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Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensedmore » SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis.« less

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging themore » PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R² values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.« less

Maximum light use efficiency (ε_max) represents a plant's capacity to convert light into carbon during photosynthesis. Although prior studies have explored ε_max variations between sunlit and shaded leaves or its temporal ties to canopy structure, the spatial relationship between biome-level ε_max (ε_biome) and biome structure remains poorly understood. We analysed data from 320 eddy covariance sites (~855 site-years) with satellite-derived near-infrared reflectance of vegetation (NIRv) and leaf area index (LAI). We introduced NIRvN (NIRv/LAI) to isolate architectural effects from leaf quantity. Site-level ε_max was calculated and aggregated by biome to derive ε_biome. Results show ε_biome rises nonlinearly with NIRv andmore » LAI, saturating at high LAI, with crops and tropical evergreen forests deviating from this trend. Conversely, ε_biome decreases linearly with increasing NIRvN, indicating that biomes with greater NIR scattering efficiency exhibit lower ε_biome. These results enhance understanding of structural influences on carbon uptake across global biomes.« less

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasetsmore » were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R²) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R² values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.« less

Global change, particularly the changes in atmospheric CO₂ concentration, climatic variables, and nitrogen deposition, has been widely recognized and examined to have worldwide impacts on forest carbon. However, its influence on forest area required to meet the demand for timber and carbon storage and subsequent land use and land cover change (LULCC) is rarely studied. This study explores the role of global change-driven forest carbon change in shaping future global LULCC projections and investigates underlying drivers. We incorporated the global change impacts on forest carbon from the Canadian Land Surface Scheme Including Biogeochemical Cycles model simulations (driven by meteorological forcingmore » projections from two Earth system models [ESMs]) into the Global Change Analysis Model, under three combinations of shared socioeconomic pathways and representative concentration pathways (SSP126, SSP370, and SSP585). Including forest carbon change decreases the projected expansion of managed forest and managed pasture, reduces the loss of unmanaged pastures and forests, and provides more cropland. The relative change in managed forest by 2100 is -4.0%, -21.7%, and -31.9%, under SSP126, SSP370, and SSP585, respectively, when forest carbon change is considered. CO₂ fertilization is the dominant driver, increasing forest vegetation and soil carbon by 37% and 4.1%, and leading to 78.6% of the total area with a change in land use types by 2100 under SSP585. In comparison, climate change reduces forest vegetation and soil carbon by -3.5% and -0.8%, influencing 23.9% of the total area with a change in land use types by 2100 under SSP585, while nitrogen deposition has minor impacts. Using meteorological forcing data from two ESMs leads to similar impacts of forest carbon change on LULCC in terms of sign and trend but different magnitudes. This study highlights the large impact of forest carbon change on shaping future LULCC dynamics and the critical role of CO₂ fertilization.« less

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product ofmore » annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. Here, to further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.« less

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. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for >700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leafmore » water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R² (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.« less

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle overmore » the Arctic-Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCC_FLUS and LULCC_Demeter, respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between LULCC_FLUS and LULCC_Demeter, and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.« less

The 2017-2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a "Designated Targeted Observable" (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380 - 2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3-5 µm; TIR: 8-12 µm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has beenmore » formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. Finally, this effort synthesizes the findings of more than 130 scientists.« less