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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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16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Unveiling the Transferability of PLSR Models for Leaf Trait Estimation: Lessons from a Comprehensive Analysis with a Novel Global Dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least-squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47,393 records for >700 species and eight PFTs at 101 globally-distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55% and 7.0-33.12%) compared to nonspatial random cross-validation (NRCV). The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs and time due to biased validation schemes and provide guidance for future field sampling strategies and remote sensing applications.

Leaf traits↗
NASA NTRS1988

Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy

The chemical content of dry, ground leaf material sampled from deciduous and conifer tree species from sites in Alaska, Wisconsin, and California was estimated using visible and shortwave IR spectroscopy. Seven chemical components - sugar, starch, protein, cellulose, total chlorophyll, lignin, and total nitrogen - were analyzed by wet chemical methods and their concentrations regressed against log 1/rho and first and second differences of log 1/rho (where rho is measured reflectance) at wavelengths selected by stepwise regression. Predictions of chemical concentrations based on cross validation suggest that this technique may be useful for extracting vegetation canopy biochemical information by remote sensing.

Card, Don H.↗
NASA NTRS1983

Red edge measurements for remotely sensing plant chlorophyll content

The feasibility of using the wavelength of the maximum slope of the red edge of leaf reflectance spectra, Lambda(re), as an indication of plant chlorophyll status was examined in the laboratory for single leaves of several species. Lambda(re) for each sample was determined by derivative reflectance spectroscopy. A high positive correlation was found between Lambda(re) and leaf chlorophyll content for all species, although there were some differences in the quantitative nature of the relationship for plants of different types. The position of the red edge was found to be unaffected by simulated change in ground cover, but multiple leaf layers produced a shift in its position. Appropriate spectral measurements and processing for obtaining useful information from the red edge are discussed, and the potential of the red edge in relation to other spectral measurements is considered.

Horler, D. N. H.↗
NASA NTRS1988

Non-destructive plant health sensing using absorption spectroscopy

The sensor group of the 1988 EGM 4001 class, working on NASA's Controlled Ecological Life Support Systems (CELSS) project, investigated many different plant health indicators and the technologies used to test them. The project selected by the group was to measure chlorophyll levels using absorption spectroscopy. The spectrometer measures the amount of chlorophyll in a leaf by measuring the intensity of light of a specific wavelength that is passed through a leaf. The three wavelengths of light being used corresponded to the near-IR absorption peaks of chlorophyll a, chlorophyll b, and chlorophyll-free structures. Experimentation showed that the sensor is indeed measuring levels of chlorophyll a and b and their changes before the human eye can see any changes. The detector clamp causes little damage to the leaf and will give fairly accurate readings on similar locations on a leaf, freeing the clamp from having to remain on the same spot of a leaf for all measurements. External light affects the readings only slightly so that measurements may be taken in light or dark environments. Future designs and experimentation will concentrate on reducing the size of the sensor and adapting it to a wider range of plants.

Bledsoe, Jim↗
NASA NTRS1992

Scientific issues and potential remote-sensing requirements for plant biochemical content

Application of developments in imaging spectrometry to the study of terrestrial ecosystems, which began in 1983, demonstrate the potential to estimate lignin and nitrogen concentrations of plant canopies by remote-sensing techniques. Estimation of these parameters from the first principles of radiative transfer and the interactions of light with plant materials is not presently possible, principally because of lack of knowledge about internal leaf scattering and specific absorption involving biochemical compounds. From the perspective of remote-sensing instrumentation, sensors are needed to support derivative imaging spectroscopy. Biochemical absorption features tend to occur in functional groupings throughout the 1100- to 2500-nm region. Derivative spectroscopy improves the information associated with the weaker, narrower absorption features of biochemical absorption that are superimposed on the strong absolute variations due to foliar biomass, pigments, and leaf water content of plant canopies. Preliminary sensor specifications call for 8-nm bandwidths at 2-nm centers in four spectral regions (about 400 bands total) and a signal-to-noise performance of at least 1000:1 for 20 percent albedo targets in the 2000-nm region.

Peterson, David L.↗
NASA NTRS1994

The Use of High Spectral Resolution Bands for Estimating Absorbed Photosynthetically Active Radiation (A Par)

Most remote sensing estimations of vegetation variables such as Leaf Area Index (LAI), Absorbed Photosynthetically Active Radiation (APAR), and phytomass are made using broad band sensors with a bandwidth of approximately 100 nm. However, high resolution spectrometers are available and have not been fully exploited for the purpose of improving estimates of vegetation variables. A study directed to investigate the use of high spectral resolution spectroscopy for remote sensing estimates of APAR in vegetation canopies in the presence of nonphotosynthetic background materials such as soil and leaf litter is presented. A high spectral resolution method defined as the Chlorophyll Absorption Ratio Index (CARI) was developed for minimizing the effects of nonphotosynthetic materials in the remote estimates of APAR. CARI utilizes three bands at 550, 670, and 700 nm with bandwidth of 10 nm. Simulated canopy reflectance of a range of LAI were generated with the SAIL model using measurements of 42 different soil types as canopy background. CARI obtained from the simulated canopy reflectance was compared with the broad band vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Simple Ratio (SR)). CARI reduced the effect of nonphotosynthetic background materials in the assessment of vegetation canopy APAR more effectively than broad band vegetation indices.

Kim, Moon S.↗
NASA NTRS2022

Determination of Chlorophyll Content in Selected Grass Communities of Krkonoše Mts. Tundra Based on Laboratory Spectroscopy and Aerial Hyperspectral Data

The study focuses on the determination of chlorophyll content in four prevailing grasses in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. We compared two methods for determination of leaf chlorophyll content (LCC) – spectrophotometric determination in the laboratory, and the LCC assessed by fluorescence portable chlorophyll meter CCM-300. Relationships were established between the LCCs and vegetation indices calculated from leaf spectra acquired with contact probe coupled with an ASD FieldSpec4 Wide-Res spectroradiometer. Canopy chlorophyll contents (CCC) were computed from the LCCs and green leaf area index (LAI), and modelled based on the field spectra measured by the spectroradiometer and the hyperspectral images acquired by Headwall Nano-Hyperspec® mounted on the DJI Matrice 600 Pro drone. The calculations are performed on datasets acquired in June, July and August 2020 together and separately for species and months. In general, the correlations based on June datasets work the best at both levels: median R 2 for all indices was 0.52 for all species together at leaf level and median R 2 = 0.47 at the canopy level (vegetation indices computed from field spectra). Canopy chlorophyll content map was created based on the results of stepwise multiple linear regression. The R 2 was 0.42 when using four wavelengths from the red and red edge spectral region. We attribute the weak model performance to a combination of several factors: leaf structure may bias LCC from laboratory measurements, effects of LAI variability on CCC, and the sampling design, probably not covering the whole phenology equally for all studied species.

L. Cervena↗
NASA NTRS2005

The Flora Mission for Ecosystem Composition, Disturbance and Productivity

Global land use and climate variability alter ecosystem conditions - including structure, function, and biological diversity - at a pace that requires unambiguous observations from satellite vantage points. Current global measurements are limited to general land cover, some disturbances, vegetation leaf area index, and canopy energy absorption. Flora is a pathfinding mission that provides new measurements of ecosystem structure, function, and diversity to understand the spatial and temporal dynamics of human and natural disturbances, and the biogeochemical and physiological responses of ecosystems to disturbance. The mission relies upon high-fidelity imaging spectroscopy to deliver full optical spectrum measurements (400-2500 nm) of the global land surface on a monthly time step at 45 meter spatial resolution for three years. The Flora measurement objectives are: (i) fractional cover of biological materials, (ii) canopy water content, (iii) vegetation pigments and light-use efficiency, (iv) plant functional types, (v) fire fuel load and fuel moisture content, and (vi) disturbance occurrence, type and intensity. These measurements are made using a multi-parameter, spectroscopic analysis approach afforded by observation of the full optical spectrum. Combining these measurements, along with additional observations from multispectral sensors, Flora will far advance global studies and models of ecosystem dynamics and change.

Asner, Gregory P.↗
NASA NTRS2003

Biogeochemistry of fluoride in a plant-solution system

Fluoride (F-) pollutants can harm plants and the animals feeding on them. However, it is largely unknown how complexing and chelating agents affect F bioavailability. Two studies were conducted that measured F- bioavailability and uptake by rice (Oryza sativa L.). In the first study, rice was grown in solution culture (pH 5.0) with 0, 2, or 4 mM F- as KF to compare the interaction of F- with humic acid (HA) and with a conventional chelating agent, N-hydroxyethylenthylenediaminetriacetic acid (HEDTA). In the second study, F was supplied at 0, 0.5, 1.0, and 2.0 mM KF with an additional 2 mM F- treatment containing solution Ca at 2x (2 mM Ca) the level used in the first study, to test the effect added Ca had on F- availability and uptake. Total biomass was greatest with HEDTA and F- < 1 mM. Leaf and stem F concentrations increased exponentially as solution F- increased linearly, with nearly no F partitioning into the seed. Results suggest that F was taken up as HF0 while F- uptake was likely restricted. Additionally, F- competed with HA for Ca, thus preventing the formation of Ca-HA flocculents. The addition of soluble Ca resulted in the precipitation of CaF2 solids on the root surface, as determined by tissue analysis and energy dispersive X-ray spectroscopy.

NASA Discipline Life Support Systems↗
NASA NTRS2024

Deciphering the Spectra of Flowers to Map Landscape-scale Blooming Dynamics

Like leaves, floral coloration is driven by inherent optical properties, which are determined by pigments, scattering structure, and thickness. However, establishing the relative contribution of these factors to canopy spectral signals is usually limited to in-situ observations. Modeling flowering dynamics (e.g., blooming duration, spatial distribution) at the landscape scale may reveal insights into ecological processes and phenological adaptations to environmental changes. Multitemporal visible to shortwave infrared (VSWIR) imaging spectroscopy observations are especially suited for such efforts. Reflectance in this spectral range is sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signals using a time series of imagery from the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) collected as part of the SBG High-Frequency Time Series (SHIFT) campaign as a case study. Airborne data were collected weekly during the spring of 2022 across two natural reserves in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels at two time points during the campaign. The processed data was used to investigate flowering species' spectro-temporal variation and spatial distribution using Spectral Mixture Residual, Gaussian clustering techniques, and a proposed narrow-band flowering index. Linear spectral unmixing allowed the computation of the weighted contribution of four major high-variance endmembers (leaves, flowers, soil, dark) and low-variance residual signal that comprises subtle spectral features used to track biophysical processes. The reflectance residual was projected on a low principal component basis to characterize flowering clusters' variation and spatial distribution based on the Gaussian mixture model, providing an uncertainty metric to assess the results. Mapping flowering events from modeling spectro-temporal dynamics throughout the season, from pre-blooming to post-flowering stages, allowed us to identify gradient variations in spectral features within the VSWIR spectral range linked to flowering pigments. Time series of the Mixture Residual Blooming Index and the Red-Edge Normalized Difference Vegetation Index revealed specific flowering and greenness phenophases across the two main species (Coreopsis gigantea, Artemisia californica) in the flowering areas. Overall, our approach opens opportunities for future satellite monitoring of floral cycles at broader scales.

Yoseline Angel↗
NASA NTRS1993

Relationships between pigment composition variation and reflectance for plant species from a coastal savannah in California

Advances in imaging spectroscopy have indicated that remotely sensed reflectance measurements of the plant canopy may be used to identify and qualify some classes of canopy biochemicals; however, the manner in which differences in biochemical compositions translate into differences is not well understood. Most frequently, multiple linear regression routines have been used to correlate narrow band reflectance values with measured biochemical concentrations. Although some success has been achieved with such methods for given data sets, the bands selected by multiple regression are not consistent between data sets, nor is it always clear what physical or biological basis underlies the correlation. To examine the relationship between biochemical concentration and leaf reflectance signal we chose to focus on the visible spectrum where the primary biochemical absorbances are due to photosynthetic pigments. Pigments provide a range of absorbance features, occur over a range of concentrations in natural samples, and are ecophysiologically important. Concentrations of chlorophyll, for example, have been strongly correlated to foliar nitrogen levels within a species and to photosynthetic capacity across many species. In addition pigments effectively absorb most of the photosynthetically active radiation between 400-700 nm, a spectral region for which silicon detectors have good signal/noise characteristics. Our strategy has been to sample a variety of naturally occurring species to measure leaf reflectance and pigment compositions. We hope to extend our understanding of pigment reflectance effects to interpret small overlapping absorbances of other biochemicals in the infrared region. For this reason, selected samples were also tested to determine total nitrogen, crude protein, cellulose, and lignin levels. Leaf reflectance spectra measured with AVIRIS bandwidths and wavelengths were compared between species and within species and for differences between seasons, for changes in the the shape of the spectra. We attempt to statistically correlate these shape changes with differences in pigment compositions. In parallel with our comparisons of pigment composition and leaf reflectance, we have modified the PROSPECT leaf reflectance model to test the contributions of pigments or pigment group concentrations. PROSPECT considers a leaf as a multi-layer dielectric plane with an uneven surface. Jacquemoud adapted the basic analysis of Allen for surface effects, a leaf thickness factor, and the absorption of water and chlorophyll (actually all pigments) and the plant matrix. Our modifications to PROSPECT in the forward direction include breaking out the pigment concentration parameter into separate components for chlorophyll a and b and a number of xanthophylls and carotenes, and introducing a shift and convolution function to model the spread and shift from their in vitro measurements to their in vivo state. Further, we have considered how the matrix elements (i.e., all biochemicals and structural effects not modeled explicity) vary with species.

Ustin, Susan L.↗
NASA NTRS2023

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

This study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8-day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission.

Hyperspectral Remote Sensing↗
NASA NTRS2013

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Vegetation↗
NASA NTRS2009

Studies of Minerals, Organic and Biogenic Materials through Time-Resolved Raman Spectroscopy

A compact remote Raman spectroscopy system was developed at NASA Langley Research center and was previously demonstrated for its ability to identify chemical composition of various rocks and minerals. In this study, the Raman sensor was utilized to perform time-resolved Raman studies of various samples such as minerals and rocks, Azalea leaves and a few fossil samples. The Raman sensor utilizes a pulsed 532 nm Nd:YAG laser as excitation source, a 4-inch telescope to collect the Raman-scattered signal from a sample several meters away, a spectrograph equipped with a holographic grating, and a gated intensified CCD (ICCD) camera system. Time resolved Raman measurements were carried out by varying the gate delay with fixed short gate width of the ICCD camera, allowing measurement of both Raman signals and fluorescence signals. Rocks and mineral samples were characterized including marble, which contain CaCO3. Analysis of the results reveals the short (approx.10-13 s) lifetime of the Raman process, and shows that Raman spectra of some mineral samples contain fluorescence emission due to organic impurities. Also analyzed were a green (pristine) and a yellow (decayed) sample of Gardenia leaves. It was observed that the fluorescence signals from the green and yellow leaf samples showed stronger signals compared to the Raman lines. Moreover, it was also observed that the fluorescence of the green leaf was more intense and had a shorter lifetime than that of the yellow leaf. For the fossil samples, Raman shifted lines could not be observed due the presence of very strong short-lived fluorescence.

Garcia, Christopher S.↗