Search Results (1,504)

Accurately estimating leaf area index (LAI) is vital for evaluating crop growth and predicting yields. Conventional approaches, however, often struggle due to the limited representativeness of available data and the complex structure of plant canopies, which reduce their reliability across diverse canopy architectures and observation conditions. To overcome these challenges, this work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques. Representative 3D maize canopy scenarios were generated using the LESS model, producing synthetic LiDAR point clouds constrained by realistic structural parameters. A deep learning model based on PointNet++ was trained, and transfer learning (TL) was employed to facilitate knowledge transfer from simulated to actual measured data. The TL-enhanced model demonstrated significant improvement, with R² rising from 0.537 to 0.842 and RMSE dropping from 0.541 to 0.288 m²·m⁻². Moreover, retrieval performance was notably affected by scanning mode, angle, and stem diameter, achieving optimal results under TLS acquisition, moderate scanning angles, and intermediate stem widths. These findings suggest that integrating 3D RTM-generated synthetic point clouds with transfer learning is an effective strategy for enhancing the robustness and generalization of LiDAR-based LAI retrieval. Full article

The presence of vegetation in urban green areas is a factor that helps minimize urban environmental and human health problems. This study aimed to analyze the effect of different vegetation types and tree densities in green areas on thermal comfort and outdoor wind speed in a city with a tropical savanna climate. Computer simulations were used with the ENVI-Met software (version 5.1.1envi), with hypothetical scenarios where central green areas present different tree species defined by the Leaf Area Index (LAI), forming compositions calculated by different Vegetation Cover Indices (VCI). The results showed that scenarios with dense canopy tree vegetation (combined or not with sparse canopy species) and scenarios with the highest vegetation cover densities showed the greatest reductions in Physiological Equivalent Temperature (PET) values, while wind speed did not show a simple direct correlation with PET, although a diurnal relationship with the analyzed densities was observed. In light of the above, it was found that promoting thermal comfort outdoors requires prioritizing the maximization of vegetation cover so as not to create physical barriers to wind flow, especially in the afternoon. Full article

Tomato yellow leaf curl virus (TYLCV) is one of the most destructive viral diseases causing severe yield losses in tomato production worldwide. This study investigated the effects of TYLCV infection on plant growth, photosynthetic physiological responses, and yield formation in greenhouse-grown tomatoes and evaluated the applicability of physiological trait-based yield prediction models. Two large-fruited tomato cultivars widely cultivated in Korean protected horticulture systems, ‘Daphnis’ and ‘Pink Star’, were inoculated with TYLCV under greenhouse conditions, and their growth, physiological responses, and yield characteristics were compared under high- and low-temperature growing seasons. TYLCV infection significantly reduced leaf length, leaf width, and leaf area index (LAI), and decreased both flowering truss number and fruit-setting truss number, resulting in reduced total yield. Physiological analyses showed that infected plants exhibited decreases in the OJIP fluorescence rise curve and Fv/Fm values, indicating a reduced photochemical efficiency in photosystem II. In addition, A–Ci response curve analysis revealed a reduction in net photosynthetic rate, suggesting limited carbon assimilation capacity. Total yield showed significant positive correlations with maximum net photosynthetic rate (Amax), Fv/Fm, and Ci300. GGE and GT biplot analyses further indicated that yield was closely associated with photosynthetic performance and canopy development traits. A multiple regression model based on physiological traits and virus infection status explained a significant proportion of the variation in tomato yield (R² = 0.367), indicating that TYLCV infection acts as a key limiting factor for yield reduction. These findings demonstrate that TYLCV infection restricts tomato productivity through reduced photosynthetic efficiency and altered canopy structure. Moreover, physiological trait-based yield prediction approaches may provide a useful framework for evaluating productivity under viral infection conditions and for developing data-driven crop management strategies in greenhouse tomato production systems. Full article

Under the pronounced warming–wetting trend in Northwest China, understanding vegetation responses to the redistribution of hydrothermal resources is essential for interpreting regional ecohydrological processes. Here, we developed a bivariate Long Short-Term Memory (LSTM) model to simulate leaf area index (LAI) dynamics for four representative vegetation types (cold temperate forest, shrubland, grassland, and cropland), using air temperature and soil moisture as predictors. The model reproduces seasonal vegetation phenology well across vegetation types (R² > 0.9), indicating that LSTM effectively captures the cumulative and lagged effects of hydrothermal drivers. However, its performance diverges at the interannual scale. Interannual variability in grasslands in water-limited environments is reasonably represented (R² = 0.31), consistent with their sensitivity to short-term hydroclimatic variability under warming–wetting conditions. In contrast, the model fails to reproduce the observed long-term greening trend in forests when driven solely by hydrothermal variables. This contrast suggests distinct underlying mechanisms across ecosystem types. Grassland dynamics are closely linked to high-frequency hydroclimatic variability, whereas forest growth appears to be governed by slower processes and low-frequency drivers, including CO₂ fertilization, nitrogen deposition, and ecological inertia. As a result, hydrothermal variables alone are insufficient to explain long-term forest dynamics. Overall, these findings highlight a transition from water-limited to energy- and process-limited controls across vegetation types and underscore the limitations of purely climate-driven models. Integrating biogeochemical processes or process-based constraints into machine learning frameworks may therefore be necessary to improve predictions of long-term vegetation change under climate change. Full article

Over the past two decades, extreme climate and weather events have become increasingly frequent in the United States, and the carbon–water cycle of corn ecosystems has shown high sensitivity to climate change. However, traditional simulation methods that rely on coarse-scale reanalysis data are unable to reflect changes in local water and heat conditions accurately. This study combines in situ meteorological observations with remote sensing, using a long short-term memory model to simulate the daily gross primary productivity (GPP) and evapotranspiration (ET) of 684 corn-growing meteorological stations in the United States. In summer, GPP and ET showed a spatial pattern of gradual decrease from the humid eastern region to the arid western region, and the multi-year daily averages at meteorological stations showed a single-peak pattern. The sensitivity of GPP and ET changes is mainly influenced by leaf area index (LAI) and shortwave radiation downward changes, which together explain more than 90% of the main variation in GPP and ET at the meteorological stations. The 2012 drought caused a general decline in GPP and ET, with the peak occurring approximately 15 days earlier than usual. Water use efficiency (GPP/ET) decreased at 85% of the sites (p < 0.05), but photosynthesis per unit leaf area (GPP/LAI) increased at 63% of the sites (p < 0.05). This study demonstrates the importance of meteorological station-scale data for understanding carbon–water flux dynamics in cornfields. Integrating the models developed in this study with medium-to-long-term climate projections will further guide climate-informed agricultural water management and provide reliable accounting and pricing tools for agricultural land carbon markets and carbon trading. Full article

The leaf area index (LAI) is a key parameter reflecting vegetation canopy structure and growth status. This study systematically compares the performance of spectral and texture features derived from Sentinel-2A imagery for LAI retrieval in winter wheat and maize. Multiple vegetation indices and gray-level co-occurrence matrix (GLCM) texture features were extracted, and three types of texture indices—Normalized Difference Texture Index (NDTI), Ratio Texture Index (RTI), and Difference Texture Index (DTI)—were constructed. Modeling was performed using Partial Least Squares Regression (PLSR) and Gaussian Process Regression (GPR). Results show that red-edge vegetation indices and mean texture features (e.g., NDVI_M) are robust predictors for both crops, with correlation coefficients reaching 0.87 for winter wheat and 0.83 for maize. Texture indices further enhance the representation of canopy structural information; the optimal NDTI achieved |R| > 0.88 for both crops, though the specific feature pairs were crop-specific. Using the proposed two-stage feature optimization strategy combined with GPR, the LAI estimation accuracy for winter wheat reached R² = 0.87 with RMSE = 0.41 on an independent test set, while for maize the accuracy was R² = 0.75 with RMSE = 0.38. The strategy significantly improved accuracy for winter wheat (uniform canopy) but yielded limited gains for maize (heterogeneous canopy), largely due to differences in canopy architecture. This study demonstrates that integrating multi-dimensional features with nonlinear modeling enhances LAI estimation accuracy. By providing a side-by-side comparative evaluation across two contrasting crop canopies, this study underscores the necessity of crop-adaptive feature selection and modeling strategies. The findings offer practical guidance rather than a universal model for large-scale crop monitoring in agricultural remote sensing. Full article

Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R²) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research. Full article

In saline–alkali environments, soil salinity imposes severe abiotic stress on maize growth by inhibiting root activity and nutrient uptake. Traditional destructive sampling methods struggle to enable cross-growth stage, large-scale dynamic fertilizer effect assessment. This study, conducted in saline–alkali farmlands of Inner Mongolia, utilized UAV multispectral remote sensing to extract 20 vegetation indices and 40 texture parameters, constructing a multi-source feature set. An ensemble learning framework integrating Random Forest (RF), Decision Tree (DTR), AdaBoost and Gradient Boosting Regression (GBR) was developed to achieve precise monitoring of maize plant height, leaf area index (LAI), and yield. In addition, the study aimed to evaluate the dynamic effects of seven fertilizer treatments (six controlled-release composite fertilizers, T1–T6, and conventional CK) and to identify the optimal fertilization scheme, with particular emphasis on comparing the two best-performing treatments, T1 and T2. Results showed that: (1) The ensemble model improved prediction robustness, with R² values of 0.88, 0.76, and 0.76 for plant height, LAI, and yield across the entire growth cycle, respectively. The integration of texture features effectively mitigated spectral saturation during peak growth stages (e.g., tasseling and filling). (2) For fertilizer evaluation, T1 performed best in growth and yield at jointing, tasseling, and filling stages, with a yield increase rate of up to 40.18% at the jointing stage. Although T2 slightly outperformed T1 in yield increase at maturity (15.42%), T1 was identified as the optimal fertilizer scheme for the region based on whole-growth-stage growth performance, measured yield, LAI, and yield increase rate. These results demonstrate that UAV-based multi-source feature fusion combined with ensemble learning provides an effective and non-destructive approach for fertilizer evaluation and precision nutrient management in saline–alkali regions. Full article

Based on leaf area index (LAI) and enhanced vegetation index (EVI) datasets, this study systematically analyzes the spatial distribution and temporal variation characteristics of vegetation index trends at the global scale, clarifying the overall pattern of global greening and the seasonal differences in vegetation greening between eastern China and India. Regions with significant greening in China and India were selected as sensitivity zones, and a coupled land–atmosphere model was used to simulate seasonal differences in the climate response to greening. The findings reveal that: (1) Vegetation greening in eastern China is most pronounced in summer, whereas in India, the greening effect is most prominent in autumn; (2) The synergistic greening of both regions induces a year-round cooling effect in southeastern China, whereas northeastern China experiences summer warming and cooling in the other seasons. Furthermore, spring greening in China and India leads to a pronounced and widespread cooling across the mid-to-high latitudes of Eurasia. (3) In terms of precipitation, southwestern China shows an increasing trend in summer rainfall, while southeastern China shows a decreasing trend. In India, synergistic greening leads to spring and summer warming and autumn and winter cooling, with the cooling and increased precipitation effects being most significant in autumn. Full article

Accurate quantification of ecohydrological processes is essential for effective water and carbon management in terrestrial ecosystems. Traditional simulations mainly rely on mechanistic models, yet their accuracy is often limited by inconsistencies in representing physical processes and uncertainties in parameterization. Integrating remote sensing signals offers a promising way to reduce these uncertainties and enhance model applicability. In this study, in-situ observations from a wheat cropland in the Guanzhong Plain were used to simulate gross primary productivity (GPP) and latent heat flux (LE) by comparing a forward model (STEMMUS-SCOPE) with a remote sensing-driven inverse model (STEMMUS-MLR). We further examined the role of solar-induced chlorophyll fluorescence (SIF), an emerging proxy for photosynthesis, as an input to improve mechanistic modeling of GPP and LE. Results show that STEMMUS-MLR outperformed STEMMUS-SCOPE in estimating water and carbon fluxes, demonstrating that incorporating SIF effectively reduces bias associated with uncertainties in parameters and forcing data. The contribution of SIF was quantified using Random Forest regression and Shapley additive explanations (SHAP), revealing that SIF markedly reduced the dependence of GPP and LE simulations on shortwave radiation (SW), air temperature (Ta), and leaf area index (LAI). These findings highlight the critical role of SIF in ecohydrological modeling of semi-arid cropland ecosystems and provide a scientific basis for advancing process understanding and improving the precision management of water and carbon budgets in terrestrial ecosystems. Full article

Traditional leaf area index (LAI) measurement methods are destructive, time-consuming, and labor-intensive. In this study, 282 four-year-old central-leader apple trees were used as research subjects. Canopy reflectance spectra in the range of 4000–10,000 cm⁻¹ were collected, and the corresponding true LAI values were measured destructively by harvesting all leaves from a representative branch of each tree using a leaf area meter. The dataset was randomly divided into training (70%) and testing (30%) sets. Eight spectral pretreatment methods were compared. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to extract characteristic wavelengths. Subsequently, both a BP neural network and a Support Vector Machine (SVM) model for LAI prediction were constructed. The optimal model was selected based on evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE). The combined preprocessing of MSC and SD yielded the optimal results, screening out 26 characteristic wavelengths. The SVM linear kernel model (c = 5, g = 0.3) constructed based on MSC + SD preprocessing performed best, achieving a validation set R² of 0.90, MAE of 0.2117, MBE of −0.1214, and MAPE of 16.09%. The performance on the training set and validation set was comparable, with no overfitting observed. The MSC + SD preprocessing combined with CARS feature screening and SVM linear kernel modeling enables rapid, non-destructive estimation of apple canopy LAI, providing an effective technical tool for precision orchard management. Full article

Leaf Area Index is a crucial biophysical variable, and its accurate estimation is essential for understanding vegetation dynamics. However, cloud cover significantly restricts optical remote sensing, hindering the generation of spatially continuous Leaf Area Index products. Remote sensing foundation models offer novel solutions to this challenge. This study presents an end-to-end framework based on the fine-tuned Prithvi foundation model for direct LAI retrieval from cloud-contaminated 30 m Harmonized Landsat and Sentinel-2 imagery. By mapping inputs directly to Hi-GLASS reference labels, the proposed architecture processes cloud contamination and vegetation signals simultaneously and circumvents the error propagation inherent in cascaded retrieval pipelines. Results demonstrate that the end-to-end LAI retrieval model significantly outperforms cascaded variants, achieving a superior R² (0.78) and lower RMSE (0.57). Furthermore, predictive accuracy exhibits a distinct U-shaped trajectory relative to the temporal mean cloud fraction, reaching an inflection point at 50–60% occlusion, which highlights the model’s implicit regularization capacity under severe atmospheric interference. This work establishes that direct feature learning with foundation models offers a more robust and streamlined pathway for generating continuous biophysical products from imperfect optical observations, prioritizing quantitative fidelity over artificial perceptual sharpness. Full article

Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R² = 0.82 and 0.91) and tree height (R² = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R² = 0.73 and 0.68) and crown volume (R² = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R² = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions. Full article

Sowing space and depth critically influence wheat canopy architecture, yet their layer-specific effects remain poorly understood. This two-year field study evaluated the effects of three sowing spaces (1.5, 3.0, 4.5 cm) and three sowing depths (2, 3, 6 cm) on canopy projection area, leaf inclination angle, leaf area distribution, and leaf area index (LAI) of dryland wheat (Triticum aestivum ‘Ningmai 13’) in Luhe, Nanjing, China, using image-based phenotyping with manual validation. Narrow spacing (1.5 cm) with intermediate depth (3 cm) produced the largest canopy projection area (0.239–0.245 m²) and an increase in leaf erectness in the middle canopy layer (+23% above average). The highest LAI values (4.23–4.28 m² m⁻²) were achieved with narrow spacing (A₁B₁, A₁B₂), demonstrating that dense canopies can be established under dryland conditions. Grain yield (g/plant) was measured as a supporting agronomic indicator; the highest yield per plant (14.36 g/plant) was observed in A₃B₁. Image-based measurements showed excellent agreement with manual methods (R² > 0.97 for all traits), validating the phenotyping pipeline. These findings contribute to a deeper understanding of how sowing parameters shape wheat canopies in dryland systems. Full article

Grapevine growth and physiological performance are strongly influenced by biotic and abiotic stresses occurring during the growing season. Plant stimulants are increasingly applied in viticulture as management tools aimed at supporting plant physiological processes and improving plant performance under variable environmental conditions; however, cultivar-specific responses to different application strategies remain insufficiently characterized. The aim of this study was to evaluate the effects of foliar plant stimulant application strategies differing in application frequency and phenological timing on selected physiological and canopy-related indicators in Slovak grapevine cultivars (Vitis vinifera L.) under field conditions. The assessed parameters included leaf chlorophyll a and b contents, chlorophyll a/b ratio, leaf area index (LAI), vegetation indices (NDVI and PRI), cluster weight, and basic must composition. Grapevines were subjected to three treatment variants: a control without plant stimulant application, a variant with two foliar applications, and a variant with three foliar applications of commercial biostimulants (Tecamin Max, Tecamin Flower, and Tecamin Brix) performed at key phenological stages during the growing season. Plant stimulant applications were associated with variations in leaf chlorophyll content and LAI values, particularly under repeated application strategies. NDVI and PRI complemented leaf-level measurements by capturing cultivar-dependent differences in canopy condition and photosynthetic regulation throughout the season. Responses of cluster weight and must composition to plant stimulant application were moderate and varied among cultivars, indicating cultivar-specific responses. Although no consistent increase in cluster yield was observed, treated variants showed higher sugar content and lower titratable acidity in several cultivars, indicating differences in grape composition and ripening-related traits. Overall, the results indicate that foliar plant stimulant application strategies can influence physiological and canopy-level grapevine traits in a cultivar-dependent manner. The combined use of leaf-level, canopy-level, and spectral indicators provides a practical framework for evaluating plant stimulant strategies under field conditions and supports their application in sustainable viticulture. Full article