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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Warming, permafrost thaw and increased nitrogen availability as drivers for plant composition and growth across the Tibetan Plateau

Permafrost-affected ecosystems are subject to warming and thawing, which can increase the availability of subsurface nitrogen (N) with consequences in otherwise N-limited tundra and alpine vegetation. Here, we quantify the extent of warming and permafrost thawing and the corresponding effects on nitrogen availability and plant growth based on a 20-year survey across 14 sites on the Tibetan Plateau. The survey showed that most sites have been subject to warming and thawing and that the upper permafrost zone across all sites was rich in inorganic N, mainly as ammonium. We further explore the efficiency of plants to utilize 15 N-labelled inorganic N over five years following 15 N addition at the permafrost table far below the main root zone. The 15 N experiment showed that deep-rooted plant species were able to utilize the labelled N. A SEM model suggests that changes in vegetation can be explained by both active layer warming and permafrost thawing and the associated changes in inorganic nitrogen availability. Our results highlight a feedback mechanism of climate warming, in which released plant-available N may favour deep-rooted plants. This can explain important changes in plant composition and growth across the sites on the Tibetan Plateau.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Propagation modeling for land mobile satellite systems

A simplified empirical model for predicting primary fade statistics for a vegetatively shadowed mobile satellite signal is presented, and predictions based on the model are presented using propagation parameter values from experimental data. Results from the empirical model are used to drive a propagation simulator to produce the secondary fade statistics of average fade duration.

Barts, R. Michael↗
NASA NTRS2005

A Rapid Approach to Modeling Species-Habitat Relationships

A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an habitat suitability index (HSI) and an objective model selection procedure are discussed.

Carter, Geoffrey M.↗
NASA NTRS1991

Remote measurement of soil moisture over vegetation using infrared temperature measurements

Better methods for remote sensing of surface evapotranspiration, soil moisture, and fractional vegetation cover were developed. The objectives were to: (1) further develop a model of water movement through the soil/plant/atmosphere system; (2) use this model, in conjunction with measurements of infrared surface temperature and vegetation fraction; (3) determine the magnitude of radiometric temperature response to water stress in vegetation; (4) show at what point one can detect that sensitivity to water stress; and (5) determine the practical limits of the methods. A hydrological model that can be used to calculate soil water content versus depth given conventional meteorological records and observations of vegetation cover was developed. An outline of the results of these initiatives is presented.

Carlson, Toby N.↗
NASA NTRS2011

Radar and Lidar Radar DEM

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

remote sensing↗
NASA NTRS1980

Simulation of solar radiation absorption in vegetation canopies

A solar radiation canopy absorption model, including multiple scattering effects, was developed and tested for a lodgepole pine (Pinus contorta) canopy. Reflectance above the canopy, spectral transmittance to the ground layer, and geometric and spectral measurements of canopy elements were made. Relatively large differentials occurred in spectral absorption by canopy layers, especially in the photosynthetically active region, as a function of solar zenith angle. In addition, the proportion of total global irradiance absorbed by individual layers varied greatly as a function of solar zenith angle. However, absorption by the entire canopy system remained relatively constant.

Kimes, D. S.↗
NASA NTRS1982

Hierarchical modeling for image classification

As part of the California Integrated Remote Sensing System's (CIRSS) San Bernardino County Project, the use of data layers from a geographic information system (GIS) as an integral part of the Landsat image classification process was investigated. Through a hierarchical modeling technique, elevation, aspect, land use, vegetation, and growth management data from the project's data base were used to guide class labeling decisions in a 1976 Landsat MSS land cover classification. A similar model, incorporating 1976-1979 Landsat spectral change data in addition to other data base elements, was used in the classification of a 1979 Landsat image. The resultant Landsat products were integrated as additional layers into the data base for use in growth management, fire hazard, and hydrological modeling.

Likens, W.↗
NASA NTRS1984

A comparison between active and passive sensing of soil moisture from vegetated terrains

A comparison between active and passive sensing of soil moisture over vegetated areas is studied via scattering models. In active sensing three contributing terms to radar backscattering can be identified: (1) the ground surface scatter term; (2) the volume scatter term representing scattering from the vegetation layer; and (3) the surface volume scatter term accounting for scattering from both surface and volume. In emission three sources of contribution can also be identified: (1) surface emission; (2) upward volume emission from the vegetation layer; and (3) downward volume emission scattered upward by the ground surface. As ground moisture increases, terms (1) and (3) increase due to increase in permittivity in the active case. However, in passive sensing, term (1) decreases but term (3) increases for the same reason. This self conpensating effect produces a loss in sensitivity to change in ground moisture. Furthermore, emission from vegetation may be larger than that from the ground. Hence, the presence of vegetation layer causes a much greater loss of sensitivity to passive than active sensing of soil moisture.

Fung, A. K.↗
NASA NTRS1985

A comparison between active and passive sensing of soil moisture from vegetated terrains

A comparison between active and passive sensing of soil moisture over vegetated areas is studied via scattering models. In active sensing three contributing terms to radar backscattering can be identified: (1) the ground surface scatter term; (2) the volume scatter term representing scattering from the vegetation layer; and (3) the surface volume scatter term accounting for scattering from both surface and volume. In emission three sources of contribution can also be identified: (1) surface emission; (2) upward volume emission from the vegetation layer; and (3) downward volume emission scattered upward by the ground surface. As ground moisture increases, terms (1) and (3) increase due to increase in permittivity in the active case. However, in passive sensing, term (1) decreases but term (3) increases for the same reason. This self compensating effect produces a loss in sensitivity to change in ground moisture. Furthermore, emission from vegetation may be larger than that from the ground. Hence, the presence of vegetation layer causes a much greater loss of sensitivity to passive than active sensing of soil moisture.

Fung, A. K.↗
NASA NTRS2018

Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar

Ecological modeling and forecasting are essential tools for the understanding of complex vegetation dynamics. The parametric demands of some of these models are often lacking or scant for threatened ecosystems, particularly in diverse tropical ecosystems. One such ecosystem and also one of the world's biodiversity hotspots, Madagascar's lowland rainforests, have disappeared at an alarming rate. The processes that drive tree species growth and distribution remain as poorly understood as the species themselves. We investigated the application of the process-based individual-based FORMIND model to successfully simulate a Madagascar lowland rainforest using previously collected multi-year forest inventory plot data. We inspected the model's ability to characterize growth and species abundance distributions over the study site, and then validated the model with an independently collected forest-inventory dataset from another lowland rainforest in eastern Madagascar. Following a comparative analysis using inventory data from the two study sites, we found that FORMIND accurately captures the structure and biomass of the study forest, with r(squared) values of 0.976, 0.895, and 0.995 for 1:1 lines comparing observed and simulated values across all plant functional types for aboveground biomass (tonnes/ha), stem numbers, and basal area (m(squared)/ha), respectively. Further, in validation with a second study forest site, FORMIND also compared well, only slightly over-estimating shade-intermediate species as compared to the study site, and slightly under-representing shade-tolerant species in percentage of total aboveground biomass. As an important application of the FORMIND model, we measured the net ecosystem exchange (NEE, in tons of carbon per hectare per year) for 50 ha of simulated forest over a 1000-year run from bare ground. We found that NEE values ranged between 1 and -1 t Cha(exp -1)year(exp -1), consequently the study forest can be considered as a net neutral or a very slight carbon sink ecosystem, after the initial 130 years of growth. Our study found that FORMIND represents a valuable tool toward simulating forest dynamics in the immensely diverse Madagascar rainforests.

Forest Productivity↗
NASA NTRS1985

Active Microwave Properties of Vegetation Canopies

Potential users of radar imagery need a better fundamental understanding of the capabilities of radar systems for vegetation studies than past studies provide. One approach is the use of theoretical models to predict observable active microwave properties of vegetation. This in turn requires accurate observations of backscattering coefficients and other active microwave properties in field research studies. The background document for the SRAEC program emphasizes the need to relate electromagnetic parameters to classical biophysical descriptors and to understand the role of polarization, especially cross-polarization. The broad goal of this study is to increase the understanding of the effects of canopy structure on the active microwave properties of vegetation canopies, with particular attention to polarization.

Paris, J. F.↗
NASA NTRSDate not supplied

The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina

Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.

Nazmus Shams Sazib↗
DOE OSTI2024

Snow distribution patterns revisited: A physics-based and machine learning hybrid approach to snow distribution mapping in the sub-Arctic Supporting Data

Snow in the Arctic and sub-Arctic is highly variable at fine scales, with deep drifts and shallow scoured areas creating a complex pattern of snow on the landscape. This fine-scale variation in snow is driven primarily by landscape and vegetation properties. Some landscape features, such as river beds, will rapidly fill in with snow during the wintertime due to high winds, while shrubs will trap blowing snow, resulting in drifts. Meanwhile, snow will blow off of exposed areas, resulting in abnormally shallow snow. These complex interactions between wind, vegetation, and terrain are difficult to represent well with physically-based models, but machine learning techniques have shown promise in the past. Here, we propose a hybrid modeling approach, where we use machine learning derived snow pattern maps to inform SnowModel, a physically-based snow process model. We develop and test this technique at the Teller 27 Seward Peninsula NGEE-Arctic study site. This dataset includes 5 *.nc files of model inputs and outputs plus one user guide (*.pdf). We present the data we use to drive SnowModel (vegetation type, digital elevation model), the snow pattern maps used to inform SnowModel (Standardized Depth Values maps, Machine Learning Snow Distribution Pattern), and our machine learning, SnowModel, and Hybrid snow depth and snow water equivalent results. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Estimating surface soil moisture from satellite microwave measurements and a satellite derived vegetation index

Normalized 18-GHz microwave brightness temperatures, T(B), and a vegetation index determined from satellite radiometer data are combined with climatically modeled surface moisture estimates to constrain a simple physically based soil moisture model. It is found that the normalized T(B) values correlated well with soil moisture when the data were segregated by vegetation index range, but less so when all the data were combined. By using the vegetation index parameter, the model is shown to account for about 70 percent of the variability in modeled surface soil moisture.

Owe, Manfred↗
NASA NTRS1994

Vegetation stress as a feedback mechanism in midlatitude drought

An atmospheric general circulation model with land surface properties represented by the Simplified Simple Biosphere Model is used to investigate the effect of soil moisture and vegetation stress on drought in the mid-latitudes. An idealized land-sea distribution with simple topography is used to remove as many external sources of climate variation as possible. The land consists of a single, flat, rectangular continent covered with prairie vegetation and centered on 44 deg N of an aqua planet. A control integration of 4 years is performed, and several sets of seasonal anomaly integrations are made to test the sensitivity of seasonal climate to low initial (1 April) soil moisture and dormant vegetation like what would occur during a severe drought. It is found that the inclusion of dormant vegetation during the spring and early summer greatly reduces evapotranspiration by eliminating transpiration. This affects local climate more strongly as summer progresses. Low initial soil moisture, combined with dormant vegetation, leads to a severe drought. The reduction in precipitation is much greater in magnitude than that due to low soil moisture alone, and greater than the sum of the effects computed separately. Although the short-term drought is more severe, the dormancy of the vegetation prevents further depletion of moisture in the root zone of the soil, so soil moisture begins to rebound toward the middle of summer.

Dirmeyer, Paul A.↗
DOE OSTI2025

Utility of near‐surface phenology in estimating productivity and evapotranspiration across diverse ecosystems

Abstract Agroecosystems, which include row crops, pasture, and grass and shrub grazing lands, are sensitive to changes in management, weather, and genetics. To better understand how these systems are responding to changes, we need to improve monitoring and modeling carbon and water dynamics. Vegetation Indices (VIs) are commonly used to estimate gross primary productivity (GPP) and evapotranspiration (ET), but these empirical relationships are often location and crop specific. There is a need to evaluate if VIs can be effective and, more general, predictors of ecosystem processes through time and across different agroecosystems. Near‐surface photographic (red‐green‐blue) images from PhenoCam can be used to calculate the VI green chromatic coordinate (G CC ) and offer a pathway to improve understanding of field‐scale relationships between VIs and GPP and ET. We synthesized observations spanning 76 site‐years across 15 agroecosystem sites with PhenoCam G CC and GPP or ET estimates from eddy covariance (EC) to quantify interannual variability (IAV) in the relationship between GPP and ET and G CC across. We uncovered a high degree of variability in the strength and slopes of the G CC ∼ GPP and ET relationships (R 2 = 0.1 ‐ 0.9) within and across production systems. Overall, G CC is a better predictor of GPP than ET (R 2 = 0.64 and 0.54, respectively), performing best in croplands (R 2 = 0.91). Shrub‐dominated systems exhibit the lowest predictive power of G CC for GPP and ET but have less IAV in slope. We propose that PhenoCam estimates of G CC could provide an alternative approach for predictions of ecosystem processes.

Environmental Sciences & Ecology↗
DOE OSTI2021

Gridded daily weather data for North America with comprehensive uncertainty quantification

Abstract Access to daily high-resolution gridded surface weather data based on direct observations and over long time periods is essential for many studies and applications including vegetation, wildlife, soil health, hydrological modelling, and as driver data in Earth system models. We present Daymet V4, a 40-year daily meteorological dataset on a 1 km grid for North America, Hawaii, and Puerto Rico, providing temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. The dataset represents several improvements from a previous version, and this data descriptor provides complete documentation for updated methods. Improvements include: reductions in the timing bias of input reporting weather station measurements; improvement to the three-dimensional regression model techniques in the core algorithm; and a novel approach to handling high elevation temperature measurement biases. We show cross-validation analyses with the underlying weather station data to demonstrate the technical validity of new dataset generation methods, and to quantify improved accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Controls and relationships of soil organic carbon abundance and persistence vary across pedo–climatic regions

One of the largest uncertainties in the terrestrial carbon cycle is the timing and magnitude of soil organic carbon (SOC) response to climate and vegetation change. This uncertainty prevents models from adequately capturing SOC dynamics and challenges the assessment of management and climate change effects on soils. Reducing these uncertainties requires simultaneous investigation of factors controlling the amount (SOC abundance) and duration (SOC persistence) of stored C. We present a global synthesis of SOC and radiocarbon profiles (n Profile = 597) to assess the timescales of SOC storage. We use a combination of statistical and depth–resolved compartment models to explore key factors controlling the relationships between SOC abundance and persistence across pedo–climatic regions and with soil depth. This allows us to better understand (i) how SOC abundance and persistence covary across pedo–climatic regions and (ii) how the depth dependence of SOC dynamics relates to climatic and mineralogical controls on SOC abundance and persistence. We show that SOC abundance and persistence are differently related; the controls on these relationships differ substantially between major pedo–climatic regions and soil depth. For example, large amounts of persistent SOC can reflect climatic constraints on soils (e.g., in tundra/polar regions) or mineral absorption, reflected in slower decomposition and vertical transport rates. In contrast, lower SOC abundance can be found with lower SOC persistence (e.g., in highly weathered tropical soils) or higher SOC persistence (e.g., in drier and less productive regions). We relate variable patterns of SOC abundance and persistence to differences in the processes constraining plant C input, microbial decomposition, vertical C transport and mineral SOC stabilization potential. This process–oriented grouping of SOC abundance and persistence provides a valuable benchmark for global C models, highlighting that pedo–climatic boundary conditions are crucial for predicting the effects of climate change and soil management on future C abundance and persistence.

54 ENVIRONMENTAL SCIENCES↗