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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

TropiRoot 1.0: Database of tropical root characteristics across environments

Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.

FRED↗
DOE OSTI2024

Regulation of Electron Cloud Density by Electronic Effects of Substituents to Optimize Photocatalytic H 2 Evolution of Carbon Dots

Carbon dots (CDs) have a wide light absorption range, which are promising candidates for photocatalytic water splitting H 2 evolution, but they show low activity for hydrogen evolution reaction (HER) due to the slow transfer efficiencies of photogenerated carriers. The electron cloud density of photocatalyst is essential for the separation and migration of photogenerated carriers. In this study, the effect mechanism of electron cloud density regulated by the substituents with different electronic effects on the photocatalytic HER of glucose-based CDs is clarified. CDs-SO 3 H and CDs-OH are first obtained by introducing electron-drawing group (–SO 3 H) and electron-donating group (–OH) using a tailoring post-processing strategy. Experimental results show that the H 2 yield catalyzed by CDs-SO 3 H is 89.95 µmol•g –1 in 4 h, which is about four times that of CDs, and 7.4 times that of CDs-OH. The –SO 3 H induces a much negative energy band and high electron cloud density on CDs edges, promoting the separation and transfer of photogenerated carriers; while the CDs-OH exhibits a high positive charge density and an upward energy band, hindering the surface complexation of electron-hole pairs and HER. Furthermore, this study will provide an insight into the design of CDs catalysts with efficient photocatalytic HER at a molecular level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tree-level carbon stock estimations across diverse species using multi-source remote sensing integration

Forests are critical carbon sinks, and remote sensing has been increasingly widely used for forest monitoring and biomass estimations. However, species-specific tree-level studies remain limited. In this study, we demonstrated the feasibility of integrating UAV-based LiDAR with high-resolution optical satellite imagery (0.5 m) to estimate biomass for individual trees across different species. The proposed method accurately estimated biomass for 53 trees (R² = 0.82, rRMSE = 0.44), with species-specific datasets, showing an average 25.2% increase in R² and a 14.8% reduction in rRMSE. A novel vegetation index combining forest structure parameters with vegetation indices (VIs) was developed using high-resolution multispectral satellite data (3 m) to explore its relationship with individual tree biomass. Combining forest structural parameters with VIs further improved estimation accuracy, achieving an R²of 0.89 and an rRMSE of 0.34. Species-specific datasets show an 11.6% increase in R²compared to methods without VIs, and a 22.2% improvement over methods using only VIs. SHapley Additive exPlanations (SHAP) analysis shows that the volume feature played a key role in model performance and remained stable throughout the training process. Altogether, the proposed approach enhances individual tree biomass and carbon sink estimations, showing great potential for large-scale precise forest carbon monitoring using multi-source remote sensing data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management

Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources worldwide. Tracking and mitigating GHG emissions require a pivot to operational monitoring of regional carbon flux and stock changes. The current GHG observing system is addressing needs at two distinct scales: 1) Local scale (< 1 km), related to anthropogenic point source emissions, and 2) global scales (> 1000 km), related to land and ocean carbon sinks. More focus on intermediate (10–1000 km) scales is needed to more effectively monitor progress in reducing carbon emissions, enhancing removals, and maintaining sinks. Representatives from carbon cycle biomass and flux communities across United States government agencies and academic institutions met in September 2024 to discuss the rationale and scientific context for more effectively implementing an operational system for GHG monitoring in support of urban and national carbon management needs. To guide development of this system, we propose a multi-tiered global spaceborne observing framework for carbon flux and stock, prioritizing: 1) frequent GHG partial columns for carbon emissions and removals; 2) continuous time series and data fusion of biomass from Lidar and Synthetic Aperture Radar (SAR) for carbon stocks, and 3) expanded coverage of tropical, high latitude, and oceanic regions to monitor carbon cycle tipping points and feedbacks. This system should be complemented by expanded surface and airborne networks for oceanic and terrestrial/aquatic ecosystems for calibration, ground truthing, and study of under-sampled regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Borate-assisted alkaline extraction of hemicellulose from switchgrass with enhanced structural stability and purity

Valorization of non-cellulosic polysaccharides is crucial for enhancing the economic competitiveness of biorefinery processes. In this study, a mixture of boric acid and sodium hydroxide was employed to efficiently extract hemicellulose from holocellulose switchgrass. Borate-assisted alkaline extraction resulted in a higher xylan content (59.5 %) compared to conventional alkaline extraction. Here, the hemicellulose fractions derived from the borate-alkaline treatment exhibited a higher molecular weight (M w = 51.2 kDa) and a relatively lower degree of polydispersity (1.28), indicating improved structural stability. The presence of borate had a protective effect against chain scission, preserving glucuronic acid residues and increasing galactose content. Additionally, borate improved hemicellulose purity, with up to 74.1 % of the extracted hemicellulose being suitable for further enzymatic applications. Extended extraction time further enhanced hemicellulose recovery, reaching 97.9 % under NaOH/boric acid conditions while maintaining structural integrity, as confirmed by SEM, FTIR and 2D HSQC NMR analyses. These findings provide insights into the role of borate in optimizing hemicellulose extraction and improving its potential for bioconversion processes.

Borate alkaline↗
DOE OSTI2025

Identifying Receptor Kinase Substrates Using an 8000 Peptide Kinase Client Library Enriched for Conserved Phosphorylation Sites

In eukaryotic organisms, protein kinases regulate diverse protein activities and signaling pathways through phosphorylation of specific protein substrates. Isolating and characterizing kinase substrates is vital for defining downstream signaling pathways. The kinase-client (KiC) assay is an in vitro synthetic peptide LC-MS/MS phosphorylation assay that has enabled identification of protein substrates (i.e., clients) for various protein kinases. For example, previous use of a 2100-member (2k) peptide library identified substrates for the extracellular ATP receptor-like kinase, P2K1. Many P2K1 clients were confirmed by additional in vitro and in planta studies, including integrin-linked kinase 4, for which we provide the evidence herein. In addition, we developed a new KiC peptide library containing 8000 (8k) peptides based on phosphorylation sites primarily from Arabidopsis thaliana datasets. The 8k peptides are enriched for sites with conservation in other angiosperm plants, with the paired goals of representing functionally conserved sites and usefulness for screening kinases from diverse plants. Screening the 8k library with the active P2K1 kinase domain identified 177 phosphopeptides, including calcineurin B–like protein and G protein alpha subunit 1, which functions in cellular calcium signaling. We confirmed that P2K1 directly phosphorylates calcineurin B–like protein and G protein alpha subunit 1 through in vitro kinase assays. This expanded 8k KiC assay will be a useful tool for identifying novel substrates across diverse plant protein kinases, ultimately facilitating the exploration of previously undiscovered signaling pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment

Cataloging and classifying trees in the urban environment is a crucial step in urban and environmental planning; however, manual collection and maintenance of this data is expensive and time-consuming. Although algorithmic approaches that rely on remote sensing data have been developed for tree detection in forests, they generally struggle in the more varied urban environment. This work proposes a novel end-to-end deep learning method for the detection of trees in the urban environment from remote sensing data. Specifically, we develop and train a novel PointNet-based neural network architecture to predict tree locations directly from LiDAR data augmented with multi-spectral imagery. We compare this model to a number of high-performing baselines on a large and varied dataset in the Southern California region, and find that our method outperforms all baselines in terms of tree detection ability (75.5% F-score) and positional accuracy (2.28 meter root mean squared error), while being highly efficient. We then analyze and compare the sources of errors, and how these reveal the strengths and weaknesses of each approach. Our results highlight the importance of fusing spectral and structural information for remote sensing tasks in complex urban environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

RSPECT: A PROSPECT-based model incorporating the real structure of rice leaves

Radiative transfer models (RTMs) describe how light is absorbed, scattered, and transmitted within leaves by simulating mechanistic light propagation processes. The PROSPECT model is based on measurable parameters (the leaf biochemical content) and a non-measurable parameter (the leaf anatomical structure represented by the leaf structure parameter (N)). The effect of N on the optical properties of leaves has been investigated through a number of local and global sensitivity analyses. Other studies have directly evaluated the effect of the leaf anatomical structure on spectral reflectance, particularly in the near infrared region. However, the relationship between N and the anatomical structure is unclear. Here, in this study, we leveraged eLeaf, a ray tracing-based 3D rice leaf simulator, to establish relationships between leaf anatomical features and spectral properties, enabling us to replace N in the PROSPECT-4 model with measurable leaf anatomical parameters and develop the RSPECT model. The leaf thickness at minor vein, leaf thickness at bulliform cells, mesophyll thickness at minor vein, and distance between two minor veins could be used to predict N effectively. The RSPECT model achieved spectral simulation accuracy comparable to PROSPECT-4 and was more suitable for parameter inversion of the physical and chemical properties of rice leaves, with relative root mean square errors of 7.4% for chlorophyll content, 5.6% for equivalent water thickness, and 7.5% for dry matter content. In conclusion, RSPECT improves radiative transfer modeling by integrating measurable anatomical features and provides a framework for extending this approach to other vegetation types.

hyperspectral↗
DOE OSTI2025

Strategies to mitigate urban heat: Effects on overheating and cloud formation

This study evaluates the effectiveness of various urban heat island (UHI) overheating mitigation strategies and their associated impacts on urban cloud dynamics and thermal processes. This study shows cloud-resolving and urban-resolving modeling results estimating the impact of Houston-Galveston heat mitigation scenarios and other resilient strategies contemplated in the Climate Adaptation Plan and Resilient Houston reports. The simulated scenarios include high intensity green rooftops, rooftop photovoltaic solar panels, enhanced urban irrigation, white/cool roofs and roads, and street trees. We contrast the adaptation scenarios against a present baseline case, a no city scenario and a larger and denser city as projected by the Houston-Galveston Area Council for 2045. During the daytime, cooling strategies such as cool roofs, cool roads, and green roofs exhibit superior performance in mitigating urban overheating. At night, enhanced urban irrigation emerges as the most effective cooling intervention. Cooling strategies significantly reduce sensible heat flux partitioning during the day, a process that reduces the uplift of air, suppressing the formation of urban shallow cumulus clouds. The extent of urban cloud mixing ratio is reduced in proportion to the decrease in sensible heating. Under the BEP-Tree scenario, which includes wind effects and evapotranspiration driven by a stomatal conductance model, urban trees demonstrated negligible environmental cooling effects and minimal urban cloud modifications. In contrast, the scenarios with more urban cooling and higher latent heat fluxes led to suppressed urban clouds. The net cooling effect achieved by the heat mitigation strategies is influenced by a combination of indirect processes, including the reduction of downwelling longwave radiation flux, due to reduced cloud presence, while some warming is attributed to a modest increase in shortwave radiation that offsets the cooling benefit. Additionally, reduced heat dissipation, weakened thermal gradients, and diminished vertical mixing over urban areas further moderate the cooling potential. These findings highlight the pivotal role of clouds and moist atmospheric processes in shaping the UHI effect and offer insights for designing more effective urban cooling strategies.

albedo changes↗
DOE OSTI2025

A U.S. Scientific Community Vision for Sustained Earth Observations of Greenhouse Gases to Support Local to Global Action

Managing carbon stocks in the land, ocean, and atmosphere under changing climate requires a globally‐integrated view of carbon cycle processes at local and regional scales. The growing Earth Observation (EO) record is the backbone of this multi‐scale system, providing local information with discrete coverage from surface measurements and regional information at global scale from satellites. Carbon flux information, anchored by inverse estimates from spaceborne Greenhouse Gas (GHG) concentrations, provides an important top‐down view of carbon emissions and sinks, but currently lacks global continuity at assessment and management scales (<100 km). Partial‐column data can help separate signals in the boundary layer from the overlying atmosphere, providing an opportunity to enhance surface sensitivity and bring flux resolution down from that of column‐integrated data (100–500 km). Based on a workshop held in September 2024, the carbon cycle community envisions a carbon observation system leveraging GHG partial columns in the lower and upper troposphere to weave together information across scales from surface and satellite EO data, and integration of top‐down/bottom‐up analyses to link process understanding to global assessment.

Parazoo, Nicholas C. [California Institute of Tech↗
DOE OSTI2026

Trees halve urban heat island effect globally but unequal benefits only modestly mitigate climate-change warming

Although tree cover reduces the urban heat island, no global estimate quantifies air temperature reductions by contemporary or future tree cover, currently and with climate change. Here, we estimate these reductions for all 8,919 large urban areas. Current urban tree cover mitigates 41–49% of the maximum potential air-temperature urban heat island that would occur in the absence of tree canopy. Tree canopy reduces summer air temperature by a population-weighted mean of 0.15 ± 0.03 °C, with wide variation (0.0–2.7 °C), benefiting 914 (805–1040, 95% CI) million people by >0.25 °C. Cooling benefits are greater in already cooler areas: high-income countries and suburbs. Current and plausible future tree cover mitigate only ~10% (6.7–18% and 6.3–17%, respectively) of the median mid-century climate-change warming under a moderate emission scenario. Our results suggest tree canopy expansion in densely settled low-income urban areas is necessary for equitable urban heat island mitigation and climate adaptation.

McDonald, Robert I. [The Nature Conservancy (TNC),↗
DOE OSTI2025

Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes

Increasing leaf area and extending vegetation growing seasons are two primary drivers of global greening, which has emerged as one of the most significant responses to climate change. However, it remains unclear how these two leaf acclimation strategies would vary across forests at a large spatial scale. Here, in this study, using multiple satellite-based datasets and field measurements, we analysed the temporal changes (Δ) in maximal leaf area index (LAI max ) and length of the growing season (LOS) from 2002 to 2021 across deciduous broadleaf forests (DBFs) in the middle to high latitudes. Contrary to the widely held assumption of coordination, our results revealed a negative correlation between ΔLAI max and ΔLOS. Notably, the trade-offs between ΔLAImax and ΔLOS were strongly explained by stand age. Younger DBFs, with lower baseline LAI max , predominantly located in eastern Asia, displayed an increase in LAI max with small changes in LOS. This acquisitive strategy facilitated younger DBFs to grow more photosynthetically efficient leaves with low leaf mass per area, enhancing their light use efficiency. Conversely, older DBFs with a higher baseline LAI max , primarily located in North America and Europe, extended their LOS by increasing leaf mass per area. This conservative strategy facilitated older DBFs to produce thicker, but less photosynthetically efficient leaves, resulting in decreased light use efficiency. Our findings offer new insights into the contrasting changes in leaf area and growing season length and highlight their divergent impacts on ecosystem functioning.

Wang, Fangyi [Sun Yat-Sen Univ., Zhuhai (China)]↗