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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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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Effects of varying soil moisture contents and vegetation canopies on microwave emissions

Results of NASA airborne passive microwave scans of bare and vegetated fields for comparison with ground truth tests are discussed and a model for atmospheric scattering of radiation by vegetation is detailed. On-board radiometers obtained data at 21, 2.8, and 1.67 cm during three passes over each of 46 fields, 28 of which were bare and the others having wheat or alfalfa. Ground-based sampling included moisture in five layers down to 15 cm in addition to soil temperature. The relationships among the brightness temperature and soil moisture, as well as the surface roughness and the vegetation canopy were examined. A model was developed for the dielectric coefficient and volume scattering for a vegetation medium. L- to C-band data were found useful for retrieving soil information directly. A surface moisture content of 5-35% yielded an emissivity of 0.9-0.7. The data agreed well with a combined multilayer radiative transfer model with simple roughness correction.

Burke, H.-H. K.↗
NASA NTRS1992

Remote sensing of vegetation canopy photosynthetic and stomatal conductance efficiencies

The problem of remote sensing the canopy photosynthetic and stomatal conductance efficiencies is investigated with the aid of one- and three-dimensional radiative transfer methods coupled to a semi-empirical mechanistic model of leaf photosynthesis and stomatal conductance. Desertlike vegetation is modeled as clumps of leaves randomly distributed on a bright dry soil with partial ground cover. Normalized difference vegetation index (NDVI), canopy photosynthetic (Ep), and stomatal efficiencies (Es) are calculated for various geometrical, optical, and illumination conditions. The contribution of various radiative fluxes to estimates of Ep is evaluated and the magnitude of errors in bulk canopy formulation of problem parameters are quantified. The nature and sensitivity of the relationship between Ep and Es to NDVI is investigated, and an algorithm is proposed for use in operational remote sensing.

Myneni, R. B.↗
DOE OSTI2020

Organizing principles for vegetation dynamics

Understanding vegetation dynamics is very challenging because of the multitude of contributing processes at 64widely different spatial and temporal scales. In this Perspective we propose that understanding of vegetation dynamics can be improved, permitting better predictions, based on organizing principles that constrain plant and ecosystem be haviour: natural selection, self-organization, and entropy maximization. Although these ideas are increasingly used,a limited common understanding of their theoretical basis has prevented their full potential to be realized. We explain the power of natural selection-based optimality to predict photosynthesis and carbon allocation responses to multiple environmental drivers, and how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities, and how entropy maximization can distinguish between stochastic and deterministic drivers of vegetation patterns. In combination with empirical exploration of patterns in plant functional variation, these principles can accelerate the development of dynamic vegetation models as well as trait-based ecology resting on strengthened theoretical and empirical foundations.

Geosciences↗
NASA NTRS1992

Remote sensing of solar radiation absorbed and reflected by vegetated land surfaces

1D and 3D radiative-transfer models have been used to investigate the problem of remotely sensed determination of vegetated land surface-absorbed and reflected solar radiation. Calculations were conducted for various illumination conditions to determine surface albedo, soil- and canopy-absorbed photosynthetically active and nonactive radiation, and normalized difference vegetation index. Simple predictive models are developed on the basis of the relationships among these parameters.

Myneni, Ranga B.↗
DOE OSTI2026

Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms

Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.

CO2 flux estimate↗
NASA NTRS1999

Quantifying BRDF Effects in Comparing Landsat-7 and AVIRIS Near-Simultaneous Acquisitions for Studies of High Plains Vegetation Cover

Approximately 100,000 sq. km of the High Plains of the central United States are covered by sand dunes and sand sheets deposited during the Holocene. Soil-dating evidence shows that there were at least four periods of dune reactivation during major droughts in the last 10,000 years. The dunes in this region are anchored by vegetation. We have undertaken a study of land-use change in the High Plains from 1985 to the present using Landsat 5 TM and Landsat 7 ETM+ images to map variation in vegetation cover during wet and dry years. Mapping vegetation cover of less than 20% is important in modeling potential surface reactivation since at this level the vegetation no longer sufficiently shields sandy surfaces from movement by wind. Landsat TM data have both the spatial resolution and temporal coverage to facilitate vegetation cover analysis for model development and verification. However, there is still the question of how accurate TM data are for the measurement of both growing and senescent vegetation in and and semi-arid regions. AVIRIS provides both high spectral resolution as well as high signal-to-noise ratio and can be used to test the accuracy of Landsat TM and ETM+ data. We have analyzed data from AVIRIS flown nearly concurrently with a Landsat 7 overpass. The comparison between an AVIRIS image swath of 11 km width subtending a 30 deg. angle and the same area covered by a 0.8 deg. angle from Landsat required accounting for the BRDF. A normalization technique using the ratio of the reflectances from registered AVIRIS and Landsat data proved superior to the techniques of column averaging on AVIRIS data alone published previously by Kennedy et al. This technique can be applied to aircraft data covering a wider swath angle than AVIRIS to develop BRDF responses for a wide variety of surfaces more efficiently than from ground measurements.

Goetz, A. F. H.↗
DOE OSTI2025

Vertical canopy gradients of respiration drive plant carbon budgets and leaf area index

Despite its importance for determining global carbon fluxes, leaf respiration remains poorly constrained in land surface models (LSMs). We tested the sensitivity of the Energy Exascale Earth System Model Land Model – Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) to variation in the canopy gradients of leaf maintenance respiration (R dark ). We ran global and point simulations varying the canopy gradient of R dark to explore the impacts on forest structure, composition, and carbon cycling. In global simulations, steeper canopy gradients of R dark lead to increased understory survival and leaf biomass. Leaf area index (LAI) increased up to 77% in tropical regions compared with the default parameterization, improving alignment with remotely sensed benchmarks. Global vegetation carbon varied from 308 Pg C to 449 Pg C across the ensemble. In tropical forest simulations, steeper gradients of R dark had a large impact on successional dynamics. Results show the importance of canopy gradients in leaf traits and fluxes for determining plant carbon budgets and emergent ecosystem properties such as competitive dynamics, LAI, and vegetation carbon. The high-model sensitivity to canopy gradients in R dark highlights the need for more observations of how leaf traits and fluxes vary along light micro-environments to inform critical dynamics in LSMs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2007

Using Satellite Remote Sensing Data in a Spatially Explicit Price Model

Famine early warning organizations use data from multiple disciplines to assess food insecurity of communities and regions in less-developed parts of the World. In this paper we integrate several indicators that are available to enhance the information for preparation for and responses to food security emergencies. The assessment uses a price model based on the relationship between the suitability of the growing season and market prices for coarse grain. The model is then used to create spatially continuous maps of millet prices. The model is applied to the dry central and northern areas of West Africa, using satellite-derived vegetation indices for the entire region. By coupling the model with vegetation data estimated for one to four months into the future, maps are created of a leading indicator of potential price movements. It is anticipated that these maps can be used to enable early warning of famine and for planning appropriate responses.

Brown, Molly E.↗
NASA NTRS1994

The reflection for dense plant canopies from the one-angle radiative transfer equation

An essential component of remote sensing of vegetation canopies from satellites is fundamental understanding. Since passive remote is driven by photons, the modeling of photon interactions with vegetation is a basic building block in that understanding. Several such photon transport models have been developed during the past two decades and continue to be developed. Different approaches have been followed including monte carlo, radiosity methods, geometric shadowing, and radiative transfer. In general, each approach has application for canopies with specific attributes. This presentation concerns the application of radiative transfer to dense vegetation canopies in which the soil does not participate. The approach taken here is novel in that a consistent theory for photon transport for non-rotationally invariant leaf scattering is developed in a canopy with a general leaf angle distribution (LAD). The theory is limited to the one-angle approximation (azimuthally averaged radiance) and is based on Chandrasekhar's analytical theory. While such a model is admittedly only approximate, it does fulfill a unique function in our search for understanding. In particular, the model is simple in its construct yet contains the essential features of canopy architecture that are mainly responsible for observed responses. Thus, this model will not only be a predictive tool but also an educational one. The mathematical setting is the radiative transfer equation in a dense (semiinfinite) canopy. The leaf scattering phase function is assumed to be Lambertian with different reflectance and transmittance. In addition, abaxial and adaxial differentiation is allowed which effectively destroys optical reciprocity. The analytical solution for the canopy BRDF is obtained by manipulation of the integral transport equation (a la Chandrasekhar) for a general LAD. With discretization of the. leaf angle, the resulting set of integral equations are solved iteratively including an acceleration procedure when the single scatter albedo is near one (in the NIR). Results will be compared to the LARS soybean canopy radiances as well as to broadleaf results from a recent Ames experiment.

Ganapol, B. D.↗
NASA NTRS2021

The 2019-2020 Australian drought and bushfires altered the partitioning of hydrological fluxes

Though coarse in spatial resolution, the nearly all weather measurements from passive microwave sensors can help in improving the spatiotemporal coverage of optical and thermal infrared sensors for monitoring vegetation changes on the land surface. This study demonstrates the use of vegetation optical depth retrievals from the Soil Moisture Active Passive mission for capturing the vegetation alterations from the recent 2019-2020 Australian bushfires and drought. The impact of vegetation disturbance s on terrestrial water budget is examined by assimilating the vegetation optical depth retrievals into a dynamic phenology model. The results demonstrate that assimilating vegetation optical depth observations lead to improved simulation of evapotranspiration, runoff, and soil moisture states. The study also demonstrates that the vegetation changes from the 2019-2020 Australian drought and fires led to significant modifications in the partitioning of evaporative and runoff fluxes, resulting in increased bare soil evaporation, reduced transpiration, and higher runoff.

data assimilation↗
DOE OSTI2020

Understanding the eco-geomorphologic feedback of coastal marsh under sea level rise: vegetation dynamic representations, processes interaction, and parametric sensitivity

A growing number of coastal eco-geomorphologic modeling studies have been conducted to understand coastal marsh evolution under sea-level rise (SLR). Although these models quantify marsh topographic change as a function of sedimentation and erosion, their representations of vegetation dynamics that control organic sedimentation differ. How vegetation dynamic schemes contribute to simulation outcomes is not well quantified. Additionally, the sensitivity of modeling outcomes to parameter selection in the available formulations has not been rigorously tested to date, especially under the influence of an accelerating SLR. In this paper, we used a coastal eco-geomorphologic model with different vegetation dynamic schemes to investigate the eco-geomorphologic feedbacks of coastal marshes and parametric sensitivity under SLR scenarios. We found that marsh platform relief increased with sea level rise rate. The simulations with different vegetation schemes exhibited different spatial-temporal variations in elevation and biomass. The nonlinear Spartina scheme presented the most resilient prediction with generally the highest marsh accretion and vegetation biomass, and the least elevation relief under SLR. But the linear Spartina scheme predicts the lowest unvegetated-vegetated ratio. We also found that vegetation-related parameters and sediment diffusivity, which were not well measured or discussed in previous studies, were identified as some of the most critical parameters. Additionally, the model sensitivity to vegetation-related parameters increased with SLR rates. The identified most sensitive parameters may inform how to appropriately choose modeling representations of key processes and parameters for different coastal marsh landscapes under SLR, and demonstrate the importance of future field measurements of these key parameters.

58 GEOSCIENCES↗
DOE OSTI2020

Projected changes in the terrestrial and oceanic regulators of climate variability across sub-Saharan Africa

Future changes in the sign and intensity of ocean–land–atmosphere interactions have been insufficiently studied, despite implications for regional climate change projections, extreme event statistics, and seasonal climate predictability. In response to this deficiency, the present study focuses on projected responses to the enhanced greenhouse effect in: (1) the mean state of the atmosphere and land surface; (2) oceanic and terrestrial drivers of sub-Saharan climate variability; and (3) total seasonal climate predictability of sub-Saharan Africa, a region known for its pronounced land–atmosphere coupling. Analysis focuses on output from 23 Earth System Models in the Coupled Model Intercomparison Project Phase Five for the late twentieth and twenty-first centuries. It is projected that the greatest warming across sub-Saharan Africa will occur over the Sahel, the monsoon season will become more persistent into late summer and autumn, short rains in the Horn of Africa (HOA) will intensify, and leaf area index will increase across the HOA. Stepwise Generalized Equilibrium Feedback Assessment, i.e. a multivariate statistical approach, is applied to the model output over sub-Saharan Africa in order to explore the oceanic and terrestrial drivers of regional climate. The models indicate that the study region’s climate variability is dominated by oceanic drivers, with secondary contributions from soil moisture and very modest impacts from vegetation. Overall, the general model consensus of future projections indicates a concerning diminished seasonal predictability of sub-Saharan African regional climate based on key oceanic and terrestrial predictors and an elevated role of the land surface (associated with soil moisture anomalies) compared to oceanic drivers in regulating regional climate variability.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Relationship of attenuation in a vegetation canopy to physical parameters of the canopy

A discrete scatter model is employed to compute the radiometric response (i.e. emissivity) of a layer of vegetation over a homogeneous ground. This was done to gain insight into empirical formulas for the emissivity which have recently appeared in the literature and which indicate that the attenuation through the canopy is proportional to the water content of the vegetation and inversely proportional to wavelength raised to a power around unity. The analytical result assumes that the vegetation can be modeled by a sparse layer of discrete, randomly oriented particles (leaves, stalks, etc.). The attenuation is given by the effective wave number of the layer obtained from the solution for the mean wave using the effective field approximation. By using the Ulaby-El Rayes formula to relate the dielectric constant of the vegetation to its water content, it can be shown that the attenuation is proportional to water content. The analytical form offers insight into the dependence of the empirical parameters on other variables of the canopy, including plant geometry (i.e. shape and orientation of the leaves and stalks of which the vegetation is comprised), frequency of the measurement and even the physical temperature of the vegetation. Solutions are presented for some special cases including layers consisting of cylinders (stalks) and disks (leaves).

Karam, M. A.↗
NASA NTRS1986

Estimation of canopy parameters for inhomogeneous vegetation canopies from reflectance data. I - Two-dimensional row canopy

A canopy-reflectance (CR) model for row-planted vegetation is presented. Its use of an estimation of important biophysical variables like leaf-area index (LAI) and average leaf angle (ALA) from bidirectional CR data is discussed. Using field-measured CR data for a partially covered soybean canopy, it is shown that one can accurately estimate LAI, ALA and extent of percentage of ground cover from CR data.

Goel, N. S.↗
DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Monitoring primary production in the Sahel with AVHRR observations

Field and satellite measurements made in the Gourma region of Mali in 1986 are analyzed. A comprehensive model for the relationship between vegetation indices and primary production is described, and findings are made on primary production, solar radiation, normalized difference vegetation indices, and the efficiency of photosynthetic fixation of solar radiation. The model appears to provide a reasonable description of the gross production. By omitting variables form the model the relative significance of each one can be assessed, and it turns out that both the corrections made to the AVHRR data and the inclusion of daily solar radiation integrals are much less important than are the satellite NDVI measurements.

Prince, Stephen D.↗
DOE OSTI2023

Carbon and Water Fluxes of the Boreal Evergreen Needleleaf Forest Biome Constrained by Assimilating Ecosystem Carbonyl Sulfide Flux Observations

Gross primary production (GPP) by boreal forests is highly sensitive to environmental changes. However, GPP simulated by land surface models (LSMs) remains highly uncertain due to the lack of direct photosynthesis observations at large scales. Carbonyl sulfide (COS) has emerged as a promising proxy to improve the representation of GPP in LSMs. Because COS is absorbed by vegetation following the same diffusion pathway as CO2 during photosynthesis and not emitted back to the atmosphere, incorporating a mechanistic representation of vegetation COS uptake in LSMs allows using COS observations to refine GPP representation. Here, we perform ecosystem COS flux and GPP data assimilations to constrain the COS- and GPP-related parameters in the ORCHIDEE LSM for boreal evergreen needleleaf forests (BorENF). Assimilating ecosystem COS fluxes at Hyytiälä forest increases the simulated net ecosystem COS uptake by 14%. This increase largely results from changes in the internal conductance to COS, highlighting the need to improve the representation of COS internal diffusion and consumption. Moreover, joint assimilation of ecosystem COS flux and GPP at Hyytiälä improves the simulated latent heat flux, contrary to the GPP-only data assimilation, which fails to do so. Finally, we scaled this assimilation framework up to the boreal region and find that the joint assimilation of COS at Hyytiälä and GPP fluxes at 10 BorENF sites increases the modeled vegetation COS uptake up to 18%, but not GPP. Therefore, this study encourages the use of COS flux observations to inform GPP and latent heat flux representations in LSMs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Soybean canopy reflectance modeling data sets

Numerous mathematical models of the interaction of radiation with vegetation canopies have been developed over the last two decades. However, data with which to exercise and validate these models are scarce. During three days in the summer of 1980, experiments are conducted with the objective of gaining insight about the effects of solar illumination and view angles on soybean canopy reflectance. In concert with these experiment, extensive measurements of the soybean canopies are obtained. This document is a compilation of the bidirectional reflectance factors, agronomic, characteristics, canopy geometry, and leaf, stem, and pod optical properties of the soybean canopies. These data sets should be suitable for use with most vegetation canopy reflectance models.

Ranson, K. J.↗