Search Results (2,706)

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

Weed interference and herbicide-induced phytotoxicity, particularly from HPPD inhibitors such as tembotrione, represent significant limitations to yield stability in grain sorghum. Developing strategies to enhance crop tolerance without compromising weed control is of high practical interest. This study tested the hypothesis that a commercial Ascophyllum nodosum-based biostimulant can mitigate tembotrione-induced oxidative stress and phytotoxicity in sorghum without compromising the weed-control activity of the herbicide. Sorghum plants at the V4 phenological stage (four fully expanded leaves) were subjected to five treatments: (1) untreated control; (2) biostimulant application alone; (3) tembotrione application alone; (4) simultaneous application of tembotrione and biostimulant; and (5) tembotrione followed by biostimulant application after six days of application (6 DAT). After 10 days of treatment, photosynthetic pigment synthesis, primary photochemistry, gas exchange, antioxidant metabolism, phytotoxicity levels, growth parameters, and yield indices were evaluated. The results support the hypothesis that A. nodosum-based biostimulants can act as effective mitigating agents. The biostimulant sustained carotenoid levels and preserved the stability of the photosynthetic apparatus (PSII), counteracting HPPD enzyme inhibition caused by the herbicide. Isolated biostimulant application upregulated net photosynthesis by 60%, while simultaneous co-application with tembotrione preserved membrane integrity and the leaf area index. Furthermore, the efficacy of the mitigation strategy was highly time-dependent, as simultaneous co-application proved superior to the delayed (6 DAT) intervention. From an agronomic perspective, the biostimulant reduced visual injury and restored the grain number per plant to control levels under simultaneous co-application, although the final yield of combined treatments did not differ statistically from either the untreated control or the treatment of tembotrione alone. This study shows that the integration of A. nodosum extracts into the chemical management of sensitive crops represents a viable biotechnological strategy to enhance herbicide selectivity and yield stability. Full article

Urban canyons, integral components of the built environment, significantly influence microclimatic conditions and thermal comfort. This review investigates their combined effects with green infrastructure on thermal comfort, offering a comprehensive framework for supporting urban design and greening strategies. The review is based on a structured literature analysis of peer-reviewed studies retrieved from major scientific databases (Scopus and Web of Science), following defined selection and screening criteria. Urban canyon orientation determines solar exposure and its interaction with prevailing wind patterns, affecting ventilation and heat dissipation. The urban canyon aspect ratio influences shading and airflow regulation, while their sky view factor moderates radiative cooling and daylight availability. Urban greening—encompassing street trees, green roofs, and vertical green walls—complements urban geometry by reducing air temperatures, enhancing evapotranspiration, and modifying local wind dynamics. Tree shading can reduce the physiological equivalent temperature in urban canyons, mitigating extreme heat stress. Key vegetative parameters, such as leaf area index and canopy density, are critical for quantifying cooling contributions. Key findings underscore the role of higher aspect ratios in enhancing shading and ventilation while they emphasize the critical influence of street orientation and sky view factor on microclimatic regulation. Vegetation emerges as a vital component, with tree shading contributing substantially to cooling effects and reducing physiological equivalent temperature. The beneficial synergistic interaction between urban geometry and vegetation optimizes thermal comfort. Tailored strategies based on urban canyon typologies balance urban development with environmental sustainability. The proposed framework provides actionable strategies for designing resilient and thermally optimized urban spaces, promoting climate-adaptive urban planning by addressing the dual challenges of the urban heat island and thermal discomfort in cities. 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

This study evaluated the morphological development of 23 Coffea canephora clones in Espírito Santo to identify materials with superior vigor and quality for commercial and breeding purposes. Seedlings from cuttings were arranged in a completely randomized design with ten replicates and assessed at the commercial dispatch stage. Shoot and root growth, biomass, leaf area (LA), Dickson Quality Index (DQI), structural ratios (shoot/root ratio, SRR; height/diameter ratio, HDR), and anatomical traits were measured. Data were analyzed using analysis of variance with Scott–Knott clustering, Pearson correlation, and Principal Component Analysis (PCA). Significant variability was observed among clones. Clones 88, VR3, 8, and LB33 showed the highest stem diameter (SD), total dry mass (TDM), LA, and DQI, with balanced shoot and root development. Leaf area correlated strongly with SD, number of leaves (NL), biomass, and DQI, confirming its role as a seedling quality indicator. PCA identified two groups: a high-performance group with greater vigor and biomass, and a lower-performance group including clones 7, MR04, and VR4. The convergence of methods confirms the robustness of the results. Overall, clones 88, VR3, 8, and LB33 demonstrate superior agronomic potential at the seedling stage, offering promising options for nurseries, growers, and clonal selection programs. Full article

The application of biostimulants is a promising tool for enhancing plant growth and crop quality in the context of sustainable and resilient agricultural production. This study evaluated four microbial biostimulants (IMB1–4) on Sonchus oleraceus L. under field and pot cultivation. Our results indicate that the growing system was a more dominant factor than biostimulants in influencing plant performance. For morphological and growth traits, biostimulants generally had a neutral or negative impact compared with untreated plants, with IMB3 consistently showing the lowest performance. Field-grown plants, especially the untreated ones, excelled in plant weight and leaf count, while pot-grown plants treated with IMB2 and IMB4 achieved higher leaf weight per plant, leaf area, and chlorophyll index (SPAD). Specifically, untreated field plants recorded the highest biomass, whereas IMB2 and IMB4 optimized leaf traits in pots. Biostimulant applications enhanced fat content and energetic value, with IMB1 and IMB2 yielding the highest protein levels. Pot cultivation favored the accumulation of nitrogen, phosphorus, and sodium, while IMB2-treated pot plants proved most effective for maximizing overall nutrient content. The phytochemical profile also varied by system: pot-grown plants yielded higher total phenols, particularly with IMB3, while field-grown plants recorded higher flavonoids, especially with IMB4. Furthermore, untreated or IMB3-treated pot plants exhibited the highest antioxidant activity, significantly outperforming field-grown counterparts. In conclusion, while biostimulants did not improve morphological and growth traits, they significantly enhanced the nutritional and phytochemical quality of S. oleraceus L., particularly in the pot cultivation system, where specific biostimulants (IMB2 and IMB3) resulted in nutrient-dense crops with high antioxidant value. Full article

This study evaluated the effects of different light-emitting diode (LED) spectral qualities on the early growth of kale at the baby-leaf harvest stage in a plant factory with artificial lighting (PFAL) by integrating morphological traits, biomass accumulation, plant quality indices, vegetation indices, and chlorophyll a fluorescence. Two kale (Brassica oleracea L.) cultivars, ‘Jellujon’ and ‘Manchoo Collard’, were grown for four weeks under monochromatic red, green, and blue LEDs, a purple composite LED with far-red wavelengths, and three white LEDs with different correlated color temperatures (3000, 4100, and 6500 K). Blue LED increased shoot height by approximately 14–28%, depending on cultivar and comparison among the white LED treatments, but this elongation did not translate into superior biomass production. In contrast, white LEDs, particularly at 3000–4100 K, increased leaf area to 24.2–24.9 cm² and SPAD units to 47.3–50.2, whereas blue or green LEDs generally resulted in smaller leaves and lower SPAD units. Shoot dry weight under 3000–4100 K white LEDs reached 0.25–0.26 g in ‘Jellujon’ and 0.26–0.29 g in ‘Manchoo Collard’, approximately twofold higher than under blue or green LEDs. Compactness, Dickson quality index, root investment ratio, and leaf efficiency index were also more favorable under white LEDs, indicating improved plant sturdiness and structural stability. Green LED light was associated with lower maximum photochemical efficiency (Φ_Po) and greater energy dissipation (Φ_Do and DI_o/RC), whereas photochemical reflectance index and PI_ABS tended to be more favorable under selected white LED treatments, although these responses were partly cultivar- and treatment-dependent. Taken together, among the LED spectral quality treatments tested, 3000–4100 K white LEDs provided the most consistently favorable conditions for producing structurally robust, high-quality kale at the early growth stage in PFAL systems. The purple LED showed partial advantages in leaf development and selected physiological responses, but these effects were less consistent across cultivars and indices. 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

Slightly saline–alkali soils represent an important but underutilized land resource in northern China, and optimizing planting patterns is essential for improving sorghum productivity under such marginal conditions. This study aimed to evaluate the effects of wide–narrow row spacing combined with different planting densities on the canopy structure, photosynthetic performance, and grain yield of brewing sorghum. A field experiment was conducted from 2022 to 2024 at the Yulin Experimental Station in Shaanxi Province, China, using the brewing sorghum cultivar Liaonuo 16. Four planting treatments were established: wide–narrow row spacing (80/60 cm) with three planting densities (105,000, 112,500, and 120,000 plants ha⁻¹) and uniform row spacing (60 cm) with 112,500 plants ha⁻¹ as the control. Wide–narrow row spacing combined with higher planting density significantly improved canopy structure and light interception. The treatment with 120,000 plants ha⁻¹ increased light interception in the middle and lower canopy layers during flowering and grain filling by 8.7% and 25.58%, respectively, and enhanced total canopy light interception by 3.33% and 1.96%. Moreover, the leaf area index and photosynthetic capacity were improved, resulting in a 10.1% increase in grain yield compared with the uniform row spacing treatment. Wide–narrow row spacing combined with a planting density of 120,000 plants ha⁻¹ effectively optimizes canopy structure and enhances sorghum productivity in slightly saline–alkali soils, providing a practical cultivation strategy for improving resource use efficiency in marginal farmlands. Full article

Salinity is a major constraint for lettuce production, affecting plant growth, physiological status, and market quality. This study evaluated the combined effects of increasing salinity levels (S₀: non-saline control; S₃₀, S₆₀, and S₁₂₀ mM NaCl) and Trichoderma harzianum inoculation on morphological, physiological, and quality-related traits of lettuce. Increasing salinity levels resulted in significant reductions in growth-related parameters, particularly leaf area, shoot biomass, root volume, and cutting resistance (CR), with the most pronounced decreases observed at S₁₂₀. In contrast, several physiological and quality-related parameters showed different response patterns. Membrane stability index (MSI) and chlorophyll index remained relatively stable across salinity treatments, while total soluble solids (C) increased with increasing salinity, indicating osmotic adjustment under stress conditions. Leaf color parameters showed reductions in lightness and chroma at higher salinity levels, suggesting structural and optical changes in leaves rather than severe pigment degradation. The effects of Trichoderma on plant growth were limited and did not consistently mitigate growth reductions under salinity. However, inoculation influenced several physiological and quality-related traits, including MSI and TSS, indicating a role in physiological regulation and stress adaptation rather than direct growth promotion. Multivariate analyses indicated that salinity was the main factor contributing to treatment separation, whereas Trichoderma application influenced the overall trait profile without consistently increasing growth parameters. Overall, the results suggest that under saline conditions, Trichoderma may contribute to stress tolerance and physiological stability rather than directly increasing plant growth, and its effectiveness depends on stress severity. Full article

Cowpeas are a legume widely consumed in Brazil. Given this, the objective of this study was to investigate the presence of metals (loids) in pods and leaves of Vigna unguiculata located near a highway with high vehicle traffic and a landfill, and to assess possible risks to human health. Pod and leaf samples were collected at nine points between the highway and the landfill. The elements were analyzed by inductively coupled plasma optical emission spectroscopy (ICP-OES) and quantified. The risk to human health was assessed using risk quotient and risk index values. A quantitative analysis of the chemical elements was also performed using the maximum tolerable intake level. Element concentrations were higher in the leaves than in the pods. The human health risk analysis showed that the average daily consumption of both pods (44 g/day) and leaves (67 g/day) may pose a chronic health risk to adult men and women, due to simultaneous exposure to multiple metals. It was concluded that the plant is contaminated and that its ingestion can cause toxicity, warranting warnings against cultivating areas near anthropogenic activities that may be contaminated with heavy metals, thereby affecting nutritional safety. 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

Leaf Area Index (LAI) is a fundamental parameter for characterizing the growth of tea (Camellia sinensis L.). However, in rugged mountainous regions, the combined effects of topographic relief and canopy structural heterogeneity severely constrain the accuracy of UAV-based multispectral LAI retrieval. This study develops an integrated framework combining topographic correction with interpretable machine learning to improve LAI estimation. We utilized a UAV multispectral dataset collected during the peak growing season from a typical tea-growing region in Fujian Province, China (altitude range: 58–186 m), comprising a total of 90 samples. Three topographic correction methods, including Sun–Canopy–Sensor (SCS), SCS with C correction (SCS+C), and Minnaert+SCS, were evaluated in combination with Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models. Results indicated that the SCS+C algorithm outperformed other methods by effectively accounting for direct and diffuse radiation components, thereby reducing topographic dependence while maintaining radiometric consistency across heterogeneous surfaces. The XGBoost model combined with SCS+C correction achieved the highest performance (R² = 0.8930, RMSE = 0.6676, nRMSE = 7.93%, MAE = 0.4936, Bias = −0.0836). SHapley Additive exPlanations (SHAP) analysis revealed a structure-dominated retrieval mechanism, in which red-band textural features (Correlation_R) exhibited higher importance than conventional vegetation indices. Compared with previous studies that primarily focus on either topographic correction or model development, this study provides quantitative insights into the underlying retrieval mechanisms. This framework improves the precision of tea LAI retrieval in complex terrains and provides a robust methodological basis for digital management in mountainous agriculture. Full article