Integrating in-situ data and remote sensing for spatiotemporal assessment of alpine vegetation

Integrating in-situ data and remote sensing for spatiotemporal assessment of alpine vegetation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating in-situ data and remote sensing for spatiotemporal assessment of alpine vegetation Jose Manuel Álvarez-Martínez, Clara Espinosa del Alba, Corrado Marcenò, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8491360/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Alpine plant communities are highly sensitive to environmental change, making effective monitoring essential to guide conservation in mountain environments where soil properties and topographic heterogeneity strongly constrain vegetation patterns. This study evaluates the potential of remote sensing indicators to capture spatial variation in alpine vegetation driven by soil and topography, as well as short-term temporal dynamics of key soil properties measured in situ. Alpine vegetation was surveyed at 40 sites distributed across four mountain massifs in the southwestern Cantabrian Mountains (Spain), recording plant community composition, soil properties and spatial structure linked to topographic variation. In addition, soil temperature and water potential were monitored over complete annual cycles from 2021 to 2025 in four representative sampling plots. Spatial and temporal field observations were coupled with co-temporal Sentinel-2 time series to derive indicators related to surface temperature, moisture and primary production. Distance-based redundancy analyses and generalized linear mixed models were used to assess the role of remote sensing indicators in explaining vegetation composition and temporal trends in soil conditions. Alpine plant communities were primarily structured by soil properties associated with water-holding capacity, together with spatial structure reflecting fine-scale topographic heterogeneity. Among remote sensing indicators, only the Soil-Adjusted Vegetation Index (SAVI) was significantly associated with vegetation composition, highlighting its potential as a proxy for productivity in topographically complex alpine landscapes. In contrast, all remote sensing variables proved effective in capturing short-term dynamics of soil temperature and water stress, particularly during climatic extremes, although their sensitivity varied with spatial scale. Our results demonstrate that integrating in-situ vegetation data with remote sensing provides a robust and scalable framework for assessing alpine ecosystems across space and time. While satellite-derived indicators can successfully capture topography-mediated compositional gradients and functional responses related to water and temperature, careful scale selection and continuous calibration between field and remote sensing data are essential to avoid misinterpretation in ecologically heterogeneous and complex terrains. This integrative approach is critical for improving biodiversity monitoring and informing conservation strategies in alpine environments under accelerating climate change. Alpine Plant Communities Cantabrian Mountains Ecosystem Monitoring Multispectral Imagery Remote Sensing Sentinel-2 Vegetation Mapping Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Alpine ecosystems are characterized by specialized vegetation adapted to extreme environmental conditions (Körner & Kèorner 1999 ) making them particularly sensitive to climate change and anthropogenic disturbances (Scherrer & Körner 2011 ; Steinbauer et al. 2018 ). The strong environmental filtering characterizing alpine vegetation, driven by factors such as topography, climate and soil properties, plays a crucial role in shaping plant community composition and distribution (He et al. 2023 ). These communities also harbor a high proportion of endemic and endangered species, many of which have limited dispersal capacity (Grabherr et al. 2011 ; Graae et al. 2018 ). Furthermore, alpine vegetation serves as refugia for cold-adapted species, making their preservation crucial for maintaining ecological resilience and functional diversity in mountain systems (Gottfried et al. 2012). However, rising temperatures and altered precipitation regimes are causing significant changes in these ecosystems, threatening the persistence of many high-altitude endemic taxa (Dirnböck et al. 2011 ). In temperate mountains, the upward expansion of lowland species in response to warming (García-Romero et al. 2010 ; Jiménez-Alfaro et al. 2014 ) facilitates novel biotic interactions and competitive pressures that further exacerbate their vulnerability (Alexander et al. 2015 ). This interplay between environmental constrains and biodiversity responses highlights the importance of monitoring alpine communities to detect early warning signals of biodiversity decline (Elsen & Tingley 2015 ). Traditionally, mapping and monitoring alpine vegetation have relied on intensive field methods, which provide high ecological detail but are limited in their spatial scope due to accessibility restrictions, leading to temporal inconsistencies in repeated sampling (Wipf et al. 2006 ; Björk & Molau 2007 ). A promising tool for mapping and monitoring community composition, functional responses and temporal trends in alpine vegetation is the integration of expert-based field data with remote sensing missions (such as Landsat-5 to 9 and Sentinel-2) that provide high spatial resolution and frequent revisit times of multispectral sensors (Pettorelli et al. 2014 ; Chu 2020 ). As described by (Proença et al. 2017 ), indicators based on remote sensing offer a scalable, repeatable and cost-effective means of capturing biodiversity distribution, condition and dynamics, consolidating information from varying observation sources (i.e., extensive and intensive monitoring schemes and ecological field studies). In alpine ecosystems, remote sensing has been applied from land cover classification to monitoring vegetation dynamics and responses to climate and land-use change (Álvarez-Martínez et al. 2018 ; De Simone et al. 2020 ; Wakulińska & Marcinkowska-Ochtyra 2020 ). Recent advances also include phenological analyses using time series to detect seasonal patterns such as greening and flowering (Gómez et al. 2016 ; Trullén et al. 2022 ) and the estimation of plant functional traits, e.g. chlorophyll content, specific leaf area, or dry matter content at the community level (Ustin & Gamon 2010 ; Schweiger et al. 2017 ). Despite their capabilities, remote sensing applications face key challenges. One of them is the need to calibrate vegetation properties with high-resolution in-situ data to link ground-truth information with spectral signatures obtained from remote sensing metrics (Pettorelli et al. 2014 ; Dronova & Taddeo 2022 ). This is especially challenging in alpine environments with complex terrain and spatial heterogeneity, posing additional issues such as spectral mixing and limited sensitivity to fine-scale ecological gradients (e.g., moisture or microclimate), which are better captured through long-term in-situ data (Rocchini et al. 2010 ; Helm et al. 2024 ). Uncertainty also persists in detecting local-scale vegetation trends in response to temporally dynamic drivers like temperature and soil water availability (Myers-Smith et al. 2020 ). A common limitation lies in the widespread use of large buffer zones around field plots to extract spectral data, which, while accounting for geolocation uncertainty, often include mixed vegetation patterns and reduce ecological specificity (Perrone et al. 2023 ; Rossi & Gholizadeh 2023 ). In contrast, matching high-resolution remote sensing data with finely mapped in-situ plots improves ecological precision at the cost of reducing representativeness to only one or two pixels. An intermediate approach is to delineate vegetation communities at spatial scales aligned with sensor resolution, enhancing the accuracy of vegetation–spectrum relationships, improving the integration of in-situ data and remote sensing for mapping and monitoring alpine ecosystems. In this study, we integrate in-situ vegetation and soil data collected in the field with remote sensing indicators obtained from satellite imagery to address spatial and temporal characterization of alpine ecosystems. Specifically, we ask the following key questions: 1) To what extent do remote sensing indicators explain spatial patterns in alpine vegetation composition compared to field-measured environmental variables? 2) Can remote sensing metrics effectively capture short-term temporal dynamics in soil moisture and temperature, as measured through in-situ observations? To investigate these matters, we coupled Sentinel-2 satellite data with field-based measurements across a siliceous mountain range in the north of Spain, evaluating the capabilities of remote sensing to cope with alpine vegetation and soil properties across space and through time. By comparing remote sensing indicators with traditional ecological data, we aim to advance the methodological framework for long-term monitoring of these complex systems, contributing to improved biodiversity conservation and ecosystem management strategies in worldwide mountain environments. 2. Methods To guide this study, Fig. 1 presents the conceptual framework that integrates in-situ ecological sampling and remote sensing to define key environmental variables and to assess spatial and temporal variation in alpine ecosystems. 2.1 Study area The study area is located in the southwestern Cantabrian mountains (NW Spain) between 716990.66–741953.25 E (longitude) and 4741208.08–4767462.49 N (latitude). The area includes three siliceous mountain massifs with most summits reaching elevations above 2000 m a.s.l.. Alpine vegetation is influenced by Mediterranean climate and dominates habitats above the treeline (approximately 1700 m a.s.l,; González Le Barbier et al. 2025). We focused on alpine grasslands occupying summits and nearby areas, which are permanent communities composed of perennial herbaceous plants adapted to short growing seasons, frequent snow cover, low temperatures and drought. Growing season stretches from March to early November, with a mean annual soil temperature of 8ºC (soil values recorded from 2021 to 2025, own unpublished data), but with a 2-month dry period in summer (average precipitation of 160 mm and mean annual air temperature is 15.5 ºC). Yearly, snow cover ranges from 13 to 116 days. Mean daily water stress during the growing season ranges from − 0.32 to -0.52 MPa (soil data collected from 2021to 2025, own unpublished data). Frequent species include Festuca summilusitana , Luzula caespitosa , Carex asturica and Dianthus langeanus , as well as cushion-forming and chamaephytic species such as Silene ciliata and Thymus praecox . These grasslands often develop on shallow, rocky soils and exhibit high beta-diversity across microhabitats shaped by topography (e.g., ridges, depressions, moist patches and snowbeds). 2.2 In-situ data collection We followed a systematic sampling of alpine grasslands along the extent of the siliceous massifs in the study area (Fig. 2 ). Vegetation plots were established along the mountain summits and the ridges among them at 500 m intervals between one site and the following. When a new (unrecorded) species appeared along the track between two contiguous sites, we sampled additional plots to cover the variation of the study vegetation. At each site, we delineated a circular plot with a 3 m radius and recorded the abundance of all vascular plant species present. The sampling resulted in 40 plots accounting for 96 species. Species were classified based on their mean cover across plots, with those exceeding 12% considered dominant, adapting the threshold established by Mariotte ( 2014 ) to our study vegetation with very low plant cover. Based on species composition, the plots were classified in two broad habitat types linked to phytosociological classes as defined in {Mucina, 2016 #93}: (1) Strip grasslands (n = 30), corresponding to Festucetea indigesta class, typically found on shallow soils subjected to extreme cold and cryoturbation; and (2) Nardus swards (n = 10), corresponding to Nardetea stricta class, associated with deeper soils and persistent snow cover lasting until early summer (Gutiérrez-Girón & Gavilán 2010 ). Local species richness ranged from 7 to 33 species per plot, with a mean of 15 ± 0.7 (Standard Error, SE). The most dominant species were Luzula caespitosa , with a mean cover of 18.7 ± 3.43 (SE) and Festuca summilusitana , with 12.6 ± 2.88 (SE). In each plot, we measured environmental variables (Table 1 ) including: (1) topographic features from a high-resolution (5-m resolution) elevation model (Gomez-Gutierrez et al. 2011 ), (2) climatic conditions at 200 m resolution (Ninyerola et al. 2005 ) and (3) soil properties measured in 200 g of soil collected from three random samples within each site. To assess spatial structure, we evaluated spatial autocorrelation among plots using Principal Coordinates of Neighbor Matrices (PCNMs), allowing to describe the spatial distribution patterns of both vegetation and environmental features (Lewis et al. 2014 ). Table 1 Environmental variables measured in the field across 40 vegetation plots sampled in the western Cantabrian mountains (NW Spain). Variables Values range Description Topography Altitude 1820 /2171 Elevation in meters above sea level, measured with submetric GPS Northness -0.97 / 1 Orientation to north (dimensionless) Eastness -0.99 / 0.99 Orientation to east (dimensionless) Slope 1 / 45 Measure of steepness relative to the horizontal plane (degrees) Climatic conditions Annual radiation 1812 / 2320 Annual radiation (W/m2) Annual precipitation 848 / 1352 Annual precipitation (mm) Annual average temperatures 40 / 58 Average annual temperatures in ºC Average maximum temperatures 99 /114 Average of annual maximum temperatures in ºC Average minimum temperatures -22 / -2 Average of annual minimum temperatures in ºC Soil properties Bulk density 0.85 / 28.14 Ratio between the dry mass of solids and the undisturbed soil volume in g/mL. Can be used as an estimation of soil compactation and relate to water retention capacity Organic matter 2.1 / 45.3 Carbon content 1.2 / 26.3 Nitrogen content 0.064 / 1.74 Sand coarse 19 / 95 Percentage of sand with granulometry superior to 0.25 mm. Sand fine 2.6 / 43.7 Percentage of sand with granulometry inferior to 0.25 mm. Highly correlated with sand coarse Silt 2.2 / 52.3 Percentage of particles between 0.05-0.002mm. Highly correlated with sand coarse Clay 0 / 19.6 Percentage of particles < 0.002 mm. Correlated with sand coarse and pH 3.89 / 4.85 Defined as the negative logarithm of the hydrogen ion concentration, indication of the acidity or alkalinity of the soil. P 2.1 / 64.4 Content of phosphorous in the soil 2.3 Remote sensing indicators We compiled a comprehensive database of remote sensing indicators using Google Earth Engine (GEE), a cloud-based geospatial platform that enables large-scale processing of satellite imagery (Gorelick et al. 2017 ; Yang et al. 2022 ). Specifically, we processed Landsat time series from USGS (Hermosilla et al. 2016 ) at 30 meters of spatial resolution and Sentinel-2 imagery from the ESA archive (Orusa et al. 2023 ) at 10 m of spatial resolution, to derive three main categories of remote sensing based indicators informing on community structure and functionality: (1) land surface temperature (LST), (2) soil and vegetation moisture and (3) vegetation productivity. The use of GEE enables efficient processing and integration of multi-temporal RS data, facilitating robust and scalable environmental assessments across spatio-temporal scales. Land Surface Temperature (LST) was estimated with two downscaling approaches: (1) 30 m resolution from Landsat-8 imagery (LST_L8) and (2) 10 m resolution from Sentinel-2 using Robust Linear Regression (S2_RLS_LST). LST reflects ground-emitted thermal radiation, influenced by surface properties like vegetation and soil (Sobrino et al. 2004 ). Surface moisture was assessed with two indices, NDMI, which captures vegetation water content (Monteiro et al. 2024 ) and TCW, which estimates canopy water stored in tissues (Lastovicka et al. 2020 ). Vegetation condition and productivity were evaluated using five indices: NDVI, SAVI, EVI, MCARI and MCARI_OSAVI. NDVI and SAVI indicate greenness (Wakulińska & Marcinkowska-Ochtyra 2020 ), with SAVI correcting for soil background (Abril-Colón et al. 2022 ), while EVI enhances detection in high-biomass areas (Zhen et al. 2023 ). MCARI and MCARI_OSAVI estimate chlorophyll and stress, with the latter minimizing soil effects (Guerini Filho et al. 2020 ). All indices were aggregated seasonally (spring, summer, autumn) to align with field sampling and assure pixel consistency for spatial analysis (Latifovic et al. 2015 ) (Section 2.4 ). For temporal analysis, one representative variable from each group (LST, moisture, vegetation) was selected (Section 2.5 ). 2.4 Spatial patterns of alpine vegetation We first explored the relationships between environmental factors (Table 1 ) and vegetation composition by performing distance-based redundancy analyses (db-RDA) on species cover data, transformed using the Hellinger method to reduce the influence of large values and zero inflation. We conducted two complementary analyses, one including all recorded species and another limited to dominant species. This allowed evaluating whether dominant species, which contribute most to vegetation structure and spectral signals, showed consistent patterns with the full community data. For each dataset, we applied a stepwise forward selection procedure to identify significant predictors, evaluating model performance using adjusted R² and permutation tests (P < 0.05; Blanchet et al., 2008). All environmental variables (topography, climate and soil) were initially assessed for multicollinearity, excluding those highly correlated within each thematic group (r > 0.7). We retained minimum temperature, radiation and precipitation as key climatic variables. Selected soil properties included bulk density, sand, silt, clay, pH and phosphorus. For remote sensing indicators, we kept one representative variable per group: LST (summer and autumn), NDMI and TCW (summer) and summer SAVI, chosen for its robustness against soil background effects. To assess spatial patterns in species composition, we applied Principal Coordinates of Neighbour Matrices (PCNM) (Borcard & Legendre 2012 ), generating independent variables free from collinearity. The first PCNM axes represent broad-scale patterns, while later axes capture finer spatial variation. To avoid model overfitting, we retained only significant variables and PCNM vectors identified via partial RDA. For RS-based indicators we applied the same variable selection procedure. Pairwise correlation analyses were also conducted to reduce redundancy among predictors, ensuring that at least one representative variable from each group was retained in the final RDA model (i.e. land surface temperature, soil moisture and vegetation indices). 2.5 Monitoring of soil properties To better understand how alpine vegetation responds to environmental variability, we integrated satellite time series with in-situ measurements of soil moisture stress (i.e. water potential) and temperature. We recorded hourly measurements at four selected study sites from June 2021 to June 2025 (see Fig. 2 ). These sites were representative of the strip grasslands, where water stress is a key environmental driver. A MicroLog SP3 datalogger (EMS Brno, Czech Republic) was buried at a depth of 5 cm. Temperature measurements had an accuracy of ± 0.3°C within a range of -40°C to 60°C. Soil water potential was measured using two Delmhorst gypsum sensors, which operate within a range of 0.1 to 14.5 bars, the latter representing the permanent wilting point. To align field data with satellite overpasses, we averaged the temperature and water potential values recorded between 11:00 and 14:00 each day. Days with mean temperatures below 5°C were excluded, as this threshold is commonly used to define the growing season {Leeper, 2021 #57;Körner, 2021 #94}. Exploratory data analysis revealed that water potential responded in a binary manner, distinguishing between stress (> 14 bars) and no stress (< 14 bars). Consequently, we derived a binary “water stress” variable for subsequent analyses. After data filtering and processing, a total of 2643 observations were retained. In parallel, we processed a complete time series (maximum temporal resolution available, i.e. one image every 5-days) of Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI) and enhanced spatial resolution Land Surface Temperature (S2_RLS_LST) indices for the same period (2021 to 2025), using Sentinel-2 imagery (10-m pixel size) within the Google Earth Engine (GEE) platform. The processing pipeline began by filtering images based on acquisition dates and selecting the relevant spectral bands: red (B4), near-infrared (B8) and shortwave infrared (B11). To ensure data quality, scenes with more than 30% cloud cover were excluded. A multi-step pixel-level masking routine was then applied to remove residual atmospheric and surface noise. Additionally, the Scene Classification Layer (SCL) was used to filter out saturated pixels and to mask cloud shadows, high-probability clouds, cirrus, snow, ice and water bodies. This approach ensured a consistent and high-quality time series for the derivation of vegetation and moisture indicators across all study sites. See Appendix 1 for details on the code applied. To relate in-situ vegetation with remote sensing, we extracted spectral data using circular buffers of 10 m, 50 m and 100 m radius around each plot (Fig. 3 ). Within each buffer, mean SAVI, NDMI and S2_RLS_LST values were calculated to assess the sensitivity of spectral signals to spatial context. This multi-scale approach addresses spatial uncertainty arising from satellite sensor resolution and geolocation accuracy, while enabling the evaluation of how buffer size influences model performance. Across buffer sizes, temperature and soil water potential were strongly and positively correlated, indicating synchronous responses to environmental conditions. SAVI showed moderate to strong correlations with LST, especially at 100 m (r = 0.786), suggesting a strong link between vegetation activity and surface temperature. NDMI was consistently and negatively correlated with LST across scales, reflecting its sensitivity to moisture stress. Overall, these patterns highlight the complementary nature of RS indicators, with SAVI and LST capturing vegetation–temperature interactions and NDMI providing independent information on moisture availability. We finally compared in-situ measurements of water stress (as a binomial response variable) and soil temperature (Gaussian response) with concurrent NDMI, SAVI and LST values by fitting Generalized Linear Mixed Models (GLMMs). RS-derived variables were used as fixed effects and site was included as a random effect. Due to potential multicollinearity among RS variables, which can obscure individual effects and inflate standard errors, we assessed each predictor separately and did not rely on combined GLMM models for interpretation. The number of valid observations (n) varied with buffer size (634 for 100 m, 591 for 50 m and 544 for 10 m) due to cloud cover and other disturbances in satellite imagery at different extents around each sampling location. 2.6 Statistical analysis We used R (R Core Team 2013 ) for all statistical analyses. To explore the spatial relationships between field measurements and satellite variables, we conducted Redundancy Analysis (RDA) using the vegan package (Oksanen 2015 ). For temporal assessments, we employed generalized linear mixed models (GLMMs) implemented in the glmmtmb package (Magnusson et al. 2017 ) to test whether remote sensing variables explained field measurements of temperature and water stress. Model assumptions for GLMMs were checked using the DHARma package (Hartig 2016 ) and pseudo-R² values were obtained using the r2 function from the performance package (Lüdecke et al. 2021 ). In GLMMs, temperature was modeled with a Gaussian family and water stress with a binomial family using NDMI, SAVI and LST as fixed factors jointly and in individual models for each family. The four sites were included as random factors. 3. Results 3.1 Spatial patterns of alpine vegetation Using environmental variables (see Table 1 ), partial RDAs revealed that soil properties and spatial structure play a key role in shaping alpine plant communities, with consistent results using the full species assemblage and only dominant species (see Appendix 2, a for details). In the full species dataset, soil variables explained the most variation in community composition, with clay being the strongest contributor, followed by silt and bulk density. Spatial structure (PCNM3) also played a role, while altitude had a minor effect. In the dominant-species model, soil influence was even stronger, with clay, silt and bulk density again ranking highest. Spatial factors (PCNM2 and PCNM3) captured fine-scale gradients and altitude explained a slightly larger portion of variation. Climate variables were not significant in either model. By using this subset of explanatory variables for each family, final RDA (Appendix 2, b) identified consistent and significant predictors applied to all and dominant species. Among soil properties, clay content emerged as the most robust predictor, followed by silt and bulk density. The spatial component PCNM3 was also significant in both models, while PCNM2 was selected only for the dominant species. These models explained 15.1–20.6% of the variance in vegetation composition (adjusted R²). Figure 4 shows that the RDA-selected variables in final models show strong agreement in both groups, suggesting scale-invariant environmental controls on vegetation patterns. In contrast, RDAs computed with the remote sensing variables (Appendix 2, c) yielded a more selective set of significant predictors. The Soil-Adjusted Vegetation Index (SAVI) from summer was the only spectral variable retained when modeling vegetation patterns, showing the strongest significance of all predictors analyzed in both datasets (F = 6.83, p = 0.002 for all species; F = 8.86, p = 0.002 for dominant species), explaining 15–19% of the variance. Other indices (NDMI, TCW, LST) were excluded during model selection due to lack of significance. These results support the complementary value of integrating field-based soil parameters and RS indicators for explaining alpine vegetation structure. 3.2 Monitoring soil properties Temporal assessment of soil properties revealed scale-dependent relationships between remote sensing indicators and field-measured water stress and temperature. NDMI was negatively associated with water stress, with the strongest and most significant signal observed at the 50 m buffer (Estimate = − 3.50, p = 0.013), suggesting that lower moisture availability was reliably detected at intermediate spatial scales (Table 2 ). Although the relationship weakened at 100 m and 10 m buffers, the direction of effect remained consistent. SAVI showed a strong positive association with water stress across all buffers, with highly significant effects (all p < 0.01) and the strongest model performance at 100 m (R² fixed + random = 0.14), indicating that higher vegetation activity correlates with periods of increased soil water demand. Land Surface Temperature (LST) was also positively and significantly associated with water stress at all spatial scales ( p < 0.001), with increasing effect size and model performance as buffer size decreased, reaching the highest explanatory power at 10 m (R² fixed + random = 0.15). Model assumptions were evaluated through residual analysis and outlier detection, revealing moderate violations in several cases, particularly for LST and NDMI models at 100 m and 10 m buffers, where residuals showed non-random patterns or influential outliers. Only SAVI and NDMI passed assumption checks, supporting a more robust interpretation at intermediate scales. Table 2 Generalized Linear Mixed Model (GLMM) results for water stress (binomial family). The number of valid observations varied with buffer size (634 for 100 m, 591 for 50 m and 544 for 10 m). Buffer RS variable Estimate Std. Error z-value p-value R2 100 m NDMI -2,0554 1,3733 -1,497 1,34E-01 ns Fixed + random 0,03 Fixed 0,01 SAVI 3,1833 0,6142 5,183 2,19E-07 *** Fixed + random 0,14 Fixed 0,08 LST 0,08781 0,01719 5,108 3,26E-07 *** Fixed + random 0,11 Fixed 0,08 50 m NDMI -3,5032 1,4126 -2,48 1,31E-02 * Fixed + random 0,05 Fixed 0,02 SAVI 2,6066 0,69 3,778 1,58E-04 *** Fixed + random 0,12 Fixed 0,04 LST 0,1022 0,0209 4,89 1,01E-06 *** Fixed + random 0,13 Fixed 0,09 10 m NDMI -2,4275 1,2766 -1,902 5,72E-02 ns Fixed + random 0,06 Fixed 0,01 SAVI 2,2293 0,7607 2,93 3,39E-03 ** Fixed + random 0,08 Fixed 0,03 LST 0,1226 0,02523 4,858 1,18E-06 *** Fixed + random 0,15 Fixed 0,11 In turn, all remote sensing indicators showed varying levels of association with soil temperature, with SAVI emerging as the strongest and most consistent predictor across spatial scales (Table 3 ). At every buffer size, SAVI was highly significant ( p < 2e–16), with higher effect sizes (Estimate = 20.12) and the highest explained variance (R² fixed = 0.32) at finer spatial resolution, indicating a strong correspondence with field-measured soil temperature. Land Surface Temperature (LST) was also a highly significant predictor at all scales, with slightly lower R² values (0.12–0.13), supporting its utility as a direct thermal proxy. In contrast, NDMI showed weak or inconsistent relationships. While it was significant only at 50 m, its explanatory power was low (R² fixed = 0.01) and non-significant at 10 m. Assumption checks revealed some violations across models, particularly for NDMI and LST, where residuals showed non-random patterns, outliers and singularity at all scales. Despite these limitations, SAVI consistently yielded the best model fit and residual patterns, especially at 50 m and 10 m. Table 3 Generalized Linear Mixed Model (GLMM) results for soil temperature (gaussian family). The number of valid observations varied with buffer size (634 for 100 m, 591 for 50 m and 544 for 10 m). Buffer RS variable Estimate Std. Error z value p-value R2 100 m NDMI -8,125 4,6922 -1,73 8,33E-02 ns Fixed + random NA Fixed 0,01 SAVI 19,233 1,693 11,361 < 2e-16 *** Fixed + random 0,21 Fixed 0,18 LST 0,43916 0,04518 9,72 < 2e-16 *** Fixed + random NA Fixed 0,13 50 m NDMI -10,191 4,474 -2,28 2,27E-02 * Fixed + random NA Fixed 0,01 SAVI 19,539 2,051 9,526 < 2e-16 *** Fixed + random 0,21 Fixed 0,15 LST 0,46196 0,05169 8,937 < 2e-16 *** Fixed + random NA Fixed 0,12 10 m NDMI 0,1239 4,4124 0,03 9,78E-01 ns Fixed + random 0,00 Fixed 0,00 SAVI 20,123 2,415 8,331 < 2e-16 *** Fixed + random 0,17 Fixed 0,32 LST 0,4795 0,0571 8,399 < 2e-16 *** Fixed + random NA Fixed 0,12 Figure 5 summarizes the relationship between RS indicators and field-based measurements across scales. LST showed a consistent positive association with both water stress and soil temperature at all buffer sizes. NDMI was negatively related to water stress, especially at 50 m, but showed weak patterns for temperature. SAVI emerged as the strongest predictor of soil temperature, particularly at finer scales (10–50 m) and also captured water stress dynamics at broader scales (100 m). Overall, intermediate spatial buffers (50 m) offered a good balance between ecological sensitivity and model stability. 4. Discussion 4.1 Spatial patterns of vegetation Our results support the importance of environmental filtering as the main mechanism driving alpine vegetation composition (Dirnböck et al. 2003 ; Jiménez-Alfaro et al. 2024 ), with soil properties (particularly clay content, silt and bulk density) together with soil water-holding capacity, emerging as primary drivers of community assembly, filtering out how species traits cope with harsh conditions (Bello et al. 2013 ). These findings align with previous studies emphasizing the role of edaphic conditions, particularly soil texture and water retention, in shaping alpine plant distributions (Winkler et al. 2019 ; Buri et al. 2020 ). Spatial structure, represented by PCNM axes, also contributed significantly, reflecting fine-scale ecological gradients and spatial autocorrelation patterns not captured by environmental variables alone. In contrast, climatic variables had limited explanatory power, likely due to the narrow climatic gradient within the study area and the overriding influence of micro-environmental heterogeneity (Guil et al. 2009 ). Beyond field-based evidence, remote sensing demonstrated its potential to capture part of the ecological patterns and processes observed on the ground, offering a complementary approach to traditional methods (Coppin et al. 2004 ; Álvarez-Martínez et al. 2018 ). The significant correlations between satellite-derived soil moisture indices and field-measured properties suggests that remote sensing data can serve as a cost-effective tool for ecosystem mapping in inaccessible alpine regions (Pettorelli et al. 2025 ). Furthermore, the incorporation of vegetation indices, such as SAVI, demonstrated their utility in differentiating plant communities along environmental gradients, confirming the applicability of spectral data for spatial biodiversity assessments (Helfenstein et al. 2022 ). Interestingly, the redundancy analysis (RDA) revealed that while field-measured variables explained a higher proportion of variance in community composition, remote sensing indicators still captured significant ecological responses. This suggests that although spectral proxies do not fully replace in-situ measurements, they provide a valuable approximation of ecological conditions, particularly when field access is limited. The consistency of SAVI in explaining vegetation patterns across different community datasets (all species vs. dominant species) further supports their robustness as a functional indicator (Cavender-Bares et al. 2020 ; Cavender-Bares et al. 2025 ). However, the redundancy analysis also revealed a key limitation of remote sensing proxies. While useful, they do not fully capture the ecological specificity provided by direct soil measurements. Field-based variables explained a greater proportion of variance in community composition, particularly those related to soil texture and structure. This reinforces the idea that remote sensing indicators and field-based parameters are not interchangeable but complementary, the former offering spatial and temporal scalability, the latter providing mechanistic and process-level ecological understanding while achieving large-scale mapping and monitoring in an efficient manner. Integrating both data sources allows to bridge scales and perspectives in vegetation analysis. The inclusion of stellite-based indicators alongside edaphic and spatial variables strengthens model performance and interpretation, suggesting that remote sensing can enhance, not replace, traditional ecological monitoring. Moreover, the close agreement between models based on all species and those focused only on dominant species supports the scale-invariant influence of certain environmental filters, particularly soil texture, in determining vegetation composition. Together, these findings emphasize the value of multiscale, multi-source approaches in vegetation monitoring and point to the utility of functional remote sensing for long-term monitoring of biodiversity under changing environmental conditions. These approaches may be especially important in alpine regions with difficult access, a lack of previous data, or with endemic and threatened vegetation (Balsamo et al. 2018 ; Ganem et al. 2022 ).Oviedo 4.2 Monitoring soil properties Beyond community patterns, our study analysed soil properties controlling key ecosystem functions through time. It is well-known that variations in soil moisture, particularly during the summer season, represent a critical limiting factor for plant growth and community persistence in abiotic-limited environments (Schwinning & Kelly 2013 ). In this context, the ability of satellite-based soil moisture, productivity and surface reflectance indices to reflect seasonal variations provides a valuable tool to move beyond static patterns and explore how vegetation responds to temporal variability in soil conditions, offering insight into the physiological processes underlying the spatial structure previously described (Cavender-Bares et al. 2020 ). At this regard, while Fig. 5 illustrates strong and significant associations between remote sensing and field-based measurements, it is important to note that these relationships are atemporal. The scatterplots do not depict a continuous temporal trend across the study period (2021–2025) but rather summarize the overall correlation between high and low values of vegetation and soil indicators regardless of the specific time of year. Consequently, these results should not be interpreted as representing seasonal dynamics, e.g., a decline in SAVI from spring to late summer, but instead as evidence that lower SAVI or NDMI values tend to co-occur with higher water stress or lower soil moisture across the dataset as a whole. This distinction is crucial for interpreting model performance and ecological meaning. Although the data span multiple years and sampling dates, the patterns captured in these models reflect general co-variation rather than directional change or intra-annual dynamics. One step beyond, remote sensing indicators varied in their predictive power, highlighting differences in spectral sensitivity to thermal and hydric stress, as well as the influence of buffer size on signal quality and ecological interpretability. Indeed, a key methodological challenge in linking field-based vegetation data with remote sensing observations lies in the use of spatial patterns for spectral data extraction. This study revealed scale-dependent effects in this regard for alpine vegetation monitoring. Although buffers are practical for dealing with geolocation uncertainty and sub-pixel heterogeneity, this method involves a critical trade-off. While larger buffers can stabilize spectral measurements by averaging over a broader area, they often encompass a mix of vegetation types, especially in topographically complex and ecologically diverse alpine environments, which led to spectral mixing and weaken the specificity of community-level indicators (Helfenstein et al. 2022 ). Despite this, including multiple buffer sizes allowed us to explicitly test how landscape heterogeneity affects predictive performance. While alpine plant communities respond to microhabitat gradients (e.g., snowmelt patterns) operating at local scales (Scherrer & Körner 2011 ), Sentinel-2 related indicators offer frequently more comprehensive insights at intermediate buffers, balancing spatial resolution and ecological signal strength and outperforming at finer and larger scales in model accuracy and statistical robustness. This aligns with (Malanson et al. 2023 ), who found that intermediate scales best capture alpine ecosystem heterogeneity by balancing community purity and landscape context. The general lower performance of 100 m buffers likely stems from sensor limitations and spectral variability in a larger and more rugged terrain, which accommodates different vegetation types across environmental gradients (Rocchini et al. 2018 ). This links to (Jiménez-Alfaro et al. 2024 ) cautioning that coarse resolutions may conflate species-specific signals in biodiverse alpine mosaics. Oppositely, 10 m resolution fails to represent adequately target communities responses with one to three Sentinel-2 pixels within, likely due to the high spatial heterogeneity of alpine environments (Liu & Xia 2010 ). This trade-off highlights the importance of selecting appropriate spatial scales when coupling remote sensing with field data (Wu & Li 2009 ; Walsh et al. 2023 ). To address this issue, a suitable solution would be focusing on the actual spatial extent of target plant communities (Coppin et al. 2004 ; Álvarez‐Martínez et al. 2018) rather than generic buffers around plot locations. This patch-based approach may reduce signal contamination from adjacent plant assemblages and other land cover types, improving the ecological relevance of the derived biodiversity monitoring metrics. The complementary roles of remote sensing indicators in capturing the temporal dynamics of soil moisture and temperature are particularly noteworthy. Land Surface Temperature (LST) consistently showed a strong and positive association with both water stress and soil temperature across all spatial scales, underscoring its utility as a direct thermal proxy. Its performance remained stable from coarse (100 m) to fine (10 m) spatial resolutions, making it a reliable indicator for detecting heat-related stress and warming trends in alpine environments (Fu & Sun 2022 ). NDMI, by contrast, exhibited a consistent negative relationship with water stress and temperature, reflecting its sensitivity to canopy moisture and leaf water content (Wang et al. 2013 ). This signal was most robust at the 50 m buffer, suggesting that intermediate spatial integration improves signal clarity while maintaining ecological relevance. The relative insensitivity of NDMI to buffer size supports its application for drought monitoring across varying spatial contexts. SAVI, on the other hand, was more strongly associated with soil temperature (positive correlation, p < 0.01), particularly at finer spatial scales where vegetation structure and cover are more homogeneously captured. For water stress, SAVI showed significant positive associations at 50 m and 100 m buffers, reflecting its role as an indicator of vegetation vigor and productivity under moderate hydric conditions. This distinction suggests that while NDMI is more directly tied to plant water content and physiological drought, SAVI may reflect secondary responses, such as reduced biomass or photosynthetic activity under cumulative stress conditions. The dual sensitivity of SAVI and NDMI mirrors findings from other alpine and high-elevation ecosystems, such as the Tibetan Plateau (Zhang et al. 2023 ), where SAVI captured thermal niche dynamics while NDMI tracked spatial patterns of hydric limitation. These findings highlight the value of combining spectral indices to disentangle the distinct but overlapping effects of temperature and moisture stress in alpine vegetation (Helfenstein et al. 2022 ). LST consistently captured soil temperature and water stress across all buffer sizes, though it showed higher residual variance at finer scales, warranting cautious interpretation. NDMI reliably reflected moisture limitation, with stable negative associations across scales, making it suitable for monitoring drought-related stress. SAVI stood out as the most robust predictor overall, strongly linked to soil temperature at all scales and to water stress at broader buffers (50–100 m). Its seasonal trends aligned with field-measured soil parameters, especially under summer drought, confirming its utility as a functional indicator of vegetation productivity and thermal exposure. Our results further suggest that coarser spatial buffers may better capture the environmental heterogeneity relevant to alpine vegetation, smoothing local variability while enhancing model stability. In contrast, finer-scale buffers (10 m) introduced statistical instability without improving predictive power, underscoring important trade-offs in scale selection. This insight is critical when designing remote sensing monitoring systems in fragmented alpine mosaics, where spatial structure and ecological signal strength must be carefully balanced (Cavender-Bares et al. 2025 ). 5. Conclusion This study underscores the strength of integrating field-based observations with remote sensing to monitor alpine vegetation structure and stress responses. Soil properties, particularly texture and water retention, emerged as key drivers of community composition, reflecting strong environmental filtering in these ecosystems. Among remote sensing indicators, SAVI consistently outperformed others in predicting both spatial vegetation patterns and soil temperature, especially at intermediate spatial scales, while NDMI reliably captured canopy moisture dynamics and LST served as a stable thermal proxy. Our results highlight the importance of scale in remote sensing applications. Intermediate spatial assessments offered the best balance between ecological relevance and model stability, whereas finer resolutions introduced statistical noise and coarser ones blurred community-specific signals. These insights are essential for improving the accuracy and interpretability of spectral indicators in complex alpine mosaics. By capturing both compositional and functional variation, Sentinel-2-derived indices offer a scalable and cost-effective approach for long-term biodiversity monitoring. As alpine systems face accelerating climate-driven changes, such integrative frameworks will be key to anticipating ecological shifts, informing conservation strategies and supporting adaptive management in mountain environments worldwide. Declarations Author Contribution We follow the CRediT taxonomy (Contributor Roles Taxonomy) to specify individual author contributions: Conceptualization (C.M., B.J.-A.); Methodology (all); Formal analysis (C.E.d.A., G.H.R.); Investigation (J.M.A.-M., C.E.d.A.); Writing – original draft (J.M.A.-M., C.E.d.A.); Writing – review & editing (all); Supervision (J.M.A.-M., B.J.-A.) Acknowledgement We thank Teresa García Gutiérrez for technical assistance in the laboratory, and all members of the VegBioLab team at IMIB, Jorge, Alicia, Marta, et al., for their support. This work was conducted within the framework of the project Monitoring of Terrestrial habitats by Integrating Vegetation Archive Time series in Europe (MOTIVATE), funded by the European Commission through Biodiversa+, the European Biodiversity Partnership under the EU Biodiversity Strategy 2030. References Abril-Colón I, Alonso JC, Palacín C, Álvarez‐Martínez JM, Ucero A (2022) Short‐distance nocturnal migration in an island endemic bustard. Ibis 164:1145–1159 Alexander JM, Diez JM, Levine JM (2015) Novel competitors shape species’ responses to climate change. Nature 525:515–518 Álvarez-Martínez JM, Jiménez‐Alfaro B, Barquín J, Ondiviela B, Recio M, Silió‐Calzada A, Juanes JA (2018) Modelling the area of occupancy of habitat types with remote sensing. Methods Ecol Evol 9:580–593 Balsamo G, Agustì-Parareda A, Albergel C, Arduini G, Beljaars A, Bidlot J, Blyth E, Bousserez N, Boussetta S, Brown A (2018) Satellite and in situ observations for advancing global Earth surface modelling: A review. Remote Sensing 10: 2038 Bello Fd, Lavorel S, Lavergne S, Albert CH, Boulangeat I, Mazel F, Thuiller W (2013) Hierarchical effects of environmental filters on the functional structure of plant communities: a case study in the French Alps. Ecography 36:393–402 Björk RG, Molau U (2007) Ecology of alpine snowbeds and the impact of global change. Arct Antarct Alp Res 39:34–43 Borcard D, Legendre P (2012) Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology 93:1473–1481 Buri A, Grand S, Yashiro E, Adatte T, Spangenberg JE, Pinto-Figueroa E, Verrecchia E, Guisan A (2020) What are the most crucial soil variables for predicting the distribution of mountain plant species? A comprehensive study in the Swiss Alps. J Biogeogr 47:1143–1153 Cavender-Bares J, Gamon JA, Townsend PA (2020) The use of remote sensing to enhance biodiversity monitoring and detection: A critical challenge for the twenty-first century. Remote sensing of plant biodiversity : 1–12 Cavender-Bares J, Meireles JE, Pinto-Ledezma J, Schuman MC, Townsend P, Trowbridge A (2025) Spectral biology across scales in changing environments. Chu D (2020) Remote Sensing of Land Use and Land Cover in Mountain Region. Springer Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Review ArticleDigital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25:1565–1596 De Simone W, Di Musciano M, Di Cecco V, Ferella G, Frattaroli AR (2020) The potentiality of Sentinel-2 to assess the effect of fire events on Mediterranean mountain vegetation. Plant Sociol 57:11–22 Dirnböck T, Dullinger S, Grabherr G (2003) A regional impact assessment of climate and land-use change on alpine vegetation. J Biogeogr 30:401–417 Dirnböck T, Essl F, Rabitsch W (2011) Disproportional risk for habitat loss of high-altitude endemic species under climate change. Glob Change Biol 17:990–996 Dronova I, Taddeo S (2022) Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales. J Ecol 110:1460–1484 Elsen PR, Tingley MW (2015) Global mountain topography and the fate of montane species under climate change. Nat Clim change 5:772–776 Fu G, Sun W (2022) Temperature sensitivities of vegetation indices and aboveground biomass are primarily linked with warming magnitude in high-cold grasslands. Sci Total Environ 843:157002 Ganem KA, Xue Y, Rodrigues AA, Franca-Rocha W, Oliveira MT, Carvalho NSd., Cayo EYT, Rosa MR, Dutra AC, Shimabukuro YE (eds) (2022) Mapping South America’s drylands through remote sensing—A review of the methodological trends and current challenges. Remote Sensing 14: 736 García-Romero A, Muñoz J, andrés N, Palacios D (2010) Relationship between climate change and vegetation distribution in the Mediterranean mountains: Manzanares Head valley, Sierra De Guadarrama (Central Spain). Clim Change 100:645–666 Gomez-Gutierrez A, Schnabel S, Contador FL, Marín RG (2011) Testing the quality of open-access DEMs and their derived attributes in Spain: SRTM, GDEM and PNOA DEM. In: Proceedings of Geomorphometry 2011 Conference, Redlands, CA, USA , pp. 53–56 Gómez C, White JC, Wulder MA (2016) Optical remotely sensed time series data for land cover classification: A review. ISPRS J photogrammetry Remote Sens 116:55–72 Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27 Gottfried M, Pauli H, Futschik A, Akhalkatsi M, Barančok P, Alonso B, Coldea JL, Dick G, Erschbamer J B. & Fernández Calzado, M.a.R. 2012. Continent-wide response of mountain vegetation to climate change. Nat Clim change 2: 111–115 Graae BJ, Vandvik V, Armbruster WS, Eiserhardt WL, Svenning J-C, Hylander K, Ehrlén J, Speed JD, Klanderud K, Bråthen KA (2018) Stay or go–how topographic complexity influences alpine plant population and community responses to climate change. Perspect Plant Ecol Evol Syst 30:41–50 Grabherr G, Gottfried M, Pauli H (2011) Global change effects on alpine plant diversity. Biodivers Hotspots: Distribution Prot Conserv Prior Areas : 149–163 Guerini Filho M, Kuplich TM, Quadros FLD (2020) Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data. Int J Remote Sens 41:2861–2876 Guil N, Hortal J, Sánchez-Moreno S, Machordom A (2009) Effects of macro and micro-environmental factors on the species richness of terrestrial tardigrade assemblages in an Iberian mountain environment. Landscape Ecol 24:375–390 Gutiérrez-Girón A, Gavilán RG (2010) Spatial patterns and interspecific relations analysis help to better understand species distribution patterns in a Mediterranean high mountain grassland. Plant Ecol 210:137–151 Hartig F (2016) DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. CRAN: Contributed Packages He X, Arif M, Zheng J, Ni X, Yuan Z, Zhu Q, Wang J, Ding D, Li C (2023) Plant diversity patterns along an elevation gradient: the relative impact of environmental and spatial variation on plant diversity and assembly in arid and semi-arid regions. Front Environ Sci 11:1021157 Helfenstein IS, Schneider FD, Schaepman ME, Morsdorf F (2022) Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity. Remote Sens Environ 275:113024 Helm N, Chytrý K, Hülber K, Moser D, Wessely J, Gattringer A, Hausharter J, Pauli H, Winkler M, Saccone P (2024) Fine-scale alpine plant community assembly: Relative roles of environmental sorting, dispersal processes and species interactions. J Ecol 112:2745–2757 Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, Campbell LB (2016) Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. Int J Digit Earth 9:1035–1054 Jiménez-Alfaro B, Fernández‐Pascual E, Del Alba E, C., Marcenò C (2024) Spatiotemporal patterns of microclimatic buffering in relict alpine communities. J Veg Sci 35:e13242 Jiménez-Alfaro B, Gavilán RG, Escudero A, Iriondo JM, Fernández‐González F (2014) Decline of dry grassland specialists in M editerranean high‐mountain communities influenced by recent climate warming. J Veg Sci 25:1394–1404 Körner C, Kèorner C (1999) Alpine plant life: functional plant ecology of high mountain ecosystems Lastovicka J, Svec P, Paluba D, Kobliuk N, Svoboda J, Hladky R, Stych P (2020) Sentinel-2 data in an evaluation of the impact of the disturbances on forest vegetation. Remote Sensing 12: 1914 Latifovic R, Pouliot D, Sun L, Schwarz J, Parkinson W (2015) Moderate Resolution Time Sereis Data Management and Analysis: Automated Large Area Mosaicking and Quality Control. Natural Resources Canada Ottawa, ON, Canada Leeper RD, Matthews JL, Cesarini MS, Bell JE (2021) Evaluation of air and soil temperatures for determining the onset of growing season. J Geophys Research: Biogeosciences 126: e2020JG006171. Lewis RJ, Pakeman RJ, Marrs RH (2014) Identifying the multi-scale spatial structure of plant community determinants of an important national resource. J Veg Sci 25:184–197 Liu D, Xia F (2010) Assessing object-based classification: advantages and limitations. Remote Sens Lett 1:187–194 Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021) performance: an R package for assessment, comparison and testing of statistical models. J Open Source Softw 6:3139 Magnusson A, Skaug H, Nielsen A, Berg C, Kristensen K, Maechler M, van Bentham K, Bolker B, Brooks M, Brooks MM (2017) Package ‘glmmtmb’. R Package Version 0.2. 0 25 Malanson GP, Testolin R, Pansing ER, Jiménez-Alfaro B (2023) Area, environmental heterogeneity, scale and the conservation of alpine diversity. J Biogeogr 50:743–754 Mariotte P (2014) Do subordinate species punch above their weight? Evidence from above-and below-ground. New Phytol 203:16–21 Monteiro AT, Arenas-Castro S, Punalekar SM, Cunha M, Mendes I, Giamberini M, da Costa EM, Fava F, Lucas R (2024) Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data. Ecol Ind 163:112123 Myers-Smith IH, Kerby JT, Phoenix GK, Bjerke JW, Epstein HE, Assmann JJ, John C, andreu-Hayles L, Angers-Blondin S, Beck PS (2020) Complexity revealed in the greening of the Arctic. Nat Clim change 10:106–117 Ninyerola M, Fernández i, X.P., Roure JM (2005) Atlas climático digital de la Península Ibérica: metodología y aplicaciones en bioclimatología y geobotánica Oksanen J (2015) Vegan: community ecology package. R package version 2:3 Orusa T, Viani A, Cammareri D, Borgogno Mondino E (2023) A google earth engine algorithm to map phenological metrics in mountain areas worldwide with landsat collection and sentinel-2. Geomatics 3:221–238 Perrone M, Di Febbraro M, Conti L, Divíšek J, Chytrý M, Keil P, Carranza ML, Rocchini D, Torresani M, Moudrý V (2023) The relationship between spectral and plant diversity: Disentangling the influence of metrics and habitat types at the landscape scale. Remote Sens Environ 293:113591 Pettorelli N, Laurance WF, O'Brien TG, Wegmann M, Nagendra H, Turner W (2014) Satellite remote sensing for applied ecologists: opportunities and challenges. J Appl Ecol 51:839–848 Pettorelli N, Williams J, Schulte to Bühne H, Crowson M (2025) Deep learning and satellite remote sensing for biodiversity monitoring and conservation. Remote Sens Ecol Conserv 11:123–132 Proença V, Martin LJ, Pereira HM, Fernandez M, McRae L, Belnap J, Böhm M, Brummitt N, García-Moreno J, Gregory RD (2017) Global biodiversity monitoring: from data sources to essential biodiversity variables. Biol Conserv 213:256–263 Core Team R (2013) R. R: A language and environment for statistical computing Rocchini D, Balkenhol N, Carter GA, Foody GM, Gillespie TW, He KS, Kark S, Levin N, Lucas K, Luoto M (2010) Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol Inf 5:318–329 Rocchini D, Luque S, Pettorelli N, Bastin L, Doktor D, Faedi N, Feilhauer H, Féret JB, Foody GM, Gavish Y (2018) Measuring β-diversity by remote sensing: A challenge for biodiversity monitoring. Methods Ecol Evol 9:1787–1798 Rossi C, Gholizadeh H (2023) Uncovering the hidden: Leveraging sub-pixel spectral diversity to estimate plant diversity from space. Remote Sens Environ 296:113734 Scherrer D, Körner C (2011) Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J Biogeogr 38:406–416 Schweiger AK, Schütz M, Risch AC, Kneubühler M, Haller R, Schaepman ME (2017) How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response. Methods Ecol Evol 8:86–95 Schwinning S, Kelly CK (2013) Plant competition, temporal niches and implications for productivity and adaptability to climate change in water-limited environments. Funct Ecol 27:886–897 Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90:434–440 Steinbauer MJ, Grytnes J-A, Jurasinski G, Kulonen A, Lenoir J, Pauli H, Rixen C, Winkler M, Bardy-Durchhalter M, Barni E (2018) Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556:231–234 Trullén DL, Álvarez-Martínez JM, Labrador JDS, Jiménez-Alfaro B, Pérez-Silos I, Hernández-Romero G, Barquín J (2022) Espectrofenología con datos Sentinel 2: definición de curvas de referencia para la caracterización de ecosistemas forestales. Ecosistemas 31:2411–2411 Ustin SL, Gamon JA (2010) Remote sensing of plant functional types. New Phytol 186:795–816 Wakulińska M, Marcinkowska-Ochtyra A (2020) Multi-temporal sentinel-2 data in classification of mountain vegetation. Remote Sens 12:2696 Walsh SJ, Moody A, Allen TR, Brown DG (2023) Scale dependence of NDVI and its relationship to mountainous terrain. Scale in remote sensing and GIS. Routledge, pp 27–55 Wang L, Hunt Jr ER, Qu JJ, Hao X, Daughtry CS (2013) Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices. Remote Sens Environ 129:103–110 Winkler DE, Lubetkin KC, Carrell AA, Jabis MD, Yang Y, Kueppers LM (2019) Responses of alpine plant communities to climate warming. Ecosystem consequences of soil warming. Elsevier, pp 297–346 Wipf S, Rixen C, Mulder CP (2006) Advanced snowmelt causes shift towards positive neighbour interactions in a subarctic tundra community. Glob Change Biol 12:1496–1506 Wu H, Li Z-L (2009) Scale issues in remote sensing: A review on analysis, processing and modeling. Sensors 9:1768–1793 Yang L, Driscol J, Sarigai S, Wu Q, Chen H, Lippitt CD (2022) Google Earth Engine and artificial intelligence (AI): a comprehensive review. Remote Sens 14:3253 Zhang Z, Zhao Y, Lin H, Li Y, Fu J, Wang Y, Sun J, Zhao Y (2023) Comprehensive analysis of grazing intensity impacts alpine grasslands across the Qinghai-Tibetan Plateau: A meta-analysis. Front Plant Sci 13:1083709 Zhen Z, Chen S, Yin T, Gastellu-Etchegorry J-P (2023) Globally quantitative analysis of the impact of atmosphere and spectral response function on 2-band enhanced vegetation index (EVI2) over Sentinel-2 and Landsat-8. ISPRS J photogrammetry Remote Sens 205:206–226 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 11 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers invited by journal 05 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Submission checks completed at journal 03 Jan, 2026 First submitted to journal 31 Dec, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8491360","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569778331,"identity":"cff87474-9a62-4a5b-a2f7-7e8238464d84","order_by":0,"name":"Jose Manuel Álvarez-Martínez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie2RsQrCMBBArwRuCnQNFPQXTgRRFP2VhEJXHQUHC0Kd1E0U/8NZyeBSd8FFEZwLgjg4WKuDU9rRIW8IB8njHgTAYvlDnDA9TtnINgAEgIUUmY0oiykZH4VTsTA23ziJitpd143vXq/XUlNgOjGGrSQTKvIby3C/9hYUqAgwEDkKCBkzou1k7XHSVQReM4a9tzxkPKSO5tev4t7ywlDIviZCjm+llG6BnDAV1WV/RyLGapNTUEKGNaNSWfn6kNCA3Jk+H/mzxcvj0cUYVgmzz/yFmd6nlHPuLRaLxQLwAhIwPR+BaTb/AAAAAElFTkSuQmCC","orcid":"","institution":"Biodiversity Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Jose","middleName":"Manuel","lastName":"Álvarez-Martínez","suffix":""},{"id":569778335,"identity":"94ad3995-814e-4570-9eb6-d8ce987fced5","order_by":1,"name":"Clara Espinosa del Alba","email":"","orcid":"","institution":"Biodiversity Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"Espinosa del","lastName":"Alba","suffix":""},{"id":569778339,"identity":"1165dd55-d602-4f89-9af2-f6d4aa5ac7db","order_by":2,"name":"Corrado Marcenò","email":"","orcid":"","institution":"Department of Chemistry, Biology and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Corrado","middleName":"","lastName":"Marcenò","suffix":""},{"id":569778342,"identity":"2701098f-5cc8-4f51-9521-b7eda9eaaa8b","order_by":3,"name":"Gonzalo Hernández Romero","email":"","orcid":"","institution":"Biodiversity Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Gonzalo","middleName":"Hernández","lastName":"Romero","suffix":""},{"id":569778344,"identity":"7f800be7-ca6a-4c38-a1b3-d18268cb717b","order_by":4,"name":"Borja Jiménez-Alfaro","email":"","orcid":"","institution":"Biodiversity Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Borja","middleName":"","lastName":"Jiménez-Alfaro","suffix":""}],"badges":[],"createdAt":"2025-12-31 17:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8491360/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8491360/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100356322,"identity":"cf6c27ff-4fb3-4646-9b72-6fd765598d16","added_by":"auto","created_at":"2026-01-16 07:02:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1541697,"visible":true,"origin":"","legend":"","description":"","filename":"Mainmanuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/b7955dcbbb8ebd5f14a726c5.docx"},{"id":99830913,"identity":"bf5af483-e5ea-4cf9-81cd-479c5ab7b193","added_by":"auto","created_at":"2026-01-08 17:30:16","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8140,"visible":true,"origin":"","legend":"","description":"","filename":"918a385c18c74e939910954d9a998ee7.json","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/ca369764734b5971bcb58ba0.json"},{"id":99830908,"identity":"4af37e8c-3068-4ca7-b56c-c165f6824c7d","added_by":"auto","created_at":"2026-01-08 17:30:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45508,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/1f77573e63e0f07ddc1635cf.docx"},{"id":100356690,"identity":"32151a02-e0b8-4abb-a33d-e0e67407ffdf","added_by":"auto","created_at":"2026-01-16 07:16:59","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184802,"visible":true,"origin":"","legend":"","description":"","filename":"918a385c18c74e939910954d9a998ee71enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/27903ff0e361e2cf1bad35ee.xml"},{"id":100356852,"identity":"18ba41b5-5ef8-4de3-ad92-66d3b05d26a7","added_by":"auto","created_at":"2026-01-16 07:17:48","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135812,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/d33c1a96d79b270629953844.png"},{"id":100357006,"identity":"4b10d0cf-ff2b-4f0a-9dfe-839c16ecdbf0","added_by":"auto","created_at":"2026-01-16 07:18:15","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176467,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/707aec817e148b858739f4d1.png"},{"id":100356497,"identity":"1c8b46c4-9cc1-4e5b-a566-351f191805f7","added_by":"auto","created_at":"2026-01-16 07:13:43","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":379351,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/96523fec4c16c4e8c7bd50dc.png"},{"id":99830918,"identity":"e35801c4-3d18-4f26-9c5f-51603cf1019e","added_by":"auto","created_at":"2026-01-08 17:30:16","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26965,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/3fa37437252eacd1452815bd.png"},{"id":99830916,"identity":"002a8ded-324e-47f9-b20d-5ce8386d64c7","added_by":"auto","created_at":"2026-01-08 17:30:16","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96088,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/9c0af3de9bb5872493877253.png"},{"id":99830919,"identity":"288388b7-e418-4d43-89f4-9242e01287e5","added_by":"auto","created_at":"2026-01-08 17:30:16","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182690,"visible":true,"origin":"","legend":"","description":"","filename":"918a385c18c74e939910954d9a998ee71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/dcde3045581dba21c4911ec1.xml"},{"id":99830920,"identity":"7568ef1d-9636-4432-bb27-fc87ae43467e","added_by":"auto","created_at":"2026-01-08 17:30:16","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190977,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/38c3b6c6ee853520c22130d5.html"},{"id":99830903,"identity":"fa21266c-5191-4f4b-8c80-ef4a74ae6f8a","added_by":"auto","created_at":"2026-01-08 17:30:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":366320,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral framework for assessing spatial and temporal variation in alpine ecosystems with a combination of in-situ data (i.e. vegetation and soil physical properties) and remote sensing indicators. Numbers in white circles represent the methods subsections.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/eca402c8e7eee4238a9e383a.jpeg"},{"id":99830905,"identity":"4b932243-16f0-45e0-ad11-6fb70468cc6a","added_by":"auto","created_at":"2026-01-08 17:30:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":306095,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic location of the study area in the Cantabrian Mountains (Spain) and the distribution of the 40 sampling plots. The four plots marked with a black point: a) Rabinalto, b) La Cañada, c) Solana and d) Pico Penauta, were selected for the monitoring of soil properties described below.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/6b073086ab3efcbf28d99fdb.jpeg"},{"id":100356867,"identity":"b3b324c4-86ba-4726-a147-f2c9e24f07ac","added_by":"auto","created_at":"2026-01-16 07:17:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":395225,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial buffers of 10 m, 50 m and 100 m radii around vegetation plots selected to couple field observations with remote sensing indicators: a) Rabinalto, b) La Cañada, c) Solana and d) Pico Penauta (see Figure 2 for location). We observe in all cases how increasing buffer size captures greater landscape heterogeneity around sampling sites.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/e286dd6a6c1acb3f25dbed00.jpeg"},{"id":100356359,"identity":"2f872b42-8dad-456c-9fa7-3d0007919521","added_by":"auto","created_at":"2026-01-16 07:04:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76308,"visible":true,"origin":"","legend":"\u003cp\u003eBiplots with significant variables in final RDA models of environmental variables, using both the full species assemblage (left) and dominant species only (right).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/eaf6d027b0806d6b755d2efc.png"},{"id":100356491,"identity":"97c1a4fb-0c4c-49a6-9b04-4176a0cad5d7","added_by":"auto","created_at":"2026-01-16 07:11:59","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":441048,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between remote sensing indicators (LST, NDMI, SAVI) and in-situ measurements of soil water stress and temperature across spatial scales: a) 100 m, b) 50 m and c) 10 m buffer sizes.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/dc43b34d91458075c49f9bab.jpeg"},{"id":100376815,"identity":"67822ac5-b295-4458-b833-dbfab784306b","added_by":"auto","created_at":"2026-01-16 08:45:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2495612,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/52f8f7f3-d2e6-45ef-b8e8-68c272dbb628.pdf"},{"id":100356736,"identity":"e9eb4b39-31c2-4f6d-9263-1241bba04e80","added_by":"auto","created_at":"2026-01-16 07:17:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":45508,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8491360/v1/21282b9348dec52eb9a74698.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating in-situ data and remote sensing for spatiotemporal assessment of alpine vegetation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlpine ecosystems are characterized by specialized vegetation adapted to extreme environmental conditions (K\u0026ouml;rner \u0026amp; K\u0026egrave;orner \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) making them particularly sensitive to climate change and anthropogenic disturbances (Scherrer \u0026amp; K\u0026ouml;rner \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Steinbauer et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The strong environmental filtering characterizing alpine vegetation, driven by factors such as topography, climate and soil properties, plays a crucial role in shaping plant community composition and distribution (He et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These communities also harbor a high proportion of endemic and endangered species, many of which have limited dispersal capacity (Grabherr et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Graae et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, alpine vegetation serves as refugia for cold-adapted species, making their preservation crucial for maintaining ecological resilience and functional diversity in mountain systems (Gottfried et al. 2012). However, rising temperatures and altered precipitation regimes are causing significant changes in these ecosystems, threatening the persistence of many high-altitude endemic taxa (Dirnb\u0026ouml;ck et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In temperate mountains, the upward expansion of lowland species in response to warming (Garc\u0026iacute;a-Romero et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jim\u0026eacute;nez-Alfaro et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) facilitates novel biotic interactions and competitive pressures that further exacerbate their vulnerability (Alexander et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This interplay between environmental constrains and biodiversity responses highlights the importance of monitoring alpine communities to detect early warning signals of biodiversity decline (Elsen \u0026amp; Tingley \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditionally, mapping and monitoring alpine vegetation have relied on intensive field methods, which provide high ecological detail but are limited in their spatial scope due to accessibility restrictions, leading to temporal inconsistencies in repeated sampling (Wipf et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bj\u0026ouml;rk \u0026amp; Molau \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A promising tool for mapping and monitoring community composition, functional responses and temporal trends in alpine vegetation is the integration of expert-based field data with remote sensing missions (such as Landsat-5 to 9 and Sentinel-2) that provide high spatial resolution and frequent revisit times of multispectral sensors (Pettorelli et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chu \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As described by (Proen\u0026ccedil;a et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), indicators based on remote sensing offer a scalable, repeatable and cost-effective means of capturing biodiversity distribution, condition and dynamics, consolidating information from varying observation sources (i.e., extensive and intensive monitoring schemes and ecological field studies). In alpine ecosystems, remote sensing has been applied from land cover classification to monitoring vegetation dynamics and responses to climate and land-use change (\u0026Aacute;lvarez-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; De Simone et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wakulińska \u0026amp; Marcinkowska-Ochtyra \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent advances also include phenological analyses using time series to detect seasonal patterns such as greening and flowering (G\u0026oacute;mez et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Trull\u0026eacute;n et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the estimation of plant functional traits, e.g. chlorophyll content, specific leaf area, or dry matter content at the community level (Ustin \u0026amp; Gamon \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Schweiger et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite their capabilities, remote sensing applications face key challenges. One of them is the need to calibrate vegetation properties with high-resolution in-situ data to link ground-truth information with spectral signatures obtained from remote sensing metrics (Pettorelli et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dronova \u0026amp; Taddeo \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is especially challenging in alpine environments with complex terrain and spatial heterogeneity, posing additional issues such as spectral mixing and limited sensitivity to fine-scale ecological gradients (e.g., moisture or microclimate), which are better captured through long-term in-situ data (Rocchini et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Helm et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Uncertainty also persists in detecting local-scale vegetation trends in response to temporally dynamic drivers like temperature and soil water availability (Myers-Smith et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A common limitation lies in the widespread use of large buffer zones around field plots to extract spectral data, which, while accounting for geolocation uncertainty, often include mixed vegetation patterns and reduce ecological specificity (Perrone et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rossi \u0026amp; Gholizadeh \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, matching high-resolution remote sensing data with finely mapped in-situ plots improves ecological precision at the cost of reducing representativeness to only one or two pixels. An intermediate approach is to delineate vegetation communities at spatial scales aligned with sensor resolution, enhancing the accuracy of vegetation\u0026ndash;spectrum relationships, improving the integration of in-situ data and remote sensing for mapping and monitoring alpine ecosystems.\u003c/p\u003e \u003cp\u003eIn this study, we integrate in-situ vegetation and soil data collected in the field with remote sensing indicators obtained from satellite imagery to address spatial and temporal characterization of alpine ecosystems. Specifically, we ask the following key questions: 1) To what extent do remote sensing indicators explain spatial patterns in alpine vegetation composition compared to field-measured environmental variables? 2) Can remote sensing metrics effectively capture short-term temporal dynamics in soil moisture and temperature, as measured through in-situ observations? To investigate these matters, we coupled Sentinel-2 satellite data with field-based measurements across a siliceous mountain range in the north of Spain, evaluating the capabilities of remote sensing to cope with alpine vegetation and soil properties across space and through time. By comparing remote sensing indicators with traditional ecological data, we aim to advance the methodological framework for long-term monitoring of these complex systems, contributing to improved biodiversity conservation and ecosystem management strategies in worldwide mountain environments.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eTo guide this study, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the conceptual framework that integrates in-situ ecological sampling and remote sensing to define key environmental variables and to assess spatial and temporal variation in alpine ecosystems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study area is located in the southwestern Cantabrian mountains (NW Spain) between 716990.66\u0026ndash;741953.25 E (longitude) and 4741208.08\u0026ndash;4767462.49 N (latitude). The area includes three siliceous mountain massifs with most summits reaching elevations above 2000 m a.s.l.. Alpine vegetation is influenced by Mediterranean climate and dominates habitats above the treeline (approximately 1700 m a.s.l,; Gonz\u0026aacute;lez Le Barbier et al. 2025). We focused on alpine grasslands occupying summits and nearby areas, which are permanent communities composed of perennial herbaceous plants adapted to short growing seasons, frequent snow cover, low temperatures and drought. Growing season stretches from March to early November, with a mean annual soil temperature of 8\u0026ordm;C (soil values recorded from 2021 to 2025, own unpublished data), but with a 2-month dry period in summer (average precipitation of 160 mm and mean annual air temperature is 15.5 \u0026ordm;C). Yearly, snow cover ranges from 13 to 116 days. Mean daily water stress during the growing season ranges from \u0026minus;\u0026thinsp;0.32 to -0.52 MPa (soil data collected from 2021to 2025, own unpublished data). Frequent species include \u003cem\u003eFestuca summilusitana\u003c/em\u003e, \u003cem\u003eLuzula caespitosa\u003c/em\u003e, \u003cem\u003eCarex asturica\u003c/em\u003e and \u003cem\u003eDianthus langeanus\u003c/em\u003e, as well as cushion-forming and chamaephytic species such as \u003cem\u003eSilene ciliata\u003c/em\u003e and \u003cem\u003eThymus praecox\u003c/em\u003e. These grasslands often develop on shallow, rocky soils and exhibit high beta-diversity across microhabitats shaped by topography (e.g., ridges, depressions, moist patches and snowbeds).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 In-situ data collection\u003c/h2\u003e \u003cp\u003eWe followed a systematic sampling of alpine grasslands along the extent of the siliceous massifs in the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Vegetation plots were established along the mountain summits and the ridges among them at 500 m intervals between one site and the following. When a new (unrecorded) species appeared along the track between two contiguous sites, we sampled additional plots to cover the variation of the study vegetation. At each site, we delineated a circular plot with a 3 m radius and recorded the abundance of all vascular plant species present. The sampling resulted in 40 plots accounting for 96 species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecies were classified based on their mean cover across plots, with those exceeding 12% considered dominant, adapting the threshold established by Mariotte (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) to our study vegetation with very low plant cover. Based on species composition, the plots were classified in two broad habitat types linked to phytosociological classes as defined in {Mucina, 2016 #93}: (1) Strip grasslands (n\u0026thinsp;=\u0026thinsp;30), corresponding to \u003cem\u003eFestucetea indigesta\u003c/em\u003e class, typically found on shallow soils subjected to extreme cold and cryoturbation; and (2) \u003cem\u003eNardus\u003c/em\u003e swards (n\u0026thinsp;=\u0026thinsp;10), corresponding to \u003cem\u003eNardetea stricta\u003c/em\u003e class, associated with deeper soils and persistent snow cover lasting until early summer (Guti\u0026eacute;rrez-Gir\u0026oacute;n \u0026amp; Gavil\u0026aacute;n \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Local species richness ranged from 7 to 33 species per plot, with a mean of 15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (Standard Error, SE). The most dominant species were \u003cem\u003eLuzula caespitosa\u003c/em\u003e, with a mean cover of 18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43 (SE) and \u003cem\u003eFestuca summilusitana\u003c/em\u003e, with 12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88 (SE).\u003c/p\u003e \u003cp\u003eIn each plot, we measured environmental variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) including: (1) topographic features from a high-resolution (5-m resolution) elevation model (Gomez-Gutierrez et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), (2) climatic conditions at 200 m resolution (Ninyerola et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and (3) soil properties measured in 200 g of soil collected from three random samples within each site. To assess spatial structure, we evaluated spatial autocorrelation among plots using Principal Coordinates of Neighbor Matrices (PCNMs), allowing to describe the spatial distribution patterns of both vegetation and environmental features (Lewis et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\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\u003eEnvironmental variables measured in the field across 40 vegetation plots sampled in the western Cantabrian mountains (NW Spain).\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValues range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTopography\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1820 /2171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation in meters above sea level, measured with submetric GPS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.97 / 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrientation to north (dimensionless)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.99 / 0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrientation to east (dimensionless)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 / 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasure of steepness relative to the horizontal plane (degrees)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClimatic conditions\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual radiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1812 / 2320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual radiation (W/m2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e848 / 1352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual precipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average temperatures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 / 58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage annual temperatures in \u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage maximum temperatures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 /114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage of annual maximum temperatures in \u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage minimum temperatures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-22 / -2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage of annual minimum temperatures in \u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSoil properties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBulk density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 / 28.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio between the dry mass of solids and the undisturbed soil volume in g/mL. Can be used as an estimation of soil compactation and relate to water retention capacity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 / 45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 / 26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.064 / 1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSand coarse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 / 95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of sand with granulometry superior to 0.25 mm.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSand fine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 / 43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of sand with granulometry inferior to 0.25 mm. Highly correlated with sand coarse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 / 52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of particles between 0.05-0.002mm. Highly correlated with sand coarse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 / 19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of particles\u0026thinsp;\u0026lt;\u0026thinsp;0.002 mm. Correlated with sand coarse and\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.89 / 4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDefined as the negative logarithm of the hydrogen ion concentration, indication of the acidity or alkalinity of the soil.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 / 64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContent of phosphorous in the soil\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 \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Remote sensing indicators\u003c/h2\u003e \u003cp\u003eWe compiled a comprehensive database of remote sensing indicators using Google Earth Engine (GEE), a cloud-based geospatial platform that enables large-scale processing of satellite imagery (Gorelick et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Specifically, we processed Landsat time series from USGS (Hermosilla et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) at 30 meters of spatial resolution and Sentinel-2 imagery from the ESA archive (Orusa et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) at 10 m of spatial resolution, to derive three main categories of remote sensing based indicators informing on community structure and functionality: (1) land surface temperature (LST), (2) soil and vegetation moisture and (3) vegetation productivity. The use of GEE enables efficient processing and integration of multi-temporal RS data, facilitating robust and scalable environmental assessments across spatio-temporal scales.\u003c/p\u003e \u003cp\u003eLand Surface Temperature (LST) was estimated with two downscaling approaches: (1) 30 m resolution from Landsat-8 imagery (LST_L8) and (2) 10 m resolution from Sentinel-2 using Robust Linear Regression (S2_RLS_LST). LST reflects ground-emitted thermal radiation, influenced by surface properties like vegetation and soil (Sobrino et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Surface moisture was assessed with two indices, NDMI, which captures vegetation water content (Monteiro et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and TCW, which estimates canopy water stored in tissues (Lastovicka et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Vegetation condition and productivity were evaluated using five indices: NDVI, SAVI, EVI, MCARI and MCARI_OSAVI. NDVI and SAVI indicate greenness (Wakulińska \u0026amp; Marcinkowska-Ochtyra \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with SAVI correcting for soil background (Abril-Col\u0026oacute;n et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while EVI enhances detection in high-biomass areas (Zhen et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). MCARI and MCARI_OSAVI estimate chlorophyll and stress, with the latter minimizing soil effects (Guerini Filho et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). All indices were aggregated seasonally (spring, summer, autumn) to align with field sampling and assure pixel consistency for spatial analysis (Latifovic et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e). For temporal analysis, one representative variable from each group (LST, moisture, vegetation) was selected (Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Spatial patterns of alpine vegetation\u003c/h2\u003e \u003cp\u003eWe first explored the relationships between environmental factors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and vegetation composition by performing distance-based redundancy analyses (db-RDA) on species cover data, transformed using the Hellinger method to reduce the influence of large values and zero inflation. We conducted two complementary analyses, one including all recorded species and another limited to dominant species. This allowed evaluating whether dominant species, which contribute most to vegetation structure and spectral signals, showed consistent patterns with the full community data. For each dataset, we applied a stepwise forward selection procedure to identify significant predictors, evaluating model performance using adjusted R\u0026sup2; and permutation tests (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Blanchet et al., 2008). All environmental variables (topography, climate and soil) were initially assessed for multicollinearity, excluding those highly correlated within each thematic group (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7). We retained minimum temperature, radiation and precipitation as key climatic variables. Selected soil properties included bulk density, sand, silt, clay, pH and phosphorus. For remote sensing indicators, we kept one representative variable per group: LST (summer and autumn), NDMI and TCW (summer) and summer SAVI, chosen for its robustness against soil background effects. To assess spatial patterns in species composition, we applied Principal Coordinates of Neighbour Matrices (PCNM) (Borcard \u0026amp; Legendre \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), generating independent variables free from collinearity. The first PCNM axes represent broad-scale patterns, while later axes capture finer spatial variation. To avoid model overfitting, we retained only significant variables and PCNM vectors identified via partial RDA. For RS-based indicators we applied the same variable selection procedure. Pairwise correlation analyses were also conducted to reduce redundancy among predictors, ensuring that at least one representative variable from each group was retained in the final RDA model (i.e. land surface temperature, soil moisture and vegetation indices).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Monitoring of soil properties\u003c/h2\u003e \u003cp\u003eTo better understand how alpine vegetation responds to environmental variability, we integrated satellite time series with in-situ measurements of soil moisture stress (i.e. water potential) and temperature. We recorded hourly measurements at four selected study sites from June 2021 to June 2025 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These sites were representative of the strip grasslands, where water stress is a key environmental driver. A MicroLog SP3 datalogger (EMS Brno, Czech Republic) was buried at a depth of 5 cm. Temperature measurements had an accuracy of \u0026plusmn;\u0026thinsp;0.3\u0026deg;C within a range of -40\u0026deg;C to 60\u0026deg;C. Soil water potential was measured using two Delmhorst gypsum sensors, which operate within a range of 0.1 to 14.5 bars, the latter representing the permanent wilting point. To align field data with satellite overpasses, we averaged the temperature and water potential values recorded between 11:00 and 14:00 each day. Days with mean temperatures below 5\u0026deg;C were excluded, as this threshold is commonly used to define the growing season {Leeper, 2021 #57;K\u0026ouml;rner, 2021 #94}. Exploratory data analysis revealed that water potential responded in a binary manner, distinguishing between stress (\u0026gt;\u0026thinsp;14 bars) and no stress (\u0026lt;\u0026thinsp;14 bars). Consequently, we derived a binary \u0026ldquo;water stress\u0026rdquo; variable for subsequent analyses. After data filtering and processing, a total of 2643 observations were retained.\u003c/p\u003e \u003cp\u003eIn parallel, we processed a complete time series (maximum temporal resolution available, i.e. one image every 5-days) of Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI) and enhanced spatial resolution Land Surface Temperature (S2_RLS_LST) indices for the same period (2021 to 2025), using Sentinel-2 imagery (10-m pixel size) within the Google Earth Engine (GEE) platform. The processing pipeline began by filtering images based on acquisition dates and selecting the relevant spectral bands: red (B4), near-infrared (B8) and shortwave infrared (B11). To ensure data quality, scenes with more than 30% cloud cover were excluded. A multi-step pixel-level masking routine was then applied to remove residual atmospheric and surface noise. Additionally, the Scene Classification Layer (SCL) was used to filter out saturated pixels and to mask cloud shadows, high-probability clouds, cirrus, snow, ice and water bodies. This approach ensured a consistent and high-quality time series for the derivation of vegetation and moisture indicators across all study sites. See Appendix 1 for details on the code applied.\u003c/p\u003e \u003cp\u003eTo relate in-situ vegetation with remote sensing, we extracted spectral data using circular buffers of 10 m, 50 m and 100 m radius around each plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Within each buffer, mean SAVI, NDMI and S2_RLS_LST values were calculated to assess the sensitivity of spectral signals to spatial context. This multi-scale approach addresses spatial uncertainty arising from satellite sensor resolution and geolocation accuracy, while enabling the evaluation of how buffer size influences model performance. Across buffer sizes, temperature and soil water potential were strongly and positively correlated, indicating synchronous responses to environmental conditions. SAVI showed moderate to strong correlations with LST, especially at 100 m (r\u0026thinsp;=\u0026thinsp;0.786), suggesting a strong link between vegetation activity and surface temperature. NDMI was consistently and negatively correlated with LST across scales, reflecting its sensitivity to moisture stress. Overall, these patterns highlight the complementary nature of RS indicators, with SAVI and LST capturing vegetation\u0026ndash;temperature interactions and NDMI providing independent information on moisture availability. We finally compared in-situ measurements of water stress (as a binomial response variable) and soil temperature (Gaussian response) with concurrent NDMI, SAVI and LST values by fitting Generalized Linear Mixed Models (GLMMs). RS-derived variables were used as fixed effects and site was included as a random effect. Due to potential multicollinearity among RS variables, which can obscure individual effects and inflate standard errors, we assessed each predictor separately and did not rely on combined GLMM models for interpretation. The number of valid observations (n) varied with buffer size (634 for 100 m, 591 for 50 m and 544 for 10 m) due to cloud cover and other disturbances in satellite imagery at different extents around each sampling location.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe used R (R Core Team \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) for all statistical analyses. To explore the spatial relationships between field measurements and satellite variables, we conducted Redundancy Analysis (RDA) using the vegan package (Oksanen \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For temporal assessments, we employed generalized linear mixed models (GLMMs) implemented in the glmmtmb package (Magnusson et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to test whether remote sensing variables explained field measurements of temperature and water stress. Model assumptions for GLMMs were checked using the DHARma package (Hartig \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and pseudo-R\u0026sup2; values were obtained using the r2 function from the performance package (L\u0026uuml;decke et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In GLMMs, temperature was modeled with a Gaussian family and water stress with a binomial family using NDMI, SAVI and LST as fixed factors jointly and in individual models for each family. The four sites were included as random factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial patterns of alpine vegetation\u003c/h2\u003e \u003cp\u003eUsing environmental variables (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), partial RDAs revealed that soil properties and spatial structure play a key role in shaping alpine plant communities, with consistent results using the full species assemblage and only dominant species (see Appendix 2, a for details). In the full species dataset, soil variables explained the most variation in community composition, with clay being the strongest contributor, followed by silt and bulk density. Spatial structure (PCNM3) also played a role, while altitude had a minor effect. In the dominant-species model, soil influence was even stronger, with clay, silt and bulk density again ranking highest. Spatial factors (PCNM2 and PCNM3) captured fine-scale gradients and altitude explained a slightly larger portion of variation. Climate variables were not significant in either model.\u003c/p\u003e \u003cp\u003eBy using this subset of explanatory variables for each family, final RDA (Appendix 2, b) identified consistent and significant predictors applied to all and dominant species. Among soil properties, clay content emerged as the most robust predictor, followed by silt and bulk density. The spatial component PCNM3 was also significant in both models, while PCNM2 was selected only for the dominant species. These models explained 15.1\u0026ndash;20.6% of the variance in vegetation composition (adjusted R\u0026sup2;). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the RDA-selected variables in final models show strong agreement in both groups, suggesting scale-invariant environmental controls on vegetation patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, RDAs computed with the remote sensing variables (Appendix 2, c) yielded a more selective set of significant predictors. The Soil-Adjusted Vegetation Index (SAVI) from summer was the only spectral variable retained when modeling vegetation patterns, showing the strongest significance of all predictors analyzed in both datasets (F\u0026thinsp;=\u0026thinsp;6.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002 for all species; F\u0026thinsp;=\u0026thinsp;8.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002 for dominant species), explaining 15\u0026ndash;19% of the variance. Other indices (NDMI, TCW, LST) were excluded during model selection due to lack of significance. These results support the complementary value of integrating field-based soil parameters and RS indicators for explaining alpine vegetation structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Monitoring soil properties\u003c/h2\u003e \u003cp\u003eTemporal assessment of soil properties revealed scale-dependent relationships between remote sensing indicators and field-measured water stress and temperature. NDMI was negatively associated with water stress, with the strongest and most significant signal observed at the 50 m buffer (Estimate = \u0026minus;\u0026thinsp;3.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), suggesting that lower moisture availability was reliably detected at intermediate spatial scales (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although the relationship weakened at 100 m and 10 m buffers, the direction of effect remained consistent. SAVI showed a strong positive association with water stress across all buffers, with highly significant effects (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the strongest model performance at 100 m (R\u0026sup2; fixed\u0026thinsp;+\u0026thinsp;random\u0026thinsp;=\u0026thinsp;0.14), indicating that higher vegetation activity correlates with periods of increased soil water demand. Land Surface Temperature (LST) was also positively and significantly associated with water stress at all spatial scales (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with increasing effect size and model performance as buffer size decreased, reaching the highest explanatory power at 10 m (R\u0026sup2; fixed\u0026thinsp;+\u0026thinsp;random\u0026thinsp;=\u0026thinsp;0.15). Model assumptions were evaluated through residual analysis and outlier detection, revealing moderate violations in several cases, particularly for LST and NDMI models at 100 m and 10 m buffers, where residuals showed non-random patterns or influential outliers. Only SAVI and NDMI passed assumption checks, supporting a more robust interpretation at intermediate scales.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Mixed Model (GLMM) results for water stress (binomial family). The number of valid observations varied with buffer size (634 for 100 m, 591 for 50 m and 544 for 10 m).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuffer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRS variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ez-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e100 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,0554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,3733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,34E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,1833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,6142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,19E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,08781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,01719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,26E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e50 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3,5032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,4126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,31E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,6066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,58E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,1022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,0209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,01E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e10 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,4275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,2766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,72E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,2293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,7607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,39E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,1226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,02523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,18E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn turn, all remote sensing indicators showed varying levels of association with soil temperature, with SAVI emerging as the strongest and most consistent predictor across spatial scales (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At every buffer size, SAVI was highly significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2e\u0026ndash;16), with higher effect sizes (Estimate\u0026thinsp;=\u0026thinsp;20.12) and the highest explained variance (R\u0026sup2; fixed\u0026thinsp;=\u0026thinsp;0.32) at finer spatial resolution, indicating a strong correspondence with field-measured soil temperature. Land Surface Temperature (LST) was also a highly significant predictor at all scales, with slightly lower R\u0026sup2; values (0.12\u0026ndash;0.13), supporting its utility as a direct thermal proxy. In contrast, NDMI showed weak or inconsistent relationships. While it was significant only at 50 m, its explanatory power was low (R\u0026sup2; fixed\u0026thinsp;=\u0026thinsp;0.01) and non-significant at 10 m. Assumption checks revealed some violations across models, particularly for NDMI and LST, where residuals showed non-random patterns, outliers and singularity at all scales. Despite these limitations, SAVI consistently yielded the best model fit and residual patterns, especially at 50 m and 10 m.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Mixed Model (GLMM) results for soil temperature (gaussian family). The number of valid observations varied with buffer size (634 for 100 m, 591 for 50 m and 544 for 10 m).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuffer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRS variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e100 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,6922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,33E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,43916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,04518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e50 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10,191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,27E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,46196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,05169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e10 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,1239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,4124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,78E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,4795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,0571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u0026thinsp;+\u0026thinsp;random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the relationship between RS indicators and field-based measurements across scales. LST showed a consistent positive association with both water stress and soil temperature at all buffer sizes. NDMI was negatively related to water stress, especially at 50 m, but showed weak patterns for temperature. SAVI emerged as the strongest predictor of soil temperature, particularly at finer scales (10\u0026ndash;50 m) and also captured water stress dynamics at broader scales (100 m). Overall, intermediate spatial buffers (50 m) offered a good balance between ecological sensitivity and model stability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Spatial patterns of vegetation\u003c/h2\u003e \u003cp\u003eOur results support the importance of environmental filtering as the main mechanism driving alpine vegetation composition (Dirnb\u0026ouml;ck et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Jim\u0026eacute;nez-Alfaro et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with soil properties (particularly clay content, silt and bulk density) together with soil water-holding capacity, emerging as primary drivers of community assembly, filtering out how species traits cope with harsh conditions (Bello et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These findings align with previous studies emphasizing the role of edaphic conditions, particularly soil texture and water retention, in shaping alpine plant distributions (Winkler et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Buri et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Spatial structure, represented by PCNM axes, also contributed significantly, reflecting fine-scale ecological gradients and spatial autocorrelation patterns not captured by environmental variables alone. In contrast, climatic variables had limited explanatory power, likely due to the narrow climatic gradient within the study area and the overriding influence of micro-environmental heterogeneity (Guil et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond field-based evidence, remote sensing demonstrated its potential to capture part of the ecological patterns and processes observed on the ground, offering a complementary approach to traditional methods (Coppin et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; \u0026Aacute;lvarez-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The significant correlations between satellite-derived soil moisture indices and field-measured properties suggests that remote sensing data can serve as a cost-effective tool for ecosystem mapping in inaccessible alpine regions (Pettorelli et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the incorporation of vegetation indices, such as SAVI, demonstrated their utility in differentiating plant communities along environmental gradients, confirming the applicability of spectral data for spatial biodiversity assessments (Helfenstein et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Interestingly, the redundancy analysis (RDA) revealed that while field-measured variables explained a higher proportion of variance in community composition, remote sensing indicators still captured significant ecological responses. This suggests that although spectral proxies do not fully replace in-situ measurements, they provide a valuable approximation of ecological conditions, particularly when field access is limited. The consistency of SAVI in explaining vegetation patterns across different community datasets (all species vs. dominant species) further supports their robustness as a functional indicator (Cavender-Bares et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cavender-Bares et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the redundancy analysis also revealed a key limitation of remote sensing proxies. While useful, they do not fully capture the ecological specificity provided by direct soil measurements. Field-based variables explained a greater proportion of variance in community composition, particularly those related to soil texture and structure. This reinforces the idea that remote sensing indicators and field-based parameters are not interchangeable but complementary, the former offering spatial and temporal scalability, the latter providing mechanistic and process-level ecological understanding while achieving large-scale mapping and monitoring in an efficient manner. Integrating both data sources allows to bridge scales and perspectives in vegetation analysis. The inclusion of stellite-based indicators alongside edaphic and spatial variables strengthens model performance and interpretation, suggesting that remote sensing can enhance, not replace, traditional ecological monitoring. Moreover, the close agreement between models based on all species and those focused only on dominant species supports the scale-invariant influence of certain environmental filters, particularly soil texture, in determining vegetation composition. Together, these findings emphasize the value of multiscale, multi-source approaches in vegetation monitoring and point to the utility of functional remote sensing for long-term monitoring of biodiversity under changing environmental conditions. These approaches may be especially important in alpine regions with difficult access, a lack of previous data, or with endemic and threatened vegetation (Balsamo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ganem et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Oviedo\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Monitoring soil properties\u003c/h2\u003e \u003cp\u003eBeyond community patterns, our study analysed soil properties controlling key ecosystem functions through time. It is well-known that variations in soil moisture, particularly during the summer season, represent a critical limiting factor for plant growth and community persistence in abiotic-limited environments (Schwinning \u0026amp; Kelly \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this context, the ability of satellite-based soil moisture, productivity and surface reflectance indices to reflect seasonal variations provides a valuable tool to move beyond static patterns and explore how vegetation responds to temporal variability in soil conditions, offering insight into the physiological processes underlying the spatial structure previously described (Cavender-Bares et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At this regard, while Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates strong and significant associations between remote sensing and field-based measurements, it is important to note that these relationships are atemporal. The scatterplots do not depict a continuous temporal trend across the study period (2021\u0026ndash;2025) but rather summarize the overall correlation between high and low values of vegetation and soil indicators regardless of the specific time of year. Consequently, these results should not be interpreted as representing seasonal dynamics, e.g., a decline in SAVI from spring to late summer, but instead as evidence that lower SAVI or NDMI values tend to co-occur with higher water stress or lower soil moisture across the dataset as a whole. This distinction is crucial for interpreting model performance and ecological meaning. Although the data span multiple years and sampling dates, the patterns captured in these models reflect general co-variation rather than directional change or intra-annual dynamics.\u003c/p\u003e \u003cp\u003eOne step beyond, remote sensing indicators varied in their predictive power, highlighting differences in spectral sensitivity to thermal and hydric stress, as well as the influence of buffer size on signal quality and ecological interpretability. Indeed, a key methodological challenge in linking field-based vegetation data with remote sensing observations lies in the use of spatial patterns for spectral data extraction. This study revealed scale-dependent effects in this regard for alpine vegetation monitoring. Although buffers are practical for dealing with geolocation uncertainty and sub-pixel heterogeneity, this method involves a critical trade-off. While larger buffers can stabilize spectral measurements by averaging over a broader area, they often encompass a mix of vegetation types, especially in topographically complex and ecologically diverse alpine environments, which led to spectral mixing and weaken the specificity of community-level indicators (Helfenstein et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite this, including multiple buffer sizes allowed us to explicitly test how landscape heterogeneity affects predictive performance. While alpine plant communities respond to microhabitat gradients (e.g., snowmelt patterns) operating at local scales (Scherrer \u0026amp; K\u0026ouml;rner \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), Sentinel-2 related indicators offer frequently more comprehensive insights at intermediate buffers, balancing spatial resolution and ecological signal strength and outperforming at finer and larger scales in model accuracy and statistical robustness. This aligns with (Malanson et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who found that intermediate scales best capture alpine ecosystem heterogeneity by balancing community purity and landscape context. The general lower performance of 100 m buffers likely stems from sensor limitations and spectral variability in a larger and more rugged terrain, which accommodates different vegetation types across environmental gradients (Rocchini et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This links to (Jim\u0026eacute;nez-Alfaro et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) cautioning that coarse resolutions may conflate species-specific signals in biodiverse alpine mosaics. Oppositely, 10 m resolution fails to represent adequately target communities responses with one to three Sentinel-2 pixels within, likely due to the high spatial heterogeneity of alpine environments (Liu \u0026amp; Xia \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This trade-off highlights the importance of selecting appropriate spatial scales when coupling remote sensing with field data (Wu \u0026amp; Li \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Walsh et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To address this issue, a suitable solution would be focusing on the actual spatial extent of target plant communities (Coppin et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; \u0026Aacute;lvarez‐Mart\u0026iacute;nez et al. 2018) rather than generic buffers around plot locations. This patch-based approach may reduce signal contamination from adjacent plant assemblages and other land cover types, improving the ecological relevance of the derived biodiversity monitoring metrics.\u003c/p\u003e \u003cp\u003eThe complementary roles of remote sensing indicators in capturing the temporal dynamics of soil moisture and temperature are particularly noteworthy. Land Surface Temperature (LST) consistently showed a strong and positive association with both water stress and soil temperature across all spatial scales, underscoring its utility as a direct thermal proxy. Its performance remained stable from coarse (100 m) to fine (10 m) spatial resolutions, making it a reliable indicator for detecting heat-related stress and warming trends in alpine environments (Fu \u0026amp; Sun \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). NDMI, by contrast, exhibited a consistent negative relationship with water stress and temperature, reflecting its sensitivity to canopy moisture and leaf water content (Wang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This signal was most robust at the 50 m buffer, suggesting that intermediate spatial integration improves signal clarity while maintaining ecological relevance. The relative insensitivity of NDMI to buffer size supports its application for drought monitoring across varying spatial contexts. SAVI, on the other hand, was more strongly associated with soil temperature (positive correlation, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), particularly at finer spatial scales where vegetation structure and cover are more homogeneously captured. For water stress, SAVI showed significant positive associations at 50 m and 100 m buffers, reflecting its role as an indicator of vegetation vigor and productivity under moderate hydric conditions. This distinction suggests that while NDMI is more directly tied to plant water content and physiological drought, SAVI may reflect secondary responses, such as reduced biomass or photosynthetic activity under cumulative stress conditions. The dual sensitivity of SAVI and NDMI mirrors findings from other alpine and high-elevation ecosystems, such as the Tibetan Plateau (Zhang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), where SAVI captured thermal niche dynamics while NDMI tracked spatial patterns of hydric limitation.\u003c/p\u003e \u003cp\u003eThese findings highlight the value of combining spectral indices to disentangle the distinct but overlapping effects of temperature and moisture stress in alpine vegetation (Helfenstein et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). LST consistently captured soil temperature and water stress across all buffer sizes, though it showed higher residual variance at finer scales, warranting cautious interpretation. NDMI reliably reflected moisture limitation, with stable negative associations across scales, making it suitable for monitoring drought-related stress. SAVI stood out as the most robust predictor overall, strongly linked to soil temperature at all scales and to water stress at broader buffers (50\u0026ndash;100 m). Its seasonal trends aligned with field-measured soil parameters, especially under summer drought, confirming its utility as a functional indicator of vegetation productivity and thermal exposure. Our results further suggest that coarser spatial buffers may better capture the environmental heterogeneity relevant to alpine vegetation, smoothing local variability while enhancing model stability. In contrast, finer-scale buffers (10 m) introduced statistical instability without improving predictive power, underscoring important trade-offs in scale selection. This insight is critical when designing remote sensing monitoring systems in fragmented alpine mosaics, where spatial structure and ecological signal strength must be carefully balanced (Cavender-Bares et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study underscores the strength of integrating field-based observations with remote sensing to monitor alpine vegetation structure and stress responses. Soil properties, particularly texture and water retention, emerged as key drivers of community composition, reflecting strong environmental filtering in these ecosystems. Among remote sensing indicators, SAVI consistently outperformed others in predicting both spatial vegetation patterns and soil temperature, especially at intermediate spatial scales, while NDMI reliably captured canopy moisture dynamics and LST served as a stable thermal proxy. Our results highlight the importance of scale in remote sensing applications. Intermediate spatial assessments offered the best balance between ecological relevance and model stability, whereas finer resolutions introduced statistical noise and coarser ones blurred community-specific signals. These insights are essential for improving the accuracy and interpretability of spectral indicators in complex alpine mosaics. By capturing both compositional and functional variation, Sentinel-2-derived indices offer a scalable and cost-effective approach for long-term biodiversity monitoring. As alpine systems face accelerating climate-driven changes, such integrative frameworks will be key to anticipating ecological shifts, informing conservation strategies and supporting adaptive management in mountain environments worldwide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWe follow the CRediT taxonomy (Contributor Roles Taxonomy) to specify individual author contributions: Conceptualization (C.M., B.J.-A.); Methodology (all); Formal analysis (C.E.d.A., G.H.R.); Investigation (J.M.A.-M., C.E.d.A.); Writing \u0026ndash; original draft (J.M.A.-M., C.E.d.A.); Writing \u0026ndash; review \u0026amp; editing (all); Supervision (J.M.A.-M., B.J.-A.)\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Teresa Garc\u0026iacute;a Guti\u0026eacute;rrez for technical assistance in the laboratory, and all members of the VegBioLab team at IMIB, Jorge, Alicia, Marta, et al., for their support. This work was conducted within the framework of the project Monitoring of Terrestrial habitats by Integrating Vegetation Archive Time series in Europe (MOTIVATE), funded by the European Commission through Biodiversa+, the European Biodiversity Partnership under the EU Biodiversity Strategy 2030.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbril-Col\u0026oacute;n I, Alonso JC, Palac\u0026iacute;n C, \u0026Aacute;lvarez‐Mart\u0026iacute;nez JM, Ucero A (2022) Short‐distance nocturnal migration in an island endemic bustard. Ibis 164:1145\u0026ndash;1159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander JM, Diez JM, Levine JM (2015) Novel competitors shape species\u0026rsquo; responses to climate change. Nature 525:515\u0026ndash;518\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aacute;lvarez-Mart\u0026iacute;nez JM, Jim\u0026eacute;nez‐Alfaro B, Barqu\u0026iacute;n J, Ondiviela B, Recio M, Sili\u0026oacute;‐Calzada A, Juanes JA (2018) Modelling the area of occupancy of habitat types with remote sensing. Methods Ecol Evol 9:580\u0026ndash;593\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalsamo G, Agust\u0026igrave;-Parareda A, Albergel C, Arduini G, Beljaars A, Bidlot J, Blyth E, Bousserez N, Boussetta S, Brown A (2018) Satellite and in situ observations for advancing global Earth surface modelling: A review. \u003cem\u003eRemote Sensing\u003c/em\u003e 10: 2038\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBello Fd, Lavorel S, Lavergne S, Albert CH, Boulangeat I, Mazel F, Thuiller W (2013) Hierarchical effects of environmental filters on the functional structure of plant communities: a case study in the French Alps. Ecography 36:393\u0026ndash;402\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBj\u0026ouml;rk RG, Molau U (2007) Ecology of alpine snowbeds and the impact of global change. Arct Antarct Alp Res 39:34\u0026ndash;43\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorcard D, Legendre P (2012) Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology 93:1473\u0026ndash;1481\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuri A, Grand S, Yashiro E, Adatte T, Spangenberg JE, Pinto-Figueroa E, Verrecchia E, Guisan A (2020) What are the most crucial soil variables for predicting the distribution of mountain plant species? A comprehensive study in the Swiss Alps. J Biogeogr 47:1143\u0026ndash;1153\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavender-Bares J, Gamon JA, Townsend PA (2020) The use of remote sensing to enhance biodiversity monitoring and detection: A critical challenge for the twenty-first century. \u003cem\u003eRemote sensing of plant biodiversity\u003c/em\u003e: 1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavender-Bares J, Meireles JE, Pinto-Ledezma J, Schuman MC, Townsend P, Trowbridge A (2025) Spectral biology across scales in changing environments.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu D (2020) Remote Sensing of Land Use and Land Cover in Mountain Region. Springer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Review ArticleDigital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25:1565\u0026ndash;1596\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Simone W, Di Musciano M, Di Cecco V, Ferella G, Frattaroli AR (2020) The potentiality of Sentinel-2 to assess the effect of fire events on Mediterranean mountain vegetation. Plant Sociol 57:11\u0026ndash;22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDirnb\u0026ouml;ck T, Dullinger S, Grabherr G (2003) A regional impact assessment of climate and land-use change on alpine vegetation. J Biogeogr 30:401\u0026ndash;417\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDirnb\u0026ouml;ck T, Essl F, Rabitsch W (2011) Disproportional risk for habitat loss of high-altitude endemic species under climate change. Glob Change Biol 17:990\u0026ndash;996\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDronova I, Taddeo S (2022) Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales. J Ecol 110:1460\u0026ndash;1484\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsen PR, Tingley MW (2015) Global mountain topography and the fate of montane species under climate change. Nat Clim change 5:772\u0026ndash;776\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu G, Sun W (2022) Temperature sensitivities of vegetation indices and aboveground biomass are primarily linked with warming magnitude in high-cold grasslands. Sci Total Environ 843:157002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanem KA, Xue Y, Rodrigues AA, Franca-Rocha W, Oliveira MT, Carvalho NSd., Cayo EYT, Rosa MR, Dutra AC, Shimabukuro YE (eds) (2022) Mapping South America\u0026rsquo;s drylands through remote sensing\u0026mdash;A review of the methodological trends and current challenges. \u003cem\u003eRemote Sensing\u003c/em\u003e 14: 736\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Romero A, Mu\u0026ntilde;oz J, andr\u0026eacute;s N, Palacios D (2010) Relationship between climate change and vegetation distribution in the Mediterranean mountains: Manzanares Head valley, Sierra De Guadarrama (Central Spain). Clim Change 100:645\u0026ndash;666\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomez-Gutierrez A, Schnabel S, Contador FL, Mar\u0026iacute;n RG (2011) Testing the quality of open-access DEMs and their derived attributes in Spain: SRTM, GDEM and PNOA DEM. In: \u003cem\u003eProceedings of Geomorphometry 2011 Conference, Redlands, CA, USA\u003c/em\u003e, pp. 53\u0026ndash;56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez C, White JC, Wulder MA (2016) Optical remotely sensed time series data for land cover classification: A review. ISPRS J photogrammetry Remote Sens 116:55\u0026ndash;72\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18\u0026ndash;27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGottfried M, Pauli H, Futschik A, Akhalkatsi M, Barančok P, Alonso B, Coldea JL, Dick G, Erschbamer J B. \u0026amp; Fern\u0026aacute;ndez Calzado, M.a.R. 2012. Continent-wide response of mountain vegetation to climate change. Nat Clim change 2: 111\u0026ndash;115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraae BJ, Vandvik V, Armbruster WS, Eiserhardt WL, Svenning J-C, Hylander K, Ehrl\u0026eacute;n J, Speed JD, Klanderud K, Br\u0026aring;then KA (2018) Stay or go\u0026ndash;how topographic complexity influences alpine plant population and community responses to climate change. Perspect Plant Ecol Evol Syst 30:41\u0026ndash;50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrabherr G, Gottfried M, Pauli H (2011) Global change effects on alpine plant diversity. Biodivers Hotspots: Distribution Prot Conserv Prior Areas : 149\u0026ndash;163\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerini Filho M, Kuplich TM, Quadros FLD (2020) Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data. Int J Remote Sens 41:2861\u0026ndash;2876\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuil N, Hortal J, S\u0026aacute;nchez-Moreno S, Machordom A (2009) Effects of macro and micro-environmental factors on the species richness of terrestrial tardigrade assemblages in an Iberian mountain environment. Landscape Ecol 24:375\u0026ndash;390\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuti\u0026eacute;rrez-Gir\u0026oacute;n A, Gavil\u0026aacute;n RG (2010) Spatial patterns and interspecific relations analysis help to better understand species distribution patterns in a Mediterranean high mountain grassland. Plant Ecol 210:137\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartig F (2016) DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. \u003cem\u003eCRAN: Contributed Packages\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe X, Arif M, Zheng J, Ni X, Yuan Z, Zhu Q, Wang J, Ding D, Li C (2023) Plant diversity patterns along an elevation gradient: the relative impact of environmental and spatial variation on plant diversity and assembly in arid and semi-arid regions. Front Environ Sci 11:1021157\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelfenstein IS, Schneider FD, Schaepman ME, Morsdorf F (2022) Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity. Remote Sens Environ 275:113024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelm N, Chytr\u0026yacute; K, H\u0026uuml;lber K, Moser D, Wessely J, Gattringer A, Hausharter J, Pauli H, Winkler M, Saccone P (2024) Fine-scale alpine plant community assembly: Relative roles of environmental sorting, dispersal processes and species interactions. J Ecol 112:2745\u0026ndash;2757\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, Campbell LB (2016) Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. Int J Digit Earth 9:1035\u0026ndash;1054\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJim\u0026eacute;nez-Alfaro B, Fern\u0026aacute;ndez‐Pascual E, Del Alba E, C., Marcen\u0026ograve; C (2024) Spatiotemporal patterns of microclimatic buffering in relict alpine communities. J Veg Sci 35:e13242\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJim\u0026eacute;nez-Alfaro B, Gavil\u0026aacute;n RG, Escudero A, Iriondo JM, Fern\u0026aacute;ndez‐Gonz\u0026aacute;lez F (2014) Decline of dry grassland specialists in M editerranean high‐mountain communities influenced by recent climate warming. J Veg Sci 25:1394\u0026ndash;1404\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026ouml;rner C, K\u0026egrave;orner C (1999) Alpine plant life: functional plant ecology of high mountain ecosystems\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLastovicka J, Svec P, Paluba D, Kobliuk N, Svoboda J, Hladky R, Stych P (2020) Sentinel-2 data in an evaluation of the impact of the disturbances on forest vegetation. \u003cem\u003eRemote Sensing\u003c/em\u003e 12: 1914\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatifovic R, Pouliot D, Sun L, Schwarz J, Parkinson W (2015) Moderate Resolution Time Sereis Data Management and Analysis: Automated Large Area Mosaicking and Quality Control. Natural Resources Canada Ottawa, ON, Canada\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeeper RD, Matthews JL, Cesarini MS, Bell JE (2021) Evaluation of air and soil temperatures for determining the onset of growing season. J Geophys Research: Biogeosciences 126: e2020JG006171.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis RJ, Pakeman RJ, Marrs RH (2014) Identifying the multi-scale spatial structure of plant community determinants of an important national resource. J Veg Sci 25:184\u0026ndash;197\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu D, Xia F (2010) Assessing object-based classification: advantages and limitations. Remote Sens Lett 1:187\u0026ndash;194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026uuml;decke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021) performance: an R package for assessment, comparison and testing of statistical models. J Open Source Softw 6:3139\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagnusson A, Skaug H, Nielsen A, Berg C, Kristensen K, Maechler M, van Bentham K, Bolker B, Brooks M, Brooks MM (2017) Package \u0026lsquo;glmmtmb\u0026rsquo;. \u003cem\u003eR Package Version 0.2. 0\u003c/em\u003e 25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalanson GP, Testolin R, Pansing ER, Jim\u0026eacute;nez-Alfaro B (2023) Area, environmental heterogeneity, scale and the conservation of alpine diversity. J Biogeogr 50:743\u0026ndash;754\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMariotte P (2014) Do subordinate species punch above their weight? Evidence from above-and below-ground. New Phytol 203:16\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro AT, Arenas-Castro S, Punalekar SM, Cunha M, Mendes I, Giamberini M, da Costa EM, Fava F, Lucas R (2024) Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data. Ecol Ind 163:112123\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyers-Smith IH, Kerby JT, Phoenix GK, Bjerke JW, Epstein HE, Assmann JJ, John C, andreu-Hayles L, Angers-Blondin S, Beck PS (2020) Complexity revealed in the greening of the Arctic. Nat Clim change 10:106\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNinyerola M, Fern\u0026aacute;ndez i, X.P., Roure JM (2005) Atlas clim\u0026aacute;tico digital de la Pen\u0026iacute;nsula Ib\u0026eacute;rica: metodolog\u0026iacute;a y aplicaciones en bioclimatolog\u0026iacute;a y geobot\u0026aacute;nica\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen J (2015) Vegan: community ecology package. R package version 2:3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrusa T, Viani A, Cammareri D, Borgogno Mondino E (2023) A google earth engine algorithm to map phenological metrics in mountain areas worldwide with landsat collection and sentinel-2. Geomatics 3:221\u0026ndash;238\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerrone M, Di Febbraro M, Conti L, Div\u0026iacute;šek J, Chytr\u0026yacute; M, Keil P, Carranza ML, Rocchini D, Torresani M, Moudr\u0026yacute; V (2023) The relationship between spectral and plant diversity: Disentangling the influence of metrics and habitat types at the landscape scale. Remote Sens Environ 293:113591\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePettorelli N, Laurance WF, O'Brien TG, Wegmann M, Nagendra H, Turner W (2014) Satellite remote sensing for applied ecologists: opportunities and challenges. J Appl Ecol 51:839\u0026ndash;848\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePettorelli N, Williams J, Schulte to B\u0026uuml;hne H, Crowson M (2025) Deep learning and satellite remote sensing for biodiversity monitoring and conservation. Remote Sens Ecol Conserv 11:123\u0026ndash;132\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProen\u0026ccedil;a V, Martin LJ, Pereira HM, Fernandez M, McRae L, Belnap J, B\u0026ouml;hm M, Brummitt N, Garc\u0026iacute;a-Moreno J, Gregory RD (2017) Global biodiversity monitoring: from data sources to essential biodiversity variables. Biol Conserv 213:256\u0026ndash;263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCore Team R (2013) R. R: A language and environment for statistical computing\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocchini D, Balkenhol N, Carter GA, Foody GM, Gillespie TW, He KS, Kark S, Levin N, Lucas K, Luoto M (2010) Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol Inf 5:318\u0026ndash;329\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocchini D, Luque S, Pettorelli N, Bastin L, Doktor D, Faedi N, Feilhauer H, F\u0026eacute;ret JB, Foody GM, Gavish Y (2018) Measuring β-diversity by remote sensing: A challenge for biodiversity monitoring. Methods Ecol Evol 9:1787\u0026ndash;1798\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi C, Gholizadeh H (2023) Uncovering the hidden: Leveraging sub-pixel spectral diversity to estimate plant diversity from space. Remote Sens Environ 296:113734\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherrer D, K\u0026ouml;rner C (2011) Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J Biogeogr 38:406\u0026ndash;416\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchweiger AK, Sch\u0026uuml;tz M, Risch AC, Kneub\u0026uuml;hler M, Haller R, Schaepman ME (2017) How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response. Methods Ecol Evol 8:86\u0026ndash;95\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwinning S, Kelly CK (2013) Plant competition, temporal niches and implications for productivity and adaptability to climate change in water-limited environments. Funct Ecol 27:886\u0026ndash;897\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSobrino JA, Jim\u0026eacute;nez-Mu\u0026ntilde;oz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90:434\u0026ndash;440\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinbauer MJ, Grytnes J-A, Jurasinski G, Kulonen A, Lenoir J, Pauli H, Rixen C, Winkler M, Bardy-Durchhalter M, Barni E (2018) Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556:231\u0026ndash;234\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrull\u0026eacute;n DL, \u0026Aacute;lvarez-Mart\u0026iacute;nez JM, Labrador JDS, Jim\u0026eacute;nez-Alfaro B, P\u0026eacute;rez-Silos I, Hern\u0026aacute;ndez-Romero G, Barqu\u0026iacute;n J (2022) Espectrofenolog\u0026iacute;a con datos Sentinel 2: definici\u0026oacute;n de curvas de referencia para la caracterizaci\u0026oacute;n de ecosistemas forestales. Ecosistemas 31:2411\u0026ndash;2411\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUstin SL, Gamon JA (2010) Remote sensing of plant functional types. New Phytol 186:795\u0026ndash;816\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWakulińska M, Marcinkowska-Ochtyra A (2020) Multi-temporal sentinel-2 data in classification of mountain vegetation. Remote Sens 12:2696\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalsh SJ, Moody A, Allen TR, Brown DG (2023) Scale dependence of NDVI and its relationship to mountainous terrain. Scale in remote sensing and GIS. Routledge, pp 27\u0026ndash;55\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Hunt Jr ER, Qu JJ, Hao X, Daughtry CS (2013) Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices. Remote Sens Environ 129:103\u0026ndash;110\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinkler DE, Lubetkin KC, Carrell AA, Jabis MD, Yang Y, Kueppers LM (2019) Responses of alpine plant communities to climate warming. Ecosystem consequences of soil warming. Elsevier, pp 297\u0026ndash;346\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWipf S, Rixen C, Mulder CP (2006) Advanced snowmelt causes shift towards positive neighbour interactions in a subarctic tundra community. Glob Change Biol 12:1496\u0026ndash;1506\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H, Li Z-L (2009) Scale issues in remote sensing: A review on analysis, processing and modeling. Sensors 9:1768\u0026ndash;1793\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Driscol J, Sarigai S, Wu Q, Chen H, Lippitt CD (2022) Google Earth Engine and artificial intelligence (AI): a comprehensive review. Remote Sens 14:3253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Zhao Y, Lin H, Li Y, Fu J, Wang Y, Sun J, Zhao Y (2023) Comprehensive analysis of grazing intensity impacts alpine grasslands across the Qinghai-Tibetan Plateau: A meta-analysis. Front Plant Sci 13:1083709\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhen Z, Chen S, Yin T, Gastellu-Etchegorry J-P (2023) Globally quantitative analysis of the impact of atmosphere and spectral response function on 2-band enhanced vegetation index (EVI2) over Sentinel-2 and Landsat-8. ISPRS J photogrammetry Remote Sens 205:206\u0026ndash;226\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"alpine-botany","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"albo","sideBox":"Learn more about [Alpine Botany](http://link.springer.com/journal/35)","snPcode":"35","submissionUrl":"https://www.editorialmanager.com/albo/default2.aspx","title":"Alpine Botany","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Alpine Plant Communities, Cantabrian Mountains, Ecosystem Monitoring, Multispectral Imagery, Remote Sensing, Sentinel-2, Vegetation Mapping","lastPublishedDoi":"10.21203/rs.3.rs-8491360/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8491360/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlpine plant communities are highly sensitive to environmental change, making effective monitoring essential to guide conservation in mountain environments where soil properties and topographic heterogeneity strongly constrain vegetation patterns. This study evaluates the potential of remote sensing indicators to capture spatial variation in alpine vegetation driven by soil and topography, as well as short-term temporal dynamics of key soil properties measured in situ. Alpine vegetation was surveyed at 40 sites distributed across four mountain massifs in the southwestern Cantabrian Mountains (Spain), recording plant community composition, soil properties and spatial structure linked to topographic variation. In addition, soil temperature and water potential were monitored over complete annual cycles from 2021 to 2025 in four representative sampling plots. Spatial and temporal field observations were coupled with co-temporal Sentinel-2 time series to derive indicators related to surface temperature, moisture and primary production. Distance-based redundancy analyses and generalized linear mixed models were used to assess the role of remote sensing indicators in explaining vegetation composition and temporal trends in soil conditions.\u003c/p\u003e \u003cp\u003eAlpine plant communities were primarily structured by soil properties associated with water-holding capacity, together with spatial structure reflecting fine-scale topographic heterogeneity. Among remote sensing indicators, only the Soil-Adjusted Vegetation Index (SAVI) was significantly associated with vegetation composition, highlighting its potential as a proxy for productivity in topographically complex alpine landscapes. In contrast, all remote sensing variables proved effective in capturing short-term dynamics of soil temperature and water stress, particularly during climatic extremes, although their sensitivity varied with spatial scale.\u003c/p\u003e \u003cp\u003eOur results demonstrate that integrating in-situ vegetation data with remote sensing provides a robust and scalable framework for assessing alpine ecosystems across space and time. While satellite-derived indicators can successfully capture topography-mediated compositional gradients and functional responses related to water and temperature, careful scale selection and continuous calibration between field and remote sensing data are essential to avoid misinterpretation in ecologically heterogeneous and complex terrains. This integrative approach is critical for improving biodiversity monitoring and informing conservation strategies in alpine environments under accelerating climate change.\u003c/p\u003e","manuscriptTitle":"Integrating in-situ data and remote sensing for spatiotemporal assessment of alpine vegetation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 17:30:11","doi":"10.21203/rs.3.rs-8491360/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T08:37:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T10:48:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267430793979819940635081243607886525763","date":"2026-01-11T17:42:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111016634492318291396891156696732579267","date":"2026-01-05T11:52:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-05T10:47:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T09:11:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-03T11:47:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Alpine Botany","date":"2025-12-31T16:58:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"alpine-botany","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"albo","sideBox":"Learn more about [Alpine Botany](http://link.springer.com/journal/35)","snPcode":"35","submissionUrl":"https://www.editorialmanager.com/albo/default2.aspx","title":"Alpine Botany","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9f14e2a2-06f4-463a-8960-aaa7f92f2e79","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T08:37:00+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T08:42:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 17:30:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8491360","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8491360","identity":"rs-8491360","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}