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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Disturbance, complexity, and succession of net ecosystem production in North America’s temperate deciduous forests

Century-old forests in the U.S. upper Midwest and Northeast power much of North Amer- ica’s terrestrial carbon (C) sink, but these forests’ production and C sequestration capacity are expected to soon decline as fast-growing early successional species die and are replaced by slower growing late successional species. But will this really happen? Here we marshal empirical data and ecological theory to argue that substantial declines in net ecosystem production (NEP) owing to reduced forest growth, or net primary production (NPP), are not imminent in regrown temperate deciduous forests over the next several decades. Forest age and production data for temperate deciduous forests, synthesized from published literature, suggest slight declines in NEP and increasing or stable NPP during middle successional stages. We revisit long-held hypotheses by EP Odum and others that suggest low-severity, high-frequency disturbances occurring in the region’s aging forests will, against intuition, maintain NEP at higher-than- expected rates by increasing ecosystem complexity, sustaining or enhancing NPP to a level that largely o sets rising C losses as heterotrophic respiration increases. This theoretical model is also supported by biological evidence and observations from the Forest Accelerated Succession Experiment in Michigan, USA. Ecosystems that experience high-severity disturbances that simplify ecosystem complexity can exhibit substantial declines in production during middle stages of succession. However, observations from these ecosystems have exerted a disproportionate in uence on assumptions regarding the trajectory and magnitude of age-related declines in forest production. We conclude that there is a wide ecological space for forests to maintain NPP and, in doing so, lessens the declines in NEP, with signi cant implications for the future of the North American carbon sink. Our intellectual frameworks for understanding forest C cycle dynamics and resilience need to catch up to our more complex and nuanced understanding of ecological succession.

ecological theory↗
DOE OSTI2015

Seasonal fluxes of carbonyl sulfide in a midlatitude forest

Significance The flux of carbonyl sulfide (OCS) provides a quantitative, independent measure of biospheric activity, especially stomatal conductance and carbon uptake, at the ecosystem scale. We describe the factors controlling the hourly, daily, and seasonal fluxes of OCS based on 1 year of observations in a forest ecosystem. Vegetation dominated uptake of OCS, with daytime fluxes accounting for 72% of the total uptake for the year. Nighttime fluxes had contributions from both incompletely closed stomata and soils. Net OCS emission was observed at high temperature in summer. Diurnal and seasonal variations in OCS flux show variable stoichiometry relative to photosynthetic uptake of CO 2 . An effective model framework is shown, using an explicit representation of ecosystem processing of OCS.

Commane, Róisín↗
DOE OSTI2020

Mountain biodiversity and ecosystem functions: interplay between geology and contemporary environments

Although biodiversity and ecosystem functions are strongly shaped by contemporary environments, such as climate and local biotic and abiotic attributes, relatively little is known about how they depend on long-term geological processes. Here, along a 3000-m elevational gradient with tectonic faults on the Tibetan Plateau (that is, Galongla Mountain in Medog County, China), we study the joint effects of geological and contemporary environments on biological communities, such as the diversity and community composition of plants and soil bacteria, and ecosystem functions. We find that these biological communities and ecosystem functions generally show consistent elevational breakpoints at 2000–2800 m, which coincide with Indus-Yalu suture zone fault and are similar to the elevational breakpoints of soil bacteria on another mountain range 1000 km away. Mean annual temperature, soil pH and moisture are the primary contemporary determinants of biodiversity and ecosystem functions, which support previous findings. However, compared with the models excluding geological processes, inclusion of geological effects, such as parent rock and weathering, increases 67.9 and 35.9% of the explained variations in plant and bacterial communities, respectively. Such inclusion increases 27.6% of the explained variations in ecosystem functions. The geological processes thus provide additional links to ecosystem properties, which are prominent but show divergent effects on biodiversity and ecosystem functions: parent rock and weathering exert considerable direct effects on biodiversity, whereas indirectly influence ecosystem functions via interactions with biodiversity and contemporary environments. Thus, the integration of geological processes with environmental gradients could enhance our understanding of biodiversity and, ultimately, ecosystem functioning across different climatic zones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evaluating Integrated Surface/Subsurface Permafrost Thermal Hydrology Models in ATS (v0.88) Against Observations from a Polygonal Tundra Site: Modeling Archive

This Modeling Archive supports the NGEE Arctic publication "Evaluating integrated surface/subsurface permafrost thermal hydrology models in ATS (v0.88) against observations from a polygonal tundra site" available in the Geoscientific Model Development journal. We evaluated the integrated surface/subsurface permafrost thermal hydrology models in the Advanced Terrestrial Simulator (ATS) against field observations in polygonal tundra at the Barrow Environmental Observatory. We simulated thermal hydrology of three-dimensional ice-wedge polygons with generic but broadly representative surface microtopography. We drove the simulations with meteorological data and observed water table elevations in ice-wedge polygon troughs. With limited calibration of the soil properties and evaporation model parameters, the simulations were found to be consistent with observed water tables, active layer thickness, snowpack depth, evaporation, and observed soil temperatures at several depths in trough, rim, and center. The study builds confidence in the emerging class of integrated surface/subsurface permafrost simulators and provides an optimized set of model parameters for use in projections of permafrost dynamics in a warming climate. This dataset includes python, html, pdf, EXO, H5, xlsx, and csv files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Documenting the function of non-cultivated microorganisms in terrestrial ecosystems

The terrestrial biosphere contains a large fraction of global carbon and nearly 70% of the organic carbon in these systems is found in soils. Global changes in atmospheric CO 2 , temperature, precipitation, and ecosystem N inputs, are expected to impact primary production and carbon inputs to soils, but it remains difficult to predict the response of soil processes to anthropogenic change. Models that predict soil C-cycling as a function of ecosystem properties do not explain well variation in soil processes. Our difficulty in predicting the response of soil processes to environmental change suggests a need for a greater understanding of the biotic mechanisms that govern the soil C-cycle. Changes in microbial community structure and function have been proposed to impact soil C-cycling. However, our ability to predict the impacts of these changes on terrestrial ecosystems is constrained by our limited understanding of mechanisms that drive microbial processes in soil systems. We have applied a newly developed approach of Microbial Food Web Mapping to chart the carbon assimilation dynamics of microbial taxa in soils. This method made it possible to track different classes of 13C-labeled substrates into the DNA of nearly every member of the soil community simultaneously over time. Microbial Food Web Mapping was used to identify and characterize microbial taxa that fill key roles in the soil carbon cycle. We mapped the assimilation of carbon into thousands of discrete soil microorganisms as a function of soil carbon content and pH. In so doing we characterized assimilatory carbon metabolism, an essential component of soil C-cycling, for a vast array of uncultivated soil microorganisms. Further, we characterized the spatial and temporal dynamics of these microorganisms across a series of sites representing variation in soil characteristics. This project revealed fundamental aspects of soil C-cycling and provided ecological and metabolic insights on diverse uncultivated soil organisms that are widespread and play major roles in the global C-cycle. The genetic capacity of microbial communities can be studied through ‘omic approaches but it remains difficult to make direct links between the genetic capacity of microorganisms and their function in the soil C-cycle. Microbial Food Web Mapping makes it possible to link gene sequences to soil C-cycle processes as they occur in soil. This approach allows us to characterize the activity of non-cultivated microorganisms in a range of terrestrial systems. This data will be used to build a base of information about the role of non-cultivated organisms in critical C-cycle processes in terrestrial ecosystems and will provide insight on the manner in which soil communities metabolize soil organic matter. The results generated by this project will improve our ability to examine the impacts of management decisions, soil history, and environmental change on the behavior of microbial communities in terrestrial ecosystems, revealing the ecological mechanisms by which microbes regulate both C mineralization and carbon retention in soils, and improving our ability to predict changes in terrestrial ecosystem processes in the face of accelerating global change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Functional but not taxonomic diversity increases productivity of Populus in the southeastern United States

Plant interactions like competition and facilitation impact ecosystem function and resilience. Improving our understanding of the relationships between these interactions and community productivity has important implications for managers of production systems in forestry and agriculture as well as conservation science. Populus spp. are an excellent model system for exploring how inter‐ and intraspecific interactions impact ecosystem functions, such as productivity, in forest plantations. In this study, we compared aboveground productivity of six Populus clones from three different taxa grown in monoclonal and mixed‐clonal plots. The different mixture treatments were intended to experimentally test aboveground biomass response to contrasting levels of taxonomic diversity and functional diversity based on nitrogen use characteristics of Populus clones. We hypothesize that functional diversity would be more important than taxonomic diversity in increasing aboveground productivity of mixed‐clonal plantings compared to monocultures. In addition, a subset of treatments was carried out on additional sites representing a productivity gradient in order to determine if the relationship between biodiversity and productivity in these systems diminished at more productive sites as suggested by the stress‐gradient hypothesis. We found that functionally diverse mixtures of clones had greater yield of aboveground biomass than the average of their constituent monocultures, while more taxonomically diverse mixes of clones did not differ from the average of their constituent monocultures. However, when reestablished on sites with extremely high or low productivity, the best performing clone mixture also did not differ from the average of its constituent monocultures. Our results suggest that intimate clone mixtures of Populus have the potential to significantly increase productivity, but results vary by mixture and by site. To capitalize on positive biodiversity effects on yield in production systems, targeted mixtures based on divergent functional traits linked to different use and acquisition strategies for site‐specific limiting resources are most likely to be successful.

BEF↗
DOE OSTI2018

Land Surface Cooling Induced by Sulfate Geoengineering Constrained by Major Volcanic Eruptions

Based on observations of large volcanic eruptions1 and dedicated model simulations2, solar radiation management by stratospheric sulfate aerosol injection (SRM-SAI) is thought to be one of the most promising methods to counteract anthropogenic global warming3. However, coordinated simulations of SRM-SAI using Earth System Models (ESMs) reveal fundamental uncertainties in the climate response to geoengineering, including global cooling and any resulting risks to natural and human ecosystems. Here we identify an emergent relationship4,5,6,7 linking the long-term global land surface cooling due to SRM-SAI and the short-term cooling following the 20th century major volcanic eruptions across an ESM ensemble. This emergent relationship, combined with observations and reanalysis, is used to constrain the global land surface temperature (GLST) response to reduced downward solar radiation. Based on these constraints, we find that GLST decreases by approximately 0.43 K per W.m-2 ([0.18,0.67]), which is 22% smaller in magnitude than the model mean of an unconstrained ensemble of ESMs simulating SRM-SAI and 46% smaller in magnitude than some past estimates8. These new estimates may affect how trade-offs between cost, risk and effectiveness of SRM-SAI might be considered.

Plazzotta, Maxime↗
DOE OSTI2023

Assessing carbon storage capacity and saturation across six central US grasslands using data–model integration

Abstract. Future global changes will impact carbon (C) fluxes and pools in most terrestrial ecosystems and the feedback of terrestrial carbon cycling to atmospheric CO2. Determining the vulnerability of C in ecosystems to future environmental change is thus vital for targeted land management and policy. The C capacity of an ecosystem is a function of its C inputs (e.g., net primary productivity – NPP) and how long C remains in the system before being respired back to the atmosphere. The proportion of C capacity currently stored by an ecosystem (i.e., its C saturation) provides information about the potential for long-term C pools to be altered by environmental and land management regimes. We estimated C capacity, C saturation, NPP, and ecosystem C residence time in six US grasslands spanning temperature and precipitation gradients by integrating high temporal resolution C pool and flux data with a process-based C model. As expected, NPP across grasslands was strongly correlated with mean annual precipitation (MAP), yet C residence time was not related to MAP or mean annual temperature (MAT). We link soil temperature, soil moisture, and inherent C turnover rates (potentially due to microbial function and tissue quality) as determinants of carbon residence time. Overall, we found that intermediates between extremes in moisture and temperature had low C saturation, indicating that C in these grasslands may trend upwards and be buffered against global change impacts. Hot and dry grasslands had greatest C saturation due to both small C inputs through NPP and high C turnover rates during soil moisture conditions favorable for microbial activity. Additionally, leaching of soil C during monsoon events may lead to C loss. C saturation was also high in tallgrass prairie due to frequent fire that reduced inputs of aboveground plant material. Accordingly, we suggest that both hot, dry ecosystems and those frequently disturbed should be subject to careful land management and policy decisions to prevent losses of C stored in these systems.

58 GEOSCIENCES↗
DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗
DOE OSTI2015

Influence of explicit Phaeocystis parameterizations on the global distribution of marine dimethyl sulfide

Dimethyl sulfide (DMS) is a biogenic organosulfur compound which contributes strongly to marine aerosol mass and the determination of cloud condensation nuclei over the remote oceans. Since uncertainties in DMS flux to the atmosphere lead to large variations in climate forcing, the global DMS distribution has been the subject of increasingly complex dynamic simulations. DMS concentrations are directly controlled by marine ecosystems. Phaeocystis is a major DMS producer but is often omitted from global reduced sulfur mechanisms. Here we incorporate this phytoplankton group into the marine ecosystem-biogeochemical module of the Community Earth System Model. To examine its role in the ocean sulfur cycle, an earlier DMS model has been enhanced to include new knowledge gained over the last few years. Results from the baseline run show that simulated Phaeocystis biomass generally agrees with observations, with high concentrations near the Antarctic continent and between 50° and 60° north. Given the new explicit Phaeocystis representation, the DMS distribution shows significant improvements, especially regarding the amplitude and location of high-latitude peaks. The simulated global mean surface DMS value is 2.26 nM, comparable to an estimate of 2.34 nM from the latest climatology extrapolated based on observations. The total oceanic DMS source to the atmosphere is 20.4 Tg S/yr, on the low side of previous estimates. Comparisons with and without Phaeocystis show that the group dominates DMS distributions in temperate and cold waters, contributing 13% of the global flux. The proportion may increase as sea ice declines and should be considered in climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Advancing wildlife connectivity in land use planning: a case study with four-toed salamanders

Stable habitat connections that wildlife can safely traverse are essential to biodiversity conservation and healthy ecosystems. We developed high-resolution landscape connectivity models to predict resistance to movement by a threatened wetland-obligate amphibian, the four-toed salamander ( Hemidactylium scutatum ), and identified priority management areas on the 13,000-ha Department of Energy Oak Ridge Reservation (ORR) from 2019 to 2022. We developed a resistance surface based on aerial light detection and ranging data (LiDAR), >30 years of field-based mapping of forest, hydrologic, and geologic features, and contemporary population surveys, alongside derived predictors at <1-m resolution. We then modeled predicted movement corridors using a circuit theory-based modeling approach. We worked closely with land management and natural resources personnel to integrate ecological modeling with broader land use priorities, monetary costs, and feasibility. We identified important terrestrial and aquatic areas on ORR and simulated management scenarios to promote stable connections for four-toed salamanders. This approach allowed us to narrow down a list of 438 potential habitat manipulation sites to 10 sites where open-bottomed culverts and buffers could be implemented. This smaller-scale restoration approach produced a similar increase in landscape connectivity while costing <20% of a larger-scale approach based on barrier removal. We successfully identified feasible, cost-effective management strategies that integrated knowledge from a variety of sources. In conclusion, we offer a strategy that permitted integration of wildlife management goals into infrastructure upgrades wherein wildlife was not an initial consideration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Influence of sun zenith angle on canopy clumping and the resulting impacts on photosynthesis

Addressing the impact of vegetation architecture on shortwave radiation transfer in land surface models is important for accurate weather forecasting, carbon budget estimates, and climate predictions. This paper investigates to what extent it is possible to retrieve structural parameters of two different parameterization schemes from direct transmittance derived from digital hemispherical photography and 3D radiative transfer modeling for two study sites with different vegetation canopy architectures. Neglecting the representation of 3D canopy structure in radiative transfer schemes leads to significant errors in shortwave radiation partitioning (up to 3.5 times more direct transmittance in the 3D model). Structural parameters, referred to as whole canopy ‘clumping indices’, were obtained in order to evaluate the impact of angular variation in clumping on shortwave radiation transfer. Impacts on photosynthesis were evaluated at site level with the UKESM land surface model, JULES. A comparison between flux tower derived and modeled photosynthesis indicates that considering zenith angular variations of structural parameters in the radiative transfer scheme of the UKESM land surface model significantly improves photosynthesis prediction in light limited ecosystems (from RMSE = 2.91 μmol CO 2 .m -2 .s -1 to RMSE = 1.51 μmol CO 2 .m -2 .s -1 , 48% smaller), typically with enhanced photosynthesis from bottom layers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Innovative urban forestry governance in Melbourne?: Investigating “green placemaking” as a nature-based solution

Highlights: • Scholars have called for a re-assessment of urban environmental governance. • We examine the City of Melbourne's Urban Forest Strategy (UFS). • Co-governance arrangements in the UFS foster situated decision making pluralism. • Melbourne’s UFS links aims of biodiversity to those of socio-ecological resilience. A nature-based approach to climate resilience aims to challenge and re-frame conventional environmental management methods by refocusing solutions from technological strategies to socio-ecological principles such as human well-being and community-based governance models, thereby improving and legitimizing the delivery of ecosystem services (ES). There are, however, many challenges to applying a socio-ecological agenda to urban climate resilience and thereby re-framing ES delivery as community and people focused, a knowledge gap extensively outlined in the environmental governance literature. In this paper, we aim to contribute to this re-assesment of urban environmental governance by examining the City of Melbourne's approach to urban re-naturing governance from a place-based perspective. Here we focus on the city's internationally-acclaimed urban forest strategy (UFS), investigating how and to which extent the governance arrangements embedded within the UFS draw strength from diverse perspectives and allow for institutional arrangements that support “situated” reflexive decision making and co-creation. We find that Melbourne's UFS governance process fosters green placemaking by re-focusing climate adaptation solutions from technological strategies to situated socio-ecological principles such as human well-being and community-based decision making. In this sense, this case provides valuable insight for the broader UGI governance field regarding the opportunities and challenges associated with a socio-cultural approach to urban re-naturing and ES delivery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

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 datasets 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 (R 2 ) 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 R 2 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.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Stressors Reveal Ecosystems' Hidden Characteristics

Abstract Vegetation responds dynamically to local microclimates at both short and long time scales via mechanisms ranging from physiological behaviors, such as stomatal closure, to acclimation and adaptation. These responses influence the carbon, water, and energy cycles directly and are therefore crucial to understanding and predicting Earth system responses to a changing climate. Several recent studies have demonstrated that differences in microclimate can induce structural and functional acclimations, and potentially adaptations, within the same ecosystem. Such microclimate divergence can be caused by variability in slopes, disturbance history, or even localized resource availability. Ecosystem stressors such as low soil water availability, limited photoperiod, or high vapor pressure deficit have been shown to reveal the large impact of the subtle differences within these systems such as the number of sun versus shade leaves or differences in whole‐plant water acquisition and use. These findings highlight the linkages between plant canopy structure and ecosystem function, alongside the need for comprehensive analyses of vegetation within the broader context of its environment. This commentary addresses some of the key implications of ecosystem stress responses and accompanying acclimations across three ecosystem types for ecosystem ecology, plant physiology, ecohydrology and trait‐based modeling of vegetation‐climate dynamics.

Matheny, Ashley M.↗