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 the 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 space-borne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. These findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Competition for land is intensifying as humanity seeks to meet growing demands for food, fuel, and fiber while protecting ecosystems and storing carbon. A critical but overlooked factor is how environmental and management-driven changes in forest carbon stocks reshape this competition. Here we show that rising forest carbon stocks, driven by CO₂ fertilization, climate change, and management intensification, can substantially expand natural land, including the unmanaged forests, unmanaged pasture, grasslands, and shrublands essential for biodiversity and carbon storage. By 2100, combined environmental and management effects increase global natural land area by 4.1% under a low-emission pathway and 13.4% under a high-emission pathway, with gains concentrated in the Northern Hemisphere. These findings reveal that forest carbon dynamics fundamentally alter projected land use competition, a feedback absent from most climate and land use assessments.

High spatial resolution hyperspectral imagery (HR-HSI) is essential for fine-scale ecological and environmental monitoring, yet current spaceborne sensors are constrained by a trade-off between spectral and spatial resolution. Hyperspectral reconstruction through data fusion offers a promising pathway to generate HR-HSI, but most existing approaches are trained on synthetic, perfectly aligned datasets and their performance under real cross-sensor conditions remains uncertain. Here, we develop a deep learning fusion framework that reconstructs HR-HSI by integrating low-resolution hyperspectral imagery from NASA's Earth Surface Mineral Dust Source Investigation (EMIT) with high-resolution multispectral imagery from PlanetScope. The model was evaluated across three ecologically distinct landscapes in the western United States using airborne AVIRIS-NG and AVIRIS-3 imagery as reference data. The proposed framework consistently outperformed seven peer state-of-the-art fusion models, achieving a best spectral angle mapper (SAM) of 2.64 and a root mean squared error (RMSE) of 0.0267 when validated against AVIRIS observations. Additional metrics (MAE = 0.0174, ERGAS = 3.62, PSNR = 28.87) further confirmed strong spectral fidelity and reconstruction accuracy across the study regions. Stratified analyses across vegetation density gradients (NDVI classes), spectral subregions (VIS, NIR, and SWIR), and pixel-level distributions demonstrated strong robustness in heterogeneous landscapes. A sensor inter-calibration pipeline ensured radiometric consistency between EMIT and PlanetScope, improving the reliability of the fusion inputs. Overall, this study demonstrates that the proposed deep learning framework enables scalable, high-fidelity HR-HSI reconstruction from current satellite observations, supporting fine-scale applications in vegetation trait retrieval, ecosystem monitoring, and environmental change analysis.

Rapid agricultural expansion has driven forest loss worldwide. Deforestation reduces evaporation, potentially decreasing precipitation and crop yields far beyond agricultural frontiers. Here, we quantified the teleconnection impacts of Amazon deforestation on precipitation and soybean yields across Brazil during 1982-2018, using a Lagrangian moisture tracking model. Our analysis indicates that tree evaporation contributes one-third of growing-season precipitation, yet recent deforestation decreased seasonal precipitation by 6 to 30% across Brazilian soybean states, with replacement land covers providing limited compensation for the losses. Although precipitation declines were most pronounced near Amazonian deforestation, the largest yield reductions (227 kton, or [Formula: see text]6% loss) occurred in the southern state of Rio Grande do Sul. Cumulatively, deforestation-driven precipitation declines resulted in a total soybean production loss of [Formula: see text]700 kton. These findings reveal that expanding agriculture into forests undermines yields in established croplands, potentially creating a feedback where yield losses drive demand for additional forest clearing. Under continued deforestation and climate change, this feedback is likely to intensify, threatening Brazilian rainfed agriculture into the future.

Small wetlands remain underappreciated emission sources in the global methane budget. Using 30-m remote sensing data, we identify 160 million small wetlands (0.001-1 km & sup2;) in non-forested regions worldwide, contributing 24% of the total wetland methane emissions. Notably, methane emissions from small wetlands increased significantly over 2003-2022, with very small wetlands (<0.1 km(2)) dominating the magnitude and growth of small-wetland emissions.

Forest canopy height is a fundamental control on aerodynamic conductance and surface energy partitioning, but its influence on evapotranspiration (ET) at a global scale remains under-investigated. We combined biophysical theory with Community Land Model version 5 (CLM5) simulations to quantify how canopy height affects the magnitude and partitioning of global forest ET. By uniformly scaling canopy height by up to ±20%, we first establish the intrinsic sensitivity of ET to canopy structure: a 20% increase in canopy height reduces global transpiration by 4.15% while increasing canopy evaporation by 2.94%, as enhanced aerodynamic conductance shifts energy partitioning between latent and sensible heat fluxes. We then prescribe canopy height fields derived from GEDI (circa 2020) and ICESat-1 GLAS (2003-2009) to assess how differences in data source, observation epoch, and spatial aggregation methods (maximum, mean, median) propagate into simulated ET. The ET response to canopy height is strongly biome dependent: tropical broadleaf evergreen forests show modest relative responses but the largest absolute flux perturbations; temperate broadleaf forests show the strongest relative responses, propagating directly to total ET; and boreal broadleaf deciduous forests display opposite-sign changes in canopy evaporation and transpiration that nearly cancel in total ET, hiding the height bias in flux partitioning. Differences among canopy height datasets and aggregation methods produced up to 1.76% divergence in global ET and up to 4.93% in individual flux components such as transpiration, with substantially larger differences at regional and biome scales. Our results highlight the need for updated canopy height constraints and standardized aggregation protocols to reduce structural uncertainty in the representation of vegetation-atmosphere coupling in Earth system models.

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 sensed 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.

The full text of this preprint has been withdrawn by the authors in order to comply with an institutional policy on preprints. Therefore, the authors do not wish this work to be cited as a reference.

Limiting global warming to 1.5 °C requires aggressive climate pledges, but their impact on land-use strategies remains underexplored. Now, a study reveals that these commitments may drive large-scale cropland loss, intensifying food security risks, especially in the global south.

Wetlands are the single largest natural source of atmospheric methane (CH _4 ), contributing approximately 30% of total surface CH _4 emissions, and they have been identified as the largest source of uncertainty in the global CH _4 budget based on the most recent Global Carbon Project CH _4 report. High uncertainties in the bottom–up estimates of wetland CH _4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH _4 emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH _4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH _4 emissions, as well as how they are used in top–down inversions. These bottom–up estimates, using (1) empirical biogeochemical modeling (e.g. WetCHARTs: 125–208 TgCH _4 yr ^−1 ); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH _4 yr ^−1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH _4 yr ^−1 ). Bottom–up estimates are subject to significant uncertainties (∼80 Tg CH _4 yr ^−1 ), and the ranges of different estimates do not overlap, further amplifying the overall uncertainty when combining multiple data products. These substantial uncertainties highlight gaps in our understanding of wetland CH _4 biogeochemistry and wetland inundation dynamics. Major tropical and arctic wetland complexes are regional hotspots of CH _4 emissions. However, the scarcity of satellite data over the tropics and northern high latitudes offer limited information for top–down inversions to improve bottom–up estimates. Recent advances in surface measurements of CH _4 fluxes (e.g. FLUXNET-CH _4 ) across a wide range of ecosystems including bogs, fens, marshes, and forest swamps provide an unprecedented opportunity to improve existing bottom–up estimates of wetland CH _4 estimates. We suggest that continuous long-term surface measurements at representative wetlands, high fidelity wetland mapping, combined with an appropriate modeling framework, will be needed to significantly improve global estimates of wetland CH _4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH _4 observations directed at wetlands.

Maximum light use efficiency (εmax) represents a plant's capacity to convert light into carbon during photosynthesis. Although prior studies have explored εmax variations between sunlit and shaded leaves or its temporal ties to canopy structure, the spatial relationship between biome-level εmax (εbiome) 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 εmax was calculated and aggregated by biome to derive εbiome. Results show εbiome rises nonlinearly with NIRv and LAI, saturating at high LAI, with crops and tropical evergreen forests deviating from this trend. Conversely, εbiome decreases linearly with increasing NIRvN, indicating that biomes with greater NIR scattering efficiency exhibit lower εbiome. These results enhance understanding of structural influences on carbon uptake across global biomes.

Solar-induced fluorescence (SIF), a small light signal emitted during the photosynthetic process, is a powerful tool for tracking gross primary productivity (GPP) across scales, particularly in evergreen needleleaf forests, which are traditionally challenging to monitor with remote sensing. Terrestrial biosphere models (TBMs) that incorporate a SIF module can help address the spatiotemporal limitations of field and satellite observations and explain variations in the SIF-GPP relationship across different temporal scales and ecosystems. In this study, we developed TECs-SIF, a TBM that integrates the spectral invariant property-based radiative transfer model across leaf and canopy scales to simultaneously simulate canopy SIF emissions and GPP and investigate how the SIFGPP relationship varies across forest ecosystems. We calibrated and validated TECs-SIF using data from three evergreen needleleaf forest and one deciduous broadleaf forest AmeriFlux sites: Southern Old Black Spruce (CAObs), Delta Junction (US-xDJ), Niwot Ridge Forest (US-NR1), and University of Michigan Biological Station AmeriFlux site (US-UMB). Our results show that TECs-SIF as a promising tool that accurately simulates SIF and GPP across various temporal scales (Hourly: SIF: R2 = 0.48-0.87, Root Mean Squared Error (RMSE) = 0.03-0.12 W/m2/mu m/sr; GPP: R2 = 0.60-0.79, RMSE = 1.82-5.31 mu mol/m2/s; Daily: SIF: R2 = 0.64-0.91, RMSE = 0.02-0.09 W/m2/mu m/sr; GPP: R2 = 0.89-0.97, RMSE = 0.51-2.05 mu mol/m2/s), captures nonlinear relationships at hourly intervals, and linear trends at daily and monthly scales. Meanwhile, SIF-GPP relationship is sitedependent across temporal scales, influenced by canopy structure (e.g., CI) and leaf traits.

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30 degrees-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45 degrees segment offered accurate estimation of CI (bias within +/- 0.1, RMSE < 0.2) and PAI (bias within +/- 0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15 degrees segment (PAI bias within +/- 0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15 degrees segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68 degrees, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30 degrees-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

The product of near-infrared reflectance of vegetation and photosynthetic active radiation (NIRvP) is a new tool for monitoring gross primary productivity (GPP) dynamics in terrestrial ecosystems, due to the discovered linear correlation between NIRvP and GPP. While remote sensing-based NIRvP is considerably influenced by sensor geometry, such geometry impacts on the NIRvP-GPP relationship remain underexplored. In this study, we calculate NIRvP using observations from the Deep Space Climate Observatory (DSCOVR) that provide unique hotspot observation geometry in which the sensor viewing angle coincides with the sun direction. We evaluated the linear correlation between NIRvP and GPP in both the common nadir direction and the special hotspot direction. The results indicate that NIRvP in the hotspot direction significantly outperforms that in the nadir direction for tracking GPP variations across different ecosystems from diurnal to daily scales. This conclusion is further supported by data from the MODerate resolution Imaging Spectroradiometer (MODIS) and simulations using the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. Our research highlights the value of using the unconventional hotspot-based sun-tracking satellite observations for a more accurate characterization of GPP dynamics in terrestrial ecosystems.

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 forcing 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.

The Paris Agreement aims to combat climate change by limiting global temperature rise to well below 2°C, with aspirations of reducing it to 1.5°C by the end of the century. However, debates are intensifying over the pace and direction of energy transitions needed to meet these goals. Our study highlights significant uncertainties in the terrestrial carbon cycle and their implications for mitigation efforts and energy transition pathways. We used the results from the TRENDY Model‐Intercomparison Project to represent terrestrial carbon cycle simulations from 11 models using the Hector model, the simple climate model coupled to a multisector integrated assessment model, the Global Change Analysis Model. Focusing on scenarios limiting global warming to 1.5°C by the end of the century, we assessed the uncertainties in energy trajectories and associated carbon prices. Our results reveal that uncertainties in terrestrial carbon cycle projections meaningfully affect the pace of global energy transitions to meet climate policy goals. The models predict the phase‐out of unabated coal power generation by 2050 ± 7 years. Additionally, ensemble simulations estimate a carbon price of 170.25 ± 38.84 $/tCO2e in 2010$ by the end of the century. Our findings highlight the critical need to refine models and integrate updated data to improve the reliability of carbon cycle projections and guide effective climate policy development. Specifically, enhancing the representation of the terrestrial carbon cycle in integrated assessment models is essential. Addressing these uncertainties is crucial for informed decision‐making and effective implementation of strategies to achieve long‐term climate objectives.

Gross Primary Productivity (GPP) estimates from terrestrial biosphere models (TBMs) are often uncertain due to limited constraints on vegetation biochemical and biophysical properties. Remote sensing offers promising opportunities to reduce these uncertainties, yet its full potential remains understudied. Here, we conducted model-data fusion experiments, including Observing System Simulation Experiments (OSSEs), and Observing System Experiments (OSEs) at the Harvard Forest site, using the Terrestrial Ecosystem Carbon cycle simulator (TECs) with an embedded spectral invariant theory-based radiative transfer model. In OSSEs, we assimilated synthetic hyperspectral reflectance, multispectral reflectance, and Leaf Area Index (LAI) into TECs to evaluate their effect under the ideal conditions. In OSEs, we assimilated PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral reflectance (620-1000 nm), MODerate resolution Imaging Spectroradiometer (MODIS) multispectral reflectance (broadband red and near-infrared), and MODIS-derived LAI to optimize model parameters, including several key vegetation traits such as leaf chlorophyll content (Cab), maximum carboxylation rate at 25 degrees C (V-cmax25), and LAI. Results show that hyperspectral reflectance consistently outperforms multispectral reflectance and LAI in improving GPP estimates and reducing uncertainties, with RMSE decreasing from 2.68 to 1.18 mu mol CO2 m(-2) s(-1) in OSSEs, and from 6.74 to 5.42 mu mol CO2 m(-2) s(-1) in OSEs. This is because hyperspectral information better constrains seasonal variations in canopy structure and Cab. Meanwhile, both hyperspectral and multispectral reflectance outperform LAI, with information from both canopy structural parameters and leaf biochemical properties, thus offering a joint constraint on GPP simulations. Our findings highlight that remotely sensed reflectance data, particularly hyperspectral reflectance, have great potential to improve photosynthesis modeling and reduce uncertainties in GPP estimates within TBMs.

Tropospheric ozone has significant impacts on human health, atmospheric oxidizing capacity and global climate change. Despite stringent control measures, summer ozone pollution in the arid and semi-arid regions of China has increased. The average ozone (90th percentile of the daily maximum 8-h sliding average) concentration in Lanzhou increased by 53 mu g m-3 from 2013 to 2021. The primary factors driving changes in ozone are not clear, hampering the implementation of effective control strategies. This study aims to address the above issue by using the WRF-Chem model, as well as observational data in three arid and semi-arid urban stations (Lanzhou, Yinchuan, and Xining) from 2013 to 2021. The results indicated that changes in anthropogenic emissions such as NOX (nitrogen oxides), VOCS (volatile organic compounds), and particulate matter, led to a 0.98 ppb increase while meteorological variations contributed to a 1.68 ppb increase in summer daytime ozone concentration in Lanzhou from 2013 to 2021. Anthropogenic emissions primarily affect ozone levels by influencing the chemical and vertical mixing processes in the atmosphere. Changes in ozone concentration in Lanzhou from 2013 to 2021 were primarily influenced by meteorological variations rather than changes in anthropogenic emissions. Observational data in Lanzhou, Yinchuan, and Xining showed that summer ozone concentration exhibited stronger correlation with relative humidity than with temperature, which is a unique phenomenon compared to those cities in humid regions in eastern China. This study enhances our understanding of the mechanisms underlying ozone pollution in industrial cities located in arid and semi-arid regions.

Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.