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Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade‐offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long‐table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the ‘rtry’ R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, ‘rtry’ avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. ‘rtry’ is therefore easy to use even for beginners in R. Notably, ‘rtry’ does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While ‘rtry’ primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The ‘rtry’ package is available on the Comprehensive R Archive Network (CRAN;https://cran.r‐project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI‐BGC‐Functional‐Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows.

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear.

Imaging spectroscopy is a powerful tool used to support diverse Earth science and applications objectives, ranging from understanding and mitigating widespread impacts of climate change to management of water at farm‐scale. Community studies, such as those deployed by NASA's Surface Biology and Geology and ESA's Copernicus Hyperspectral Imaging Mission for the Environment, have offered new and tangible insights into user needs that are then incorporated into overall mission planning and design. These technologies and tools will be key to develop and consolidate downstream services for users and resource management, given the current pressures on the environment posed by climate change and population growth. This process has highlighted the degree to which planned mission capabilities are responsive to community needs. In this study, we analyze user requirements belonging to the Italian Copernicus User Forum and to the user pool of NASA's Surface Biology and Geology community for the synergic use of hyperspectral imaging technology, providing a reference for the development of earth observation services and the consolidation of existing ones. In addition, potential cross‐mission coordination is analyzed to highlight key benefits—(a) addressing shared community needs around products requiring more frequent temporal revisit and (b) shared resources and community expertise around algorithm development. This paper discusses the critical role of early engagement with users to establish a community of practice ready to work with high spatial resolution imaging spectroscopy data sets. The main outcome is a guide for the synergetic use of hyperspectral mission and data together with the identification of the main gaps between user needs and satellite capabilities influencing the development of key national and trans‐national downstream services.

The relationship between nutrient cycling and water quality in mixed-use ecosystems is driven by interactions among biotic and abiotic processes. However, the underlying processes cannot always be directly observed or modeled at broad spatial scales. Numerous empirical studies have employed land use patterns, variations in watershed physiography or disturbance regimes to characterize nutrient export from mixed-use watersheds, but simultaneously disentangling the effects of such factors remains challenging and few models directly incorporate vegetation biochemistry. Here we use structural equation models (SEMs) to assess the relative influence of foliar chemical traits (derived from imaging spectroscopy), watershed physiography, and human land use on the water quality (summer baseflow nitrate-N and soluble reactive phosphorus concentration) in watersheds across the Upper Midwestern United States. We use an SEM to link water quality (stream nitrate-nitrogen and dissolved phosphorus) to foliar retention (AVIRIS-Classic derived foliar traits related to recalcitrance), watershed retention (wetland proportion, MODIS Tasseled Cap Wetness), runoff (agricultural and urban land use), and watershed leakiness (AVIRIS-Classic foliar nitrogen, nitrogen deposition). The SEMs confirmed that variables associated with foliar retention derived from imaging spectroscopy are negatively related to watershed leakiness (standardized path coefficient = −0.892) and positively to watershed retention (standardized path coefficient = 0.705), with features related to watershed retention and runoff exerting the strongest controls on water quality (standardized path coefficients of −0.270 and 0.331 respectively). Comparing forested and agricultural watersheds, we found significantly increased importance of foliar retention to watershed leakiness in forests compared to agriculture (standardized coefficients of −1.004 and −0.764 respectively), with measures of watershed retention more important to runoff and water quality in agricultural watersheds. The results illustrate the capacity of imaging spectroscopy to provide measures of foliar traits that influence nutrient cycling in watersheds. Ultimately, the results may help focus development and restoration policies towards building more resilient landscapes that take into consideration associations among functional traits of vegetation, physiography and climate.

As climate change advances, there is a need to examine climate conditions at scales that are ecologically relevant to species. While microclimates in forested systems have been extensively studied, microclimates in grasslands have received little attention despite the climate vulnerability of this endangered biome. We employed a novel combination of iButton temperature and humidity measurements, fine-scale spatial observations of vegetation and topography collected by unpiloted aircraft system, and gridded mesoclimate products to model microclimate anomalies in temperate grasslands. We found that grasslands harbored diverse microclimates and that primary productivity (as represented by normalized difference vegetation index), canopy height, and topography were strong spatial drivers of these anomalies. Microclimate heterogeneity is likely of ecological importance to grassland organisms seeking out climate change refugia, and thus there is a need to consider microclimate complexity in the management and conservation of grassland biodiversity.