Search NASA⌕ Search

SEARCH · Search NASA

Results for “Leaf”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Global variation in the fraction of leaf nitrogen allocated to photosynthesis

Plants invest a considerable amount of leaf nitrogen in the photosynthetic enzyme ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO), forming a strong coupling of nitrogen and photosynthetic capacity. Variability in the nitrogen-photosynthesis relationship indicates different nitrogen use strategies of plants (i.e., the fraction nitrogen allocated to RuBisCO; fLNR), however, the reason for this remains unclear as widely different nitrogen use strategies are adopted in photosynthesis models. Here, we use a comprehensive database of in situ observations, a remote sensing product of leaf chlorophyll and ancillary climate and soil data, to examine the global distribution in fLNR using a random forest model. We find global fLNR is 18.2 ± 6.2%, with its variation largely driven by negative dependence on leaf mass per area and positive dependence on leaf phosphorus. Some climate and soil factors (i.e., light, atmospheric dryness, soil pH, and sand) have considerable positive influences on fLNR regionally. This study provides insight into the nitrogen-photosynthesis relationship of plants globally and an improved understanding of the global distribution of photosynthetic potential.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Protein coating composition targets nanoparticles to leaf stomata and trichomes

Plant nanobiotechnology has the potential to revolutionize agriculture. However, the lack of effective methods to deliver nanoparticles (NPs) to the precise locations in plants where they are needed impedes these technological innovations. Here, model gold nanoparticles (AuNP) were coated with citrate, bovine serum albumin (BSA) as a protein control, or LM6-M, an antibody with an affinity for functional groups unique to stomata on leaf surfaces to deliver the AuNPs to stomata. One-month-old Vicia faba leaves were exposed via drop deposition to aqueous suspensions of LM6-M-coated AuNPs and allowed to air dry. After rinsing, Au distribution on the leaf surface was investigated by enhanced dark-field microscopy and X-ray fluorescence mapping. While citrate-coated AuNPs randomly covered the plant leaves, LM6M-AuNPs strongly adhered to the stomata and remained on the leaf surface after rinsing, and BSA-AuNPs specifically targeted trichome hairs. To the authors’ knowledge, this is the first report of active targeting of live leaf structures using NPs coated with molecular recognition molecules. Finally, this proof-of-concept study provides a strategy for future targeted nanopesticide delivery research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Observed declines in leaf nitrogen explained by photosynthetic acclimation to CO 2

Widespread evidence of decreasing leaf nutrients has raised concerns about ecosystem productivity under global change. Interpreting trends in leaf nutrients has important implications for the fate of ecosystem services, particularly the role of forests in mitigating climate change and sustaining quality food sources. Here, we challenge the common interpretation that decreasing leaf nitrogen concentration (LNC) is evidence of increasing nutrient limitations on ecosystem primary productivity. Instead, we show that declines in LNC (4% decrease per 50 ppm CO 2 increase), observed across 409 European forest plots over 22 y, can be explained by reduced photosynthetic nitrogen demand. This regional trend is consistent with leaf acclimation to increasing atmospheric CO 2 according to optimality theory. This finding suggests that enhanced photosynthetic nitrogen use efficiency due to CO 2 fertilization may lead to less nitrogen uptake and/or reallocation of nitrogen for plant growth and other functions. Our results have large implications for understanding and simulating interactions between ecosystem nitrogen and carbon cycles and suggest nitrogen requirements for terrestrial carbon uptake under elevated CO 2 may be lower than previously thought.

CO2 fertilization↗
DOE OSTI2022

Photosynthetic decline in aging perennial grass is not fully explained by leaf nitrogen

Abstract Aging in perennial plants is traditionally observed in terms of changes in end-of-season biomass; however, the driving phenological and physiological changes are poorly understood. We found that 3-year-old (mature) stands of the perennial grass Miscanthus×giganteus had 19–30% lower Anet than 1-year-old M.×giganteus (juvenile) stands; 10–34% lower maximum carboxylation rates of Rubisco and 34% lower light-saturated Anet (Asat). These changes could be related to nitrogen (N) limitations, as mature plants were larger and had 14–34% lower leaf N on an area basis (Na) than juveniles. However, N fertilization restored Na to juvenile levels but compensated only 50% of the observed decline in leaf photosynthesis with age. Comparison of leaf photosynthesis per unit of leaf N (PNUE) showed that mature stands had at least 26% lower PNUE than juvenile stands across all N fertilization rates, suggesting that other factors, besides N, may be limiting photosynthesis in mature stands. We hypothesize that sink limitations in mature stands could be causing feedback inhibition of photosynthesis which is associated with the age-related decline in photosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Altered cell wall hydroxycinnamate composition impacts leaf- and canopy-level CO2 uptake and water use in rice

Abstract Cell wall properties play a major role in determining photosynthetic carbon uptake and water use through their impact on mesophyll conductance (CO2 diffusion from substomatal cavities into photosynthetic mesophyll cells) and leaf hydraulic conductance (water movement from xylem, through leaf tissue, to stomata). Consequently, modification of cell wall (CW) properties might help improve photosynthesis and crop water use efficiency (WUE). We tested this using 2 independent transgenic rice (Oryza sativa) lines overexpressing the rice OsAT10 gene (encoding a “BAHD” CoA acyltransferase), which alters CW hydroxycinnamic acid content (more para-coumaric acid and less ferulic acid). Plants were grown under high and low water levels, and traits related to leaf anatomy, CW composition, gas exchange, hydraulics, plant biomass, and canopy-level water use were measured. Alteration of hydroxycinnamic acid content led to statistically significant decreases in mesophyll CW thickness (−14%) and increased mesophyll conductance (+120%) and photosynthesis (+22%). However, concomitant increases in stomatal conductance negated the increased photosynthesis, resulting in no change in intrinsic WUE (ratio of photosynthesis to stomatal conductance). Leaf hydraulic conductance was also unchanged; however, transgenic plants showed small but statistically significant increases in aboveground biomass (AGB) (+12.5%) and canopy-level WUE (+8.8%; ratio of AGB to water used) and performed better under low water levels than wild-type plants. Our results demonstrate that changes in CW composition, specifically hydroxycinnamic acid content, can increase mesophyll conductance and photosynthesis in C3 cereal crops such as rice. However, attempts to improve photosynthetic WUE will need to enhance mesophyll conductance and photosynthesis while maintaining or decreasing stomatal conductance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Source:sink imbalance detected with leaf and canopy-level spectroscopy in a field-grown crop

The finely-tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. Here, we measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38%; free amino acids increased 22%. Partial least-squares regression models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R 2 values of 0.93 and 0.64 and %RMSE of 13% and 38% respectively. For LMA, R 2 values were 0.91 and 0.60 and %RMSE 7% and 14%; for free amino acids, R 2 was 0.53 and 0.21 with %RMSE 20% and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Data for The Leaf Economics Spectrum of Triploid and Tetraploid C4 Grass Miscanthus x giganteus

The leaf economics spectrum (LES) describes multivariate correlations in leaf structural, physiological and chemical traits, originally based on diverse C3 species grown under natural ecosystems. However, the specific contribution of C4 species to the global LES is studied less widely. C4 species have a CO2 concentrating mechanism which drives high rates of photosynthesis and improves resource use efficiency, thus potentially pushing them towards the edge of the LES. Here, we measured foliage morphology, structure, photosynthesis, and nutrient content for hundreds of genotypes of the C4 grass Miscanthus × giganteus grown in two common gardens over two seasons. We show substantial trait variations across M. × giganteus genotypes and robust genotypic trait relationships. Compared to the global LES, M. × giganteus genotypes had higher photosynthetic rates, lower stomatal conductance, and less nitrogen content, indicating greater water and photosynthetic nitrogen use efficiency in the C4 species. Additionally, tetraploid genotypes produced thicker leaves with greater leaf mass per area and lower leaf density than triploid genotypes. By expanding the LES relationships across C3 species to include C4 crops, these findings highlight that M. × giganteus occupies the boundary of the global LES and suggest the potential for ploidy to alter LES traits.

Biomass Analytics↗
DOE OSTI2022

Leaf sample detail, Feb2016-May2016, PA-SLZ, PA-PNM, PA-BCI: Panama

This data package contains details of the date, location, species and photographs of leaf samples collected on a monthly basis from Feb to May 2016 from Parque Natural Metropolitano (PA-PNM), Barro Colorado Island (PA-BCI) and Bosque Protector San Lorenzo (PA-SLZ) in Panama. Data from BCI only available for March. This data was collected as part of the 2016 El Niño-Southern Oscillation (ENSO) campaign. Included in this data package are an Excel file with data (2016ENSO_Panama_LeafSamples) and two Excel files with associated metadata. Sample photos are included in five zip files, organized by month and site. Also included is a Word document (Metadata_description_2016_ENSO_Panama) with details such as data collection methods, equipment used, and site information. Data to be used as a reference to linking related datasets including leaf water potential, leaf spectra, LMA, gas exchange and leaf chemistry (CHN, NSC). Most leaves were sampled from sunlit canopy trees. VERSION 2 update. The identification of a species from the PNM site has been corrected as follows: the identification of the tree initially identified as Pseudosamanea guachapele (ALBIED) has been revised to Albizia adinocephala (ALBIAD). The updated data package includes revised data, metadata and protocol documents updated to reflect this change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Diurnal leaf gas exchange survey, Feb2016-May2016, PA-SLZ, PA-PNM: Panama

This data package contains the results of a diurnal leaf gas exchange survey measured on sunlit canopy trees within the NGEE Tropics sites Parque Natural Metropolitano (PA-PNM) and Bosque Protector San Lorenzo (PA-SLZ) in Panama. Measurements were taken on a monthly basis from February to May of 2016. This data was collected as part of the 2016 El Niño-Southern Oscillation (ENSO) campaign. Included in this data package are two Excel files with data (2016ENSO_Panama_DiurnalGasEx, 2016ENSO_Panama_AreaCorrections) and additional Excel files with associated metadata. Also included is a Word document (Metadata_description_2016_ENSO_Panama) with details such as data collection methods, equipment used, and site information. See related datasets for further sample details, leaf water potential, LMA, leaf spectra, other gas exchange and leaf chemistry. VERSION 2 update. The identification of a species from the PNM site has been corrected as follows: the identification of the tree initially identified as Pseudosamanea guachapele (ALBIED) has been revised to Albizia adinocephala (ALBIAD). The updated data package includes revised data, metadata and protocol documents updated to reflect this change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Leaf mass area, Feb2016-May2016, PA-SLZ, PA-PNM, PA-BCI: Panama

This data package contains leaf mass data per unit area measured on a monthly basis from February to April, 2016, at the Bosque Protector San Lorenzo (PA-SLZ) and Parque Natural Metropolitano (PA-PNM) sites in Panama. Data from the Barro Colorado Island (PA-BCI) site are only available for March. This data was collected as part of the 2016 El Niño-Southern Oscillation (ENSO) campaign. Included in this data package are an Excel file with data (2016ENSO_Panama_LMA1) and two Excel files with associated metadata. Also included is a Word document (Metadata_description_2016_ENSO_Panama) with details such as data collection methods, equipment used, and site information. See related datasets for further sample details, leaf water potential, leaf spectra, gas exchange and leaf chemistry. VERSION 2 update. The identification of a species from the PNM site has been corrected as follows: the identification of the tree initially identified as Pseudosamanea guachapele (ALBIED) has been revised to Albizia adinocephala (ALBIAD). The updated data package includes revised data, metadata and protocol documents updated to reflect this change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Leaf gas exchange raw data, 2015 - 2017, at Manaus, Brazil

This data package contains raw leaf gas exchange data from the NGEE Tropics K34 tower site on a plateau near Manaus, Brazil. Data was collected using sensor and leaf sample measurements. Leaves on a target tree were chosen by accessing a branch near the tower. In some cases, the branch was naturally positioned near the tower and in other cases, a small rope was used to pull the branch closer to the tower for better access to leaves. Mature leaves were targeted in nearly all cases. The gas exchange system was hauled with a climbing rope to the level of the branch on the tower and the sensor head/leaf enclosure was installed near the branch of interest using a magic arm. We measured leaf gas exchange from 5:00 AM to 5:00 PM, randomly cycling through the approximately 20 leaves in the targeted branch(es). Data were collected over two time periods, detailed within the files. The attached data files contain raw data, sensor manuals, and PDFs with detailed methods and procedures separated into multiple folders. Data is in Excel file format. See data references for data package containing related metadata. This dataset replaces the leaf gas exchange data of two retired packages, NGT0019 and NGT0040.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Leaf-to-canopy spectral reflectance, February 2018, PA-SLZ: Panama

Canopy reflectance spectra of 37 trees were measured at the Smithsonian Tropical Research Institute (STRI) San Lorenzo Protected Area site (PA-SLZ), utilising the canopy access crane to access fully sunlit leaves. Data were recorded in the middle of the day, with 20–50 measurements for each tree. The canopy structure for each crown was captured using a digital camera. Leaf-level reflectance and leaf demographic composition (age class) was recorded in detail for a > 0.5 meter branch sampled from each canopy tree (corresponding to the position where canopy spectra were measured). This dataset includes processed canopy and leaf-level spectral measurements (*.xlsx), raw spectral data (Spectral Evolution PSR+, *.raw, *.sed), sample details (*.xlsx) and *.pdf summaries containing low resolution photographs of the measured canopies. Full resolution images are available in the related dataset, "Leaf-to-canopy spectral reflectance photographs, February 2018, PA-SLZ: Panama".

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Pre-dawn leaf water potential, San Lorenzo, Panama, 2020

This data package contains predawn leaf water potential (LWP) data for leaves sampled in the San Lorenzo forest canopy crane site in Panama (PA-SLZ) from January to March 2020. Data were collected from 31 species, from top of canopy and vertical profiles within the canopy. All data and metadata are presented in .csv files. The protocol details are provided as *.pdf. See related data packages from the BNL 2020 field campaign for leaf optical properties and gas exchange measurements. Sample information including canopy elevation and leaf area index (LAI) can be found in the related “Leaf and canopy traits” data package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Photosynthetic and stomatal response, and leaf traits, of two pine species, Talladega National Forest, Alabama, 2023

Photosynthetic response to irradiance (AQ), CO2 (ACi) and stomatal response to irradiance measurements on two pine species common to the southeast United States, long leaf pine, Pinus palustris Mill., and loblolly pine, Pinus taeda L.. Data were collected in June 2023 within the Oakmulgee District of the Talladega National Forest in west-central Alabama at the NEON site (TALL). Measurements were made with four LI-COR LI-6800 and one LI-COR LI-6400XT gas exchange systems. ACi and AQ curves enable the estimation of photosynthetic parameters (e.g., Vcmax, Jmax, Rd, aQY), while stomatal response curves enable the estimation of stomatal slope and intercept parameters. Foliar trait data (needle dimensions, leaf mass per area, leaf nitrogen concentration) is provided for all gas exchange samples. The data package files include multiple raw and processed data files (csv), metadata files (csv), the complete instrument output for all measurements in .zip format, and a pdf describing the experimental protocol.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-resolution leaf area index maps generated from unoccupied aerial system, Teller Mile 27, Seward Peninsula, Alaska

Leaf area index (LAI), a measure of the amount of one-side leaf area per ground unit, is an important indicator of plant carbon, energy, and water cycle. In the heterogeneous Arctic landscapes, it has been challenging to accurately measure LAI across species and space needed for Earth system model validation. Here, we use multispectral unoccupied aerial systems (UASs) to scale up and map leaf area index (LAI) , in a low-Arctic tundra landscape on the Seward Peninsula, Alaska. We linked previous published LAI measurements with high-resolution, UAS-collected multispectral data collected over the region of Next Generation Ecosystem Experiments in the Arctic (NGEE Arctic)’s Teller Mile Maker 27 site in 2022 to develop random forest (RF) machine learning models to predict and map LAI. 100 RF models were developed to account for uncertainties in ground LAI plot measurements and process scaling. This dataset includes a raster (*.tif) map of the mean LAI value of the 100 RF models, a raster (*.tif) map of the standard deviation of the RF-modeled LAI data, and a user guide (*.pdf).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Root and Leaf Traits of Alfalfa Exhibit Distinct Responses to Soil Microbial Communities and Environmental Stresses

Ongoing climate change is negatively impacting crop productivity globally. Past research has highlighted that a diverse soil microbial community and variation in plant traits for resource acquisition can mitigate the negative impacts of climate change factors on crop productivity. This study investigates the effects of two major environmental stressors—drought and salinity stress, on plant productivity, biomass allocation, and root and leaf trait responses under distinct soil microbial diversities. Our results showed that salinity stress had stronger negative impacts on plant productivity than drought stress. Shoot biomass decreased by 30% and 32.5% under drought and salinity stress, respectively, whereas the root biomass decreased by 32% only under salinity stress. Soil microbial diversity did not affect plant productivity. Next, root traits were mainly impacted by drought and salinity stress, whereas leaf traits were impacted by both environmental stresses and soil microbial diversity. Specific root length and specific root area decreased under drought, and root tissue density was minimal under salinity stress. Root traits were not affected by soil microbial communities. In contrast, the leaf nitrogen content increased, whereas pheophytin content (a breakdown product of chlorophyll) decreased when plants were grown in diverse microbial communities under environmental stresses, especially drought. These results highlight the importance of soil microbial diversity in impacting plant traits in response to environmental stresses. We showed that the soil microbial diversity influences both aboveground and belowground plant traits, indicating the need for better management practices to conserve and promote soil microbial diversity.

59 BASIC BIOLOGICAL SCIENCES↗