Climatic drivers of leaf area index dynamics in the Amazon Basin: Insights from remote sensing

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As a key determinant of canopy structure and productivity, LAI regulates biosphere-atmosphere interactions and regional carbon and hydrological cycles, making it vital for forest management and conservation. However, its long-term response to climate variability remains poorly characterized. This study integrates MODIS-derived LAI data (2001–2022) with meteorological records to assess how temperature anomalies, precipitation extremes, and aridity shape canopy dynamics, offering insights for adaptive forest management. A nonlinear relationship between LAI and temperature reveals a threshold of 25.3°C, beyond which LAI declines, indicating heat stress-induced canopy suppression. Precipitation positively influences LAI, with seasonal variability exerting a stronger effect than annual means, emphasizing the role of short-term hydrological fluctuations in maintaining forest productivity. The aridity index explains 23% of LAI variability, underscoring its role as a key constraint on vegetation growth. Additional meteorological factors, including water vapor pressure (R² = 0.26) and elevation (R² = 0.27), further shape LAI dynamics, reflecting interactions between land surface energy balance and atmospheric moisture availability. Post-2018 trends indicate a decline in high-LAI regions, with partial recovery by 2022, suggesting increasing climate-driven instability in Amazonian forest structure. These findings enhance understanding of tropical forest resilience, guiding conservation planning, ecosystem monitoring, and climate-adaptive management. Given the Amazon Basin’s role in global atmospheric circulation, sustained LAI monitoring is essential for refining climate-vegetation models and ensuring long-term forest stability. Leaf Area Index (LAI) Amazon Basin Climatic variability Forest productivity Carbon cycle Ecosystem resilience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Leaf Area Index (LAI) is a fundamental biophysical parameter that directly influences forest productivity, hydrological cycles, and carbon sequestration, thereby playing a crucial role in sustainable forest management (Kobayashi et al., 2010 ; Zhu et al., 2016 ; Chrysafis et al., 2020 ). As a primary determinant of canopy structure and function, LAI regulates light interception, evapotranspiration, and microclimatic conditions, linking forest ecology to broader land-atmosphere interactions (Chagas et al., 2019 ). In tropical forests, where climate variability exerts strong control over ecosystem dynamics, LAI serves as a key proxy for gross primary productivity (GPP) and canopy resilience (Nackaerts et al., 2000 ; Yin et al., 2016 ). Understanding the climatic drivers of LAI variability is therefore critical for assessing forest stability and informing management strategies aimed at enhancing ecosystem resilience under changing environmental conditions. The Amazon Basin, as the world’s largest tropical forest ecosystem, is central to global carbon and water cycles, supporting vast biodiversity and sustaining regional livelihoods through its ecosystem services (Betts et al., 2008 ; D’Amato et al., 2017 ). However, rising temperatures, shifting precipitation patterns, and intensifying droughts are altering forest structure and function, with implications for both conservation and resource management (Ciemer et al., 2021 ). While seasonal increases in solar radiation may promote canopy development in some regions, prolonged droughts often trigger declines in LAI, leading to increased tree mortality and shifts in forest composition (Caldararu et al., 2012 ; De Almeida et al., 2019 ). Such changes have profound consequences for carbon sequestration and water regulation, making it imperative to quantify how climate extremes shape LAI dynamics at large spatial and temporal scales. Despite growing interest in LAI-climate interactions, key uncertainties remain regarding the role of aridity, the long-term stability of Amazonian canopies, and the capacity of forests to recover from extreme climatic events. Addressing these knowledge gaps is essential for developing adaptive management strategies that balance conservation goals with ecosystem sustainability. Traditionally, forest LAI has been measured through ground-based techniques such as Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS). However, these methods are limited in their spatial and temporal coverage, restricting their utility for large-scale forest monitoring (Xie et al., 2023 ). Recent advances in remote sensing—particularly satellite-derived LAI products—offer a powerful alternative for assessing canopy dynamics over broad geographic regions and extended time periods. Among available datasets, the MODIS LAI product (2000–2022) provides long-term, high-resolution measurements validated against field data, making it a valuable tool for evaluating vegetation-climate interactions (Zhang et al., 2019 ; Xu et al., 2020 ). Despite these advancements, discrepancies exist between satellite-based LAI trends and field observations, particularly in drought-prone regions where remotely sensed ‘greening up’ may contrast with on-the-ground reports of canopy decline (Brando et al., 2010 ). Clarifying these inconsistencies is crucial for refining forest monitoring systems and improving decision-making in conservation and land management (Brando et al., 2010 ). Clarifying these inconsistencies is crucial for refining forest monitoring systems and improving decision-making in conservation and land management. This study integrates MODIS-derived LAI data (2001–2022) with climate records to assess how temperature, precipitation, and aridity influence canopy structure and function across the Amazon Basin. Specifically, we aim to: (1) quantify the climatic drivers of LAI variability at seasonal and interannual scales, (2) assess the role of aridity in shaping LAI responses, (3) analyze long-term trends and anomalies in canopy structure, and (4) evaluate the implications of observed LAI patterns for forest resilience and sustainable management. By combining remote sensing with land-atmosphere modeling, this study provides actionable insights into how tropical forests respond to climate variability, offering a knowledge base for adaptive management strategies that enhance forest sustainability under intensifying climatic pressures. METHODS Study area The Amazon Basin, covering approximately nine South American countries, is home to the world’s largest continuous tropical rainforest and the most extensive river system (Fig. 1 ). It is a biodiversity hotspot, sheltering a remarkable diversity of flora and fauna, including iconic species such as jaguars ( Panthera onca ), giant otters ( Pteronura brasiliensis ), and Brazil nut trees ( Bertholletia excelsa ). The region experiences a humid tropical climate, with average annual temperatures ranging from 25–27°C and rainfall exceeding 2000 mm in most areas (Davidson et al., 2012 ). The Amazon plays a pivotal role in global ecosystem services, including carbon sequestration, climate regulation, and freshwater cycling. However, it faces escalating threats from deforestation, increasingly amplified by climate-induced droughts and self-reinforcing fire feedback loops. The dynamic interplay between anthropogenic land-use changes and shifting climatic regimes is perturbing hydrological cycles, compromising ecosystem resilience, and accelerating carbon fluxes to the atmosphere, with profound implications for regional and global climate stability. By 2000, approximately 15% of the Amazon forest had been lost to deforestation (Foley et al., 2007 ), and as of 2022, cumulative forest loss has surpassed 17%, with projections indicating a potential tipping point if deforestation reaches 20–25% (Flores et al., 2024 ). Despite recent declines in deforestation rates, escalating fire regimes and climate-induced droughts continue to destabilize ecosystem integrity, disrupt biogeochemical cycles, and amplify regional climate instability. Data collection A comprehensive dataset (Table 1 ) was compiled to examine the Leaf Area Index (LAI) and climatic conditions across the Amazon Basin. LAI data were obtained from the Terra Leaf Area Index/FPAR 8-Day Global 500m (MOD15A2H.061), which provides a consistent long-term record with high temporal frequency. This dataset was selected over alternatives (e.g., AVHRR, GLASS, or GIMMS LAI4g) due to its superior spatial resolution, compatibility with MODIS-derived GPP estimates, and extensive validation against in-situ measurements (Myneni et al., 2021 ). To establish LAI as a reliable indicator of Gross Primary Productivity (GPP), we utilized annual mean GPP data from 2021 to 2022, sourced from the MOD17A2H.061 Terra Gross Primary Productivity product, with a spatial resolution of 500 m × 500 m (Running et al., 2021 ). Climatic variables, including mean annual temperature (°C), annual precipitation (mm), temperature seasonality (standard deviation ×100), and precipitation seasonality (standard deviation ×100), were sourced from the WorldClim v2.1 dataset, with a resolution of 1 km × 1 km and a temporal range from 1970 to 2000 (Fick & Hijmans, 2017 ). Additional environmental factors such as solar radiation (kJ m⁻² day⁻¹), elevation (m), and water vapor pressure (kPa) were also derived from WorldClim. Latitude information was extracted directly from the LAI dataset at the pixel level. To characterize long-term aridity conditions, aridity index values were obtained from the Global Aridity Index ET0 dataset, covering the same temporal span (Zomer et al., 2022 ). These datasets (Table 1 ) were selected to effectively capture the spatial and temporal dynamics of vegetation and climate across the study region. Table 1 Overview of datasets used in this study, including extracted variables, temporal coverage, and references Dataset Variable extracted Time span Reference MOD15A2H.061 Terra Leaf Area Index/FPAR 8-Day Global 500m Leaf Area Index (LAI) 2001–2022 (Myneni et al., 2021 ) WorldClim v2.1 Mean annual temperature (°C), Annual Precipitation (mm), Temperature seasonality (standard deviation ×100), Precipitation seasonality (standard deviation ×100), Solar Radiation (kJ m − 2 day − 1 ), Vapor Pressure (m s − 1 ), Elevation (m) 1970–2000 (Fick & Hijmans, 2017 ) Global Aridity Index ET0 (Global-AI_ET0) Aridity Index values 1970–2000 (Zomer et al., 2022 ) MOD17A2H.061: Terra Gross Primary Productivity 8-Day Global 500m Gross primary production 2021–2022 (Running et al., 2021 ) Data processing Data processing was carried out using RStudio (version 2024.04.2 Build 764). Climatic variables were first extracted at the geographic locations corresponding to the LAI dataset. MODIS LAI retrievals can be affected by cloud contamination and seasonal inconsistencies, particularly in dense tropical forests. To minimize these errors, we applied quality control flags from the MODIS product and excluded pixels with high uncertainty values. The ‘terra’ package was utilized to load and process WorldClim rasters, ensuring spatial consistency with the LAI dataset in terms of resolution and projection. To match the 500 m × 500 m resolution of the LAI data, all climatic variables were upscaled using the nearest neighbor resampling method, implemented via the ‘resample’ and ‘aggregate’ functions within the ‘terra’ package. While nearest-neighbor resampling preserves original values, it may introduce spatial discontinuities, particularly in heterogeneous landscapes, potentially affecting the precision of climate-LAI relationships. For the evaluation of long-term climatic influences, climate and environmental variables were averaged over the period from 1970 to 2000, following the convention of using pre-industrial and 20th-century baselines for assessing climate change impacts. This period was selected due to the availability of high-resolution climate data and its relevance for detecting climate-driven deviations in LAI post-2000. Temporal trends in LAI changes from 2001 to 2022 were also analyzed. Geographic coordinates from the LAI dataset were structured into a ‘SpatVector’ object, allowing efficient extraction of multiple climatic variables at each location. To enhance computational efficiency, the extraction was carried out in batches. The extracted values for mean annual temperature, annual precipitation, solar radiation, temperature and precipitation seasonality, elevation, and water vapor pressure were integrated with the LAI data. The final dataset, combining LAI with climatic variables, was exported as a CSV file for further statistical analysis. A summary of the entire data processing workflow, from collection to analysis, is presented in Fig. 2 . <> Statistical analysis Key climatic and environmental variables—such as mean annual temperature (°C), annual precipitation (mm), solar radiation (kJ m⁻² day⁻¹), temperature and precipitation seasonality, elevation (m), water vapor pressure (kPa), and aridity index—were extracted from global raster datasets (Table 1 ). The extraction process was carried out using the ‘terra’ and ‘dplyr’ packages in R. Raster data were loaded using the ‘rast’ function, and spatial coordinates from the LAI dataset were converted into a ‘SpatVector’ object to ensure precise extraction. The ‘terra::extract’ function was then applied to retrieve raster values at the corresponding locations. To optimize computational efficiency, the extraction was performed in predefined batches. For bivariate analysis of the relationship between LAI and the aridity index, the ‘biscale’ package was employed to classify the data into quantile-based categories. This approach was chosen over clustering-based methods due to its ability to maintain interpretability while capturing the nonlinear distribution of LAI values across aridity gradients. These categories were visualized with a color-coded scheme, and spatial patterns were mapped using the ‘ggplot2’ and ‘sf’ packages. The boundaries of the Amazon Basin were overlaid on the final visualization to provide geographical context. The combined bivariate map and its legend were constructed using the ‘cowplot’ package. For regression analysis, Python (v3.11.10) was used, leveraging its computational efficiency in handling large datasets. The analysis was carried out with the ‘pandas’, ‘seaborn’, ‘matplotlib’, and ‘scipy’ libraries. The dataset was first cleaned by removing missing values and filtering out observations with extreme outliers (beyond three standard deviations from the mean) or those located in areas with known data artifacts (e.g., cloud-contaminated pixels, water bodies misclassified as vegetation). A regression analysis was then performed to investigate the relationship between LAI (the dependent variable) and various climatic and environmental predictors, including aridity index, temperature, precipitation, and solar radiation. While climate variables are primary drivers of LAI variability, additional confounding factors such as soil properties, forest age, and disturbance history may also influence observed patterns. Future work should incorporate complementary datasets to disentangle the relative contributions of these factors to LAI dynamics. RESULT LAI as an indicator of gross primary productivity Leaf Area Index (LAI) exhibits a strong positive correlation with Gross Primary Productivity (GPP) across the Amazon Basin (R = 0.59, p < 0.001; Fig. 3 ), reinforcing the role of canopy structure in modulating carbon assimilation. Despite regional variability driven by factors such as soil fertility, water availability, and species composition, the overall trend demonstrates that higher LAI is associated with enhanced primary productivity. The unimodal distribution of GPP (Fig. 3 ), clustering around 0.05 kg C/m², further underscores this relationship. However, deviations from this trend suggest that climatic stressors and land-use alterations may influence ecosystem productivity. These findings validate the use of LAI as a robust proxy for estimating large-scale variations in tropical forest carbon dynamics, offering critical insights into the functional responses of the Amazon’s carbon cycle under changing environmental conditions. <> Climatic Influences on LAI Variability The spatial heterogeneity of LAI across the Amazon Basin reflects the influence of climatic variability on canopy structure. The relationship between LAI and mean annual temperature follows a unimodal Gaussian distribution (adjusted R² = 0.15; Fig. 4 a), with LAI increasing up to ~ 25.3°C before declining at higher temperatures. This indicates that moderate temperatures favor canopy development, whereas excessive warming is associated with a reduction in LAI. A weak but significant positive correlation with annual precipitation (R² = 0.07, p < 0.001; Fig. 4 b) suggests that higher rainfall generally supports greater leaf area, though with considerable variability. Seasonal climatic fluctuations exert a stronger influence on LAI. A negative correlation with temperature seasonality (R² = 0.01, p < 0.001; Fig. 4 c) implies that greater thermal variability is associated with reduced canopy cover. Similarly, precipitation seasonality exhibits a stronger negative relationship with LAI (R² = 0.06, p < 0.001; Fig. 4 d), indicating that regions with highly irregular rainfall patterns tend to have lower LAI, likely due to periodic water stress. These results underscore the greater impact of seasonal climate variability compared to mean climatic conditions in regulating LAI across the Amazon Basin. <> Aridity Impacts on LAI The spatial distribution of LAI exhibits clear gradients, with higher values concentrated in humid regions and lower values in more arid zones (Fig. 5 , top panel). The strongest vegetation cover is observed in the central and northern Amazon, where low aridity supports dense forests, whereas the southern and southwestern regions exhibit lower LAI due to drier conditions. A statistically significant but weak positive correlation between LAI and the Aridity Index (R² = 0.23, p < 0.001; Fig. 5 , bottom right) suggests that within the observed range, canopy cover tends to increase slightly with decreasing aridity, although additional factors such as soil moisture, vegetation composition, and land-use dynamics introduce variability. The histogram of the Aridity Index (Fig. 5 , bottom left) indicates that the majority of the Amazon Basin falls within the humid category (Aridity Index > 0.65), where LAI remains high. However, regions classified as dry sub-humid (0.5–0.65) and semi-arid (0.2–0.5) exhibit greater variability in LAI, reflecting the increasing influence of climatic stressors on vegetation structure. These findings highlight the complexity of aridity-vegetation interactions, where water availability is a key driver of canopy cover, but secondary environmental factors further modulate spatial patterns of forest productivity. <> Temporal Dynamics of LAI (2001–2022) Analysis of LAI trends from 2001 to 2022 reveals subtle yet ecologically significant shifts in canopy structure (Fig. 6 ). While the overall LAI distribution has remained relatively stable, regional trends indicate dynamic fluctuations. Between 2001 and 2009, LAI values were more evenly distributed, with a gradual increase in mid-range values (3–4). By 2013, a shift toward higher LAI values suggested an expansion in vegetation cover. However, this trend was not sustained, as 2018 data indicate a decline in high-LAI values, signaling localized canopy loss. The most recent 2022 dataset suggests a partial recovery, with an increase in the modal class frequency, hinting at possible stabilization following prior disturbances. These trends underscore the ongoing influence of climatic and anthropogenic factors on Amazonian forest structure, with long-term implications for ecosystem resilience. <> Relationship between LAI and Environmental Variables The relationship between LAI and environmental factors varies in strength and direction. A statistically significant but weak positive correlation is observed with latitude (R² = 0.05, p < 0.001; Fig. 7 a), indicating a slight increase in LAI at higher latitudes. In contrast, elevation exhibits a stronger negative correlation with LAI (R² = 0.27, p < 0.001; Fig. 7 b), with LAI declining as elevation increases. This pattern suggests a progressive reduction in canopy cover along altitudinal gradients. Solar radiation shows a weak negative association with LAI (R² = 0.01, p < 0.001; Fig. 7 c), implying minimal influence on canopy development. In comparison, water vapor pressure demonstrates the strongest positive correlation (R² = 0.26, p < 0.001; Fig. 7 d), highlighting a strong link between atmospheric moisture availability and LAI distribution. The overall low R² values across most environmental variables indicate that although these factors contribute to spatial LAI variations, their explanatory power remains limited. This underscores the influence of additional ecological and biophysical drivers that shape canopy structure at broader scales. <> DISCUSSION This study explores the relationship between climate variables and the Leaf Area Index (LAI) in the Amazon Basin, a critical aspect of ecosystem functioning in the face of ongoing environmental change. By considering temporal trends from 2001 to 2022, climatic drivers, and environmental interactions, we provide a detailed examination of how changes in climate—particularly precipitation, temperature, and extreme weather events—affect LAI dynamics in this globally significant biome. The results emphasize the multifaceted role of climate as a primary driver of LAI variability, offering insights into the ecological processes that underpin ecosystem resilience, biodiversity, and carbon cycling in response to global change. Climate as a Dominant Driver of LAI Dynamics Climate acts as a key regulator of LAI in the Amazon Basin, influencing both the structural and functional aspects of forest canopies. Seasonal shifts in precipitation, temperature, and relative humidity, along with extreme climatic events such as droughts and cold spells, are known to induce significant compositional changes in the canopy structure and affect aerosol microbiome dynamics (Caldararu et al., 2012 ; Souza et al., 2020 ). This study corroborates findings that reduced rainfall, driven by climate variability and deforestation, directly affects vegetation productivity, thus altering canopy greenness and LAI (Hilker et al., 2014 ). The phenomenon of "flying rivers," where trees release moisture that sustains regional rainfall, exemplifies the intricate relationship between vegetation and climate (Webb, 2018 ). However, the decreasing precipitation trend, exacerbated by human-induced land use changes, results in prolonged drought conditions, leading to the degradation of forest canopies, which heightens the vulnerability of the Amazon to wildfires and carbon release (Malavelle et al., 2019 ; Pfeifer et al., 2018 ). This feedback mechanism not only destabilizes ecosystem structure but also compromises the carbon sink capacity of the forest, reinforcing concerns over potential shifts in the Amazon’s role in global carbon dynamics (Wey et al., 2022 ). Our analysis further reveals that drought-induced shifts in LAI are particularly pronounced in forest strata. During drought events, upper-canopy trees often exhibit increased LAI due to enhanced sunlight availability, stimulating leaf expansion. In contrast, the lower canopy shows reduced LAI as water stress increases, particularly in smaller trees, which are more sensitive to moisture deficits (Smith et al., 2019 ). This vertical heterogeneity in LAI dynamics was evident during the 2015–2016 El Niño, where the varying responses of different canopy layers to extreme heat and water stress revealed complex interactions between climatic stressors and ecosystem structure (Y. Y. Liu et al., 2024 ). The long-term implications of these droughts are profound; sustained drying trends could lead to irreversible shifts in forest structure, further accelerating the conversion of the Amazon from a carbon sink to a carbon source. Beyond precipitation and drought, rising temperatures are altering the moisture regime in the Amazon. Temperature increases, compounded by decreased precipitation, elevate vapor pressure deficits, which exacerbate water stress and decrease photosynthetic efficiency (Souza et al., 2020 ). Deforestation amplifies this effect by increasing local land surface temperatures, particularly during the dry season, where the loss of canopy cover leads to an average temperature increase of 0.44°C (Baker & Spracklen, 2019 ). These temperature-induced changes in microclimates further decouple structural (LAI) and functional (photosynthetic) components of the ecosystem, leading to altered carbon dynamics and reduced forest resilience (Hu et al., 2022 ). The relationship between LAI and mean annual temperature follows a unimodal Gaussian distribution, with LAI increasing up to ~ 25.3°C before declining at higher temperatures, suggesting that moderate temperatures favor canopy development, whereas excessive warming reduces LAI. Although some studies have identified increasing LAI in certain regions of the Amazon due to climate warming and wetting (P. Liu et al., 2017 ), the localized variability—attributable to land-use activities—suggests that broader climatic shifts are increasingly influenced by anthropogenic disturbances, masking the full extent of global climate change impacts. Overall, our findings underscore the critical role of climate as a driver of LAI dynamics in the Amazon. As temperature rises and precipitation patterns become more erratic, these changes are expected to further reduce forest resilience, with cascading impacts on biodiversity, ecosystem services, and carbon storage (Boulton et al., 2021 ). With the Amazon approaching a potential tipping point toward a savanna-like state, this study highlights the need for sustained monitoring of LAI as an early warning indicator for large-scale ecological transformations, such as forest dieback, and to better understand how these changes may influence global carbon and water cycles. Environmental drivers and spatial heterogeneity of LAI The spatial variability in LAI across the Amazon Basin can be attributed to a complex suite of environmental factors, including latitude, elevation, solar radiation, and water vapor pressure. Latitude and elevation act as primary determinants of temperature and precipitation patterns, which, in turn, shape the vegetation structure and productivity across the Amazon’s latitudinal gradient (Eamus et al., 2016 ). The observed weak positive correlation between latitude and LAI suggests a gradual increase in canopy cover at higher latitudes, possibly linked to shifts in climatic conditions and vegetation composition. In contrast, the stronger negative relationship with elevation highlights a progressive decline in LAI along altitudinal gradients, reflecting the constraints imposed by reduced atmospheric moisture and lower temperatures at higher elevations (Jordão et al., 2015 ). Solar radiation is another critical driver of LAI, as it directly influences photosynthetic activity. However, the weak negative correlation observed in the results indicates that increasing solar radiation does not necessarily enhance canopy development, likely due to concurrent water limitations or photoinhibition effects (Lovelock et al., 1994 ). In contrast, Water vapor pressure, a key indicator of atmospheric moisture, strongly influences plant transpiration rates and overall canopy water status. The positive association between LAI and water vapor pressure suggests that higher atmospheric moisture availability supports greater canopy density, likely by reducing evaporative stress and sustaining leaf hydration (Y. Liu et al., 2020 ). In regions with high water vapor pressure, plant transpiration operates more efficiently, contributing to greater carbon assimilation and canopy expansion (Cernusak, 2020 ). Conversely, in areas where atmospheric moisture is low, water loss through transpiration can exceed replenishment, constraining LAI. This highlights the crucial role of atmospheric moisture in mediating forest productivity, particularly in ecosystems subject to seasonal fluctuations in humidity. Notably, taller forests exhibit greater resilience to precipitation variability but remain sensitive to changes in vapor pressure, underscoring the intricate interactions between atmospheric moisture conditions and forest structure (Giardina et al., 2018 ). Further complicating the LAI-environment relationship are anthropogenic factors such as deforestation, logging, and land-use change, which alter canopy structure and contribute to spatial heterogeneity in LAI (Qu et al., 2018 ). For instance, deforestation increases local temperatures and disrupts the balance between precipitation and evapotranspiration, exacerbating water stress and reducing LAI, particularly in forested areas undergoing anthropogenic disturbance (Nasahara et al., 2008 ). These findings emphasize the need for integrated models that account for both climatic and anthropogenic drivers of LAI variability to predict the future trajectory of Amazonian ecosystems. Implications for tropical forests and global change The resilience of tropical forests under accelerating climate change hinges on their ability to maintain functional canopy structures, with Leaf Area Index (LAI) serving as a key indicator of forest health, productivity, and adaptive capacity (Needham et al., 2025 ). As a fundamental driver of carbon sequestration, water cycling, and biodiversity support, LAI directly influences forest-climate feedback and, consequently, the sustainability of forest ecosystems. However, increasing anthropogenic pressures—including deforestation, land-use change, and altered precipitation regimes—are destabilizing LAI dynamics, posing critical challenges for forest management and conservation efforts. In the Amazon Basin, declining LAI due to rising temperatures and shifting precipitation patterns threatens to weaken the region’s carbon sink capacity, accelerating carbon loss and intensifying climate change feedback loops (P. Liu et al., 2017 ; Y. Liu et al., 2012 ; Yin et al., 2016 ). The potential shift of the Amazon from a carbon sink to a carbon source represents a major tipping point in global climate regulation, with cascading effects on carbon budgets and atmospheric stability. Furthermore, reduced canopy density disrupts key microclimatic processes such as evapotranspiration and temperature moderation, which are essential for maintaining hydrological balance and ecosystem stability (Boulton et al., 2021 ). Beyond climate regulation, LAI declines have profound consequences for forest biodiversity and ecosystem services. Loss of canopy cover increases habitat fragmentation, reduces structural complexity, and drives shifts in species composition, ultimately accelerating biodiversity loss and impairing essential ecological functions (Cowling et al., 2001 ). These disruptions extend beyond forest interiors, affecting landscape-scale processes such as pollination, seed dispersal, and water purification. Given the interconnected nature of these impacts, integrating LAI into forest management frameworks is critical for enhancing adaptive capacity and ensuring long-term ecosystem sustainability. A pathway toward sustainability requires the development of integrated models that account for both climatic and anthropogenic drivers of LAI change. Such models should incorporate remote sensing data, ecological monitoring, and land-use projections to refine our understanding of future LAI trajectories under different climate scenarios. By linking LAI dynamics to forest management strategies—such as reforestation efforts, conservation zoning, and adaptive silvicultural practices—these models can inform policies that mitigate climate risks while enhancing forest resilience. CONCLUSION This study underscores the intricate and dynamic relationship between climate and LAI in the Amazon Basin, highlighting the vulnerability of tropical forests to ongoing climatic shifts and anthropogenic disturbances. The findings reinforce the necessity of multidimensional models that integrate climate variables, vegetation indices, and human-induced impacts to improve predictions of LAI responses to environmental change. Future research should bridge the gap between satellite-derived observations and field-based measurements, enhancing the accuracy of LAI assessments across diverse forest conditions. By strengthening predictive models and incorporating LAI into adaptive forest management frameworks, we can develop proactive strategies to mitigate climate impacts, safeguard biodiversity, and sustain critical ecosystem services in the Amazon and other tropical forests worldwide. Declarations AUTHOR CONTRIBUTIONS Md Shamim Reza Saimun: conceptualization, data curation, formal analysis, investigation, methodology, resources, software, validation, visualization, writing – original draft, writing – review and editing. Md Rezaul Karim: data curation, formal analysis, investigation, methodology, software, validation, visualization, writing – original draft, writing – review and editing, supervision ACKNOWLEDGEMENT We sincerely thank Md. Abdul Halim (PhD) for his valuable ideas with the GPP analysis. 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Ambiente e Agua-An Interdisciplinary J Appl Sci 10(2):363–375 Kergoat L, Berthelot B, Royer J, Lafont S, Planton S, Douville H, Dedieu G (2002) Impact of doubled CO 2 on global-scale leaf area index and evapotranspiration: Conflicting stomatal conductance and LAI responses. J Geophys Research: Atmos 107(D24). https://doi.org/10.1029/2001jd001245 Kobayashi H, Delbart N, Suzuki R, Kushida K (2010) A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest. J Geophys Research: Biogeosciences 115:G1 Lin W, Wei N, Yuan H, Zhang S, Wei Z, Hu Y, Dong W, Dai Y, Liu S, Lu X (2023) Reprocessed MODIS Version 6.1 Leaf Area Index Dataset and Its Evaluation for Land Surface and Climate Modeling. Remote Sens 15(7):1780. https://doi.org/10.3390/rs15071780 Liu P, Hao L, Pan C, Zhou D, Liu Y, Sun G (2017) Combined effects of climate and land management on watershed vegetation dynamics in an arid environment. Sci Total Environ 589:73–88 Liu Y, Ju W, Chen J, Zhu G, Xing B, Zhu J, He M (2012) Spatial and temporal variations of forest LAI in China during 2000–2010. Chin Sci Bull 57:2846–2856 Liu Y, Kumar M, Katul GG, Feng X, Konings AG (2020) Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nat Clim Change 10(7):691–695. https://doi.org/10.1038/s41558-020-0781-5 Liu YY, van Dijk AIJM, Meir P, McVicar TR (2024) Drought and radiation explain fluctuations in Amazon rainforest greenness during the 2015–2016 drought. Biogeosciences 21(9):2273–2295 Lovelock CE, Osmond CB, Jebb M (1994) Photoinhibition and recovery in tropical plant species: Response to disturbance. Oecologia 97(3):297–307. https://doi.org/10.1007/BF00317318 Malavelle FF, Haywood JM, Mercado LM, Folberth GA, Bellouin N, Sitch S, Artaxo P (2019) Studying the impact of biomass burning aerosol radiative and climate effects on the Amazon rainforest productivity with an Earth system model. Atmos Chem Phys 19(2):1301–1326 Myneni R, Knyazikhin Y, Park T (2021) MOD15A2H MODIS/Terra leaf area Index/FPAR 8-Day L4 global 500m SIN grid V006. [Dataset]. NASA EOSDIS Land Processes Distributed Active Archive Center Nackaerts K, Coppin P, Muys B, Hermy M (2000) Sampling methodology for LAI measurements with LAI-2000 in small forest stands. Agric For Meteorol 101(4):247–250. https://doi.org/10.1016/s0168-1923(00)00090-3 Nasahara KN, Muraoka H, Nagai S, Mikami H (2008) Vertical integration of leaf area index in a Japanese deciduous broad-leaved forest. Agric For Meteorol 148(6–7):1136–1146 Needham JF, Dey S, Koven CD, Fisher RA, Knox RG, Lamour J, Lemieux G, Longo M, Rogers A, Holm J (2025) Vertical canopy gradients of respiration drive plant carbon budgets and leaf area index. New Phytol 246(1):144–157. https://doi.org/10.1111/nph.20423 Pfeifer M, Gonsamo A, Woodgate W, Cayuela L, Marshall AR, Ledo A, Paine TCE, Marchant R, Burt A, Calders K (2018) Tropical forest canopies and their relationships with climate and disturbance: Results from a global dataset of consistent field-based measurements. For Ecosyst 5(1):1–14 Qu Y, Shaker A, Silva CA, Klauberg C, Pinagé ER (2018) Remote sensing of leaf area index from LiDAR height percentile metrics and comparison with MODIS product in a selectively logged tropical forest area in Eastern Amazonia. Remote Sens 10(6):970 Querino CAS, Beneditti CA, Machado NG, da Silva MJG, da Silva Querino JKA, dos Santos Neto LA, Biudes MS (2016) Spatiotemporal NDVI, LAI, albedo, and surface temperature dynamics in the southwest of the Brazilian Amazon forest. J Appl Remote Sens 10(2):26007 Running S, Mu Q, Zhao M (2021) MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V061 [Dataset]. https://doi.org/10.5067/MODIS/MOD17A2H.061 . NASA EOSDIS Land Processes Distributed Active Archive Center Smith MN, Oliveira RC, Santos DB, Taylor TC, Woodcock T, Oliveira E, Huxman TE, Alves LF, Falk DA, Restrepo-Coupe N, Saleska SR, Chen S, Figueira M, Camargo PB, Aragão LEOC, Ferreira ML, Mcmahon SM, Stark SC (2019) Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest. New Phytol 222(3):1284–1297. https://doi.org/10.1111/nph.15726 Souza F, Sánchez-Parra B, Sadowsky MJ, Huergo LF, Andreae MO, Weber B, Mathai PP, Cruz LM, Baura VA, Balsanelli E, Souza E, Souza R, Reis R, Godoi R, Angelis I, Pauliquevis T, Pedrosa F, Ruff S, Pöhlker C, Barbosa C (2020) Influence of seasonality on the aerosol microbiome of the Amazon rainforest. Sci Total Environ 760:144092. https://doi.org/10.1016/j.scitotenv.2020.144092 Webb J (2018) Bleeding the Flying River Dry: Deforestation, Climate Change and Drought in the Amazon Health on the Frontlines Blog Series . https://amazonfrontlines.org/chronicles/bleeding-river/ Wey H, Pongratz J, Nabel JEMS, Naudts K (2022) Effects of increased drought in Amazon forests under climate change: Separating the roles of canopy responses and soil moisture. J Geophys Research: Biogeosciences, 127 (3), e2021JG006525. Xiao Z, Liang S, Zhao X, Song J, Xiang Y, Wang J (2016) Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance. IEEE Trans Geosci Remote Sens 54(9):5301–5318. https://doi.org/10.1109/tgrs.2016.2560522 Xie X, He B, Guo L, Huang L, Hao X, Zhang Y, Liu X, Tang R, Wang S (2022) Revisiting dry season vegetation dynamics in the Amazon rainforest using different satellite vegetation datasets. Agric For Meteorol 312:108704 Xie X, Su H, Li W, Liao N, Pan W, Yang Y (2023) Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods. Remote Sens 15(6):1621. https://doi.org/10.3390/rs15061621 Xu J, Volk TA, Quackenbush LJ, Im J (2020) Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions. Remote Sens 12(18):2934. https://doi.org/10.3390/rs12182934 Yin Y, Myneni RB, Wu S, Dai E, Ma D, Zhu Z (2016) Nonlinear variations of forest leaf area index over China during 1982–2010 based on EEMD method. Int J Biometeorol 61(6):977–988. https://doi.org/10.1007/s00484-016-1277-x Zhang D, Zhang Z, Liu J, Sun G, Wang Q, Liu Q, Ni W (2019) Estimation of Forest Leaf Area Index Using Height and Canopy Cover Information Extracted From Unmanned Aerial Vehicle Stereo Imagery. IEEE J Sel Top Appl Earth Observations Remote Sens 12(2):471–481. https://doi.org/10.1109/jstars.2019.2891519 Zhu W, Zeng Y, Fang X, Xiang W, Pan Q, Peng C, Lei P, Deng X, Ouyang S (2016) Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters. Biogeosciences 13(12):3819–3831. https://doi.org/10.5194/bg-13-3819-2016 Zomer RJ, Xu J, Trabucco A (2022) Version 3 of the global aridity index and potential evapotranspiration database. Sci Data 9(1):409 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 23 Jun, 2025 Reviews received at journal 23 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 30 Apr, 2025 Editor assigned by journal 15 Apr, 2025 Submission checks completed at journal 15 Apr, 2025 First submitted to journal 14 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6447042","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450881463,"identity":"c12eae2b-6bdb-438d-972f-49b8faf5748f","order_by":0,"name":"Md Shamim Reza Saimun","email":"","orcid":"","institution":"Shahjalal University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Shamim Reza","lastName":"Saimun","suffix":""},{"id":450881465,"identity":"bedad6cf-cd99-4435-a60d-d0d0da267f58","order_by":1,"name":"Md Rezaul Karim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYNCCAgYDBgbGBoYPDAw8fMRpMYBoYZwB1MIG1gwEPIS1MDAwg1QR1GLe3v7wM48BgzH/7MOtm21z7GTYGHjMH3zcY5Nnz97A+OEHphaZMweSpYFazCTOJbbdzt2WDHQYj2HjjGdpxTw8B5glezC1SEgkHABpsWE4wwjSwgzW0sxz4HBij0QCGzbXScg/bP4N0iIP0mK5rR5VC+MfbLYws4EdZgDSwrjtMKoWZmy28KSxWc4xkDA2BGq52bvtOA8bM1vhzBkH0hJ7zhxslpbBooX9+OMbbypsDOedYX924+e2ant+9uYNHz4csElsb28++PENjpAG6kRiM8NZkPgZBaNgFIyCUUA6AABwzVrmiiKceQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Toronto","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Rezaul","lastName":"Karim","suffix":""}],"badges":[],"createdAt":"2025-04-14 14:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6447042/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6447042/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-025-05673-y","type":"published","date":"2025-08-05T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81991365,"identity":"d9864e07-cc7a-47d3-9396-42438671db83","added_by":"auto","created_at":"2025-05-05 16:49:53","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1122502,"visible":true,"origin":"","legend":"\u003cp\u003eStudy site and climate of the Amazon Basin, including land cover types such as water (blue), forest (green), crops (yellow), built areas (red), and rangeland (light brown). The inset (top left) marks the basin's location in South America. The Walter-Lieth climate diagram (bottom left) presents mean monthly temperature (red line) and precipitation (blue shading) from 1901 to 2022 (CRU v4.07), highlighting the region’s humid tropical climate.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6447042/v1/91fcffcbeb06d9bd918e1bfc.jpeg"},{"id":81991361,"identity":"66a441a6-ae5f-468e-baf7-e1cf9510e89c","added_by":"auto","created_at":"2025-05-05 16:49:53","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":522105,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the steps in this study on LAI in the Amazon Basin. It illustrates the sequential process of data collection, processing, and statistical analysis to assess the impact of climate and environmental variables on LAI.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6447042/v1/b82e6edb3cfb2151669c13bc.jpeg"},{"id":81992207,"identity":"1bbf0ed9-7800-4555-91f6-616a7ed52f74","added_by":"auto","created_at":"2025-05-05 16:57:53","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1482378,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Leaf Area Index (LAI) and Gross Primary Production (GPP) in the Amazon Basin. Scatter plot depicting the correlation between GPP (kg C/m²) and LAI, with a linear regression fit (red line) showing a significant relationship. The Pearson correlation coefficient (R) and p-value (p \u0026lt; 0.001) indicate a strong and statistically significant positive relationship between GPP and LAI. The analysis focuses on data extracted from MODIS satellite products for the year 2021 to 2022.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6447042/v1/e09949bbe7c736181e6e74ee.jpeg"},{"id":81991364,"identity":"3e25504e-ce6a-43e5-b203-de0a3b0c0758","added_by":"auto","created_at":"2025-05-05 16:49:53","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1760868,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between climate variables and Leaf Area Index (LAI) in the Amazon Basin. Scatterplots show the correlations between LAI and (a) mean annual temperature (°C), (b) annual precipitation (mm), (c) temperature seasonality (SD × 100), and (d) precipitation seasonality (SD × 100). Each point represents a 500 m pixel from MODIS LAI data. Red lines indicate least-squares regression fits. Pearson correlation coefficients (R) and associated p-values are displayed in each panel.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6447042/v1/0d7c05217af5150020b816e2.jpeg"},{"id":81991374,"identity":"159f5abc-5e79-4ad1-8164-0941adc8de85","added_by":"auto","created_at":"2025-05-05 16:49:54","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2014717,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the Leaf Area Index (LAI) across the Amazon Basin (top) and its relationship with the Aridity Index (bottom). The upper panel illustrates the spatial heterogeneity of LAI, with variations influenced by aridity gradients. The lower panel presents a scatter plot depicting the relationship between LAI and the Aridity Index, with a fitted regression line (red).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6447042/v1/68abdce25cd249c0c9d476d6.jpeg"},{"id":81991370,"identity":"ef369f4d-68f0-4f70-b121-eafcbeec46a8","added_by":"auto","created_at":"2025-05-05 16:49:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2046220,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal variation in Leaf Area Index (LAI) across the study region from 2001 to 2022, with corresponding histograms illustrating LAI frequency distributions for each time point. The top panels depict LAI maps for six selected years, where colors represent LAI values ranging from 0 (blue) to 7 (yellow green), as indicated by the color bar. The bottom panels present histograms showing the frequency distribution of LAI values, highlighting shifts in vegetation cover and canopy density over time. The spatial extent of the study area remains constant across all years, with variations in LAI.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6447042/v1/86d71cf959e08fedb3ebfe93.png"},{"id":81991371,"identity":"69e83764-d0d8-48bc-8670-42889b9300d5","added_by":"auto","created_at":"2025-05-05 16:49:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":918955,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Leaf Area Index (LAI) and key environmental variables in the Amazon region. Scatterplots show the relationships between LAI and (a) latitude, (b) elevation, (c) solar radiation, and (d) water vapor pressure. Gray points represent individual data points, and blue lines indicate linear regression fits. Pearson correlation coefficients (R) and corresponding p-values are displayed in each panel.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6447042/v1/451dd8fb01f790a8491c928a.png"},{"id":88814208,"identity":"95fd0cd5-325a-40d9-8c40-ae7e0c1aaf06","added_by":"auto","created_at":"2025-08-11 16:08:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10478724,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6447042/v1/6b5d931e-daf4-4aab-b519-aca7f3d5ec2e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climatic drivers of leaf area index dynamics in the Amazon Basin: Insights from remote sensing","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLeaf Area Index (LAI) is a fundamental biophysical parameter that directly influences forest productivity, hydrological cycles, and carbon sequestration, thereby playing a crucial role in sustainable forest management (Kobayashi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chrysafis et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a primary determinant of canopy structure and function, LAI regulates light interception, evapotranspiration, and microclimatic conditions, linking forest ecology to broader land-atmosphere interactions (Chagas et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In tropical forests, where climate variability exerts strong control over ecosystem dynamics, LAI serves as a key proxy for gross primary productivity (GPP) and canopy resilience (Nackaerts et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Understanding the climatic drivers of LAI variability is therefore critical for assessing forest stability and informing management strategies aimed at enhancing ecosystem resilience under changing environmental conditions.\u003c/p\u003e \u003cp\u003eThe Amazon Basin, as the world\u0026rsquo;s largest tropical forest ecosystem, is central to global carbon and water cycles, supporting vast biodiversity and sustaining regional livelihoods through its ecosystem services (Betts et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; D\u0026rsquo;Amato et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, rising temperatures, shifting precipitation patterns, and intensifying droughts are altering forest structure and function, with implications for both conservation and resource management (Ciemer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While seasonal increases in solar radiation may promote canopy development in some regions, prolonged droughts often trigger declines in LAI, leading to increased tree mortality and shifts in forest composition (Caldararu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; De Almeida et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such changes have profound consequences for carbon sequestration and water regulation, making it imperative to quantify how climate extremes shape LAI dynamics at large spatial and temporal scales. Despite growing interest in LAI-climate interactions, key uncertainties remain regarding the role of aridity, the long-term stability of Amazonian canopies, and the capacity of forests to recover from extreme climatic events. Addressing these knowledge gaps is essential for developing adaptive management strategies that balance conservation goals with ecosystem sustainability.\u003c/p\u003e \u003cp\u003eTraditionally, forest LAI has been measured through ground-based techniques such as Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS). However, these methods are limited in their spatial and temporal coverage, restricting their utility for large-scale forest monitoring (Xie et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recent advances in remote sensing\u0026mdash;particularly satellite-derived LAI products\u0026mdash;offer a powerful alternative for assessing canopy dynamics over broad geographic regions and extended time periods. Among available datasets, the MODIS LAI product (2000\u0026ndash;2022) provides long-term, high-resolution measurements validated against field data, making it a valuable tool for evaluating vegetation-climate interactions (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these advancements, discrepancies exist between satellite-based LAI trends and field observations, particularly in drought-prone regions where remotely sensed \u0026lsquo;greening up\u0026rsquo; may contrast with on-the-ground reports of canopy decline (Brando et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Clarifying these inconsistencies is crucial for refining forest monitoring systems and improving decision-making in conservation and land management (Brando et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Clarifying these inconsistencies is crucial for refining forest monitoring systems and improving decision-making in conservation and land management.\u003c/p\u003e \u003cp\u003eThis study integrates MODIS-derived LAI data (2001\u0026ndash;2022) with climate records to assess how temperature, precipitation, and aridity influence canopy structure and function across the Amazon Basin. Specifically, we aim to: (1) quantify the climatic drivers of LAI variability at seasonal and interannual scales, (2) assess the role of aridity in shaping LAI responses, (3) analyze long-term trends and anomalies in canopy structure, and (4) evaluate the implications of observed LAI patterns for forest resilience and sustainable management. By combining remote sensing with land-atmosphere modeling, this study provides actionable insights into how tropical forests respond to climate variability, offering a knowledge base for adaptive management strategies that enhance forest sustainability under intensifying climatic pressures.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe Amazon Basin, covering approximately nine South American countries, is home to the world\u0026rsquo;s largest continuous tropical rainforest and the most extensive river system (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is a biodiversity hotspot, sheltering a remarkable diversity of flora and fauna, including iconic species such as jaguars (\u003cem\u003ePanthera onca\u003c/em\u003e), giant otters (\u003cem\u003ePteronura brasiliensis\u003c/em\u003e), and Brazil nut trees (\u003cem\u003eBertholletia excelsa\u003c/em\u003e). The region experiences a humid tropical climate, with average annual temperatures ranging from 25\u0026ndash;27\u0026deg;C and rainfall exceeding 2000 mm in most areas (Davidson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The Amazon plays a pivotal role in global ecosystem services, including carbon sequestration, climate regulation, and freshwater cycling. However, it faces escalating threats from deforestation, increasingly amplified by climate-induced droughts and self-reinforcing fire feedback loops. The dynamic interplay between anthropogenic land-use changes and shifting climatic regimes is perturbing hydrological cycles, compromising ecosystem resilience, and accelerating carbon fluxes to the atmosphere, with profound implications for regional and global climate stability. By 2000, approximately 15% of the Amazon forest had been lost to deforestation (Foley et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and as of 2022, cumulative forest loss has surpassed 17%, with projections indicating a potential tipping point if deforestation reaches 20\u0026ndash;25% (Flores et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite recent declines in deforestation rates, escalating fire regimes and climate-induced droughts continue to destabilize ecosystem integrity, disrupt biogeochemical cycles, and amplify regional climate instability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e\u003c\u003c Fig. 1 is about to be here \u003e\u003e\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eA comprehensive dataset (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was compiled to examine the Leaf Area Index (LAI) and climatic conditions across the Amazon Basin. LAI data were obtained from the Terra Leaf Area Index/FPAR 8-Day Global 500m (MOD15A2H.061), which provides a consistent long-term record with high temporal frequency. This dataset was selected over alternatives (e.g., AVHRR, GLASS, or GIMMS LAI4g) due to its superior spatial resolution, compatibility with MODIS-derived GPP estimates, and extensive validation against in-situ measurements (Myneni et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To establish LAI as a reliable indicator of Gross Primary Productivity (GPP), we utilized annual mean GPP data from 2021 to 2022, sourced from the MOD17A2H.061 Terra Gross Primary Productivity product, with a spatial resolution of 500 m \u0026times; 500 m (Running et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climatic variables, including mean annual temperature (\u0026deg;C), annual precipitation (mm), temperature seasonality (standard deviation \u0026times;100), and precipitation seasonality (standard deviation \u0026times;100), were sourced from the WorldClim v2.1 dataset, with a resolution of 1 km \u0026times; 1 km and a temporal range from 1970 to 2000 (Fick \u0026amp; Hijmans, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additional environmental factors such as solar radiation (kJ m⁻\u0026sup2; day⁻\u0026sup1;), elevation (m), and water vapor pressure (kPa) were also derived from WorldClim. Latitude information was extracted directly from the LAI dataset at the pixel level. To characterize long-term aridity conditions, aridity index values were obtained from the Global Aridity Index ET0 dataset, covering the same temporal span (Zomer et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These datasets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were selected to effectively capture the spatial and temporal dynamics of vegetation and climate across the study region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of datasets used in this study, including extracted variables, temporal coverage, and references\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable extracted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime span\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOD15A2H.061\u003c/p\u003e \u003cp\u003eTerra Leaf Area Index/FPAR 8-Day Global 500m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaf Area Index (LAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Myneni et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorldClim v2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean annual temperature (\u0026deg;C), Annual Precipitation (mm),\u003c/p\u003e \u003cp\u003eTemperature seasonality (standard deviation \u0026times;100),\u003c/p\u003e \u003cp\u003ePrecipitation seasonality (standard deviation \u0026times;100), Solar Radiation (kJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Vapor Pressure (m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Elevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1970\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Fick \u0026amp; Hijmans, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal Aridity Index ET0\u003c/p\u003e \u003cp\u003e(Global-AI_ET0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAridity Index values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1970\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Zomer et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOD17A2H.061: Terra Gross Primary Productivity 8-Day Global 500m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross primary production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Running et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e\u003c\u003c Table 1 is about to be here \u003e\u003e\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eData processing was carried out using RStudio (version 2024.04.2 Build 764). Climatic variables were first extracted at the geographic locations corresponding to the LAI dataset. MODIS LAI retrievals can be affected by cloud contamination and seasonal inconsistencies, particularly in dense tropical forests. To minimize these errors, we applied quality control flags from the MODIS product and excluded pixels with high uncertainty values. The \u0026lsquo;terra\u0026rsquo; package was utilized to load and process WorldClim rasters, ensuring spatial consistency with the LAI dataset in terms of resolution and projection. To match the 500 m \u0026times; 500 m resolution of the LAI data, all climatic variables were upscaled using the nearest neighbor resampling method, implemented via the \u0026lsquo;resample\u0026rsquo; and \u0026lsquo;aggregate\u0026rsquo; functions within the \u0026lsquo;terra\u0026rsquo; package. While nearest-neighbor resampling preserves original values, it may introduce spatial discontinuities, particularly in heterogeneous landscapes, potentially affecting the precision of climate-LAI relationships. For the evaluation of long-term climatic influences, climate and environmental variables were averaged over the period from 1970 to 2000, following the convention of using pre-industrial and 20th-century baselines for assessing climate change impacts. This period was selected due to the availability of high-resolution climate data and its relevance for detecting climate-driven deviations in LAI post-2000. Temporal trends in LAI changes from 2001 to 2022 were also analyzed.\u003c/p\u003e \u003cp\u003eGeographic coordinates from the LAI dataset were structured into a \u0026lsquo;SpatVector\u0026rsquo; object, allowing efficient extraction of multiple climatic variables at each location. To enhance computational efficiency, the extraction was carried out in batches. The extracted values for mean annual temperature, annual precipitation, solar radiation, temperature and precipitation seasonality, elevation, and water vapor pressure were integrated with the LAI data. The final dataset, combining LAI with climatic variables, was exported as a CSV file for further statistical analysis. A summary of the entire data processing workflow, from collection to analysis, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;\u0026lt; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is about to be here \u0026gt;\u0026gt;\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eKey climatic and environmental variables\u0026mdash;such as mean annual temperature (\u0026deg;C), annual precipitation (mm), solar radiation (kJ m⁻\u0026sup2; day⁻\u0026sup1;), temperature and precipitation seasonality, elevation (m), water vapor pressure (kPa), and aridity index\u0026mdash;were extracted from global raster datasets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The extraction process was carried out using the \u0026lsquo;terra\u0026rsquo; and \u0026lsquo;dplyr\u0026rsquo; packages in R. Raster data were loaded using the \u0026lsquo;rast\u0026rsquo; function, and spatial coordinates from the LAI dataset were converted into a \u0026lsquo;SpatVector\u0026rsquo; object to ensure precise extraction. The \u0026lsquo;terra::extract\u0026rsquo; function was then applied to retrieve raster values at the corresponding locations. To optimize computational efficiency, the extraction was performed in predefined batches.\u003c/p\u003e \u003cp\u003eFor bivariate analysis of the relationship between LAI and the aridity index, the \u0026lsquo;biscale\u0026rsquo; package was employed to classify the data into quantile-based categories. This approach was chosen over clustering-based methods due to its ability to maintain interpretability while capturing the nonlinear distribution of LAI values across aridity gradients. These categories were visualized with a color-coded scheme, and spatial patterns were mapped using the \u0026lsquo;ggplot2\u0026rsquo; and \u0026lsquo;sf\u0026rsquo; packages. The boundaries of the Amazon Basin were overlaid on the final visualization to provide geographical context. The combined bivariate map and its legend were constructed using the \u0026lsquo;cowplot\u0026rsquo; package.\u003c/p\u003e \u003cp\u003eFor regression analysis, Python (v3.11.10) was used, leveraging its computational efficiency in handling large datasets. The analysis was carried out with the \u0026lsquo;pandas\u0026rsquo;, \u0026lsquo;seaborn\u0026rsquo;, \u0026lsquo;matplotlib\u0026rsquo;, and \u0026lsquo;scipy\u0026rsquo; libraries. The dataset was first cleaned by removing missing values and filtering out observations with extreme outliers (beyond three standard deviations from the mean) or those located in areas with known data artifacts (e.g., cloud-contaminated pixels, water bodies misclassified as vegetation). A regression analysis was then performed to investigate the relationship between LAI (the dependent variable) and various climatic and environmental predictors, including aridity index, temperature, precipitation, and solar radiation. While climate variables are primary drivers of LAI variability, additional confounding factors such as soil properties, forest age, and disturbance history may also influence observed patterns. Future work should incorporate complementary datasets to disentangle the relative contributions of these factors to LAI dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULT","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLAI as an indicator of gross primary productivity\u003c/h2\u003e \u003cp\u003eLeaf Area Index (LAI) exhibits a strong positive correlation with Gross Primary Productivity (GPP) across the Amazon Basin (R\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), reinforcing the role of canopy structure in modulating carbon assimilation. Despite regional variability driven by factors such as soil fertility, water availability, and species composition, the overall trend demonstrates that higher LAI is associated with enhanced primary productivity. The unimodal distribution of GPP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), clustering around 0.05 kg C/m\u0026sup2;, further underscores this relationship. However, deviations from this trend suggest that climatic stressors and land-use alterations may influence ecosystem productivity. These findings validate the use of LAI as a robust proxy for estimating large-scale variations in tropical forest carbon dynamics, offering critical insights into the functional responses of the Amazon\u0026rsquo;s carbon cycle under changing environmental conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;\u0026lt; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is about to be here \u0026gt;\u0026gt;\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eClimatic Influences on LAI Variability\u003c/h2\u003e \u003cp\u003eThe spatial heterogeneity of LAI across the Amazon Basin reflects the influence of climatic variability on canopy structure. The relationship between LAI and mean annual temperature follows a unimodal Gaussian distribution (adjusted R\u0026sup2; = 0.15; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), with LAI increasing up to ~\u0026thinsp;25.3\u0026deg;C before declining at higher temperatures. This indicates that moderate temperatures favor canopy development, whereas excessive warming is associated with a reduction in LAI. A weak but significant positive correlation with annual precipitation (R\u0026sup2; = 0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) suggests that higher rainfall generally supports greater leaf area, though with considerable variability. Seasonal climatic fluctuations exert a stronger influence on LAI. A negative correlation with temperature seasonality (R\u0026sup2; = 0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) implies that greater thermal variability is associated with reduced canopy cover. Similarly, precipitation seasonality exhibits a stronger negative relationship with LAI (R\u0026sup2; = 0.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), indicating that regions with highly irregular rainfall patterns tend to have lower LAI, likely due to periodic water stress. These results underscore the greater impact of seasonal climate variability compared to mean climatic conditions in regulating LAI across the Amazon Basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;\u0026lt; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is about to be here \u0026gt;\u0026gt;\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eAridity Impacts on LAI\u003c/h2\u003e \u003cp\u003eThe spatial distribution of LAI exhibits clear gradients, with higher values concentrated in humid regions and lower values in more arid zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, top panel). The strongest vegetation cover is observed in the central and northern Amazon, where low aridity supports dense forests, whereas the southern and southwestern regions exhibit lower LAI due to drier conditions. A statistically significant but weak positive correlation between LAI and the Aridity Index (R\u0026sup2; = 0.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, bottom right) suggests that within the observed range, canopy cover tends to increase slightly with decreasing aridity, although additional factors such as soil moisture, vegetation composition, and land-use dynamics introduce variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe histogram of the Aridity Index (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, bottom left) indicates that the majority of the Amazon Basin falls within the humid category (Aridity Index\u0026thinsp;\u0026gt;\u0026thinsp;0.65), where LAI remains high. However, regions classified as dry sub-humid (0.5\u0026ndash;0.65) and semi-arid (0.2\u0026ndash;0.5) exhibit greater variability in LAI, reflecting the increasing influence of climatic stressors on vegetation structure. These findings highlight the complexity of aridity-vegetation interactions, where water availability is a key driver of canopy cover, but secondary environmental factors further modulate spatial patterns of forest productivity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;\u0026lt; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is about to be here \u0026gt;\u0026gt;\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eTemporal Dynamics of LAI (2001\u0026ndash;2022)\u003c/h2\u003e \u003cp\u003eAnalysis of LAI trends from 2001 to 2022 reveals subtle yet ecologically significant shifts in canopy structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). While the overall LAI distribution has remained relatively stable, regional trends indicate dynamic fluctuations. Between 2001 and 2009, LAI values were more evenly distributed, with a gradual increase in mid-range values (3\u0026ndash;4). By 2013, a shift toward higher LAI values suggested an expansion in vegetation cover. However, this trend was not sustained, as 2018 data indicate a decline in high-LAI values, signaling localized canopy loss. The most recent 2022 dataset suggests a partial recovery, with an increase in the modal class frequency, hinting at possible stabilization following prior disturbances. These trends underscore the ongoing influence of climatic and anthropogenic factors on Amazonian forest structure, with long-term implications for ecosystem resilience.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;\u0026lt; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e is about to be here \u0026gt;\u0026gt;\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eRelationship between LAI and Environmental Variables\u003c/h2\u003e \u003cp\u003eThe relationship between LAI and environmental factors varies in strength and direction. A statistically significant but weak positive correlation is observed with latitude (R\u0026sup2; = 0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), indicating a slight increase in LAI at higher latitudes. In contrast, elevation exhibits a stronger negative correlation with LAI (R\u0026sup2; = 0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), with LAI declining as elevation increases. This pattern suggests a progressive reduction in canopy cover along altitudinal gradients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSolar radiation shows a weak negative association with LAI (R\u0026sup2; = 0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), implying minimal influence on canopy development. In comparison, water vapor pressure demonstrates the strongest positive correlation (R\u0026sup2; = 0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed), highlighting a strong link between atmospheric moisture availability and LAI distribution. The overall low R\u0026sup2; values across most environmental variables indicate that although these factors contribute to spatial LAI variations, their explanatory power remains limited. This underscores the influence of additional ecological and biophysical drivers that shape canopy structure at broader scales.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;\u0026lt; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e is about to be here \u0026gt;\u0026gt;\u003c/h2\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study explores the relationship between climate variables and the Leaf Area Index (LAI) in the Amazon Basin, a critical aspect of ecosystem functioning in the face of ongoing environmental change. By considering temporal trends from 2001 to 2022, climatic drivers, and environmental interactions, we provide a detailed examination of how changes in climate\u0026mdash;particularly precipitation, temperature, and extreme weather events\u0026mdash;affect LAI dynamics in this globally significant biome. The results emphasize the multifaceted role of climate as a primary driver of LAI variability, offering insights into the ecological processes that underpin ecosystem resilience, biodiversity, and carbon cycling in response to global change.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eClimate as a Dominant Driver of LAI Dynamics\u003c/h2\u003e \u003cp\u003eClimate acts as a key regulator of LAI in the Amazon Basin, influencing both the structural and functional aspects of forest canopies. Seasonal shifts in precipitation, temperature, and relative humidity, along with extreme climatic events such as droughts and cold spells, are known to induce significant compositional changes in the canopy structure and affect aerosol microbiome dynamics (Caldararu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Souza et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study corroborates findings that reduced rainfall, driven by climate variability and deforestation, directly affects vegetation productivity, thus altering canopy greenness and LAI (Hilker et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The phenomenon of \"flying rivers,\" where trees release moisture that sustains regional rainfall, exemplifies the intricate relationship between vegetation and climate (Webb, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the decreasing precipitation trend, exacerbated by human-induced land use changes, results in prolonged drought conditions, leading to the degradation of forest canopies, which heightens the vulnerability of the Amazon to wildfires and carbon release (Malavelle et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pfeifer et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This feedback mechanism not only destabilizes ecosystem structure but also compromises the carbon sink capacity of the forest, reinforcing concerns over potential shifts in the Amazon\u0026rsquo;s role in global carbon dynamics (Wey et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur analysis further reveals that drought-induced shifts in LAI are particularly pronounced in forest strata. During drought events, upper-canopy trees often exhibit increased LAI due to enhanced sunlight availability, stimulating leaf expansion. In contrast, the lower canopy shows reduced LAI as water stress increases, particularly in smaller trees, which are more sensitive to moisture deficits (Smith et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This vertical heterogeneity in LAI dynamics was evident during the 2015\u0026ndash;2016 El Ni\u0026ntilde;o, where the varying responses of different canopy layers to extreme heat and water stress revealed complex interactions between climatic stressors and ecosystem structure (Y. Y. Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The long-term implications of these droughts are profound; sustained drying trends could lead to irreversible shifts in forest structure, further accelerating the conversion of the Amazon from a carbon sink to a carbon source.\u003c/p\u003e \u003cp\u003eBeyond precipitation and drought, rising temperatures are altering the moisture regime in the Amazon. Temperature increases, compounded by decreased precipitation, elevate vapor pressure deficits, which exacerbate water stress and decrease photosynthetic efficiency (Souza et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Deforestation amplifies this effect by increasing local land surface temperatures, particularly during the dry season, where the loss of canopy cover leads to an average temperature increase of 0.44\u0026deg;C (Baker \u0026amp; Spracklen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These temperature-induced changes in microclimates further decouple structural (LAI) and functional (photosynthetic) components of the ecosystem, leading to altered carbon dynamics and reduced forest resilience (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The relationship between LAI and mean annual temperature follows a unimodal Gaussian distribution, with LAI increasing up to ~\u0026thinsp;25.3\u0026deg;C before declining at higher temperatures, suggesting that moderate temperatures favor canopy development, whereas excessive warming reduces LAI. Although some studies have identified increasing LAI in certain regions of the Amazon due to climate warming and wetting (P. Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the localized variability\u0026mdash;attributable to land-use activities\u0026mdash;suggests that broader climatic shifts are increasingly influenced by anthropogenic disturbances, masking the full extent of global climate change impacts.\u003c/p\u003e \u003cp\u003eOverall, our findings underscore the critical role of climate as a driver of LAI dynamics in the Amazon. As temperature rises and precipitation patterns become more erratic, these changes are expected to further reduce forest resilience, with cascading impacts on biodiversity, ecosystem services, and carbon storage (Boulton et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the Amazon approaching a potential tipping point toward a savanna-like state, this study highlights the need for sustained monitoring of LAI as an early warning indicator for large-scale ecological transformations, such as forest dieback, and to better understand how these changes may influence global carbon and water cycles.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eEnvironmental drivers and spatial heterogeneity of LAI\u003c/h2\u003e \u003cp\u003eThe spatial variability in LAI across the Amazon Basin can be attributed to a complex suite of environmental factors, including latitude, elevation, solar radiation, and water vapor pressure. Latitude and elevation act as primary determinants of temperature and precipitation patterns, which, in turn, shape the vegetation structure and productivity across the Amazon\u0026rsquo;s latitudinal gradient (Eamus et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The observed weak positive correlation between latitude and LAI suggests a gradual increase in canopy cover at higher latitudes, possibly linked to shifts in climatic conditions and vegetation composition. In contrast, the stronger negative relationship with elevation highlights a progressive decline in LAI along altitudinal gradients, reflecting the constraints imposed by reduced atmospheric moisture and lower temperatures at higher elevations (Jord\u0026atilde;o et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSolar radiation is another critical driver of LAI, as it directly influences photosynthetic activity. However, the weak negative correlation observed in the results indicates that increasing solar radiation does not necessarily enhance canopy development, likely due to concurrent water limitations or photoinhibition effects (Lovelock et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). In contrast, Water vapor pressure, a key indicator of atmospheric moisture, strongly influences plant transpiration rates and overall canopy water status. The positive association between LAI and water vapor pressure suggests that higher atmospheric moisture availability supports greater canopy density, likely by reducing evaporative stress and sustaining leaf hydration (Y. Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In regions with high water vapor pressure, plant transpiration operates more efficiently, contributing to greater carbon assimilation and canopy expansion (Cernusak, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, in areas where atmospheric moisture is low, water loss through transpiration can exceed replenishment, constraining LAI. This highlights the crucial role of atmospheric moisture in mediating forest productivity, particularly in ecosystems subject to seasonal fluctuations in humidity. Notably, taller forests exhibit greater resilience to precipitation variability but remain sensitive to changes in vapor pressure, underscoring the intricate interactions between atmospheric moisture conditions and forest structure (Giardina et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther complicating the LAI-environment relationship are anthropogenic factors such as deforestation, logging, and land-use change, which alter canopy structure and contribute to spatial heterogeneity in LAI (Qu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, deforestation increases local temperatures and disrupts the balance between precipitation and evapotranspiration, exacerbating water stress and reducing LAI, particularly in forested areas undergoing anthropogenic disturbance (Nasahara et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These findings emphasize the need for integrated models that account for both climatic and anthropogenic drivers of LAI variability to predict the future trajectory of Amazonian ecosystems.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImplications for tropical forests and global change\u003c/h2\u003e \u003cp\u003eThe resilience of tropical forests under accelerating climate change hinges on their ability to maintain functional canopy structures, with Leaf Area Index (LAI) serving as a key indicator of forest health, productivity, and adaptive capacity (Needham et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a fundamental driver of carbon sequestration, water cycling, and biodiversity support, LAI directly influences forest-climate feedback and, consequently, the sustainability of forest ecosystems. However, increasing anthropogenic pressures\u0026mdash;including deforestation, land-use change, and altered precipitation regimes\u0026mdash;are destabilizing LAI dynamics, posing critical challenges for forest management and conservation efforts.\u003c/p\u003e \u003cp\u003eIn the Amazon Basin, declining LAI due to rising temperatures and shifting precipitation patterns threatens to weaken the region\u0026rsquo;s carbon sink capacity, accelerating carbon loss and intensifying climate change feedback loops (P. Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Y. Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The potential shift of the Amazon from a carbon sink to a carbon source represents a major tipping point in global climate regulation, with cascading effects on carbon budgets and atmospheric stability. Furthermore, reduced canopy density disrupts key microclimatic processes such as evapotranspiration and temperature moderation, which are essential for maintaining hydrological balance and ecosystem stability (Boulton et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond climate regulation, LAI declines have profound consequences for forest biodiversity and ecosystem services. Loss of canopy cover increases habitat fragmentation, reduces structural complexity, and drives shifts in species composition, ultimately accelerating biodiversity loss and impairing essential ecological functions (Cowling et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These disruptions extend beyond forest interiors, affecting landscape-scale processes such as pollination, seed dispersal, and water purification. Given the interconnected nature of these impacts, integrating LAI into forest management frameworks is critical for enhancing adaptive capacity and ensuring long-term ecosystem sustainability.\u003c/p\u003e \u003cp\u003eA pathway toward sustainability requires the development of integrated models that account for both climatic and anthropogenic drivers of LAI change. Such models should incorporate remote sensing data, ecological monitoring, and land-use projections to refine our understanding of future LAI trajectories under different climate scenarios. By linking LAI dynamics to forest management strategies\u0026mdash;such as reforestation efforts, conservation zoning, and adaptive silvicultural practices\u0026mdash;these models can inform policies that mitigate climate risks while enhancing forest resilience.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study underscores the intricate and dynamic relationship between climate and LAI in the Amazon Basin, highlighting the vulnerability of tropical forests to ongoing climatic shifts and anthropogenic disturbances. The findings reinforce the necessity of multidimensional models that integrate climate variables, vegetation indices, and human-induced impacts to improve predictions of LAI responses to environmental change. Future research should bridge the gap between satellite-derived observations and field-based measurements, enhancing the accuracy of LAI assessments across diverse forest conditions. By strengthening predictive models and incorporating LAI into adaptive forest management frameworks, we can develop proactive strategies to mitigate climate impacts, safeguard biodiversity, and sustain critical ecosystem services in the Amazon and other tropical forests worldwide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd Shamim Reza Saimun:\u0026nbsp;\u003c/strong\u003econceptualization, data curation, formal analysis, investigation, methodology, resources, software, validation, visualization, writing \u0026ndash; original draft, writing \u0026ndash; review and editing.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd Rezaul Karim:\u0026nbsp;\u003c/strong\u003edata curation, formal analysis, investigation, methodology, software, validation, visualization, writing \u0026ndash; original draft, writing \u0026ndash; review and editing, supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Md. Abdul Halim (PhD) for his valuable ideas with the GPP analysis. His expertise and insights significantly contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaker JCA, Spracklen DV (2019) Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front Forests Global Change 2:47\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBetts RA, Malhi Y, Roberts JT (2008) The future of the Amazon: New perspectives from climate, ecosystem and social sciences. 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Sci Data 9(1):409\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Leaf Area Index (LAI), Amazon Basin, Climatic variability, Forest productivity, Carbon cycle, Ecosystem resilience","lastPublishedDoi":"10.21203/rs.3.rs-6447042/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6447042/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Amazon Basin, a critical carbon sink, is increasingly vulnerable to climate change, yet the mechanisms governing Leaf Area Index (LAI) variability under meteorological influences remain uncertain. As a key determinant of canopy structure and productivity, LAI regulates biosphere-atmosphere interactions and regional carbon and hydrological cycles, making it vital for forest management and conservation. However, its long-term response to climate variability remains poorly characterized. This study integrates MODIS-derived LAI data (2001\u0026ndash;2022) with meteorological records to assess how temperature anomalies, precipitation extremes, and aridity shape canopy dynamics, offering insights for adaptive forest management. A nonlinear relationship between LAI and temperature reveals a threshold of 25.3\u0026deg;C, beyond which LAI declines, indicating heat stress-induced canopy suppression. Precipitation positively influences LAI, with seasonal variability exerting a stronger effect than annual means, emphasizing the role of short-term hydrological fluctuations in maintaining forest productivity. The aridity index explains 23% of LAI variability, underscoring its role as a key constraint on vegetation growth. Additional meteorological factors, including water vapor pressure (R\u0026sup2; = 0.26) and elevation (R\u0026sup2; = 0.27), further shape LAI dynamics, reflecting interactions between land surface energy balance and atmospheric moisture availability. Post-2018 trends indicate a decline in high-LAI regions, with partial recovery by 2022, suggesting increasing climate-driven instability in Amazonian forest structure. These findings enhance understanding of tropical forest resilience, guiding conservation planning, ecosystem monitoring, and climate-adaptive management. Given the Amazon Basin\u0026rsquo;s role in global atmospheric circulation, sustained LAI monitoring is essential for refining climate-vegetation models and ensuring long-term forest stability.\u003c/p\u003e","manuscriptTitle":"Climatic drivers of leaf area index dynamics in the Amazon Basin: Insights from remote sensing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 16:49:49","doi":"10.21203/rs.3.rs-6447042/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-23T06:13:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T04:47:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237771598645559283657042738652213076325","date":"2025-06-02T15:45:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87165764925389422807846379003225185462","date":"2025-06-02T11:30:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192408622068720588569835382721831480459","date":"2025-06-01T09:53:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T19:09:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14201028462730741975810574471857994016","date":"2025-05-02T04:55:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-30T04:39:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-15T06:49:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T06:47:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2025-04-14T14:32:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4fc6b563-5912-4833-b8f4-5ca5841e3b23","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T16:03:42+00:00","versionOfRecord":{"articleIdentity":"rs-6447042","link":"https://doi.org/10.1007/s00704-025-05673-y","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2025-08-05 15:58:02","publishedOnDateReadable":"August 5th, 2025"},"versionCreatedAt":"2025-05-05 16:49:49","video":"","vorDoi":"10.1007/s00704-025-05673-y","vorDoiUrl":"https://doi.org/10.1007/s00704-025-05673-y","workflowStages":[]},"version":"v1","identity":"rs-6447042","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6447042","identity":"rs-6447042","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}