Search Results (1,089)

Accurate crop classification in complex and heterogeneous agricultural landscapes is often challenged by mixed-pixel effects and spatial autocorrelation. This study proposes a prior-guided crop classification framework that integrates accessible Moderate Resolution Imaging Spectroradiometer (MODIS) optical and Sentinel-1 synthetic aperture radar (SAR) time-series data with explicit phenological and structural priors. By embedding physically meaningful constraints into temporal feature learning, the model shifts from purely data-driven learning toward biophysically interpretable discrimination between crop types and background classes. Performance was rigorously evaluated using spatial cross-validation (SCV) to ensure geographic independence. Results demonstrate that the prior-guided CNN achieves an overall accuracy (OA) of 98.66% and a Kappa of 0.9832, outperforming unguided deep learning and conventional machine learning models. Notably, the framework exhibits high spatial robustness, with a minimal performance gap between random and spatial validation (ΔOA = 0.0049). In addition to improving classification accuracy, integrating phenological features with SAR-based prior information enhances the stability of non-crop categories in fragmented scenarios, while leveraging readily available medium-resolution data to support large-scale applications. These findings demonstrate that embedding physically meaningful prior knowledge into multi-source time-series learning improves classification accuracy while enhancing spatial generalizability and interpretability. More broadly, the proposed framework offers a transferable paradigm for integrating domain knowledge with deep learning, providing a practical and scalable solution for crop mapping in heterogeneous agricultural landscapes using widely accessible medium-resolution data. Full article

Urban agriculture plays a critical yet increasingly complex role in sustainable urban development, especially in high-density regions undergoing rapid transformation. Accurate mapping of its spatial distribution and functional composition remains a methodological challenge due to its fragmented landscape, small plot sizes, and multifunctional nature. This study addresses this gap by developing and applying a novel hierarchical classification framework that integrates agricultural land cover types with key socio-economic functions to map urban agriculture in the Pearl River Delta (PRD), China. This framework is structured around agricultural land categories (i.e., cropland, garden, forest, grass, and water body) and further delineated by two primary production functions, planting and breeding, with a third functional dimension, leisure activities, proposed as a conceptual extension for future research. Using unmanned aerial vehicle (UAV) imagery and high-resolution satellite data, we constructed a spatial sample database for urban agriculture. The random forest algorithm was applied to classify urban agriculture with Gaofen-2 imagery, generating detailed spatial distribution maps across the study area, with consistently reliable overall accuracy (79.07–81.82%), though this may be slightly optimistic due to potential spatial autocorrelation between training and testing samples. While the framework performed exceptionally well for spectrally and spatially distinct classes such as water bodies and perennial plantations, challenges remained in discriminating among annual field crops due to spectral similarity. These findings underscore the potential of integrating multi-temporal remote sensing data to capture phenological variations for improved classification. This study provides a replicable, functionally informed mapping approach that not only advances the methodological toolkit for urban agriculture characterization but also offers a valuable evidence base for land use planning, agricultural policy, and sustainable urban development in rapidly urbanizing regions. Full article

Accurate estimation of crop development, water use and yield is essential for improving irrigation management in Mediterranean agricultural systems under increasing climate variability. However, many crop models require extensive input data and technical expertise, limiting their operational use by farmers and technicians. This study proposes an integrated agro-modelling framework that combines thermal time modelling, satellite-derived vegetation indices and simplified yield estimation approaches to assess maize phenology, crop water use and productivity under real farming conditions. A key component of the framework is the use of the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series to dynamically identify crop growth stages and derive actual basal crop coefficients (K_{cb act}), enabling the estimation of actual crop transpiration (T_{c act}). These NDVI-based estimates of actual K_cb and T_c were evaluated against simulations from the previously calibrated soil water balance model SIMDualKc. The results showed that the temporal profiles of the NDVI successfully captured the progression of the maize growth stages, although some discrepancies were observed during early stages of development due to the effects of the soil background and the satellite revisit intervals. An empirical relationship between the NDVI and K_cb was developed using multi-year observations and model simulations, improving crop transpiration estimation under field conditions. The NDVI-based approach adequately reproduced daily transpiration dynamics with good agreement with SIMDualKc simulations, yielding RMSE values of 0.11–0.69 mm d⁻¹ and errors generally below 21% of the mean transpiration rate. Seasonal transpiration estimates showed stronger agreement once canopy cover reached its maximum. The integrated AEZ–Stewart modelling framework incorporating NDVI-based transpiration estimations provided accurate yield predictions, with RMSE values of 1.7–2.3 t ha⁻¹ (representing less than 14% of the observed yields). Overall, the proposed framework demonstrates strong potential as a practical and scalable decision-support tool for irrigation management and yield assessment in Mediterranean maize systems. Its novelty lies in the operational integration of NDVI-derived crop development and transpiration estimates within a simplified yield modelling structure, offering a transferable approach applicable to other regions and cropping systems where satellite data are available. Full article

This study assesses the impacts of climate change (CC) on maize production in Bosnia and Herzegovina, comparing ten maize-producing municipalities and using Gradiška as a case study. Agroclimatic indicators and ISAREG-based soil water balance simulations were used to evaluate regional suitability for future maize production. Projections indicate substantial increases in average temperatures of 2 to 6 Celsius by the end of the century, depending on the RCP scenario, together with important reductions in accumulated mean precipitation, particularly during summer. Rising temperatures accelerate maize phenology, shortening growth cycles and enabling double-cropping opportunities for short-season cycles. Medium-season cycles may become feasible in most regions, while long-season cycles remain constrained in high-altitude areas due to thermal requirements. Rainfed maize in Gradiška is expected to face increased relative evapotranspiration deficits under future ‘hot & dry’ conditions, with potential relative yield losses due to water deficit of up to 12%. Irrigated maize shows a variation in irrigation requirements from −26% to +8% relative to the baseline, which reflects the combined effect of a shortened crop growth cycle under higher temperatures and increased evapotranspiration demand under drier conditions. Regions with high soil water-holding capacity are the most resilient, while areas with shallow soils or Mediterranean climates are more vulnerable under future conditions. The findings underscore the need for agronomic adaptation measures to the projected CC impacts, including supplemental irrigation, drought-tolerant cultivars, and potential adjustment of sowing. Full article

Fine-scale crop type information is essential for agricultural monitoring, irrigation management, and food security assessment. This study mapped three major crops—wheat, corn, and sunflower—in the Hetao Irrigation District, China, using multi-temporal Sentinel-2 optical imagery and Sentinel-1 SAR observations at the parcel scale. A multi-source feature set, including spectral bands, vegetation and red-edge indices, moisture-related variables, radar backscatter coefficients, and derived radar features, was constructed from the full growing season. An LSTM network was used to learn temporal representations of crop phenological dynamics, and the resulting embeddings were then combined with traditional machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for final classification. The results show that the hybrid framework substantially improves classification performance compared with the corresponding non-LSTM classifiers. Among all tested models, XGBoost + LSTM achieved the best performance, with an overall accuracy of 93.61%, a Kappa coefficient of 91.66%, and a mean IoU of 87.41%. The class-wise F1-scores were 85.61% for wheat, 97.22% for corn, and 87.27% for sunflower. Additional experiments further confirmed the advantages of parcel-based aggregation in improving spatial consistency and reducing mixed-field noise. The proposed framework provides a promising parcel-scale workflow for crop type mapping in fragmented irrigation districts, while its transferability across years and regions still requires further validation. Full article

Accurate identification of cultivated land planting types is essential for agricultural resource management and national food security. Traditional optical remote sensing approaches are susceptible to weather interference in cloudy regions, making continuous crop growth monitoring challenging to achieve. To address this limitation, this study proposes a crop classification framework based on time-series Sentinel-1A SAR imagery combined with Recurrent Neural Networks (RNN), using Chongming Island, Shanghai as the experimental area. The framework integrates backscattering coefficients (VV, VH, VV/VH ratio) with polarimetric decomposition parameters (entropy H, scattering angle alpha, anisotropy A) as multi-dimensional temporal input features, and employs decision-level voting to obtain plot-level classification results. Experiments on three classification tasks (Rice versus Non-Rice, Wheat versus Non-Wheat, and multi-class rotation patterns) demonstrate that the proposed method achieves pixel-level accuracies of 99.72%, 99.60%, and 98.39% respectively using the six-dimensional BSPD model, with plot-level F1 scores exceeding 0.990 across all tasks. The fusion of polarimetric decomposition features reduces classification errors by up to 70% compared with backscattering-only features, particularly improving discrimination of phenologically overlapping crop categories. These results confirm that multi-dimensional temporal features extracted from dense time-series SAR imagery significantly enhance crop classification accuracy in all-weather conditions. Full article

Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches. Full article

Monitoring avocado (Persea americana Mill., cv. Semil-34) in tropical mountain landscapes of Cambita, San Cristóbal, Dominican Republic is inherently complex due to the pronounced topographical and climatic heterogeneity that modulates the crop’s ecophysiological responses, specifically vegetative vigor, carbon allocation, and the synchronization of reproductive flushes. This study integrates 5-year (2020–2025) Sentinel-2 time series, ERA5-Land climatic variables (air temperature, total precipitation, and radiation), and geomorphometric covariates to explain variability in yield and fruit quality. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Red Edge (NDRE), and Normalized Difference Moisture Index (NDMI), were analyzed using Partial Least Squares Regression (PLSR) to characterize phenological dynamics and rank dominant predictors. The results revealed coherent spectral phenological trajectories; however, a significant inverse relationship was detected between canopy vigor and yield during reproductive phases. High vegetation index values were significantly and negatively associated with lower production (r = −0.58, p < 0.0021), reflecting a potential source–sink imbalance. Topography functioned as a structural filter, regulating root drainage and productive stability across the landscape. While yield variability was partially explainable (R² = 0.38), internal fruit quality, measured as dry matter content, exhibited comparatively high environmental stability. A central contribution of this research lies in identifying the “vigor paradox” in cv. Semil-34 and the suggestion that topography may exert a stronger influence than direct spectral signals under tropical hillside conditions. These findings provide an exploratory framework for anticipating yield and fruit quality through satellite remote sensing or UAVs, supporting site-specific management decisions in mountain agricultural systems. Full article

Accurate, scalable estimation of rice planting dates is essential for climate-adaptive management in multi-cropping regions, yet most models rely on static calendars, which fail to capture climate-driven shifts and bias simulated yield responses. This study aims to develop a climate-driven, spatially explicit framework to simulate dynamic transplanting dates across diverse multi-cropping systems in monsoon Asia. Utilizing daily AgERA5 reanalysis and Monsoon Asia Rice Calendar (MARC) data from 2019 to 2020, we present Geo-ROCKET. The framework integrates an automated K-means clustering workflow to delineate bimodal planting windows and employs random convolutional kernel transforms with adaptive geographic neighborhoods to capture local climate heterogeneity. Evaluated by area-weighted mean absolute error (MAE), the model achieves high accuracy across six seasons (MAE 6.53–12.50 days), outperforming six traditional ROCKET and ensemble baselines while preserving smooth spatial error fields. Sensitivity experiments reveal that a 15-day bias in the previous harvest date can increase transplanting error to 10.8–17.8 days, emphasizing the importance of sequential consistency. By providing dynamic, climate-sensitive inputs, Geo-ROCKET improves the accuracy of crop modeling for climate impact projections. This framework offers a flexible tool for characterizing human management decisions and evaluating adaptation strategies in intensive agricultural systems. Full article

This paper explores the feasibility of early-season crop classification based on Sentinel-2-time series using the TimeSen2Crop dataset (≈1 million pixels, 16 crops). The aim of the study was to evaluate the spectral-phenological separability of crops during the season and compare the performance of classical tabular algorithms, deep sequence models, and a seasonally oriented hybrid stacking scheme. Based on multispectral observations, a feature set was formed from 9 optical channels and 13 vegetation indices for 30 dates. F-criteria were calculated, confirming a sharp increase in interclass separability during the active vegetative growth phase and substantiating three time series truncation scenarios (early, early + mid-season, and full season). Random Forest (macro-F1: 0.46/0.74/0.75) was used as the base tabular model. LSTM, BiLSTM, GRU, 1D-CNN, and Transformer were trained in parallel, with Transformer showing the best results among the deep architectures (0.42/0.68/0.78). The main contribution of the work is a hybrid multi-layer stacking scheme combining heterogeneous base algorithms and OOF meta-features, which provides the highest quality (0.51/0.83/0.86) in all scenarios. The obtained results confirm the effectiveness of phenology-oriented selection of time windows, informative indices, and hybrid ensemble learning for improving the accuracy of early-season crop monitoring. Full article

Nai plum (Prunus salicina Lindl. var. cordata) is a high-value fruit crop in southern China, yet its post-harvest quality is often compromised by fruit browning, a major constraint to storage and marketability. Addressing this challenge requires a deeper understanding of the species’ reproductive biology, which underpins both fruit set and cultivar improvement. In this study, we characterized the flowering biological characteristics of Nai plum accessions introduced from Yanling and Liuyang (Hunan Province) and Shaoguan and Lechang (Guangdong Province). Using field observations combined with microscopic and submicroscopic techniques, we documented flowering phenology, flowering dynamics, floral organ traits, pollen viability and stigma receptivity. The flowering period was in March, lasting 26–28 d, and the group blooming period was divided into three stages: Initial opening stage, Full blooming stage, and Final flowering stage. The single-flower opening process was divided into eight stages. Pollen viability followed a unimodal curve, peaking at the petal flattening stage (PF) across all accessions, though peak values varied by provenances. Stigmas were of the wet type, with receptivity following a weak–strong–weak pattern; peak receptivity occurred at early flowering (EF) and PF in most accessions. The EF of Nai plum from Yangling (S1) lasted for 7 h, and PF lasts for 28 h. The EF of Nai plum from Yangling (S2) lasted for 3 h, and the PF lasted for 11 h. Both the EF and the PF of Nai plum from Shaoguan (S3) lasted for 14 h. The bud white stage (BW) of Nai plum from Lechang (S4) lasted for 6 h and the EF lasted for 7 h. The EF of Nai plum from Liuyang (S5) lasts for 7 h, and the PF lasted for 28 h. These findings clarify the reproductive phenology and floral biology of Nai plum, providing foundational knowledge that can inform breeding strategies and cultivation practices aimed at improving fruit set and, ultimately, post-harvest quality. Full article

This study evaluated the susceptibility of common bean (Phaseolus vulgaris L.) cultivars to Euschistus heros feeding across various phenological stages. Three cultivars (IPR Curió, IPR Sabiá, and IPR Urutau) were infested with 0.5 insects per plant for eight days starting at anthesis and 8, 16, 24, 32, and 40 days after flowering (DAF) using a randomized block design with five replicates. E. heros did not significantly impact grain yield or reproductive abscission, except for the IPR Curió cultivar during flowering, which demonstrated substantial qualitative damage. Feeding injury resulted in increased grain punctures and the grading of commercial classification to Type 2. The most critical susceptibility period occurred during the grain-filling stages (16–24 DAF). IPR Curió was the most sensitive cultivar, exhibiting Type 2 status at both 16 and 24 DAF. These findings demonstrate that although common beans exhibit quantitative tolerance to E. heros at the tested density, qualitative damage during grain development significantly compromises marketability and value. Integrated Pest Management (IPM) should prioritize protecting the crop during mid-to-late reproductive stages to ensure that grain quality standards are met. Full article

Accurate monitoring of crop phenology is essential for precision agriculture and yield forecasting. However, satellite-derived time series often suffer from inherent noise, such as residual atmospheric effects and mixed pixels, as well as a frequent lack of ground-truth data in agriculture. In response, this study proposes an algorithm to define the type of spectral signatures for the principal phenological stages of crops, using them as the foundation for training supervised machine learning classification models. The algorithm was developed using Fuzzy C-Means (FCM) clustering to identify the spectral signature reference groups in winter wheat across the Burgos region (Spain) during the 2020 and 2021 growing seasons. To enhance cluster independence and biological coherence, a multi-step filtering process was implemented, including spectral purity (membership degree, SAM, and SAMder) and temporal coherence filters. The filtered and labeled dataset (80% original Burgos dataset) was used to train supervised classification models (KNN and XGBoost). The models’ reliability was verified through three wheat tests (remaining 20%), labeled using other clustering techniques, and an independent barley dataset from diverse geographic locations (Valladolid and Soria). The filtering process significantly improved cluster stability by removing outliers and transition spectral signatures. The supervised models demonstrated exceptional performance; the KNN model slightly outperformed XGB, achieving a mean Accuracy of 0.977, a Kappa of 0.967, and an F1-score of 0.977 in the wheat external test. Furthermore, the model showed, when applied to barley, that its phenological spectral signatures are equivalent in shape to those of wheat, with an Accuracy of 0.965 and an F1-score of 0.974. In addition, it was verified that the type spectral signatures remain the same regardless of the location. This study presents a robust classification tool capable of labeling four key phenological stages (tillering, stem elongation, ripening, and senescence) without ground truth. By effectively removing inherent satellite noise, the proposed methodology produces organized, cleaned datasets. This structured foundation is critical for future research integrating spectral signatures with harvester data to develop high-precision yield prediction models. Full article

Accurate monitoring of nitrogen (N) status is critical for precision N management and optimizing the yield and quality of Zanthoxylum armatum var. novemfolius (ZA). However, individual sensors often struggle to simultaneously capture the biochemical variations and complex canopy structural changes of ZA. Therefore, field experiments were conducted over two consecutive years, applying four N-application rates (0, 150, 300, and 450 kg N ha⁻¹) to ZA. At each phenological stage, hyperspectral imagery and LiDAR point clouds were collected via three UAV flight altitudes (60 m, 80 m, and 100 m), and canopy nitrogen concentration (CNC) and aboveground nitrogen accumulation (AGNA) were measured. This study developed a framework by synergistically fusing UAV-derived hyperspectral imaging (HSI) and LiDAR data for CNC and AGNA monitoring. Results showed that the response of nitrogen status indicators to fertilization was phenology-specific: CNC showed no significant difference (p > 0.05) among treatments during the vigorous vegetative growth stage (VGS) but differed significantly (p < 0.05) during the fruit expansion stage (FES); AGNA differed significantly among treatments at VGS and FES (p < 0.05). The two-step screening yielded NDSI _{(732, 879)} and NDSI _{(560, 690)} as the optimal CNC indicators at VGS and FES, respectively (r = 0.83 and 0.93), whereas the NDSI _{(711, 986)} and NDSI _{(515, 736)} were identified as the optimal AGNA indicators at VGS and FES, respectively (r = 0.91 and 0.71). Across all phenological stages, Random Forest Regression consistently delivered the highest accuracy for CNC (R² = 0.93–0.98, RMSE = 0.87–1.02 g kg⁻¹) and AGNA (R² = 0.95–0.97, RMSE = 1.92–2.55 g plant⁻¹), outperforming MLR, PLSR, and SVR. This synergistic framework provides a high-precision, non-destructive methodology for the precision N monitoring of woody crops. Full article

The impacts of climate change on Mediterranean weed flora were investigated to inform future weed management strategies. Projections indicate that rising temperatures and increased atmospheric CO₂ concentrations are likely to favor ruderal species characterized by rapid phenological development and high dispersal capacity. Enhanced abiotic stressors—such as elevated temperatures, water scarcity, and increased UV-B radiation—are expected to affect crops more severely than weeds, given the latter’s greater evolutionary potential to develop stress-tolerant biotypes. Moreover, the increased frequency and intensity of extreme events (e.g., drought, flooding, and soil salinization) may reduce weed community diversity, potentially leading to dominance by a limited number of highly competitive species and consequently intensifying reliance on chemical weed control. Simplification of weed communities may also increase vulnerability to the introduction and establishment of alien species, particularly those originating from hot and arid regions, some of which may be parasitic, toxic, or allergenic. Climate change-induced phenological mismatches between flowering plants and pollinators are likely to favor wind-pollinated weed species, further compromising the aesthetic and ecological quality of agricultural landscapes. Additionally, increased production of wind-dispersed allergenic pollen, together with the anticipated rise in herbicide applications, may pose significant risks to human health. An effective agronomic strategy to address future weed scenarios should include the genetic improvement in crops to enhance adaptive plasticity, exploiting germplasm from ancestral lines and related wild species. Full article