Search Results (1,451)

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning. Full article

The Leaf Area Index (LAI) is a fundamental biophysical parameter quantifying forest canopy structure and regulating water–energy exchange. While Abies Mill. forests constitute a vital component of China’s alpine ecosystems, the spatial heterogeneity of their LAI and its sensitivity to environmental filtering remain underexplored. This study employed Random Forest (RF) and Structural Equation Modeling (SEM) to disentangle the direct and interactive effects of climate, soil, topography, and human footprint (HFP) on LAI across 17 distinct Abies forest types. The results revealed that temperature was the dominant positive driver for the overall Abies forests (Total effect = 2.197), whereas Elevation (DEM) exerted the strongest negative regulation (Total effect = −0.335). However, driver dominance varied substantially among forest types: climatic water availability was the primary constraint for Abies georgei var. smithii (Viguié & Gaussen) W.C.Cheng & L.K.Fu forest (Type 55), while DEM determined LAI in Abies fargesii Franch. forest (Type 49). Notably, we found that HFP could exert positive effects on LAI in specific communities (e.g., Abies densa Griff. forest, Type 58), likely due to understory compensation under moderate disturbance. These findings highlight the necessity of type-specific management strategies and provide a theoretical basis for predicting alpine forest dynamics under changing environments. Full article

Leaf inclination angle (LIA) and leaf area index (LAI) are important components of crop population canopy structure, which affect population photosynthetic production via altering canopy light interception and transmittance, and gas diffusion. In this study, we used a genetically diverse maize population of 378 inbred lines as materials to detect significantly associated SNPs with LIA and LAI using the mixed linear model (MLM) of genome-wide association study (GWAS). A total of 21 SNPs associated with LIA explain 6.07–10.86% of the phenotypic variation, containing two major-effect SNPs over 10%; 38 SNPs associated with LAI explain 2.91–10.36% of the phenotypic variation, containing one major-effect SNP. One candidate gene, GLCT1, significantly associated with LIA was identified, which might involve cell-wall biosynthesis. In addition, a cascade of SNPs significantly associated with LAI was identified in a single environment, and a candidate gene encoding the bHLH144 transcription factor was found. The results provide a theoretical basis for the selection of maize inbred lines with ideal canopy architecture and further investigation of the genetic mechanism of LIA and LAI. Full article

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. Full article

Canada’s forests are a critical component of the global carbon pool and play an essential role in regulating the Earth’s climate. Since 2000, increasing disturbances have reduced ecosystem resilience, raising concerns about the long-term carbon sequestration capacity of Canada’s forests. Yet, the resilience of Canada’s undisturbed forests remains poorly understood. In this study, we assessed resilience across undisturbed forests from 2001 to 2018 by applying the lag-1 autocorrelation (AR(1)) metric to leaf area index (LAI) time series. Our analyses revealed a widespread and substantial temporal shift in resilience from declining to increasing despite a persistently greening trend. These resilience transitions were most pronounced in mixed-species and intermediate-aged forests. By quantifying the influence of multiple environmental drivers, we found that variability in temperature and moisture exerted dominant controls on resilience shifts. Cooler conditions and higher moisture availability contributed to increased resilience, with the largest resilience shifts occurring in regions experiencing sustained cooling or wetting trends. These findings imply that conservation strategies favoring mixed-species and intermediate-aged forests under cooler, wetter conditions could promote long-term ecosystem stability. Full article

Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R² = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R² = 0.64 and R² = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management. Full article

Legume green manure incorporation offers a potential pathway for sustainable cropping in arid irrigated areas. This study aimed to determine whether water and nitrogen inputs could be concurrently reduced without compromising maize productivity under this practice. A two-year field experiment (2024–2025) was conducted using a split-plot design with three irrigation levels (I1: 4045, I2: 3240, I3: 2430 m³·ha⁻¹) and three nitrogen rates (N1: 360, N2: 288, N3: 216 kg·ha⁻¹). Compared with conventional management (I1N1), 20% co-reduction in water and nitrogen (I2N2) maintained stable leaf area index (LAI), net photosynthetic rate (P_n), transpiration rate (T_r), DM, and GY, while significantly increasing water use efficiency (WUE) by 7.6% and nitrogen use efficiency for grain yield (NUtEg) by 11.7%. Excessive water reduction (I3) or nitrogen reduction (N3) significantly inhibited growth and reduced yield (p < 0.05). Soil water content under I2N2 did not differ significantly from I1N1 in the 0–110 cm profile, and soil total nitrogen remained higher at silking.) Structural equation model (SEM) revealed SWC and STN indirectly affected P_n and T_r via regulating LAI and SPAD (path coefficients: 0.48–0.62), which drove DM accumulation and determined GY (R² = 0.81). These short-term results suggest that moderate water-nitrogen reduction with green manure can sustain yield while improving resource efficiency, offering a promising practice for arid irrigated maize systems, though longer-term validation is needed. Full article

With the advancement of modern agriculture and increasing scarcity of water and fertilizer resources, determining optimal water and nitrogen (N) management strategies for intercropping systems is critical for ensuring system productivity and enhancing resource-use efficiency. This study employed field experiments to investigate the effects of different water and N treatments on grain yield, aboveground biomass, leaf area index (LAI), photosynthetic parameters, chlorophyll fluorescence characteristics, and radiation use efficiency (RUE) in a maize–soybean intercropping system. The experiment consisted of three cropping systems (maize monoculture, soybean monoculture, and maize–soybean intercropping), two irrigation regimes (rain-fed and supplemental irrigation), and three N-application rates for maize (240, 180, and 120 kgN ha⁻¹). The results demonstrated that supplementary irrigation significantly enhanced the LAI and photosynthetic capacity of both maize and soybean during critical growth stages, thereby promoting increases in grain yield and aboveground biomass. Intercropping significantly improved the productivity and photosynthetic performance of maize compared to monoculture, whereas soybean exhibited a reduction under intercropping conditions. Furthermore, irrigation regime and N rate had significant interactive effects on the photosynthetic performance of maize at the tasseling stage. In the intercropping system, a 25% reduction in the conventional application rate of N for maize maintained system productivity, whereas a 50% reduction substantially decreased maize yield and photosynthetic performance. The intercropping system achieved land equivalent ratios (LERs) ranging from 1.06 to 1.11 and RUE advantages (ΔRUE) of 1.52 to 1.64, demonstrating significant superiority in land and light resource utilization. Considering both productivity and resource-use efficiency, supplemental irrigation combined with 180 kgN ha⁻¹ N application for maize represents the optimal water and N management strategy for achieving high yield and efficiency in maize–soybean intercropping systems in the Guanzhong plain. Full article

Potassium (K) is crucial for global maize (Zea mays L.) production, yet the issue of “high K fertilizer input but low utilization efficiency” in K-rich soils of Xinjiang remains underexplored. A three-year field experiment (2020, 2021, 2024) in Xinjiang evaluated the effects of reduced K application on maize growth, grain yield (GY), and K-use efficiency. Five treatments were tested: K100 (136.0 kg K₂O·ha⁻¹), K60 (83.5 kg K₂O·ha⁻¹), K40 (55.6 kg K₂O·ha⁻¹), K0 (no K), and CK (no fertilizer). The research shows that K60 significantly outperforms K100 in terms of physiological parameters (plant height + 2.7–34.7%, leaf area index (LAI) + 6.3–26.8%, dry matter + 22.0–28.8%); GY and thousand kernel weight (TKW) improved by 6.9–15.1% and 9.3–30.3%, respectively. The potassium fertilizer productivity (PFP_K) and potassium fertilizer agronomic efficiency (AE_K) increased by 78–112.3% and 176.4–2085% compared to the K100. During the three-year period, the maximum net income of K60 reached 28,206 CNY·ha⁻¹, which was 18.9–20.7% higher than that of K100. Regression analysis identified an optimal K rate of 82.2–85 kg·ha⁻¹ for maximum yield. Least squares structural equation mode (PLS-SEM) and correlation analyses revealed that moderate K reduction enhanced vegetative growth and optimized yield structure, indirectly boosting yield, thereby directly driving net income. Thus, reducing K input can achieve “lower input with higher efficiency”, offering a practical basis for optimizing K management in arid-region maize systems. Full article

The leaf area index (LAI) serves as a critical parameter for assessing wetland ecosystem functions, and accurate LAI retrieval holds substantial significance for wetland conservation and ecological monitoring. To address the spatial constraints of traditional ground-based measurements and the limited accuracy of single-source remote sensing data, this study utilized unmanned aerial vehicle (UAV)-borne hyperspectral and LiDAR sensors to acquire high-quality multi-source remote sensing data of coastal wetlands in the Yellow River Delta. Three machine learning algorithms—random forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were employed for LAI retrieval modeling. A total of 38 vegetation indices (VIs) and 12-point cloud features (PCFs) were extracted from hyperspectral imagery and LiDAR point cloud data, respectively. Pearson correlation analysis and the Shapley Additive Explanations (SHAP) method were integrated to identify and select the most informative VIs and PCFs. The performance of LAI retrieval models built on single-source features (VIs or PCFs) or multi-source feature fusion was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). The main findings are as follows: (1) Multi-source feature fusion significantly improved LAI retrieval accuracy, with the RF model achieving the highest performance (R² = 0.968, RMSE = 0.125). (2) LiDAR-derived structural metrics and hyperspectral-derived vegetation indices were identified as critical factors for accurate LAI retrieval. (3) The feature selection method integrating mean absolute SHAP values (|SHAP| values) with Pearson correlation analysis enhanced model robustness. (4) The intertidal zone exhibited pronounced spatial heterogeneity in the vegetation LAI distribution. Full article

Training system selection critically influences peach orchard productivity through its effects on canopy light environment, physiological responses, and fruit quality. This study evaluated two contrasting training systems: a 2D planar fruiting wall system (Four-Axis, 1020 trees ha⁻¹) versus a 3D Quad-V system (590 trees ha⁻¹) using two peach cultivars, fresh table ‘Platibelle’ and clingstone ‘Mirel’ in Central Macedonia, Greece. Comprehensive physiological measurements including leaf gas exchange, chlorophyll fluorescence, and fruit quality parameters were assessed across two canopy zones (lower 0–1.2 m vs. upper 1.8–3.3 m) during the 2023 and 2024 growing seasons. Results demonstrated that the 2D system achieved superior leaf area index (LAI), but lower light interception, leading to enhanced photosynthetic performance with 15–20% higher net photosynthetic rates and improved water-use efficiency compared to the 3D system. Notably, the photosynthetic apparatus of fruiting wall trees maintained significantly greater efficiency (6.26 μmol CO₂ m⁻² s⁻¹) in the lower canopy zone than in Quad-V trees (3.6 μmol CO₂ m⁻² s⁻¹), indicating a more uniform and functional light environment. The 2D system produced fruits with improved flesh firmness and color development in ‘Mirel’, while higher dry matter in ‘Platibelle’. Correlation analysis revealed that Four-Axis trees enhanced the interdependence among thermal, gas exchange, and compositional traits, reflecting a shift from morphology-driven to metabolically integrated canopy function. In terms of yield, fruiting walls achieved higher efficiency and total production (Mt ha⁻¹) in ‘Mirel’, supporting their adoption to enhance productivity and peach fruit quality in Mediterranean conditions. Full article

It might be difficult in many countries to find extended time series of measurements related to parameters of lakes’ hydrology and their interactions with catchments. Nowadays, the combined use of satellite imagery and spatially distributed hydrological models may contribute substantially to this direction. In this study, in order to assess for a long period of years a lake’s surface elevation (LSE) and its water balance components, Lake Kastoria and its catchment, under Greece’s dry-thermal conditions, were selected as the case study. This research employed the MIKE SHE coupled with the MIKE HYDRO River (MHR) hydrological modeling system, fed with precipitation and leaf area index (LAI) data coming from a ground weather station, typical values of LAI for the specific area, and satellite products from NASA for the precipitation and from Copernicus Global Land Service for the LAI. In all cases where satellite data were used, the simulation of the long-term LSE was very satisfactory, with minor to medium changes to the inflow and outflow components of the water balance in both the catchment (from 0.32 to 7.36%) and the lake (from 1.47 to 11.3%). The above changes were also reflected in the runoff coefficients. In conclusion, the above satellite products can adequately be used for the prediction of the LSE. Furthermore, a plethora of quantified information in relation to the catchment’s water balance can be extracted and used in decision-making processes. Full article

Leaf area index (LAI) is essential for understanding vegetation dynamics, ecosystem processes, and land–atmosphere interactions. Various measurement methods exist, but gap-fraction-based indirect methods are preferred due to their reduced labor and field survey time in comparison to direct methods. However, gap-fraction-based field observations, often referred to as plant area index (PAI), frequently overestimate LAI because they include woody components. To mitigate this issue, the woody-to-total-area ratio (α) can be utilized to exclude these woody components from PAI, yielding more accurate LAI estimates (hereafter referred to as LAI_adjusted). In this study, we demonstrate a novel method to estimate α using Sentinel-2-based normalized difference vegetation index (NDVI) and time-series PAI measurements. The α estimates effectively reduce the influence of woody components in PAI within deciduous broadleaf forests (DBF). Moreover, LAI_adjusted shows good agreement with the Sentinel-2 LAI, which represents effective LAI derived from the PROSAIL model. Notably, the spatial distribution of α effectively captured the expected seasonal dynamics across various forest types. In DBF, α values increased during winter due to leaf fall when compared to the growing season, while seasonal variations were relatively small in evergreen needleleaf forest (ENF). We further confirmed that our method demonstrates greater robustness with NDVI than with other vegetation indices that are more susceptible to topographic variation. Ultimately, this framework presents a promising pathway to mitigate biases in most gap-fraction-based PAI measurements, thereby enhancing the accuracy of vegetation structural assessments and supporting broader ecological and climate-related applications. Full article

Enhancing the carbon sequestration (CS) capacity of urban green spaces is crucial for mitigating global warming, environmental degradation, and urbanisation-induced issues. This study focuses on the urban community unit to establish a system of determining factors for the CS capacity of green space, considering the built-up spatial pattern and green space morphology. An interpretable machine learning approach (Random Forest + Shapley Additive exPlanations) is employed to systematically analyse the non-linear relationship of built-up spatial pattern and green space morphology factors. Results demonstrate significant urban zonal heterogeneity in green space CS, whereas southern suburban area communities exhibited higher capacity. In terms of green space morphology factors, higher fractional vegetation cover (FVC) and cohesion were positively correlated with green space CS capacity. Leaf area index (LAI), canopy density (CD), and the evergreen-broadleaf forest ratio additionally further enhanced the positive effect of two-dimensional green space factors on CS. For built-up spatial pattern factors, communities with a high green space ratio and low development intensity exhibited higher CS capacity. And the optimal ranges of FVC, LAI and CD for effective facilitation of community green space CS were identified as 0.6–0.75, 4.85–5.5 and 0.68–0.7, respectively. Moreover, cohesion, LAI and CD bolstered the CS capacity in communities with a high building density and plot ratio. This study provides a rational basis for planning and layout of green space patterns to enhance CS efficiency at the urban community scale. Full article

Leaf area index (LAI) is a pivotal biophysical parameter linking vegetation physiological processes and macro-ecological functions. Accurate large-scale LAI estimation is indispensable for agricultural management, climate change research, and ecosystem modeling. However, existing methods fail to efficiently extract integrated spatial-spectral-temporal features and lack targeted modeling of spatio-temporal dependencies, compromising the accuracy of LAI products. To address this gap, we propose STC-DeepLAINet, a Transformer-GCN hybrid deep learning architecture integrating spatio-temporal correlations via the following three synergistic modules: (1) a 3D convolutional neural networks (CNNs)-based spectral-spatial embedding module capturing intrinsic correlations between multi-spectral bands and local spatial features; (2) a spatio-temporal correlation-aware module that models temporal dynamics (by “time periods”) and spatial heterogeneity (by “spatial slices”) simultaneously; (3) a spatio-temporal pattern memory attention module that retrieves historically similar spatio-temporal patterns via an attention-based mechanism to improve inversion accuracy. Experimental results demonstrate that STC-DeepLAINet outperforms eight state-of-the-art methods (including traditional machine learning and deep learning networks) in a 500 m resolution LAI inversion task over China. Validated against ground-based measurements, it achieves a coefficient of determination (R²) of 0.827 and a root mean square error (RMSE) of 0.718, outperforming the GLASS LAI product. Furthermore, STC-DeepLAINet effectively captures LAI variability across typical vegetation types (e.g., forests and croplands). This work establishes an operational solution for generating large-scale high-precision LAI products, which can provide reliable data support for agricultural yield estimation and ecosystem carbon cycle simulation, while offering a new methodological reference for spatio-temporal correlation modeling in remote sensing inversion. Full article