ABSTRACT Accurate remote sensing inversion of mangrove canopy chlorophyll (Cab) is vital for dynamically monitoring ecosystem health. Yet canopy structure induces strong uncertainty in Cab inversion, complicating model development, parameter optimization, and inversion accuracy. To explore the influence of the canopy on Cab inversion, this paper combines PROSAIL with machine learning to analyze mangrove canopy spectral characteristics, focusing on the leaf area index (LAI) and average leaf angle (ALA), and quantifies their individual/interactive effects on Cab. Results: (1) Mangroves exhibit distinct spectral characteristics, with reflectance in the green and red-to-near-infrared range consistently intermediate between those of Phragmites australis and Spartina alterniflora. (2) Leaf structural parameter (N), LAI, and ALA modulated canopy reflectance across the spectrum, with the LAI exerting influence on overall spectral region and other traits acting locally. (3) Inverting Cab using LAI and ALA separately, support vector regression outperforms random forest, improving accuracy by 31% (R² = 0.83) and 13% (R² = 0.88) and reducing RMSE by 69.51% and 73.75%, respectively. (4) With LAI–ALA synergy, a synergistic physically constrained model achieved R² of 0.74 and RMSE of 3.07. The model showed superior performance in independent validation (R² = 0.75, RMSE = 1.42), surpassing a machine learning based on vegetation indices (R² = 0.49, RMSE = 11.24). Its practical utility has been confirmed in the Aojiang Estuary mangroves.

Central Asian vegetation is more sensitive to soil moisture drought than to heat and meteorological drought.

The timing of phenological events, such as the start of season (SOS) and end of the season (EOS), is critical to understand the response of terrestrial ecosystems to climate change. Phenology patterns in space are not easily detected in multi-layered canopy structures, such as broadleaved deciduous forests; discrepancies in measures from the ground and space are known. Lidar signals can penetrate canopy and is potentially useful to solve some of the challenges in remote sensing phenology. Here, phenology time series derived from LiDAR-based Plant Area Index (PAI) from the Global Ecosystem Dynamics Investigation (GEDI) were compared with passive optical Leaf Area Index (LAI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and further evaluated using high-resolution Sentinel-2–derived phenological metrics Results evidence clear differences in the detection of the senescence phase in broadleaved European forests at different latitudes, with GEDI-PAI estimating EOS up to 49 days later than MODIS-LAI. Sentinel-2–derived EOS dates were intermediate between MODIS and GEDI estimates (∼35 days), supporting the interpretation that GEDI captures structural signals persisting beyond optical senescence. GEDI-PAI consistently retrieved later EOS dates and longer growing season length, reflecting its sensitivity to canopy structural changes during leaf fall. Robust phenological signals were detected in broadleaved forests, whereas needleleaved forests showed limited seasonal GEDI-PAI variability. In contrast, MODIS-LAI better captures changes in leaf color and greenness and better represents fine-scale variations during the active growing season. Overall, these findings demonstrate that optical and spaceborne LiDAR sensors capture complementary aspects of forest phenology, and that their integration improves phenological characterization relevant to ecological and climate change research. • GEDI-PAI has the capability to detect seasonal variations in European forests. • GEDI-PAI and MODIS-LAI showed a high correlation in broadleaved forests. • MODIS-LAI values are higher than GEDI-PAI during the active growing period. • In contrast to MODIS-LAI, GEDI-PAI detects the senescence phase later in broadleaved forests. • GEDI-PAI_z captures foliage changes in vegetative/non-vegetative periods along vertical profile.

Accurately retrieving Sun-Induced Fluorescence (SIF) is critical for monitoring plant physiological status, yet the signal is significantly distorted by light reabsorption and scattering within the canopy. While empirical models exist for the far-red region of the spectrum, accurately accounting for the photon escape fraction in the complete Chlorophyll Fluorescence (ChlF) emission range remains challenging. Based on our previous work under monochromatic conditions, in this work we present a photophysical framework to estimate the chlorophyll fluorescence escape fraction (fesc) across the full chlorophyll emission spectrum (600-800nm) under polychromatic excitation. The methodology integrates experimental radiance measurements of Bistorta amplexicaulis with an algorithm to decouple reflectance from emission. To evaluate the model's robustness in the field, we conducted a global sensitivity analysis using a synthetic dataset generated by coupling the SMARTS atmospheric radiative transfer model with the PROSAIL canopy model. Our results demonstrate that failing to account for canopy light reabsorption and scattering can underestimate fluorescence yields by approximately 25%. We identified distinct drivers for fesc in SIF-relevant bands: fesc in the red region (687nm) is primarily governed by chlorophyll content and Leaf Area Index (LAI) due to intense fluorescence reabsorption, while fesc in the far-red region (760nm) is dominated by canopy structure and leaf inclination (LIDFa). This study provides a practical and robust estimation method for fesc at the canopy level, offering a key tool for improving the accuracy of SIF-based photosynthetic efficiency assessments in both environmental and agronomic remote sensing applications.

Increased groundwater recharge under climate change will enhance nitrogen fixation in groundwater-dependent ecosystems.

Peak greening at moderate shrinking intensity across Chinese counties.

Leaf Area Index (LAI) is a fundamental parameter linking vegetation structure with surface energy and carbon exchange in Earth system models. However, the underestimation of LAI caused by snow cover remains a persistent limitation of existing satellite products. Here, we develop a global snow-free LAI dataset covering the years 1985-2020 at 500 m resolution. The method compiles over 2700 leaf lifespan records from the TRY plant trait database, spanning observations for numerous plant species, and aggregates them into plant functional type (PFT)-specific values. It then identifies snow-affected regions using MODIS data and applies a leaf-lifespan-based correction to PFT-specific LAI values. The physiologically constrained snow-free LAI effectively corrects the underestimation of LAI in snow-affected regions. Simulations with the Common Land Model indicate that snow-free LAI improves albedo simulations over snow-covered regions by better representing vegetation masking effects and reducing positive albedo bias. Additionally, the snow-free LAI increases net radiation and gross primary productivity, and reduces snow depth. This snow-free LAI dataset provides a reliable input for modeling vegetation-snow interactions in Earth system models, supporting more accurate simulations of surface energy, water and carbon dynamics.

ABSTRACT Accurate retrieval of leaf area index (LAI) is vital for crop monitoring and genetic breeding. Although multi-modal unmanned aerial vehicle (UAV) remote sensing has advanced LAI estimation, conventional empirical models often overfit on small breeding populations and cannot disentangle the true causal effects of genetic backgrounds from confounding factors within a statistically rigorous framework. This study presents a robust framework for plot-scale maize LAI estimation across 800 breeding plots from four genetic subgroups: doubled haploid (DH), mixed, temperate (TEM), and tropical/subtropical (TST). From UAV RGB and multispectral imagery acquired at three phenological stages, we extracted 76 multi-modal features comprising point-cloud structural metrics, spectral vegetation indices, and texture features. Following a dual-criterion mutual information and multicollinearity filter, four algorithms, including traditional tree-ensembles and the Tabular Prior-data Fitted Network (TabPFN), were evaluated using nested validation. TabPFN achieved superior generalization performance, yielding a mean test R 2 of 0.778 ± 0.031, an RMSE of 0.264 ± 0.024, and an MAE of 0.203 ± 0.022, significantly outperforming tree-ensemble models (p < 0.01). Across growth stages, retrieval accuracy peaked at the expanded bell-mouth stage (R 2 = 0.802) and successfully captured the unimodal trajectory of canopy development. SHAP-based attribution showed that spectral indices contributed most to the predictions (55.9%), followed by canopy texture (26.3%), with the spatial heterogeneity metric Tex_Entropy being the most influential single feature (22.8%). When embedded as the nuisance estimator within a Double Machine Learning framework for causal inference, TabPFN confirmed that, relative to the TEM subgroup, only the DH genetic background exerted a consistent and significant negative causal effect on LAI (ATE = −0.070, p = 0.030). These results establish TabPFN as a reliable and extensible tool for non-invasive, high-throughput phenotyping in precision breeding.

Agriculture in hilly regions holds significant potential but is often undervalued in the context of food production due to the distinct terrain, microclimate, and subsistence farming practices. This study explores long-term water and energy fluxes across the years 2017-2021, over a rainfed rice-wheat system using the eddy covariance technique to evaluate evapotranspiration (ET) dynamics. Seasonal variation in ET during rice and wheat growing seasons closely follows the daily magnitude of available net energy, relative canopy cover and the supply of soil moisture. The total ET during the rice and wheat growing seasons ranged from 319.39-403.82mm and 341.81-458.29mm, respectively, with maximum daily ET values of 7.21mm day-1 for rice and 6.79mm day-1 for wheat. Path analysis was used to examine the direct and indirect effects of environmental and biophysical factors on ET, including net radiation (Rn), air temperature (Tair), vapor pressure deficit (VPD), soil water content (SWC), stomatal conductance (Gs), and leaf area index (LAI). VPD was the dominant driver of ET during the rice season, while both VPD and Rn significantly influenced ET during the wheat season. Gs was also a key factor, with stronger control during the wheat season. Notably, VPD had a negative impact on ET through Gs in both seasons. Overall, this study highlights how ET ET in rainfed rice-wheat systems interacts with environmental and biophysical factors, providing insights into crop-water relations and land-atmosphere interactions.

Horti-silvi-pasture (HSP) systems offer a climate-resilient strategy to improve fodder productivity and sustainability in semi-arid areas. This study assessed two HSP systems, Holoptelea integrifolia + Punica granatum (H1) and Holoptelea integrifolia + Annona squamosa (H2), across two spacings (5m × 5m and 5m × 4m) to determine their impacts on the growth, yield, and nutritional quality of BN hybrid grass, as well as tree biomass and carbon dynamics during 2024 and 2025 in Bundelkhand, India. The H2 system markedly enhanced growth attributes, achieving greater plant height (130.85cm), shoots per m2 (149.55), and leaf area index (4.21) in pooled analysis. It also yielded superior green (27.97 t ha- 1) and dry forage (6.17 t ha- 1) compared to H1. The proximate composition improved under H2, exhibiting elevated levels of crude protein (8.45%), crude fiber (61.64%), and total ash (13.57%). Increased spacing (5m × 5m) enhanced the growth and yield of BN hybrid grass, while reduced spacing (5m × 4m) led to comparatively elevated fiber fractions and biochemical components. Among tree species, H. integrifolia (5m × 4m) demonstrated the greatest biomass (4.60 Mg ha- 1), carbon stock (2.90 Mg ha- 1), and CO₂ mitigation potential (10.64 Mg ha- 1). Correlation study revealed significant positive correlations between growth attributes and yield, but fibre fractions exhibited negative correlations with growth attributes. The study illustrates that ideal species combinations and spacing in HSP systems can markedly enhance fodder yield, nutritional quality, and carbon sequestration, providing a sustainable approach for climate-resilient livestock farming systems.

Sustainable intensification of soybean production requires strategies that simultaneously enhance plant growth, nutrient acquisition, and microbial symbiosis, particularly in nutrient-limited soils. This experiment investigated the combined effects of organic amendments (Biochar, Vermicompost) and microbial biofertilizers [encapsulated Rhizobium and arbuscular mycorrhizal fungi (AMF)] on the morphophysiological performance of black soybean (Glycine max). All treatments, the integration of Vermicompost and Biochar with dual inoculation (encapsulated Rhizobium + AMF) consistently produced the most pronounced improvements. Leaf area index and height-diameter ratio were significantly enhanced from 21days after planting onward, with the strongest canopy expansion and structural growth observed at 28days. Biomass of shoots and roots production was maximized under Biochar + dual inoculation, surpassing all other treatments. Phosphorus uptake was significantly elevated, and AMF root colonization reached 80%, the highest across treatments. This treatment also supported the greatest nitrogen-fixing bacterial population (7.63 × 105CFUg⁻1 soil), indicating synergistic microbial interactions. Data analyses confirmed that improvements in morphophysiology, nutrient acquisition, and microbial activity were strongly interrelated, with the majority of variance explained by coordinated responses under the Biochar + dual inoculation system. The integration of Biochar with encapsulated Rhizobium and AMF represents a highly effective strategy to enhance black soybean productivity and microbial symbiosis in Inceptisol, offering a promising pathway toward sustainable crop management.

Effective weed management in faba bean (Vicia faba L.) requires precise adjustment of herbicide dose and application timing to achieve effective weed suppression while maintaining crop growth and yield. This study employed response surface methodology (RSM) to quantify and optimize the interactive effects of imazethapyr rate and application timing on weed biomass, morphophysiological traits, and yield of faba bean under field conditions in western Iran during a single 2024-2025 growing season.Imazethapyr (Pursuit® 10% SL) was applied at rates ranging from 0 to 1000 mL ha⁻¹ at pre-plant incorporated, pre-emergence, and post-emergence stages using a central composite design. Leaf area index, plant height, number of pods per plant, 100-seed weight, biological yield, grain yield, and weed dry weight were modeled using quadratic and cubic RSM functions. Strong nonlinear dose by timing interactions were observed for all responses. Intermediate imazethapyr rates (250-500 mL ha⁻¹) applied from pre-planting to early post-emergence (3-10 days after sowing) maximized canopy development, reproductive performance, biological yield, and grain yield while minimizing weed biomass. Higher rates (≥ 750 mL ha⁻¹) or late post-emergence applications reduced crop performance despite improved weed suppression, indicating phytotoxic effects. Model diagnostics showed high predictive accuracy, particularly for leaf area index, 100-seed weight, and weed dry weight. Multi-response desirability analysis identified a favorable management range balancing weed control and yield performance, indicating the value of RSM as a decision-support framework for precision herbicide management in faba bean.

Assessment of vegetation response to flow scenarios using a coupled eco-hydrological model in a semi-arid wetland.

Silicon (Si) deficiency limits plant growth, physiological efficiency, and yield in high-value crops such as Coriandrum sativum L. A field experiment was conducted on Si-deficient soil at TNAU Coconut Farm, Coimbatore, India, using coriander variety CO (CR) 4. Seven treatments in a randomized block design with three replications evaluated calcium silicate (CaSiO3), and rice husk ash (RHA) at 225, and 275kg Si ha- 1, alone, and combined with Bacillus altitudinis SSB4, across growth, physiological, biochemical, antioxidant, and yield parameters. Si + SSB4 integration significantly improved plant height (up to 60.9% over control), leaf area index, SPAD index, and shoot and root dry matter production. Leaf Si content increased by 84.1-88.4% over absolute control, and 21.7-24.5% over RDF alone. Antioxidant enzymes (CAT, POD, and SOD), and biochemical attributes (total soluble sugars, total soluble protein, total phenols, and ascorbic acid) were markedly enhanced, with total soluble protein recording the highest increase (194.8% over absolute control). RHA-based treatments marginally but non-significantly outperformed CaSiO3-based treatments. The highest leaf yield (5.46 t ha- 1) was recorded with RDF + RHA at 275kg Si ha- 1 + SSB4 (53.8% over RDF), followed by RDF + CaSiO3 at the same level (52.1%). Strong positive correlations (r = 0.863-0.990) among Si content, antioxidant enzymes, biochemical parameters, and yield confirmed a coordinated Si-mediated improvement. Integrated application of CaSiO3 or RHA with B. altitudinis SSB4 synergistically enhanced growth, antioxidant defence, biochemical quality, and leaf yield of coriander in Si-deficient soils, supporting the adoption of combined chemical-biological Si management strategies.

Contrasting 2001 and 2020 land cover states: Impacts on heterogeneous HONO chemistry and nitrate formation in China.

A field experiment was conducted in Al-Muthanna Governorate during the 2024–2025 winter season on a private farmer's land to study the effect of row spacing (55, 65, and 75 cm) and plant spacing (15, 20, 25, and 30 cm) on vegetative growth and yield traits of faba beans. The experiment followed a split-plot arrangement within a randomized complete block design (RCBD) with three replications. Row spacing treatments were assigned to the main plots, while plant spacing treatments were allocated to the sub-plots.The results indicated that increasing row spacing had a significant effect on chlorophyll content in the leaves. The 75 cm row spacing recorded the highest average SPAD value of 50.35, while the 55 cm spacing gave the lowest at 46.88. Narrow row spacing (55 cm) resulted in the highest values for leaf area index, seed yield, and biological yield, reaching 6.56, 4.64, and 22.13 tons ha⁻ ¹, respectively. In contrast, the widest spacing (75 cm) recorded the lowest averages at 4.03, 2.96, and 16.87 tons ha⁻ ¹, respectively. Row spacing had no significant effect on fertilization rate or protein content.For plant spacing, 30 cm produced the highest chlorophyll content (50.16 SPAD), while 15 cm gave the lowest (46.22 SPAD). However, the closest spacing (15 cm) resulted in the highest leaf area index and biological yield (6.22 and 25.09 tons ha⁻ ¹), whereas 30 cm spacing showed the lowest values (3.58 and 13.46 tons ha⁻ ¹). The 25 cm spacing gave the highest seed yield (4.97 tons ha⁻ ¹), while 30 cm recorded the lowest (2.34 tons ha⁻ ¹). Fertilization rate peaked at 25 cm spacing with 10.42%, though this was not statistically significant. In terms of interaction, the combination of 55 cm (row) × 15 cm (plant) produced the highest leaf area index (9.56), while 55 cm × 25 cm resulted in the highest seed yield (5.91 tons ha⁻ ¹). Additionally, the 65 cm × 25 cm combination gave the highest harvest index at 41.53%.

Integrating dynamic vegetation phenology into SWAT improves watershed nitrogen and phosphorus simulations.

Three-dimensional green quantity (3DGQ) mapping and integrated 2D-3D assessment reveal hidden urban green deficits and inequities.

The Galapagos Islands are globally recognized for their biodiversity, yet the high volume of annual visitors and dependence on food imports raise significant concerns regarding human-induced environmental pressure. The COVID-19 pandemic resulted in a complete halting of tourism and extreme mobility restrictions, presenting a unique natural experiment to quantify how the abrupt reduction of human activity affects vegetation. This study provides the first joint assessment of how the tourism and agricultural sectors reacted in tandem to this mobility shock. Using satellite-based Leaf Area Index (LAI) data and a doubly robust difference-in-differences approach, we examine vegetation density shifts across both touristic and agricultural zones. Our findings reveal a significant 51% increase in vegetation density within tourism areas, specifically the fragile Bushes and Cacti zones, following the cessation of foot traffic. Concurrently, we document a 33% increase in LAI within agricultural zones relative to the counterfactual, signaling an intensification of local production. Further, these structural shifts in vegetation density persisted even as initial restrictions were eased. These quantitative results offer novel insights into the rapid responsiveness of island landscapes to changes in the human footprint, providing a data-driven foundation for resilient land-use policy.

The vegetation in climatically heterogeneous regions exhibits significant spatial variability and temporal succession characteristics. It is crucial to obtain consistent vegetation characteristics in this region over time. Traditional single indices such as NDVI (Normalized Difference Vegetation Index), LAI (Leaf Area Index), and NPP (Net Primary Productivity) each have their own advantages, but they often show inconsistent trends when applied to complex vegetation. To effectively capture spatial heterogeneity and enhance the ecological interpretability, we propose a Dynamic Spatially Variable Weighted Synthesis Vegetation Index (DWS-SVI). Based on four Global Land Surface Satellite Dataset (GLASS) vegetation parameters (FVC (Fractional Vegetation Cover), LAI, NDVI, and NPP) and land cover types, this method employs the CRITIC method to perform dynamic weighting and generate continuous weight surfaces, ultimately synthesizing a comprehensive vegetation index at the pixel level. It combines "global trend and local adaptation" by integrating spatial heterogeneity modeling and multi-variable dynamic weighting, thereby overcoming the limitations of traditional methods in terms of spatial heterogeneity and ecological interpretability. Results show that over the past two decades, more than 69.4% of the area in the YRB has witnessed a significant improvement in vegetation conditions. The improvement was most notable in the summer, and it was mainly attributed to the improvement in the temperature and humidity conditions in this region. Compared with a single indicator, the DWS-SVI index can reflect the coordinated evolution of ecosystem structure and function, and can effectively suppress the observation errors caused by the bias of a single vegetation index, especially the "false greening" signals in transition zones and arid areas. Furthermore, the dominant factor map constructed based on DWS-SVI further reveals the differentiated driving mechanisms of ecosystems such as farmland, grassland, and forest, demonstrating that it has superior interpretability. This study provides a transferable framework for constructing spatially adaptive vegetation indices, enabling more reliable monitoring of ecosystem changes in large river basins and other climatically heterogeneous regions.