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Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on V max and J max , leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO 2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgC year − 1 to 133.94 PgC year − 1 from 2000 to 2022, with an average of 125.93 PgC year − 1 . We also found that the global GPP showed an increasing trend of 0.548 PgC year − 1 during the study period. Further analyses using the structure equation model showed that atmospheric CO 2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP. [ABSTRACT FROM AUTHOR]

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R2 of 0.72, RMSE 0.41) and FCOVER (R2 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. [ABSTRACT FROM AUTHOR]

In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters. Results revealed that the ML models were able to provide relatively accurate early yield estimates. For summer rice, eXtreme gradient boosting (XGB) was the best-performing model at all three stages (45, 60, and 90 DAT), while for kharif rice, the best-performing models at 45, 60, and 90 DAT were XGB, Neural network (NNET), and Cubist, respectively. The combined ranking of ML models showed that prediction accuracy improved as the prediction date approaches harvest, and the best prediction of yield was observed at 90 DAT for both summer and kharif rice. Overall rankings indicate that for summer rice, the top three models were XGB, NNET, and Support vector regression, while for kharif rice, these were Cubist, NNET, and Random Forest, respectively. The findings of this study offer valuable insights into the potential of the combined use of remote sensing and biophysical parameters using ML models, which enhances food security planning and resource management by enabling more informed decision-making by stakeholders such as farmers, policy planners as well as researchers. [ABSTRACT FROM AUTHOR]

Mountain ecosystems are among the most difficult areas for plant cultivation due to water erosion occurring on the slopes. Growing plants in these areas may lead to a weakening of ecosystem functions and in degradation of these areas and threatens sustainability. In this experiment, the anti-erosion effectiveness of maize, oat and spring vetch were assessed through the measuring of LAI and sheet wash from a slope where cultivation had occurred. Averaged values from the six years field experiment (2017–2022) reveal that maize achieved maximum soil protection between the 115th and 128th day of vegetation (14 days), when the LAI value equals to 3.8–4.0. The corresponding values for oats were 63–81 days of vegetation (19 days; LAI 2.4–2.7). The longest period of maximum soil protection was achieved from the cultivation of spring vetch compared to maize and oats (between the 49th and 82nd day of its vegetation, i.e., 34 days), when the LAI value was in the range of 2.2–3.0. Soil cover at their maximum development is conservative compared to mountain ecosystems, and in the case of the studied plants, the protection time varied. These relationships were quantified by simple regression equations. Additionally, taking into account the compiled climate data, the average air temperature in the years of research (2017–2022) was higher than the multi-year average (1961–2000) by 2.15 °C, which may confirm the fact that the climate is warming in the region of Southern Poland. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R2, root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management. [ABSTRACT FROM AUTHOR]

Planting geometry is one of the most important management practices that determine plant growth and yield of corn. The effects of eight planting geometries (35 × 23 cm, 40 × 21 cm, 45 × 19 cm, 50 × 18 cm, 55 × 17 cm, 60 × 16 cm, 65 × 15 cm, 70 × 15 cm) on plant growth and yields of three sweet corn hybrids (Argos F1, Challenger F1, Khan F1) were investigated under Erzurum, Türkiye conditions in 2022 and 2023 years. Variance analysis of the main factors shows a highly significant effect on whole traits but in two-way interactions some of the traits were significant and in the three-way interactions, it was insignificant. As an average of years, the number of plants per hectare at the harvest varied between 92,307 (35 × 23 cm) and 120,444 (70 × 15 cm) according to the planting geometries. The highest marketable ear number per hectare (107,456), marketable ear yield (24,887 kg ha−1), and fresh kernel yield (19,493 kg ha−1) were obtained from the 40 × 21 cm planting geometry. The results showed that the variety Khan F1 grown at 40 × 21 cm planting geometry obtained the highest marketable ear number (112,472), marketable ear yield (29,788 kg ha−1), and fresh kernel yield (22,432 kg ha−1). The plant density was positively correlated with marketable ear number (r = 0.904 **), marketable ear yield (r = 0.853 **), and fresh kernel yield (r = 0.801 **). The differences among the varieties were significant for the studied traits, except for plant density and kernel number per ear. In conclusion, the variety Khan F1 should be grown at the 40 × 21 cm planting geometry to maximize yields under study area conditions without water and nutrient limitations. [ABSTRACT FROM AUTHOR]

Based on monthly MODIS EVI and LAI data from 2001 to 2020, combined with the Standardized Precipitation Evapotranspiration Index (SPEI), this study employs Theil–Sen trend analysis, Mann–Kendall (MK) test, Hurst index analysis, and correlation analysis to comparatively analyze the overall vegetation trends, spatial distribution characteristics, and future trends of different vegetation types in Guizhou Province under varying slope conditions. The study also explores the response of vegetation to SPEI at different time scales across different slopes. The results indicate the following: (1) From 2001 to 2020, the average values of EVI (0.34%/a) and LAI (1.4%/a) during the growing season exhibited an increasing trend, with the improved vegetation areas primarily concentrated in the western region of Guizhou, while the degradation areas were mainly located in the central and eastern regions. (2) Under different slope conditions, EVI generally showed slight improvement, while LAI exhibited significant improvement, with dry-lands experiencing the largest changes. Future trends indicate continuous improvement, but the proportion of vegetation improvement area decreases with increasing slope. When the slope is less than 5°, the proportion of vegetation improvement area is the highest. (3) The positive correlation between EVI, LAI, and SPEI at different time scales is stronger than the negative correlation, with the strongest correlations observed when the slope is less than 5°. When the slope exceeds 35°, the relationship between vegetation and drought response is almost unaffected by the slope. These findings provide a scientific basis for vegetation growth monitoring and the study of climate change and vegetation interactions in Guizhou Province. [ABSTRACT FROM AUTHOR]

Abstract In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters. Results revealed that the ML models were able to provide relatively accurate early yield estimates. For summer rice, eXtreme gradient boosting (XGB) was the best-performing model at all three stages (45, 60, and 90 DAT), while for kharif rice, the best-performing models at 45, 60, and 90 DAT were XGB, Neural network (NNET), and Cubist, respectively. The combined ranking of ML models showed that prediction accuracy improved as the prediction date approaches harvest, and the best prediction of yield was observed at 90 DAT for both summer and kharif rice. Overall rankings indicate that for summer rice, the top three models were XGB, NNET, and Support vector regression, while for kharif rice, these were Cubist, NNET, and Random Forest, respectively. The findings of this study offer valuable insights into the potential of the combined use of remote sensing and biophysical parameters using ML models, which enhances food security planning and resource management by enabling more informed decision-making by stakeholders such as farmers, policy planners as well as researchers.

Several studies have demonstrated global climate change, which has happened worldwide; it significantly impacts terrestrial ecosystems. Studying vegetation phenology can provide insight into the impact of climate changes on ecosystems. Alashan Plateau Semi-Desert is located between the Tibetan Plateau and the Gobi Desert and has an arid continental climate. This area consists of highlands, and the sensitivity of natural vegetation to temperature and climate change is very high. This study aims to identify climate trends in forest cover indices using weather (meteorological) data from 1982 to 2019 across the Alashan Plateau Semi-Desert. Based on this, 13 variables that affect climate change were selected, and the possible effect of climate factors on vegetation indices was predicted using MATLAB. The results showed that over the past 38 years, three forest cover indices, LAI, FAPAR, and NDVI, decreased by about 6-10, 6-7, and 19-20 percent correspondingly, and it can be assumed that this decline is due to climate change. In general, it can be said that climate changeshave adverse effects in hot and dry regions and highlands. With the current climate change trends, such as increasing temperature and decreasing rainfall, reducing the dynamics of vegetation indices in these areas can be seen dramatically.

Introduction: Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status. Methods: In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multivegetation index fusion in the estimation of cotton LAI. Results: The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. Discussion: Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications. [ABSTRACT FROM AUTHOR]

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation. [ABSTRACT FROM AUTHOR]

Several studies have demonstrated global climate change, which has happened worldwide; it significantly impacts terrestrial ecosystems. Studying vegetation phenology can provide insight into the impact of climate changes on ecosystems. Alashan Plateau Semi-Desert is located between the Tibetan Plateau and the Gobi Desert and has an arid continental climate. This area consists of highlands, and the sensitivity of natural vegetation to temperature and climate change is very high. This study aims to identify climate trends in forest cover indices using weather (meteorological) data from 1982 to 2019 across the Alashan Plateau Semi-Desert. Based on this, 13 variables that affect climate change were selected, and the possible effect of climate factors on vegetation indices was predicted using MATLAB. The results showed that over the past 38 years, three forest cover indices, LAI, FAPAR, and NDVI, decreased by about 6-10, 6-7, and 19-20 percent correspondingly, and it can be assumed that this decline is due to climate change. In general, it can be said that climate changeshave adverse effects in hot and dry regions and highlands. With the current climate change trends, such as increasing temperature and decreasing rainfall, reducing the dynamics of vegetation indices in these areas can be seen dramatically. [ABSTRACT FROM AUTHOR]

Abstract LAI measurement using a direct method is time-consuming, while other instruments like Ceptometer, Hemispherical Photography, and LI-COR require high investment. This study aims to develop allometric equations for estimating the LAI of community forest tree species. Destructive sampling was conducted on 45 trees from three different species, i.e., Tectona grandis, Swietenia macrophylla, and Falcataria moluccana . The allometric equations were developed using regression analysis with two predictor variables, namely diameter at breast height ( D ) and tree height ( H ). LAI varied among species, wherein the highest average of LAI was recorded in S. macrophylla (1.03±0.03), followed by T. grandis (0.28±0.07) and F. moluccana (0.23±0.03). Our study found that the equation LAI = 0.01 D 1.15 was reliable as a generalized allometric equation to estimate the LAI of three species with an RMSE of 0.39. We concluded that the allometric equation could facilitate LAI estimation in community forests.

Climate change affects life in tropical landscapes by increasing temperatures, among other impacts. In addition, land transformation from forest to other land cover types typically increases temperatures of surfaces exposed to direct solar radiation. In rural areas, however, many organisms including humans live sheltered below canopies, but little is known about below-canopy surface temperatures. We assessed canopy leaf area index (LAI) and below-canopy surface temperatures in a landscape of rural lowland Sumatra, Indonesia. In this region, land-use trajectories in the past decades led from forest to monocultural plantations. We found that LAI varied more than 10-fold, with the highest values observed in forest and low values in rubber and oil palm plantations. Below-canopy surface temperatures increased significantly with decreasing LAI. A decrease in LAI by one unit (m2 leaf area per m2 ground area) increased ground surface temperature by 0.95 °C (confidence interval: 1.07–0.83 °C) around noon. Likewise, the temperatures measured on peoples’ cap or hijab surfaces under the canopy was strongly affected. For a light-brown hijab, the average noon surface temperature increased by 4.0 °C from forest to oil palm. Thus, in addition to climate warming, land transformation and associated declines in canopy LAI significantly increased below-canopy surface temperatures. Strategies for mitigating such potentially adverse temperature effects may include the integration of more and densely foliated trees into landscapes.

Leaf Area Index (LAI) is a critical vegetation structural parameter for characterizing vegetation canopy structure. Multiple LAI products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations have made a noteworthy impact on global energy fluxes, climate change, and biogeochemistry research. It is essential to examine the long-term performance of global LAI products and quantitatively evaluate their quality. However, traditional methods, which often rely on single overall accuracy metrics, fail to assess the ability to capture vegetation's internal heterogeneity and temporal dynamics. This study introduces a new approach by combining spatial heterogeneity and temporal stability to assess product consistency, using MOD15A2H and MYD15A2H as examples. We observed that the local spatial heterogeneity (within 5 × 5 pixels) of the two products exhibited similar distribution pattern. The MYD15A2H LAI exhibits better time-series stability and lower spatially heterogeneous anomalous fluctuations in the tropical forest region. Both products demonstrate sustainable usability in the time series, and their spatial heterogeneity exhibit consistent trends. In conclusion, this study shows the comparability and stability of MOD15A2H and MYD15A2H in terms of the spatiotemporal variability of LAI. For differences stemming from unavoidable reflectivity issues, it is recommended to filter for high-quality inversion results or to utilize reliable reanalysis datasets.

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season. [ABSTRACT FROM AUTHOR]

Background: Up to 34% of patients with schizophrenia are resistant to several treatment trials. Lack of continuous and adequate treatment is associated with relapse, rehospitalization, a lower effect of antipsychotic therapy, and higher risk of side effects. Long-acting injectables antipsychotics (LAI APs) enhance compliance and improve clinical outcomes and quality of life in patients with schizophrenia, and thus it may be advisable to administer two LAI APs at the same time in cases of treatment-resistant schizophrenia. The purpose of this review is to summarize the available literature regarding the combined use of two LAI APs in patients with schizophrenia or other psychotic spectrum disorders. Methods: An extensive literature search for relevant articles regarding any combination of two long-acting injectable antipsychotics has been performed from inception up to 9 February 2024, on PubMed, Scopus and APA PsycInfo, according to the PRISMA statement. Only studies reporting combination of two LAI APs and its clinical outcome in patients with schizophrenia and related disorders were selected. Results: After the selection process, nine case reports, four case series and two observational retrospective studies were included in the final analysis. All patients treated with dual LAI APs reported a good response, and no new or unexpected adverse effects due to the combination of two LAIs were reported. Different drug combinations were used, and the most frequent association resulted in aripiprazole monohydrate + paliperidone palmitate once monthly (32 times). Conclusions: Our review highlights that the treatment regimen with two concurrent LAI APs is already widely used in clinical practice and is recognized as providing a promising, effective, and relatively safe therapeutic strategy for treating the schizophrenia spectrum disorders. [ABSTRACT FROM AUTHOR]

Purpose: The basal fertilizer recommendation for growing hybrid fodder sorghum varieties in uplands involves relatively high quantities of Urea, TSP, and MOP, with rates of 100:90:65 kg/ha for SX-17 and 150:62:37 kg/ha for Dairy green. For both initial and ratoon crops, it is recommended to apply 150 kg/ha of urea as a top dressing. This study investigated the impact of reducing the fertilizer application rate to 75% and employing alternative agronomic practices on hybrid fodder sorghum varieties grown in lowland fallow paddy fields in the dry zone during the Yala and third seasons. Research Method: The agronomic management practices, including four basal fertilizer rates (Urea:TSP: MOP) of 100:90:65 (BD1), 150:62:37 (BD2), 75:68:49 (BD3), and 113:47:28 (BD4); two top-dressing urea fertilizer rates of 150 (TD1) and 112.5 kg/ha (TD2); two plant spacing configurations of 45x30 (WS) and 45x15 cm (NS); and two irrigation interval regimes, 5 days up to 30 days followed by 8 days (SI) and 7 days up to 30 days followed by 10 days (LI) were investigated. These factors were combined into four agronomic management packages: AMP1 (BD1, TD1, WS, SI), AMP2 (BD2, TD1, NS, SI), AMP3 (BD3, TD2, WS, LI), and AMP4 (BD4, TD2, NS, LI). The study used a split-plot design with three replicates and assessed various parameters, including plant height, stem diameter, plant weight, leaf area index (LAI), number of tillers, and fodder yield in both the initial fodder sorghum crop and the first ratoon crop. The crops were harvested when 50% of the panicles on the plants were at the milk and dough grain stages. Findings: The initial crop exhibited significantly heavier plants having wider stems when grown with higher fertilizer rates, wider plant spacing, and shorter irrigation intervals in AMP1. Irrespective of fertilizer rate and irrigation interval, the initial crops tended to have higher LAI with narrow plant spacing in AMP2 and AMP4. In the ratoon crops, wider plant spacing in AMP1 and AMP3 resulted in clumps with a greater number of tillers and heavier plants. However, the ratoon crops also exhibited higher LAI with narrow plant spacing in AMP2 and AMP4. Total fodder dry matter yield (DMY) in AMP2 and AMP4 with narrow plant spacing was significantly greater for both varieties. Furthermore, the DMY was not affected by fertilizer rate and irrigation interval. Value: Hybrid fodder sorghum varieties (SX-17 and Dairygreen) cultivated at higher plant density, along with reduced fertilizer (75%) and irrigation, can produce comparable fodder yield (21.1 and 26.1 MT/ha, respectively) to those grown at lower plant density with more fertilizer and irrigation (24.6 and 28.1 MT/ha, respectively) in lowland paddy fields during Yala and third seasons in the dry zone. [ABSTRACT FROM AUTHOR]

Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been attributed to a decrease in winter precipitation in the range of 56% to 72.9% and an increase in summer from 11% to 27%, according to different climate scenarios. This research attempts to understand vegetation responses to climate change variability at the floodplain level. Further, this study is an effort to enlighten our understanding of temporal climate change impacts under different climate scenarios. To achieve these aims, a semi-distributed hydrological model was applied at a sub-catchment level to simulate the Leaf Area Index (LAI). The model was simulated against future time series of climate data according to Global Climate Model (GCM) projections. The time series data underwent a non-parametric Mann–Kendall test to detect trends and assess the magnitude of change. To quantify the model's performance, calibration and validation were conducted against the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. The calibration and validation results show Nash–Sutcliffe efficiency (NSE) values of 0.85 and 0.78, respectively, suggesting the model's performance is very good. The modeling results reveal that the rainfall pattern fluctuates under climate projections within the study site, in which vegetation tends to be more vibrant during the warmer seasons. Moreover, the modeling results highlighted increases in the average projected future winter temperatures, which can help vegetation growth during winter. The results of this study may be employed for sustainable floodplain management, restoration, land-use planning, and policymaking, and help floodplain communities better prepare for and respond to changing flood patterns and related challenges under a future changing climate. [ABSTRACT FROM AUTHOR]

The monitoring of crop growth, particularly the estimation of Leaf Area Index (LAI) using optical remote sensing techniques, has been a continuous area of research. However, it has become a challenge to accurately and rapidly interpret the spatial variation of LAI under nitrogen stress. To tackle these issues, this study aimed to explore the potential for precise LAI estimation by integrating multiple features, such as average spectral reflectance (ASR), vegetation index, and textures, obtained through an unmanned aerial vehicle (UAV). The study employed the partial least squares method (PLS), extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) to build the LAI estimation model under nitrogen stress. The findings of this study revealed the following: (i) texture features generally exhibited greater sensitivity to LAI compared to ASR and VIs. (ii) Utilizing a multi-source feature fusion strategy enhanced the model's accuracy in predicting LAI compared to using a single feature. The best RP2 and RMSEP of the estimated LAI were 0.78 and 0.49, respectively, achieved by RF through the combination of ASR, VIs, and textures. (iii) Among the four machine learning algorithms, RF and SVM displayed strong potential in estimating LAI of rice crops under nitrogen stress. The RP2 of the estimated LAI using ASR + VIs + texture, in descending order, were 0.78, 0.73, 0.67, and 0.62, attained by RF, SVM, PLS, and ELM, respectively. This study analyzed the spatial variation of LAI in rice using remote sensing techniques, providing a crucial theoretical foundation for crop management in the field. [ABSTRACT FROM AUTHOR]

A forest is vulnerable to drought and plays important roles in the regulation of carbon and water cycling in a terrestrial ecosystem. Atmospheric vapor pressure deficit (VPD) has been identified as an increasingly major factor in plant functioning and has been established as a main contributor to recent drought-induced plant mortality, independent of other drivers associated with climate change. However, most previous studies have focused on the effects of climate warming and CO2 enrichment on vegetation growth, without considering the effects of an increased VPD on vegetation growth and evapotranspiration (ET) in forest ecosystems. This could lead to a large uncertainty in estimating the variability in forest carbon sinks. Based on the long-term satellite data, we investigated the response of the leaf area index (LAI) and ET to the VPD via a partial correlation analysis in this study. We also examined the temporal variability in the partial coefficients within a ten-year moving window. The results showed that over 50% of the region displayed a negative partial correlation between the LAI, ET, and VPD, and those pixels were mainly concentrated in North America and the plains of Eastern Europe. Regions with a negative trend of partial correlation in both the LAI and ET are mostly located in the plains of Eastern Europe and the Siberian Plain of western Russia, while the positive trend is mainly in South America. The plains of Eastern Europe are becoming drier, which was proved by the interannual trend of the Standardized Precipitation Evapotranspiration Index (SPEI) and soil water content (SWC). Additionally, the LAI and ET in those areas exhibited a significant positive correlation with the SWC based on the moving window average. This study suggests that the role of the VPD on vegetation will become increasingly prominent in the context of future climate change for the forest ecosystem. [ABSTRACT FROM AUTHOR]

Developing an effective and reliable integrated drought index is crucial for tracking and identifying droughts. The study employs game theory to create a spatially variable weight drought index (GTDI) by combining two single-type indices: the agricultural drought index (SSMI), which implies drought hazard-bearing conditions, and the meteorological drought index (SPEI), which implies drought hazard-causing conditions. Also, the entropy theory-based drought index (ETDI) is induced to incorporate a spatial comparison to the GTDI to illustrate the rationality of gaming weight integration. Leaf Area Index (LAI) data is employed to confirm the reliability of the GTDI in identifying drought by comparing it with the SPEI, SSMI, and ETDI. Furthermore, an assessment is conducted on the temporal trajectories and spatial evolution of the GTDI-identified drought to discuss the GTDI's advancedness in monitoring changes in hazard-causing and bearing impacts. The results showed that the GTDI has a greatly high correlation with single-type drought indices (SPEI and SSMI), and its gaming weight integration is more logical and trustworthy than the ETDI. As a result, it outperforms ETDI, SPEI, and SSMI in recognizing drought spatiotemporally, and is projected to replace single-type drought indices to provide a more accurate picture of actual drought. Additionally, GTDI exhibits the gaming feature, indicating a distinct benefit in monitoring changes in hazard-causing and bearing impacts. The case studies show drought events in the Wei River Basin are dominated by a lack of precipitation. The hazard-causing index SPEI dominates the early stages of a drought event, whereas the hazard-bearing index SSMI dominates the later stages. This study surely serves as a helpful reference for the development of integrated drought indices as well as regional drought mitigation, prevention, and monitoring. [ABSTRACT FROM AUTHOR]

A bottom-up approach was investigated to produce long-acting injectable (LAI) suspension-based formulations to overcome specific limitations of top-down manufacturing methods by tailoring drug characteristics while making the methods more sustainable and cost-efficient. A Secoya microfluidic crystallization technology-based continuous liquid antisolvent crystallization (SCT-CLASC) process was optimized and afterward compared to an earlier developed microchannel reactor-based continuous liquid antisolvent crystallization (MCR-CLASC) setup, using itraconazole (ITZ) as the model drug. After operating parameter optimization and downstream processing (i.e., concentrating the suspensions), stable microsuspensions were generated with a final solid loading of 300 mg ITZ/g suspension. The optimized post-precipitation feed suspension consisted of 40 mg ITZ/g suspension with a drug-to-excipient ratio of 53:1. Compared to the MCR-CLASC setup, where the post-precipitation feed suspensions contained 10 mg ITZ/g suspension and had a drug-to-excipient ratio of 2:1, a higher drug concentration and lower excipient use were successfully achieved to produce LAI microsuspensions using the SCT-CLASC setup. To ensure stability during drug crystallization and storage, the suspensions' quality was monitored for particle size distribution (PSD), solid-state form, and particle morphology. The PSD of the ITZ crystals in suspension was maintained within the target range of 1–10 µm, while the crystals displayed an elongated plate-shaped morphology and the solid state was confirmed to be form I, which is the most thermodynamically stable form of ITZ. In conclusion, this work lays the foundation for the SCT-CLASC process as an energy-efficient, robust, and reproducible bottom-up approach for the manufacture of LAI microsuspensions using ITZ at an industrial scale. [ABSTRACT FROM AUTHOR]

Despite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (Cab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March–April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35 µg/cm2. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R2 of 0.61–0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices. [ABSTRACT FROM AUTHOR]

The common bean (Phaseolus vulgaris) is a widely consumed legume worldwide and holds significant value in terms of direct human consumption, surpassing all other legume crops combined. This study aimed to assess the applicability of the APSIM common bean model under different water-management conditions and cultivars in southeastern Iran. A two-year field experiment was conducted at the Agricultural Research Station of Shahid Bahonar University of Kerman, Iran, spanning from 2020 to 2021. The experiment followed a split-plot design with a randomized complete block structure and included four replications. The primary irrigation factor consisted of three levels, representing 80%, 60%, and 40% of crop capacity, while the secondary factor encompassed early and late-maturity cultivars. To evaluate the model's performance, simulated and measured grain yield, total dry matter, leaf area index (LAI), and soil water content were compared using the adjusted coefficient of correlation, normalized root mean square errors (nRMSE), and model efficiency (EF). The results indicated a satisfactory agreement between predicted and observed grain yield (nRMSE = 12% and EF = 0.92). Similarly, the agreement between simulated and observed total dry matter was reasonable (nRMSE = 13% and EF = 0.83). The observed and predicted soil water content also exhibited good agreement. Consequently, the APSIM common bean model proves suitable for research purposes, particularly in the areas of irrigation and cultivar selection.

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R2 of 0.72, RMSE 0.41) and FCOVER (R2 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management.

Planting geometry is one of the most important management practices that determine plant growth and yield of corn. The effects of eight planting geometries (35 × 23 cm, 40 × 21 cm, 45 × 19 cm, 50 × 18 cm, 55 × 17 cm, 60 × 16 cm, 65 × 15 cm, 70 × 15 cm) on plant growth and yields of three sweet corn hybrids (Argos F1, Challenger F1, Khan F1) were investigated under Erzurum, Türkiye conditions in 2022 and 2023 years. Variance analysis of the main factors shows a highly significant effect on whole traits but in two-way interactions some of the traits were significant and in the three-way interactions, it was insignificant. As an average of years, the number of plants per hectare at the harvest varied between 92,307 (35 × 23 cm) and 120,444 (70 × 15 cm) according to the planting geometries. The highest marketable ear number per hectare (107,456), marketable ear yield (24,887 kg ha−1), and fresh kernel yield (19,493 kg ha−1) were obtained from the 40 × 21 cm planting geometry. The results showed that the variety Khan F1 grown at 40 × 21 cm planting geometry obtained the highest marketable ear number (112,472), marketable ear yield (29,788 kg ha−1), and fresh kernel yield (22,432 kg ha−1). The plant density was positively correlated with marketable ear number (r = 0.904 **), marketable ear yield (r = 0.853 **), and fresh kernel yield (r = 0.801 **). The differences among the varieties were significant for the studied traits, except for plant density and kernel number per ear. In conclusion, the variety Khan F1 should be grown at the 40 × 21 cm planting geometry to maximize yields under study area conditions without water and nutrient limitations.

The ecosystem services of forests, such as the water conservation function, are the combined results of diverse processes, and the modification of one part of a forest affects each ecosystem service separately via complex processes. It is necessary to develop an ecosystem service assessment model for various tree species to ensure proper forest management. In this study, a model to evaluate three ecosystem services, namely, the water supply, wood supply, and carbon sink, for various tree species in Japan is developed using many observation data from the previous literature. The integrated evaluation model consists of the forest model, hydrological model, and carbon stock assessment model. The forest model consists of the forest growth model and LAI estimation model, based on allometry. The results of the simulations for the major tree species yield the following findings: (1) Water supply varies with tree species but decreases until about 40 years of age, after which it is near constant. (2) Although beech has a larger LAI than needleleaf forests, water supply is not significantly different. (3) Broadleaf forests are more affected by thinning than needleleaf forests and tend to receive increased water supply as a result of processes such as thinning. This study enabled the evaluation of water conservation function in watersheds containing various tree species. [ABSTRACT FROM AUTHOR]

Introduction: Leaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider. Methods: Taking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples. Results: The ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R² (R² = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54) Discussion: This study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing. [ABSTRACT FROM AUTHOR]

The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm−2) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination (R2) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm−2) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm−2) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables. Key Points: Important polarimetric observables and their sensitivity toward biophysical parameters were analyzedEffective retrieval of crop parameters from a single‐date polarimetric data using Random Forest and Multiple Linear Regression was explored [ABSTRACT FROM AUTHOR]

Aim: Changes in surface thaw onset in Arctic permafrost can regulate terrestrial ecosystem dynamics. Permafrost surface thaw onset has advanced significantly because of strong Arctic warming. However, surface thaw onset change effects on vegetation during the different growing season stages remain unclear. Location: Arctic and subarctic permafrost region. Time Period: 1982–2016. Major Taxa Studied: Arctic vegetation. Methods: This study investigated surface thaw onset change effects on vegetation growth in the Arctic permafrost region by analysing the relationships between surface thaw onset dates and vegetation indices obtained via satellite remote sensing from 1982 to 2016. Results: The leaf area index (LAI) from April to June was negatively correlated (−0.16 area‐averaged partial correlation coefficient) with surface‐thawed dates in over 78.6% of the Arctic permafrost region, and the LAI from August to October was positively correlated (0.17 area‐averaged partial correlation coefficient) with surface‐thawed dates in over 79.9% of the Arctic permafrost region. These distinct relationships suggest that earlier thaw onset may have a positive effect on vegetation growth during the early stage of the growing season. However, earlier thaw onset can limit vegetation growth during the late stage of the growing season. The different relationships between surface thaw onset and LAI can be explained by moisture availability. This difference occurred because 67.3% of the Arctic permafrost region was more constrained by temperature during the early stage of the growing season, therefore, the earlier thaw onset alleviated this temperature constraint and benefited vegetation growth. During the late stage of the growing season, 47.7% of the Arctic permafrost region was more constrained by moisture. Main Conclusions: Our results suggest that surface thaw onset change effects on vegetation vary by period, and this difference requires careful consideration when predicting ecosystem changes in permafrost regions. [ABSTRACT FROM AUTHOR]

As major carbon (C) pools in cities, urban green spaces play a crucial role in reducing atmospheric carbon. To determine the importance of litterfall C storage in urban green spaces, we selected the leaf area index (LAI) as a proxy indicator for litterfall C density (LCD), and established a log-linear regression model between LCD and LAI to predict the annual litterfall C pool in large-scale urban green spaces using Sentinel-2 satellite remote sensing data. Forty-five sample units were randomly selected in typical urban green spaces in Beijing, China. A high-temperature combustion method was used to measure the LCD of the sampling units, and stepwise linear regression was used to filter the proxy indicator for LCD. The annual litterfall C pool in regions within the Fifth Ring Road was also estimated with inversion using remote sensing data. From 2015 to 2021, the estimated annual litterfall C pool was in the range of 4.5–5.8 × 1010 g, i.e., approximately 18.9% of the total C storage recorded for the urban green space, which was far greater than that observed in forest ecosystems. We concluded that the litterfall C pool in urban green spaces is seriously underestimated, and that urban tree litterfall has the potential to reduce greenhouse gas emissions if used as a carbon-neutral resource. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) serves as a crucial metric in quantifying the structure and density of vegetation canopies, playing an instrumental role in determining vegetation productivity, nutrient and water utilization, and carbon balance dynamics. In subtropical montane forests, the pronounced spatial heterogeneity combined with undulating terrain introduces significant challenges for the optical remote sensing inversion accuracy of LAI, thereby complicating the process of ground validation data collection. The emergence of UAV LiDAR offers an innovative monitoring methodology for canopy LAI inversion in these terrains. This study assesses the implications of altitudinal variations on the attributes of UAV LiDAR point clouds, such as point density, beam footprint, and off-nadir scan angle, and their subsequent ramifications for LAI estimation accuracy. Our findings underscore that with increased altitude, both the average off-nadir scan angle and point density exhibit an ascending trend, while the beam footprint showcases a distinct negative correlation, with a correlation coefficient (R) reaching 0.7. In contrast to parallel flight paths, LAI estimates derived from intersecting flight paths demonstrate superior precision, denoted by R2 = 0.70, RMSE = 0.75, and bias = 0.42. Notably, LAI estimation discrepancies intensify from upper slope positions to middle positions and further to lower ones, amplifying with the steepness of the gradient. Alterations in point cloud attributes induced by the terrain, particularly the off-nadir scan angle and beam footprint, emerge as critical influencers on the precision of LAI estimations. Strategies encompassing refined flight path intervals or multi-directional point cloud data acquisition are proposed to bolster the accuracy of canopy structural parameter estimations in montane landscapes. [ABSTRACT FROM AUTHOR]

Nowadays, there is an increasing negative environmental effect of using chemical fertilizer. For this reason, the use of biofertilizers is promoted in the agriculture sector. The purpose of this investigation was to carry out an evaluation of the effects of biological fertilizer (biofertilizers are organic materials that can be used to improve soil properties) use on the growth and agronomic characteristics of Aloysia citrodora. To achieve this aim, a two-year randomized complete blocks field experiment was carried out in central Greece. The experiment included four biological fertilizer levels (0, 50, 100 and 150 N kg ha−1) with three replications. Plant height, dry stem, dry leaves, dry total yield and leaf area index (LAI) were measured during the two growing years. The results showed that in the first and second studied years, the maximum plant height and total dry yield were observed in the BF150 treatment. Moreover, LAI was ameliorated by applying the BF100 and BF150 treatments in both studied years. A key finding in this study is that the A. citrodora ecosystem favors herbaceous plant species richness. Also, soil factors (soil organic matter, P and K) promote positive herbaceous plant diversity within the A. citrodora ecosystem. Finally, A. citrodora could be a promising medicinal plant, cultivated under Mediterranean climatic conditions. [ABSTRACT FROM AUTHOR]

To make an accurate estimation of leaf are index (LAI) based on machine learning methods and images from UAV, we compared the several mainstream machine learning methods for maize LAI prediction, such as Artificial Neural Network method (ANN), Gaussian Process Regression method (GPR), Support Vector Regression method (SVR), and Gradient Boosting Decision Tree (GBDT). For this purpose, field experiments that considering apply of different amount of organic fertilizer, different amount of inorganic fertilizer, different amount of crop residue, and different planting density were carried out. Based on these experiments, field campaign were conducted to obtain UAV multispectral images and LAI data at different growth stages in maize. Based on above data, firstly, correlation analysis was used to select LAI-sensitive spectral indices, and then the Partial Least Squares Regression method (PLSR) and ANN, GPR, SVR, GBDT were coupled to design the LAI prediction models, respectively, and their performance for LAI prediction were compared. The results showed that the LAI prediction model constructed by PLSR+GBDT method had the highest accuracy and the best stability. The models of R² and RMSE values were 0.90 and 0.25, and the verified R² and RMSE values were 0.90 and 0.29 during validation, respectively. The model based on PLSR+GPR model was followed, with R² and RMSE values of 0.86 and 0.30 during calibration, and R² and RMSE values of 0.89 and 0.29 during validation, respectively. Besides, it had faster training speed and could give the uncertainty of the prediction. The model designed by PLSR+ANN method had R² and RMSE values of 0.85 and 0.31 during calibration, and R² and RMSE values of 0.89 and 0.30 during validation, respectively. The model designed by PLSR+SVR method had R² and RMSE values of 0.86 and 0.32, and R² and RMSE values of 0.90 and 0.33, respectively. Therefore, PLSR+GBDT method and PLSR+GPR method are recommended as the optimal methods for designing maize LAI prediction models. [ABSTRACT FROM AUTHOR]

A field experiment was conducted to study response of nitrogen scheduling and weed management on growth and yield attributes of wheat (Triticum aestivum L.) at the Experimental Farm, Mata Gujri College, Shri Fatehgarh Sahib during Rabi season of year 2018-2020. The experiment laid out in Split Plot Design (SPD) with three replications. The nitrogen scheduling includes N1 -½ Basal + ¼ at 4WAS + ¼ at 8 WAS, N2 -1/3 at 4 WAS + 1/3 at 8 WAS + 1/3 at 10 WAS, N3 -¼ at 4 WAS + ¼ at 6 WAS + ¼ at 8 WAS + ¼ at 10 WAS while weed management treatment were W2 -clodinafop @ 60 g/ha, W3 -sulfosulfuron @ 25 g/ha, W4-carfentrazone @ 20g/ha along with weed free and weedy check. The results revealed that the maximum growth and yield attributes were recorded of N3 -¼ at 4 WAS + ¼ at 6 WAS + ¼ at 8 WAS + ¼ at 10 WAS which was at par N2 -1/3 at 4 WAS + 1/3 at 8 WAS + 1/3 at 10 WAS and found significantly superior over N1-½ Basal + ¼ at 4WAS + ¼ at 8 WAS.N3 -¼ at 4 WAS + ¼ at 6 WAS + ¼ at 8 WAS + ¼ at 10 WAS + W3 -sulfosulfuron @ 25 g/ha recorded significantly maximum which was significantly superior over all the treatments. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R2, root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management.

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

IntroductionIntercropping cultivation is an excellent method to increase agricultural productivity per unit area based on environmental mechanisms. In this planting system, it is necessary to select crops with different capabilities in uptake resources and establish a complementary relationship. However, legumes in intercropping cultivation can be used an alternative and sustainable way to increase efficiency. Besides, it is necessary to study the interactions in the above and belowground parts to achieve the mentioned goals. Therefore, this study aimed to investigate inter-specific interactions in millet and soybean intercropping cultures. Material and MethodsA field experiment was conducted for two years (2018-19) at the research farm of Sari agricultural science and natural resources university, Iran (33º, 36ʹ N, 53º, 03ʹ E with 43 m altitude). The experimental design was a randomized complete block with four replications. The experimental treatment was 75% Soybean + 25% Niger, 50% Soybean + 50% Niger, 25% Soybean+75%Niger based on the replacement method and monoculture. This study investigated the agronomic traits, LAI, TDW, nitrogen derived from the atmosphere, and total microbial activity in the soil in different planting ratios. Result and DiscussionThe results showed that soybean monoculture had the highest plant height during different stages of growth. Furthermore, with the increase in the share of planting soybean in intercropping cultivation, the shoot height of the millet also increased. Also, unlike the millet, the soybean succeeded in producing more leaf area indexes among different mixed cultivation ratios than monoculture. The leaf area index of millet showed a significant decrease as the proportion of soybean plantings increased. However, despite this decrease, the total dry matter of soybean exhibited a higher percentage increase at various growth stages compared to the decrease in millet yield. As a result, after 60 days of planting, the intercropping cultivation of millet and soybean demonstrated positive complementarity in terms of competition impact. Additionally, the intercropping cultivation showed a higher percentage of nitrogen derived from the atmosphere compared to soybean monoculture. During the study period, the planting ratio of 75% millet and 25% soybean exhibited the highest increase in nitrogen fixation (Ndfa), with average values of 93.84% and 83.85% at 90 days after planting, respectively. Increasing the number of millet sowing rows in the intercropping patterns played a crucial role in intensifying competition with soybeans during the early growth stages and had an impact on the biological activity of soybeans. Moreover, the microbial activity in the soil (at a depth of 0-30 cm) was higher in the different intercropping ratios compared to monocultures of soybean and millet. In addition, soybean and millet monoculture had the highest and lowest grain yields. Among the different intercropping ratios, the 75:25 in 1398 with an average of 4639.77 kg. ha-1 had the highest grain yield. Besides, the planting ratio of 25:75 and 50:50, with an average of 1.23 and 1.02, respectively, had an LER higher than one. With increasing the share of soybean planting in intercropping, the competitiveness of the plant increases, and the competition ratio of millet was higher than the unit only in the planting ratio of 25:75 (millet-soybean). Therefore, the competitiveness of millet in mixed cultivation is significantly reduced by reducing the share of planting row. Finally, increasing productivity in the 25:75 combinations reduced soybean's competitive dominance, increasing millet grain yield in comparison to expected values and improving total microbial activity. ConclusionDue to limited growth period, millet had competition played an essential role in increasing total microbial activity and the percentage of nitrogen derived from the atmosphere at a 25:75 planting ratio before the initial reproductive growth. In addition, 25:75 and 50:50 planting ratios reduce soybean's competitive dominance, resulting in enhanced productivity. Finally, the selection of complementary plants based on physiological, morphological characteristics, and competitiveness in intercropping cultivation can be effective in the above and belowground interactions.

Abstract Leaf area index (LAI) is a vital parameter reflecting vegetation structure, physio‐ecological process and growth development. Accurate estimation of mangrove LAI is fundamental for assessing the ecological restoration and sustainable development of mangrove ecosystems. To date, very few studies have explored the hybrid method of radiative transfer model (RTM) and machine‐learning model in retrieving mangrove LAI with different satellite sensors. This study investigated the capabilities of combining the PROSAIL‐D model, XGBoost (extreme gradient boosting) and remote sensing images in estimating mangrove LAI, considering the spatial resolutions and spectral vegetation indices (VIs) of Landsat‐8, Sentinel‐2, Worldview‐2 and Zhuhai‐1 images, and further explored the role of eco‐environmental factors in the spatial distribution of LAI in Gaoqiao Mangrove Reserve, China. The results showed that the Zhuhai‐1 acquires the best estimation accuracy (RVal2 (the determination coefficient of validation) = 0.86, RPD (residual prediction deviation) = 3.36 and RMSE (root mean square error) = 0.31), followed by Worldview‐2 (RVal2 = 0.84, RPD = 2.64 and RMSE = 0.33), Sentinel‐2 (RVal2 = 0.34, RPD = 1.33 and RMSE = 0.62) and Landsat‐8 (RVal2 = 0.29, RPD = 1.03 and RMSE = 0.71). The newly developed three‐band VIs (B443−B864/B443+B864−2×B561 with Landsat‐8, B490−B842/B490+B842−2×B705 with Sentinel‐2, B427−B832/B908−B832 with Worldview‐2 and B896−B700/B776−B700 with Zhuhai‐1) were efficient estimators of mangrove LAI. Moreover, elevation and species composition could greatly affect the spatial distribution of mangrove LAI. We concluded that the hybrid method of PROSAIL‐D and XGBoost model using VIs derived from Zhuhai‐1 hyperspectral image could be deemed as basic method and input variables of mapping mangrove LAI, and could be effectively and widely applied in generating mangrove LAI products at the regional and national scales.

Introduction: The Leaf area index (LAI) of source region of yellow river basin is an important indicator for environmental sustainability. Most studies focus on the trend of LAI in Yellow River Source Region (YRSR) in accordance with both climate change and human actives. However, quantifying the effect of human activities on LAI is difficult but urgently needed. Specifically, Particle Matter 2.5 (PM2.5) can be an indirect indicator of human activities.Methods: In this study, we explored the potential dependence of LAI on temperature, precipitation, and PM2.5 in different land cover types in YRSR with linear regression and correlation analysis.Results: Over the period of 2001–2020, the climate in the region has been warming and becoming more humid, leading to overall improvements in vegetation. The mean LAI values varied between seasons, with summer having the highest and winter having the lowest LAI. The analysis of the LAI trends revealed that the mean LAI has been steadily increasing, particularly in the eastern region. The correlation analysis showed a significant positive correlation between annual average LAI and both annual precipitation and temperature, indicating that temperature has a greater impact on vegetation growth. The analysis of land cover types showed that most types exhibited a unimodal trend in LAI throughout the year, except for construction land which had two distinct peaks. Human-induced land cover change had a small impact on the overall increase in LAI. Furthermore, the interannual variation of PM2.5 showed a downward trend, with a strong correlation with the trend of LAI. Additionally, multiple linear regression analysis and residual trend analysis showed that climate factors had the strongest impact on LAI.Conclusion: The study highlights the spatiotemporal variations of LAI in the YRSR and its correlation with climatic and human factors. The findings suggest that climate change plays a crucial role in the vegetation growth and LAI in the region.

Abstract The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm−2) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination (R2) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm−2) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm−2) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables.

Study region: The Yarlung Zangbo River basin (YRB), southwest China. Study focus: The vegetation conditions play a significant role in regulating river runoff under climate change. However, the effect of vegetation growth induced by climate change to runoff variation remains controversial as relevant studies are mainly focused on the influences of climate change to runoff variation and vegetation growth, respectively. In this study, the response of runoff generation to change of Leaf Area Index (LAI) inputs during 1981–2010 in YRB were explored by using the Variable Infiltration Capacity (VIC) model. New hydrological insights: The VIC model with dynamic LAI inputs performs better in the YRB, especially in the sub-basins with apparent peak clipping effect by LAI overestimation. The impact mechanism of how runoff variation is driven by LAI change in the meadow, tundra and cultivated vegetation cover is evapotranspiration-controlled by energy redistribution. However, the LAI changes in coniferous and broad-leaved forest cover affect the runoff generation mainly by redistributing energy-water to alter both evapotranspiration and surface soil moisture, so do the shrub and grassland during the growing season (Apr.∼Sep.). The results highlight implanting grassland would be efficient to reduce the flash flood runoff in a short term and balance groundwater runoff in a long run. The broad-leaved forest and coniferous forest with a longer growth cycle also play a main role in adjusting soil moisture.

Five countries in the Lancang–Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang–Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang–Mekong region, which is crucial for implementing effective conservation strategies. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is among the twenty most disabling diseases worldwide. Subjective quality of life, well-being, and satisfaction are core elements to achieving personal recovery from the disorder. Long-acting injectable second-generation antipsychotics (SGA-LAIs) represent a valid therapeutic option for the treatment of SZ as they guarantee good efficacy and adherence to treatment. The aim of this rapid review is to summarize the evidence on the efficacy of SGA-LAIs in improving subjective quality of life, well-being, and satisfaction. The PubMed database was searched for original studies using SGA, LAI, risperidone, paliperidone, aripiprazole, olanzapine, SZ, and psychosis as keywords. Twenty-one studies were included: 13 clinical trials, 7 observational studies, and 1 post hoc analysis. It has been shown that SGA-LAIs bring an improvement to specific domains of subjective and self-rated quality of life, well-being, or satisfaction in prospective observational studies without a control arm and in randomized controlled trials versus placebo. The superiority of SGA-LAIs as compared with oral equivalents and haloperidol-LAI has been reported by some randomized controlled and observational studies. Although promising, the evidence is still limited because of the lack of studies and several methodological issues concerning the choice of the sample, the evaluation of the outcome variables, and the study design. New methodologically sound studies are needed. [ABSTRACT FROM AUTHOR]

Foliar feeding has been confirmed to be the fastest way of dealing with nutrient deficiencies and increasing the yield and quality of crop products. The synthesis of chlorophyll and photosynthesis are directly related to magnesium (Mg), which operates in the improvement of plant tissues and enhances the appearance of plants. This study aimed to analyze the correlation between two biophysical variables, including the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), and seven spectral vegetation indices. The spectral indices under investigation were Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Disease–Water Stress Index (DSWI), Modified Chlorophyll Absorption Ratio Index (MCARI), the Red-Edge Inflection Point Index (REIP), and Pigment-Specific Simple Ratio (PSSRa). These indices were derived from Sentinel-2 data to investigate the impact of applying foliar applications of Mg from various sources in the production of green-onion crops. The biophysical variables were derived using field measurements and Sentinel-2 data under the effects of different sources of Mg foliar sprays. The correlation coefficient between field-measured LAI and remotely sensed, calculated LAI was 0.72 in two seasons. Concerning FAPAR, it was found that the correlation between remotely sensed calculated FAPAR and field-measured FAPAR was 0.66 in the first season and 0.89 in the second season. The magnesium oxide nanoparticle (nMgO) treatments resulted in significantly higher yields than the different treatments of foliar applications. The LAI and FAPAR variables showed a positive correlation with yield in the first season (October) and in the second season (March). Yield in treatment by nMgO varied significantly from that in the other treatments, ranging from 69-ton ha−1 in the first season to 74.9-ton ha−1 in the second season. Linear regression between LAI and PSSRa showed the highest correlation coefficient (0.90) compared with other vegetation indices in the first season. In the same season, the highest correlation coefficient (0.94) was found between FAPAR and PSSRa. In the second season, the highest accuracy to the estimate LAI was found in the correlation between MCARI and PSSRa, with correlation coefficients of 0.9 and 0.91, respectively. In the second season, the highest accuracy to the estimate FAPAR was found with the correlation between PSSRa, ARVI, and NDVI, with correlation coefficients 0.97 and 0.96, respectively. The highest correlation coefficients between vegetation indices and yield were found with ARVI and NDVI in the first season, and only with NDVI in the second season. [ABSTRACT FROM AUTHOR]

Ensuring food security requires monitoring crop growth conditions prior to harvesting. This research encompass three stages: remote sensing, crop modeling, and establishing a connection between the two. Besides, Fusion satellite images have been implemented to monitor and investigate fodder corn in three study areas. In the remote sensing stage, after implementing the downscaling algorithm and producing the leaf area index (LAI) time series, the results were compared with the estimated values from Landsat 8 and MODIS images, which were associated with overestimation in all cases. Furthermore, the results exhibited statistically significance with R2 > 95% and P-value < 0.05. The AquaCrop model was first calibrated and implemented in the crop modeling section at each growth stage based on the observational data measured in each field. The accuracy of the simulated model was checked according to the results of statistical indicators with high accuracy (NRMSE = 10% and RMSE = 0.03 (Ton/ha)) at significant level (95%) and was associated with underestimation. Using the SVM decision support algorithm, the relationship between downscaled LAI and calibrated CC (crop canopy) was estimated. This relationship was generated with 70% of the data and subsequently validated using the remaining 30% (R2 = 0.99, NRMSE = 0.01). On this basis, CC values were predicted. Finally, biomass values were compared with observed biomass values. According to the results of statistical indicators (RMSE = 0.19 (Ton/ha), NRMSE = 0.01, R2 = 0.96, P-value < 0.05), biomass estimation was highly accurate. These results demonstrate the reliability and accuracy of the model and the proposed method for simulating and estimating biomass before harvesting. [ABSTRACT FROM AUTHOR]

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products' (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T's causality is evenly distributed amongst all biomes with fractional covers between ∼10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet's ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers. [ABSTRACT FROM AUTHOR]

Monitoring vegetation change and hydrological variation is crucial as they are useful means of appraising the ecological environment and managing water resources in water-resource-sensitive regions. The leaf area index (LAI) describes water consumption in hydrologic processes and is an important vegetation variable for water budgeting in catchments. As part of the Soil and Water Assessment Tool (SWAT), LAI is a significant parameter, which links vegetation dynamics with the hydrological cycle. In the current study, we have aimed to describe the Lhasa River (LR) cumulative streamflow based on simulation scenarios obtained with the SWAT model. After dispensing a heterogeneous LAI time series developed by MODIS NDVI as a source of data at the HRU level (SWAT-synthetic LAI scenario), the study has produced a better representation of LR cumulative streamflow in terms of the selected evaluation criteria, encompassing the SWAT-baseline (SWAT-B scenario)-simulated and SWAT-built-in LAI-influenced (SWAT-LAI scenario) LR cumulative streamflow. The study has revealed a close relationship between the observed and the SWAT-SLAI-scenario-generated LR streamflow, with a similar MK trend for the study time span. The LAI has been found to share a close relationship with LR streamflow, as both the LAI and LR streamflow are found to be influenced by the rainfall received in the Lhasa River Basin (LRB). The study is instrumental in understanding the association between LR streamflow, vegetation change, and the climatic conditions of the Lhasa River Basin (LRB). [ABSTRACT FROM AUTHOR]