Plants can express considerable plasticity in leaf functional traits, but that plasticity may be constrained by coordination and trade-offs among multiple functions. The leaf economics spectrum (LES) describes a well-supported trade-off between durability versus productivity but does not capture all dimensions of leaf functional responses to environmental variation. We hypothesized that traits related to photoprotection, desiccation tolerance, and water-use efficiency (WUE) would occupy axes distinct from, and potentially orthogonal to, the LES, and that species would differ in how they resolve multifunctional trade-offs along environmental gradients, influencing the strength of within- versus between-species trait relationships. We tested these hypotheses by analysing 11 leaf traits on 219 individuals of 16 woody species in a 20.2-ha temperate woodland plot spanning a topography-driven desiccation gradient in the North American Great Plains. Using multivariate ordinations, environmental vector fitting, and complementary bivariate correlation and standardized major axis analyses, we quantified axes of trait coordination and compared relationships within and between species. More productive leaves were less desiccation tolerant, durable, and water-use efficient, invested less in carotenoid-related photoprotection, and occurred in more mesic topographic positions. Ordinations revealed novel functional relationships and trade-offs: WUE was coordinated with carotenoid-related photoprotection, with more water-use efficient leaves investing more in photoprotection and less in chlorophylls relative to carotenoids, while desiccation tolerance aligned with durability and traded off with productivity traits. Within-species analyses confirmed strong, consistent relationships among LES traits and desiccation tolerance, whereas relationships involving WUE and photoprotection-reflecting more dynamic physiological processes-varied among species, producing weak between-species correlations. Together, these patterns indicate that WUE and photoprotection are coordinated along an axis approximately orthogonal to the LES, capturing covariation among dynamic physiological rather than structural functions. Supporting the idea that how individuals resolve functional trade-offs along environmental gradients shapes between-species trait relationships, our study not only expands the leaf economics framework but also reconceptualizes it in relation to critical trade-offs involving carotenoid-related photoprotection, desiccation tolerance, and WUE. By identifying new dimensions of trait relationships, our study advances functional ecology by highlighting leaf phenotypic variation along gradients as an integrated expression of multiple interacting functions.Read the free for this article on the Journal blog.

A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or 'proximal' remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site-level eddy-covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high-spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions. We provide current best practices for data availability and metadata for proximal remote sensing: spectral reflectance, solar-induced fluorescence, thermal infrared radiation, microwave backscatter, and LiDAR. Our paper outlines the steps necessary for making these data streams more widespread, accessible, interoperable, and information-rich, enabling us to address key ecological questions unanswerable from space-based observations alone and, ultimately, to demonstrate the feasibility of these technologies to address critical questions in local and global ecology.

Grassland restoration efforts aim to reestablish vegetation cover and maintain ecosystem services. However, there is a lack of systematic evaluation of the effects of grassland restoration and management strategies on biodiversity, productivity and surface-atmosphere feedbacks affecting climate. Through a multiyear grassland restoration experiment in a tallgrass prairie site in Nebraska, USA, we investigated how different management practices affected biodiversity, productivity and surface-atmosphere feedbacks using a combination of in situ measurements and airborne hyperspectral and thermal remote sensing. Our findings indicated that management treatments affected vegetation diversity, productivity and energy balance. Higher diversity plots had higher plant growth, albedo, canopy water content and lower surface temperature, indicating clear effects of management treatments on grassland ecosystem processes influencing surface-atmosphere feedbacks of mass and energy. The coherent responses of multiple airborne remote sensing indices illustrate potential cobenefits of grassland restoration practices that enhance ecosystem productivity and biodiversity and mitigate climate change through surface-atmosphere feedbacks, offering a new strategy to address the challenges of biodiversity loss and climate change in grassland ecosystems.

To reduce its greenhouse gas (GHG) impact, the United States government plans GHG Measurement, Monitoring, and Information System (MMIS) activities to better quantify GHG sources and sinks in natural, forested, and agricultural ecosystems. The national strategy discusses several areas where a robust MMIS can be strengthened by near-surface remote sensing (RS) technology—i.e. non-contact measurement of electromagnetic signals by sensors mounted near the ground, on towers, or on drones. Here, we outline specific applications of near-surface RS for an MMIS, using tools presently available and offering guidance on improvements needed for an expansion of their applications. Near-surface RS can help quantify carbon stocks by assessing vegetation structure and function, and it can inform cross-scale understanding of ecosystem carbon processes and properties. The integration of near-surface RS tools into an MMIS will overcome some limitations and uncertainties of current carbon cycle accounting methods and carbon project implementation. Development of robust, standardized near-surface RS systems can be accomplished through transdisciplinary partnerships among government agencies, academics, land managers, and the private sector. The result will hasten achievement of MMIS objectives of improved bottom-up and top-down GHG estimation and improved accessibility and standardization of GHG data and measurements.

Data from the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI) were used to develop and test algorithms for remotely retrieving terrestrial ecosystem productivity. Gross primary productivity (GPP) was calculated from CO2 flux for 47 eddy covariance flux towers representing vegetation and climatic variability across the USA. Eight-day average GPP was matched with eight-day average mapped OCI reflectance data containing 49 spectral bands from ultraviolet through short wave infrared spectral regions. The data covered the growing season from March through September 2024. For the combination of all sites and dates, the red-edge chlorophyll index alone described 66% of the variation in GPP. Using a partial least squares regression (PLSR) on all spectral bands GPP retrieval was improved to 74%. Agricultural sites were often found to have high residuals in these regressions. By training PLSR by eco-climatic region, the overall GPP retrievals were improved to 86%. The success of these algorithms across multiple sites with different vegetation types and through the growing season demonstrates the utility of PACE OCI data to map GPP dynamics at continental scales.

The Photochemical Reflectance Index (PRI) and solar induced fluorescence (SIF) provide information on plant photosynthetic activity. PRI and SIF are both strongly influenced by irradiance, but uncertainties related to the interpretation of these light responses at large spatial scales remain, partly due to a shortage of suitable data from aircraft or satellite platforms. The goal of this study was to explore interpretations of the PRI- and SIF-light responses of trees owing to species, functional types (evergreen and deciduous) and season. Using airborne hyperspectral and ultraspectral imagery in a North American urban forest, we derived PRI, SIF, and albedo (an indicator of illumination) at the 1-m pixel level. We then quantified crown-level PRI and SIF light responses of ten different tree species at three time points from late-summer to autumnal senescence using hierarchical models. Our results confirmed that both PRI and SIF were strongly influenced by illumination with PRI decreasing and SIF increasing with illumination. Both slope and intercept of the PRI-albedo relationship changed with season, but the pattern varied among species and functional types. SIF values decreased during autumnal senescence for all species, but evergreen species exhibited less seasonal decline in the slope of SIF-albedo relationship compared to deciduous species. The PRI and SIF light responses derived from the airborne imagery offer complementary information on dynamic photosynthesis responses presumably due to varying canopy structure, pigmentation and photoprotection among species and functional types. From airborne platforms, PRI- and SIF-light responses can be used to explore the contrasting physiological responses of individual tree crowns, providing a spatially and temporally explicit view of dynamic plant traits related to photoregulation and a novel view of functional diversity for entire landscapes.

The increasing threats to grassland ecosystems from land-use/land-cover change, disturbances, and invasive species underscore the importance of monitoring grassland plant diversity. While most remote sensing studies have mainly focused on quantifying alpha-diversity (diversity within communities), less attention has been given to remotely estimating beta-diversity (diversity between communities) in naturally-assembled grasslands. In this study, we used remote sensing to map plant beta-diversity in a naturally-assembled grassland managed using prescribed fire and grazing in a North American tallgrass prairie ecosystem within the US Southern Great Plains. We aimed to assess the impact of time since fire on the relationship between in situ plant beta-diversity and remotely-sensed beta-diversity, also known as spectral beta-diversity. We collected in situ observations at 60 m x 60 m, 120 m x 120 m, 180 m x 180 m, and 240 m x 240 m plots, alongside airborne and spaceborne DESIS imaging spectroscopy data with spatial resolutions of 1 m and 30 m, respectively. To assess how management practices influenced beta-diversity, we grouped our in situ observations into three categories based on time since fire, including recently-burned (burn age <1 year), transitional (burn age of 1-2 years), and unburned (burn age >2 years). Our findings showed that the association between in situ and spectral beta-diversity was strongly influenced by time since fire. Excluding plots with high soil cover (>= 50%) improved our remote estimation of beta-diversity, despite soil exposure effects. Our study demonstrated that remote sensing of beta-diversity in grassland ecosystems is influenced by spatial scale, time since fire, and soil exposure, all of which can be addressed with scale-appropriate imaging spectrometry. This study also highlighted the capability of spaceborne imaging spectrometers to estimate beta-diversity of grassland ecosystems across large spatial domains.

Sentinel-2 satellite data enables multispectral monitoring of the earth at a high temporal revisit rate. Combining this information with a network of optical ground measurements enables a more detailed and a more complete understanding of terrestrial ecosystems. However, independent optical ground measurements often lack consistency, especially when comparing different sites in geographically remote locations. Using the very high temporal and spectral resolution offered by the automated field spectrometer systems FloX and RoX (Fluorescence Box and Reflectance Box, respectively, JB-Hyperspectral Devices GmbH, Duesseldorf, Germany), we investigated continuous time series ranging over three years and in ten different locations across Europe, Africa, America and Asia. The continuous records of ground-measured reflectance were first validated against Sentinel-2 top of canopy (TOC) reflectance to evaluate the consistency of the in-situ network. Our results suggest a good agreement of ground-measured reflectance with Sentinel-2 TOC reflectance in vegetation and snow with R2 around 0.79 in the 833 nm band and R2 up to 0.94 in the bands around 559 nm and 492 nm, demonstrating good consistency across the network. Spatial misalignment of Sentinel-2 pixel-sizes with respect to the different footprint sizes of the ten automated spectrometers on the ground, atmospheric uncertainties, sub-optimal instrument setup and spatial-temporal variable landscape heterogeneity were identified as the most relevant sources of uncertainties in the network. Comparing the Normalized Difference Vegetation Index (NDVI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI) and Enhanced Vegetation Index (EVI) between ground and satellite revealed a decreasing agreement with increasing complexity of index formulation. The best agreement between satellite and ground was exhibited by NDVI with R2 around 0.96 and relative error of 4.3% investigating vegetation and snow across all ten sites. Furthermore, we identified a seasonal pattern in residuals of NDVI between ground and satellite in an alpine ecosystem in northern Italy, which was associated with increased spatial heterogeneity due to the effects of diverse vegetation phenology and snowfall. In contrast, a random distribution of residuals was recognized in a rather uniform oak forest canopy in southern France. Clustering Sentinel-2 pixels with respect to their temporal patterns in NDVI identified similar areas seen as homogenous in the canopy of Torgnon, Italy, and Observatoire de Haute-Provence (OHP), France, each. The very high temporal resolution of NDVI measured on the ground confirmed overlap with matched homogenous areas, but must consider seasonal landscape heterogeneity. Using well-standardized and globally homogenous Sentinel-2 TOC reflectance enabled the assessment of uncertainties in ten field spectrometer sites around the world. The standardization of the automated field spectrometers, their data products and data annotation were essential prerequisites that enabled joint validation against Sentinel-2. Harmonizing optical ground measurements with respect to a satellite is promising for future research to ensure the valid intercomparison and transfer of data products across different sites in a network worldwide.

Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered. We proposed a hybrid method that integrates in-situ measurements and the data simulated by a mechanistic model to improve the estimation of maize N content. In-situ data included hyperspectral images collected by Unmanned Aerial Vehicle (UAV), and leaf and canopy N content (LNC and CNC). A mechanistic radiative transfer model (PROSAIL-PRO) was used to generate simulated data, i.e., canopy reflectance paired with target crop traits (i.e., LNC, CNC). We compared the performance from the hybrid method with a machine learning method (Gaussian Process Regression) and six different vegetation indices (VIs) on four in-situ datasets collected at three study sites from 2021 to 2022. Results show that the hybrid method consistently performed the best for LNC estimation across four testing datasets (RRMSE ranging from 10.08% to 10.84%). For CNC estimation, the hybrid method had the best estimation results on two out of the four testing datasets and performed comparably to the best method on the other two datasets (RRMSE ranging from 13.89% to 25.21%). Next, we assessed the estimation robustness of the hybrid method, the machine learning, and the best-VI by comparing the mean (µ) and standard deviation (σ) of RRMSE across diverse water and N treatments (condition #1) and different growth stages (condition #2). Among 16 total cases (two crop traits by four study sites by two conditions), the hybrid method had 11 cases of smallest µ and seven cases of smallest σ, outperforming the machine learning (0/16 for µ, 4/16 for σ) and the best-VI (5/16 for µ, 5/16 for σ). These results underscore the greater robustness of the hybrid method. This study highlights the potential of integrating in-situ measurements and simulated data to improve estimation accuracy and robustness for maize LNC and CNC. The promising performance of the hybrid method suggests its applicability to a broader range of crops and various crop traits.

Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSIuni) calculation assumes N being the sole yield-limiting factor, neglecting other factors such as soil water variability. To tackle these issues, this study compared various variables for NSI calculation and evaluated two new N stress indicators in minimizing the impact of confounding water treatment. The following ground- and aerial-derived variables were compared for NSIuni calculation: SPAD, sampled leaf and canopy N content (LNC, CNC), LNC and CNC estimated using hyperspectral images acquired by an Unmanned Aerial Vehicle, and three VIs (Normalized Difference Vegetation Index (NDVI), Normalized Red Edge Index (NDRE), and Chlorophyll Index) from the hyperspectral images. Results demonstrated that ground-measured variables outperformed aerial-based variables in deriving N-responsive NSI. Especially, LNC derived NSIuni responded to N treatment significantly in ten out of thirteen site-date datasets. For the second objective, a modified NSI (NSIw) and the NDRE/NDVI ratio were compared to NSIuni. NSIw reduced water treatment effects in over 80

The Bidirectional Reflectance Distribution Function (BRDF) quantifies the distribution of the spectral reflectance of a target surface at various viewing and illumination angles. In-field measurement of the BRDF of vegetation canopies improves the characterization of off-nadir measurements and informs radiative transfer models of canopy reflectance, where the Lambertian assumption does not hold. However, current field goniometers are unable to measure BRDF efficiently, especially for tall vegetation across the growing season because of the limitations of clearance, field accessibility, and flexibility of the sensor field of view. In this study, we explored the potential of using a large-scale cable-suspended field phenotyping system to quantify BRDF of the canopy reflectance at selected bands and Vegetation Indices (VIs) of maize and soybean canopies. The system performance benefited from the following conditions: no crop damage, full-season field accessibility of tall canopies, automatic measurement, and accurate positioning. Correlation analysis shows that a strong correlation exists among the reflectance, VIs, and the Green Pixel Fraction. The hemispheric distributions of the spectral reflectance at selected bands and VIs were quantified at multiple dates. Hot spots were observed in the backscatter direction at visible and near-infrared (NIR) bands at the largest sensor zenith angle around the Solar Principal Plane (SPP). In contrast, cold spots of the Normalized Difference Vegetation Index (NDVI) and its related VIs were observed in the backscatter direction around solar zenith angles. The row effect was found for the maize canopy at the NIR band and the Near-infrared reflectance of vegetation (NIRv). NDVI had the lowest anisotropy index values among investigated bands and VIs. This system could be further leveraged to generate rapid and detailed BRDF data at a high spatiotemporal resolution using multiple sensors.

North American evergreen forests cover large areas and influence the global carbon cycle. Satellite remote sensing has been used to track the phenology of ecosystem photosynthesis of these forests by detecting variation in vegetation optical properties associated with physiological and structural features, and most of these methods have been closely tied to vegetation greenness. However, in evergreens, the application of satellite data to monitor photosynthetic phenology is often limited by the lack of sensitivity of greenness-based indices. In this study, we identified 47 evergreen forest flux sites in North America that had MODIS observation overlapping with the flux tower records. We then calculated four vegetation indices using MODIS MAIAC data (MCD19A1), including NDVI, CCI, NIRv, and kNDVI, for the 47 flux sites and evaluated relationships between gross primary productivity (GPP) and vegetation indices across the North American evergreen forests. Our results showed that snow had substantial effects on the performance of all vegetation indices in tracking GPP phenology, particularly in the early spring when rapid changes occurred to both GPP and snow cover. Different vegetation indices were affected differently, indicating contradictory and confounding effects of snow on these indices. After correcting for the snow effects, both CCI and NIRv performed well in tracking GPP phenology, albeit for different reasons. CCI is sensitive to seasonal changes in the relative levels of chlorophyll and carotenoid pigments, which are closely tied to GPP phenology in evergreens. NIRv is sensitive to the absorbed photosynthetically active radiation and to the contribution of deciduous components to the overall optical properties. We also found that correlations between GPP and vegetation indices varied among ecoregions and climate classes. In general, regions with pronounced seasonal GPP patterns had stronger correlations between GPP and greenness-based indices than regions with weaker seasonal GPP patterns. These biome differences were less pronounced for CCI. The snow artifacts and complementary vegetation index effects reported here should be considered in any large-scale studies of GPP using reflectance-based indices from optical satellites.

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

The Photochemical Reflectance Index (PRI) provides an optical indicator of photosynthetic light-use efficiency, photoprotection, and stress in plants. Although PRI can be applied in remote sensing, its interpretation depends on irradiance, which is hard to obtain from satellite or airborne imagery. To quantify forest photoprotective responses remotely, we developed a framework for modeling and interpreting PRI-light responses of individual trees and species using airborne imaging spectrometry coupled with georeferenced forest inventory data from a temperate broad-leaved forest. We derived an irradiance proxy, used hierarchical modeling to analyze PRI-light responses, and developed a framework of physiological interpretations of model parameters as facultative and constitutive components of photoprotection. Photochemical Reflectance Index declined with illumination, and PRI-light relationships varied with landscape position and among tree crowns and species. More sun-exposed foliage had lower intercepts and slopes of the relationship, indicating greater constitutive, but less facultative, photoprotection. We show that tree photoprotective strategies can be quantified at multiple scales using airborne hyperspectral data in structurally complex forests. Our findings and approach have important implications for the remote sensing of forest stress by offering a new way to assess functional diversity through dynamic differences in photoprotection and photosynthetic downregulation and providing previsual indicators of forest stress.

Ecosystem studies often consider the co-benefits of biodiversity and carbon sequestration, but these carbon-biodiversity links can be complex and multifaceted. Recent findings in forest ecosystems emphasize the importance of looking beyond single trophic levels and the more visible, above-ground portions to consider the full range of relationships between all ecosystem components when evaluating carbon sequestration potential. Simple engineered solutions to carbon storage based on monocultures that fail to consider all costs and benefits may be deceiving and lead to inappropriate management practices. Regenerating natural ecosystems may best enhance the co-benefits of carbon sequestration and biodiversity.

In 2022 we resampled normalized difference vegetation index (NDVI) along a 100 m transect in tundra near Utqiagvik, AK that had been previously measured through the 2000–2002 growing seasons, providing an opportunity to examine a 20 year NDVI change at a 1 m resolution in a region that is experiencing increased warming and precipitation over this period. Multidecadal NDVI change was spatially variable across the transect with nearly half of the transect showing greening, about a third not showing conclusive change, and about 20% browning. In wet areas, greening (increased NDVI) was associated with increased green leaf area index, while in drier areas greening was related to changes in species cover. Browning was not related to change in species cover and appeared to be due to increased coverage of standing dead material in graminoid dominated canopies. These types of detailed observations provide insights into the interpretation of satellite based NDVI trends and emphasize the importance of microtopography and hydrology in mediating vegetation change in a warming Arctic.

Given that anthropogenic greenhouse gas (GHG) emissions must be immediately reduced to avoid drastic increases in global temperature, methane emissions have been placed center stage in the fight against climate change. Methane has a significantly larger warming potential than carbon dioxide. A large percentage of methane emissions are in the form of industry emissions, some of which can now be readily identified and mitigated. This review considers recent advances in methane detection that allow accurate and transparent monitoring, which are needed for reducing uncertainty in source attribution and evaluating progress in emissions reductions. A particular focus is on complementary methods operating at different scales with applications for the oil and gas industry, allowing rapid detection of large point sources and addressing inconsistencies of emissions inventories. Emerging airborne and satellite imaging spectrometers are advancing our understanding and offer new top-down assessment methods to complement bottom-up methods. Successfully merging estimates across scales is vital for increased certainty regarding greenhouse gas emissions and can inform regulatory decisions. The development of comprehensive, transparent, and spatially resolved top-down and bottom-up inventories will be crucial for holding nations accountable for their climate commitments.

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical prop-erties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these com-plications, direct comparisons of airborne and ground SIF across scales are rarely attempted. In this study, we combined airborne SIF data with simultaneous 'ground truth' data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715-740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O2A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.