Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “vegetation model”

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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Mission: an Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA's next investment in satellite ocean color and the study of Earth's ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument - the Ocean Color Instrument (OCI) - is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-kilometer at nadir. This includes continuous collection of spectra from 340 to 890 nanometers in 5-nanometer steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth's biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a detailed discussion of the capabilities and design concept of OCI.

Passive Remote Sensing↗
NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: An Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, as well as improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument – the Ocean Color Instrument (OCI) – is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This includes continuous collection of spectra from 340 nm to 890 nm in 5 nm steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a discussion of the capabilities and design concept of OCI.

hyperspectral radiometer↗
NASA NTRSDate not supplied

Applications of NASA’s LANCE Earth Observations for Ecosystem Assessments

As one of NASA's open and free data systems, NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides a wide range of near real-time, low latency and expedited data products from NASA and other earth science satellite missions to support users in their understanding of the value and distribution of ecosystem services. LANCE data can support the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework by both facilitating the creation of ecosystem spatial extent and condition accounts, as well as the quantification and mapping of ecosystem services, much more quickly than routine data processing allows. During the past 13 years, LANCE near real-time satellite data products (e.g., surface reflectance, albedo, vegetation height, thermal anomalies, soil moisture, snow cover etc.) have been used to produce ecosystem-related indicators such as vegetation indexes, biomass, land cover maps, fire, and flood products. These ecosystem-related indicators can be integrated into ecosystem services models, tools, and platforms to monitor land cover and land use changes in ecosystem composition in support of land and natural resource management. When using ecosystem services accounts in decision-making, low latency is essential because having up-to-date information can be critical to support decisions being made. With adequate spatial resolution, long time series length, and low latency data products, LANCE would promote the sustainable use of land and natural resources.

Tian Yao↗
NASA NTRS2010

Updates of Land Surface and Air Quality Products in NASA MAIRS and NEESPI Data Portals

Following successful support of the Northern Eurasia Earth Sciences Partner Initiative (NEESPI) project with NASA satellite remote sensing data, from Spring 2009 the NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has been working on collecting more satellite and model data to support the Monsoon Asia Integrated Regional Study (MAIRS) project. The established data management and service infrastructure developed for NEESPI has been used and improved for MAIRS support.Data search, subsetting, and download functions are available through a single system. A customized Giovanni system has been created for MAIRS.The Web-based on line data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) allows scientists to explore, quickly analyze, and download data easily without learning the original data structure and format. Giovanni MAIRS includes satellite observations from multiple sensors and model output from the NASA Global Land Data Assimilation System (GLDAS), and from the NASA atmospheric reanalysis project, MERRA. Currently, we are working on processing and integrating higher resolution land data in to Giovanni, such as vegetation index, land surface temperature, and active fire at 5km or 1km from the standard MODIS products. For data that are not archived at the GESDISC,a product metadata portal is under development to serve as a gateway for providing product level information and data access links, which include both satellite, model products and ground-based measurements information collected from MAIRS scientists.Due to the large overlap of geographic coverage and many similar scientific interests of NEESPI and MAIRS, these data and tools will serve both projects.

Shen, Suhung↗
NASA NTRS2016

Integrating Chlorophyll fapar and Nadir Photochemical Reflectance Index from EO-1/Hyperion to Predict Cornfield Daily Gross Primary Production

The concept of light use efficiency (Epsilon) and the concept of fraction of photosynthetically active ration (PAR) absorbed for vegetation photosynthesis (PSN), i.e., fAPAR (sub PSN), have been widely utilized to estimate vegetation gross primary productivity (GPP). It has been demonstrated that the photochemical reflectance index (PRI) is empirically related to ε. An experimental US Department of Agriculture (USDA) cornfield in Maryland was selected as our study field. We explored the potential of integrating fAPAR(sub chl) (defined as the fraction of PAR absorbed by chlorophyll) and nadir PRI (PRI(sub nadir)) to predict cornfield daily GPP. We acquired nadir or near-nadir EO-1/Hyperion satellite images that covered the cornfield and took nadir in-situ field spectral measurements. Those data were used to derive the PRI(sub nadir) and fAPAR (sub chl). The fAPAR (sub chl) is retrieved with the advanced radiative transfer model PROSAIL2 and the Metropolis approach, a type of Markov Chain Monte Carlo (MCMC) estimation procedure. We define chlorophyll light use efficiency Epsilon (sub chl) as the ratio of vegetation GPP as measured by eddy covariance techniques to PAR absorbed by chlorophyll (Epsilon(sub chl) = GPP/APAR (sub chl). Daily Epsilon (sub chl) retrieved with the EO-1 Hyperion images was regressed with a linear equation of PRI (sub nadir) Epsilon (sub chl) = Alpha × PRI (sub nadir) + Beta). The satellite Epsilon(sub chl- PRI (sub nadir) linear relationship for the cornfield was implemented to develop an integrated daily GPP model [GPP = (Alpha × PRI(sub nadir) + Beta) × fAPAR (sub chl) × PAR], which was evaluated with fAPAR (sub chl) and PRI (sub nadir) retrieved from field measurements. Daily GPP estimated with this fAPAR (sub chl-) PRI (nadir) integration model was strongly correlated with the observed tower in-situ daily GPP (R(sup 2) = 0.93); with a root mean square error (RMSE) of 1.71 g C mol−(sup -1) PPFD and coefficient of variation (CV) of 16.57%. Both seasonal Epsilon (sub chl) and PRI (sub nadir) were strongly correlated with fAPAR (sub chl ) retrieved from field measurements, which indicates that chlorophyll content strongly affects seasonal epsilon (sub chl) and PRI (sub nadir). We demonstrate the potential capacity to monitor GPP with space-based visible through shortwave infrared (VSWIR) imaging spectrometers such as NASA's soon to be decommissioned EO- 1/Hyperion and the future Hyperspectral Infrared Imager (HyspIRI).

HyspIRI↗
NASA NTRS1991

Microwave Brightness Of Land Surfaces From Outer Space

Mathematical model approximates microwave radiation emitted by land surfaces traveling to microwave radiometer in outer space. Applied to measurements made by Scanning Multichannel Microwave Radiometer (SMMR). Developed for interpretation of microwave imagery of Earth to obtain distributions of various chemical, physical, and biological characteristics across its surface. Intended primarily for use in mapping moisture content of soil and fraction of Earth covered by vegetation. Advanced Very-High-Resolution Radiometer (AVHRR), provides additional information on vegetative cover, thereby making possible retrieval of soil-moisture values from SMMR measurements. Possible to monitor changes of land surface during intervals of 5 to 10 years, providing significant data for mathematical models of evolution of climate.

Kerr, Yann H.↗
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 NTRS1999

Impact of Assimilating GOES-Derived Land Surface Variables into the PSU/NCAR MM5

Land surface processes are known to have a profound impact on the overlying atmosphere over a wide range of spatial and temporal scales. Many atmospheric numerical models include special parameterizations to improve the specification and partitioning of surface fluxes which are critical to the accurate prediction of warm season boundary layer behavior, organized mesoscale circulations, and convective precipitation. However, the added degrees of freedom resulting from the inclusion of vegetation and soil schemes require the specification of additional surface parameters such as vegetative resistances, green vegetation fraction, leaf area index, soil physical and hydraulic characteristics, and the vertical distribution of soil moisture. As satellite data have become more readily available in recent years, many investigations have attempted to use these new measurements to infer missing components of the surface energy budget. Sensitivity studies have shown land-skin temperature (LST) tendencies during the mid-morning hours are strongly sensitive to the surface moisture availability (a function of soil wetness and vegetation) and less sensitive to other parameters such as surface roughness. Based upon results from these and other studies, developed a simple technique that dynamically assimilates Geostationary Operational Environmental Satellite (GOES) derived land-surface products into the surface energy budget of a mesoscale model. The purpose of this paper is to demonstrate that assimilating the GOES satellite data has the potential to improve the representation of land surface characteristics within the model without prior knowledge of the land surface characteristics. The assimilation technique is presented in Section 2 and the numerical experiments are detailed in Section 3. Preliminary results and conclusions are presented in Sections 4 and 5, respectively.

Lapenta, William M.↗
NASA NTRS1982

Microwave remote sensing: Active and passive. Volume 2 - Radar remote sensing and surface scattering and emission theory

The fundamental principles of radar backscattering measurements are presented, including measurement statistics, Doppler and pulse discrimination techniques, and associated ambiguity functions. The operation of real and synthetic aperture sidelooking airborne radar systems is described, along with the internal and external calibration techniques employed in scattering measurements. Attention is given to the physical mechanisms responsible for the scattering emission behavior of homogeneous and inhomogeneous media, through a discussion of surface roughness, dielectric properties and inhomogeneity, and penetration depth. Simple semiempirical models are presented. Theoretical models involving greater mathematical sophistication are also given for extended ocean and bare soil surfaces, and the more general case of a vegetation canopy over a rough surface.

Ulaby, F. T.↗
NASA NTRS1984

Red and near-infrared sensor response to off-nadir viewing

It has first been intended to employ the Advanced Very High Resolution Radiometer (AVHRR) as a meteorological instrument to aid in detecting clouds and atmospheric temperature stratifications over large areas. However, more recently the satellite data have been used for monitoring large areas of vegetation with emphasis on the visible (VIS) and near-infrared (NIR) bands. The present investigation is concerned with the objective to model ranges in AVHRR VIS and NIR radiances and normalized differences expected under typical AVHRR viewing and illumination conditions. Atmospheric data sets reported by Dave (1978) and Dave and Braslau (1975) were used. It is shown that sensor response to atmospheric path length can be substantial for the AVHRR VIS and NIR channels and normalized difference (ND) values. However, errors can be minimized by high sun and clear atmosphere viewing.

Holben, B.↗
NASA NTRS1984

Relating the microwave backscattering coefficient to leaf area index

This paper examines the relationship between the microwave backscattering coefficient of a vegetation canopy, sigma (can, 0) and the canopy's leaf area index (LAI). The relationship is established through the development of one model for corn and sorghum and another for wheat. Both models are extensions of the cloud model of Attema and Ulaby (1978). Analysis of experimental data measured at 8.6, 13.0, 17.0, and 35.6 GHz indicates that most of the temporal variations of sigma (can, 0) can be accounted for through variations in green LAI alone, if the latter is greater than 0.5.

Ulaby, F. T.↗
NASA NTRS1992

Factors controlling sulfur gas exchange in Sphagnum-dominated wetlands

Atmosphere-peatland exchange of reduced sulfur gases was determined seasonally in fen in NH, and in an artificially-acidified fen at the Experimental Lakes Area (ELA) in Canada. Dimethyl sulfide (DMS) dominated gas fluxes at rates as high as 400 nmol/m(sup -2)hr(sup -1). DMS fluxes measured using enclosures were much higher than those calculated using a stagnant-film model, suggesting that Sphagnum regulated efflux. Temperature controlled diel and seasonal variability in DMS emissions. Use of differing enclosure techniques indicated that vegetated peatlands consume atmospheric carbonyl sulfide. Sulfate amendments caused DMS and methane thiol concentrations in near-surface pore waters to increase rapidly, but fluxes of these gases to the atmosphere were not affected. However, emission data from sites experiencing large differences in rates of sulfate deposition from the atmosphere suggested that chronic elevated sulfate inputs enhance DMS emissions from northern wetlands.

Demello, William Zamboni↗
NASA NTRS1994

Remote sensing of forest ecosystem dynamics: Measurements and modeling

The development of an integrated approach to the modeling of forest dynamics encompassing submodels of forest growth and succession, soil processes and radiation interactions, is reported. Remote sensing technology is a key element of this study in that it provides data for developing, initializing, updating, and validating the models. The objectives are reviewed, the data collected and models in use are discussed, and a framework for studying interactions between the forest growth, soil process and energy interaction components, is described. Remote sensing technology used in the study includes optical and microwave field, aircraft and satellite borne instruments. The types of data collected during intensive field and aircraft campaigns included bidirectional reflectance, thermal emittance and multifrequency, multipolarization synthetic aperture radar backscatter. Synthetic imagery of derived products such as forest biomass and NDVI (Normalized Difference Vegetative Index), and collections of ground data are being assembled in a georeferenced data base. These data are used to drive or test multidiscipline simulations of forested ecosystems. Enhancements to the modeling environment permit considerable flexibility in configuring simulations and selecting results for reporting and graphical display.

Williams, Darrel L.↗
NASA NTRS1973

Land use mapping and modelling for the Phoenix Quadrangle

The author has identified the following significant results. Comparison of 9 x 9 MSS band images and color composites made from bands 4, 5, and 6 showing vegetated areas near Phoenix during the summer and fall seasons aided in definitely establishing that certain land areas were being used as agricultural land and not as rangeland. Agricultural land, which appeared to be fallow, idle, or not irrigated, often became more readily identifiable as agricultural land when comparing different images of identical land areas which have been affected by seasonal vegetation changes. Experimentation with color density slicing portions of 9 x 9 MSS band 7 transparency showing the central urban core of phoenix enabled dense commercial and industrial areas to be separated from less dense urbanized land uses; however, loss of resolution produced results of limited usefulness. The best results in agricultural areas near Sun City were obtained using MSS band 5 imagery. Discrimination of different land uses in both urban and agricultural areas which were color density sliced was not possible to the degree of accuracy necessary to make mapping feasible. Examination of MSS transparencies and color composites allowed updating of a map of land use change in the Phoenix Quadrangle.

Place, J. L.↗
NASA NTRS1973

Land use mapping and modelling for the Phoenix Quadrangle

The author has identified the following significant results. Comparison of 9 x 9 MSS band images and color composites made from bands 4, 5, and 6 showing vegetated areas near Phoenix during the summer, fall, and winter seasons aided in definitely establishing that certain land areas were being used as agricultural land and not as rangeland. Agricultural land, which appeared to be fallow, idle, or not irrigated, often became more readily identifiable as agricultural land when comparing different images of identical land areas which have been affected by seasonal vegetation changes. Experimentation with the Bausch and Lomb Zoom Transferscope using MSS images of identical areas in the same spectral band from different time periods, with a quick flip method of alternately viewing the frame areas, enabled rapid detection of a major land use change from agricultural to urban use on the northwest fringe of the metropolitan Phoenix area. The best results in this case were obtained when comparing MSS band 5 images. Examination of MSS transparencies and color composites allowed further updating of a map of land use change in the Phoenix Quadrangle.

Place, J. L.↗
NASA NTRS2012

Use of MODIS Satellite Data to Evaluate Juniperus spp. Pollen Phenology to Support a Pollen Dispersal Model, PREAM, to Support Public Health Allergy Alerts

Juniperus spp. pollen is a significant aeroallergen that can be transported 200-600 km from the source. Local observations of Juniperus spp. phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. Methods: The Dust REgional Atmospheric Model (DREAM)is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust. We successfully modified the DREAM model to incorporate pollen transport (PREAM) and used MODIS satellite images to develop Juniperus ashei pollen input source masks. The Pollen Release Potential Source Map, also referred to as a source mask in model applications, may use different satellite platforms and sensors and a variety of data sets other than the USGS GAP data we used to map J. ashei cover type. MODIS derived percent tree cover is obtained from MODIS Vegetation Continuous Fields (VCF) product (collection 3 and 4, MOD44B, 500 and 250 m grid resolution). We use updated 2010 values to calculate pollen concentration at source (J. ashei ). The original MODIS derived values are converted from native approx. 250 m to 990m (approx. 1 km) for the calculation of a mask to fit the model (PREAM) resolution. Results: The simulation period is chosen following the information that in the last 2 weeks of December 2010. The PREAM modeled near-surface concentrations (Nm-3) shows the transport patterns of J. ashei pollen over a 5 day period (Fig. 2). Typical scales of the simulated transport process are regional.

Luvall, J. C.↗
NASA NTRS2011

Changes in the Carbon Cycle of Amazon Ecosystems During the 2010 Drought

Satellite remote sensing was combined with the NASA-CASA carbon cycle simulation model to evaluate the impact of the 2010 drought (July through September) throughout tropical South America. Results indicated that net primary production (NPP) in Amazon forest areas declined by an average of 7% in 2010 compared to 2008. This represented a loss of vegetation CO2 uptake and potential Amazon rainforest growth of nearly 0.5 Pg C in 2010. The largest overall decline in ecosystem carbon gains by land cover type was predicted for closed broadleaf forest areas of the Amazon River basin, including a large fraction of regularly flooded forest areas. Model results support the hypothesis that soil and dead wood carbon decomposition fluxes of CO2 to the atmosphere were elevated during the drought period of 2010 in periodically flooded forest areas, compared to forests outside the main river floodplains.

Potter, Christophera↗
NASA NTRS2020

Global Carbon Cycle and Climate Feedbacks in the NASA GISS ModelE2.1

We present results from the NASA GISS ModelE2.1‐G‐CC Earth System Model with coupled climate‐carbon cycle simulations that were submitted to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Coupled Climate‐Carbon Cycle MIP (C4MIP). Atmospheric CO2 concentration and carbon budgets for the land and ocean in the historical simulations were generally consistent with observations. Low simulated atmospheric CO2 concentrations during 1850‐1950 were due to excess uptake from prescribed land cover change, which erroneously replaced arid shrublands with higher biomass crops, and assumed high 2004 LAI values in vegetated lands throughout the historical simulation. At the end of the historical period, slightly higher simulated CO2 than observed resulted from the land being an insufficient net carbon sink, despite the net effect of CO2 fertilization and warming‐induced increases to leaf photosynthetic capacity. The global ocean carbon uptake agreed well with the observations with the largest discrepancies in the low latitudes. Future climate projection at 2091‐2100 agreed with CMIP5 models in the northward shift, of temperate deciduous forest climate and expansion across Eurasia along 60 °N latitude, and dramatic regional biome shifts from drying and warming in continental Europe. Carbon feedback parameters were largely similar to the CMIP5 model ensemble. For our model, the variation of land feedback parameters within the uncertainty arises from the fertilization feedback being less sensitive due to lack of increased vegetation growth, and the comparably more negative ocean carbon‐climate feedback is due to the large slowdown of the Atlantic overturning circulation.

Carbon cycle↗