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Abstract Soil nutrients and water availability are strong drivers of tropical tree species distribution across scales. However, the physiological mechanisms underlying environmental filtering along these gradients remain incompletely understood. Previous studies mostly focused on univariate variation in structural traits, but a more integrative approach combining multiple physiological traits is needed to fully portray species functional strategies.We measured nine leaf functional traits related to trees' resource capture and hydraulic strategies for 552 individuals belonging to 21 tropical tree species across an environmental gradient in Amazonian forests. Our sampling included generalist and specialist species fromterra firme(TF) and seasonally flooded (SF) forests. We tested the influence of the topographic wetness index, a proxy for soil moisture and nutrient gradients, on each trait separately and on the trait integration through multivariate indices computed from the eigenvalues of a principal component analysis on the traits of the species. Finally, we evaluated intraspecific trait variability (ITV) for generalists and specialists by calculating the coefficient of variation for each trait.Results showed that (1) the environment had a greater influence on trait syndromes than single trait variation. Moreover, (2) SF specialist species expressed a stronger leaf trait coordination than TF specialist species. Furthermore, (3) the ability of generalist species to occupy a broader range of environments was not reflected by a larger ITV than specialist species but by the capacity to change trait coordination across environments.Our work highlights the need to investigate functional strategies as multidimensional syndromes in physiological trait space to fully understand and predict species distribution along environmental gradients. Read the freePlain Language Summaryfor this article on the Journal blog. 

Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR).