, and the length of the growing season (LOS) was derived from the interval between the two phenological dates. Urban–rural gradients were constructed by integrating Global Urban Boundary (GUB) and digital elevation model (DEM) data. Theil–Sen slope estimation and the Mann–Kendall test were then applied to analyze trends in vegetation phenology, while partial correlation analysis and the XGBoost-SHAP method were used to identify the relative effects of climatic and urbanization-related factors. The results showed that (1) vegetation phenology in Beijing varied markedly across space. Mountainous areas in the northwest and southwest had a later SOS, earlier EOS, and shorter LOS, whereas the central plain and southeastern regions were characterized by an earlier SOS, later EOS, and longer LOS; (2) from 2005 to 2024, the SOS advanced significantly by −0.65 days/year, EOS showed a weaker delay of 0.46 days/year, and LOS increased significantly by 0.90 days/year, indicating an overall extension of the vegetation growing season over the past two decades. (3) Urban areas had an earlier SOS, later EOS, and longer LOS, but the trends of SOS advancement, EOS delay, and LOS extension were more pronounced in suburban and rural areas; and (4) the SOS was mainly influenced by the combined effects of nighttime lights and heat-related factors, whereas the EOS was primarily affected by temperature and nighttime lights. Along the urban–rural gradient, the importance of nighttime lights gradually decreased from urban areas to suburban and rural areas, while the role of climatic factors became relatively stronger. These findings reveal the gradient differentiation of vegetation phenological status, temporal trends, and associated drivers in Beijing under rapid urbanization, and provide useful information for urban vegetation management and ecological planning. Full article

Drought poses a major threat to ecosystem stability in arid regions. In Xinjiang, China, vegetation dynamics are highly sensitive to hydroclimatic variability, yet the evolution of drought and its ecological impacts remain insufficiently quantified. Using meteorological observations from 86 stations (1962–2021), drought dynamics were characterized using the Standardized Precipitation Evapotranspiration Index (SPEI) combined with run theory, while MODIS products (2001–2021) were used to quantify vegetation phenology and productivity. Results indicate that despite a regional warming–wetting trend, more than 97% of Xinjiang exhibits a significant increase in drought frequency and intensity after 1997, with pronounced spatial heterogeneity concentrated in southern Xinjiang. Vegetation phenology shows a significant shift, with spring onset advancing at a rate of −1.9 days decade⁻¹ and growing season length increasing by +3.8 days decade⁻¹. Vegetation productivity derived from MODIS shows strong spatial variability, with GPP and NPP exhibiting consistent increasing trends, particularly in northern Xinjiang. Multi-scale analysis reveals strong scale dependence in drought–vegetation interactions, where short-term drought (SPEI-3 and SPEI-6) exerts the strongest influence on vegetation dynamics, while long-term drought (SPEI-12) primarily controls ecosystem stability and post-drought recovery. Correlation and extreme-event analyses further indicate that seasonal drought and phenological shifts jointly regulate ecosystem productivity by altering water availability and carbon uptake periods. These results highlight a warming–wetting but drought-intensifying regime in Xinjiang and emphasize the dominant role of seasonal drought in regulating vegetation functioning under climate change. Full article

Accurate maize distribution information is critical for crop-area statistics, food-security assessment, and agricultural monitoring, but large-scale maize-mapping remains difficult in regions with limited reference samples, heterogeneous crop calendars, and frequent optical data gaps. This study proposes a phenology-adaptive maize mapping framework based on Sentinel-2 time-series imagery and an Enhanced Red-edge NDVI (ENDVIre). ENDVIre was constructed from the Sentinel-2 red-edge 4 and red-edge 2 bands to enhance the spectral response of maize during the silking-to-grain-filling stage, when maize develops a dense canopy and high chlorophyll content but is often confused with soybean. The framework first reconstructed the NDVI time series using an upper-envelope-constrained Whittaker smoother to identify key phenological stages, including sowing–emergence, vigorous growth, and maturity–harvest. NDVI, ENDVIre, and LSWI were then integrated into an interpretable decision-tree model with phenology-aligned time windows to distinguish maize from soybean, rice, wheat, and other non-maize backgrounds. The method was evaluated in six representative maize-growing regions across the United States, Brazil, China, Kenya, and Ukraine, covering different crop calendars, field sizes, and agricultural systems. The mean overall accuracy, F1-score, and Kappa coefficient across the six regions reached 93.27%, 93.14%, and 0.8652, respectively. Cross-year experiments in a winter-wheat–summer-maize rotation region from 2020 to 2024 achieved overall accuracies of 89.80–96.80%, while spatial-transfer experiments in six independent regions achieved overall accuracies of 87.40–95.40%. A comparison with existing high-resolution maize products in the Huang-Huai-Hai Plain further showed that the proposed method better balanced omission and commission errors. These results indicate that ENDVIre-based phenology rules provide an interpretable and transferable solution for maize mapping under limited-sample conditions, although persistent cloud contamination and fragmented smallholder landscapes remain important challenges. Full article

Accurate crop mapping in fragmented agricultural landscapes is challenged by overlapping crop calendars and redundancy among multi-source time-series variables. Using Sentinel-1/2 imagery from December 2022 to December 2023, we constructed 275 season-specific spectral–phenological feature–month variables (125 for summer crops and 150 for winter crops) for rice, maize, soybean, winter wheat, and winter rapeseed in Jiangsu Province, China. An auxiliary binary Random Forest (RF) was used to estimate out-of-bag (OOB) permutation-based predictive contributions and construct search priors. A prior-guided genetic algorithm (GA) then identified compact subsets, with crop-specific five-class RF models used both to evaluate candidate subsets and to produce the final classifications. A fixed stratified 80/20 development–validation split was maintained throughout the analysis, with the validation subset reserved for final assessment. August and April were the principal discriminative periods for summer and winter crops, respectively, while VH backscatter and SWIR-related indices, particularly STI and NDTI, showed recurrent predictive contributions across crops. On the independent validation subset, the optical/vegetation-index scheme, SAR-only scheme, and the complete feature library achieved mean target-crop $F_1$-scores of 78.42%, 83.74%, and 86.96%, respectively. The GA-selected subsets retained 9–39 variables and achieved a mean five-class overall accuracy of 91.77% and a mean target-crop $F_1$-score of 93.95%. After non-target classes were merged into a single background class, the integrated seasonal maps achieved overall accuracies of 81.20–95.03% on the same validation subset. Supplementary classifier comparisons indicated that subset effects depended on the crop and learning algorithm. The findings support crop-specific, interpretable dimensionality reduction within the RF workflow, while broader transferability requires multi-year and multi-region evaluation. Full article

Accurately estimating village-level winter wheat yield in coastal saline–alkali farmland is challenging because this region has strong spatial differences and multiple environmental stresses. In this study, Huanghua City, Hebei Province, was selected as a typical coastal saline–alkali area. Sentinel-2 images, climate factors, and topographic variables, including elevation, topographic wetness index, distance to the coastline, and distance to water systems, were combined to build a phenology-guided feature set for winter wheat yield prediction in coastal areas. The results showed that Phenology-Guided Feature Integration XGBoost achieved an R² of 0.6382 and an RMSE of 450.15 kg/ha, which was slightly better than Gradient Boosting (R² = 0.6256) and Random Forest (R² = 0.6098), and clearly better than SVR (R² = 0.4792), Ridge regression (R² = 0.4582), and a single Decision Tree (R² = 0.3088). Then, a three-stage branch was designed to identify the main drivers of SI, NDVI, and winter wheat yield at different stages, helping explain how environmental constraints and vegetation responses jointly affect final yield. The Three-Stage Fusion XGBoost Model achieved an R² of 0.6439, an RMSE of 446.24 kg/ha, and an MAE of 363.38 kg/ha, showing a slight improvement in prediction accuracy. SHAP analysis showed that SI, distance-related factors, elevation, TWI, and NDVI were important drivers of winter wheat yield variation. Spatial prediction results showed higher winter wheat yield in inland areas (5145 kg/ha) and lower yield in coastal areas (4198 kg/ha). This framework supports village-scale winter wheat yield prediction in coastal saline–alkali farmland and improves model interpretability. Full article

Accurate crop mapping is important for agricultural monitoring and land management; yet, identifying robust and compact feature subsets from high-dimensional multi-sensor remote sensing data remains challenging, particularly in heterogeneous agricultural landscapes affected by spatial autocorrelation. Although combining multi-sensor data provides complementary spectral and structural information, traditional workflows often neglect spatial dependence during feature evaluation, leading to over-optimistic validation metrics and spatially unstable feature subsets. To address this issue, this study proposes a hierarchical feature selection and subset optimization framework for crop mapping by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. A total of 135 multi-sensor features were constructed, including spectral bands, vegetation indices, SAR metrics, texture descriptors, and phenological statistics. To improve feature compactness and spatial robustness, a multi-stage selection strategy combining correlation-based redundancy removal, spatial cross-validation (SCV) control, Boruta, recursive feature elimination (RFE), L1 regularization, SHapley Additive exPlanations (SHAP), and Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed. Results showed that temporal and phenological features contributed more strongly to crop discrimination than static spectral or SAR features, while multi-sensor integration further improved classification stability. Notably, the proposed framework reduced the feature space from 135 to 12 variables while slightly improving classification performance. The final optimized model achieved an overall accuracy (OA) of 96.98% under SCV and generated spatially consistent crop maps at 10 m resolution. The framework provides an efficient and scalable solution for fine-scale crop mapping in complex agricultural regions and demonstrates the practical potential of incorporating spatial dependence control into feature selection for large-scale agricultural monitoring applications. Full article

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions. Full article

Early-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments. A DJI Phantom 4 Multispectral UAV was flown at 40, 80, and 120 m above ground level (AGL) over 588 M₂ genotypes under full irrigation (ENV1) and terminal drought (ENV2; irrigation withheld from reproductive onset) on four dates (July–September 2025). Structure-from-motion canopy height models were compared with ground measurements, and four spectral reflectance indices—Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), and Leaf Chlorophyll Index (LCI)—were derived from 40 m imagery. Ordinary least squares (OLS), Random Forest, and Gradient Boosting were evaluated under leave-one-genotype-out (LOGO), leave-one-environment-out (LOEO), and leave-one-date-out (LODO) cross-validation; genotypic repeatability was quantified by intraclass correlation (ICC), and drought performance was ranked by a composite Stress Tolerance Index (STI) validated against an independent breeder assessment. The 40 m altitude gave the highest height accuracy (R² = 0.812 in ENV1; 0.663 in ENV2). LOGO accuracy (R² ≈ 0.83) fell to R² ≈ 0.55 under LODO—the operationally relevant figure for a new phenological stage—and the full structural–spectral OLS model collapsed (R² = −0.203) where tree ensembles remained stable. Spectral-index repeatability was up to ~2-fold higher under stress (ICC(3,4) > 0.84). The composite STI flagged 38 elite genotypes (7.6% of 498); 10 of its top 30 were confirmed in the breeder’s 48-best selection from all 588 rows—a 4.1-fold enrichment over chance (hypergeometric p = 4.5 × 10⁻⁵). Full article

Saline–alkaline land serves as a potential arable land reserve for augmenting agricultural productivity and safeguarding food security. However, long-term monitoring of saline–alkaline land conversion remains challenging because of vegetation recovery, surface changes, hydrological modification, and agricultural phenology. Compared with CCDC and LandTrendr, the proposed MK-based framework detects conversion occurrence and timing while reducing dependence on dense observations, parameter tuning, and annual classification. This study examines the spatiotemporal dynamics of saline–alkaline land converted into paddies in Da’an City, utilizing Landsat time-series data (2007–2021) from the Google Earth Engine (GEE) platform. The analysis employed Mann–Kendall (MK) trend and mutation tests to monitor conversion processes and analyze spatiotemporal dynamics. Point-biserial correlation analysis was applied to evaluate the sensitivity of various remote sensing indices in detecting land conversion. The top fifteen indices, including the Land Surface Water Index (LSWI), Salinity Index 4 (SI4), and Salinity Index 5 (SI5), demonstrated strong correlations (|r| = 0.788–0.885) and significant pre- and post-conversion spectral differences (p < 0.01). Validation via confusion matrix confirmed that the June SI5 index attained the highest detection accuracy (overall accuracy: 94.15%; Kappa coefficient: 0.86), supporting the MK trend test’s efficacy in monitoring conversion processes. The MK mutation test achieved 80.36% temporal accuracy in determining conversion timing. The spatiotemporal analyses identified heterogeneity in saline–alkaline land conversion patterns. Spatially, large contiguous paddy fields dominated the eastern region, whereas fragmented conversion characterized the west, with minimal activity in the central zone. Temporally, the conversion area expanded rapidly before 2015 and then gradually declined, reaching a cumulative converted area of 276.29 km² by 2021. This study elucidates spatiotemporal conversion dynamics to guide sustainable land use. Full article