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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Improving NASA GEOS Atmospheric CO2 Simulations by Calibrating CASA Surface Fluxes with an Empirical Sink

With the adoption of the Paris climate accord, efforts to monitor and understand both anthropogenic and natural carbon sources and sinks are increasing across the world. Given their low latency and global coverage, satellite observations of atmospheric carbon dioxide (CO2) are poised to make important contributions to this field. The combination of satellite data and high resolution global models can be used to monitor changes in carbon fluxes and to evaluate the consistency of nationally reported emissions estimates in support of multiple stakeholder communities. However, a consistent challenge to such work has been the high latency of surface carbon flux estimates, which are often not available for a year or more. This presentation describes the construction of surface carbon flux estimates meant to improve the near real time simulation of atmospheric CO2 with NASA's Goddard Earth Observing System (GEOS) general circulation model. The surface flux estimates begin with a collection of bottom-up fluxes which incorporate satellite measurements in their construction, e.g. vegetation indices in the Carnegie-Ames-Stanford Approach (CASA) and nighttime lights in the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). From there, we take the additional step of using an empirical sink to calibrate terrestrial net biospheric exchange (NBE) to estimated values from atmospheric inversion systems. This approach removes a known, systematic bias in predicted atmospheric mixing ratios. Using these fluxes in a free running simulation, the model is able to reproduce in situ measurements with the same skill as when it uses gridded fluxes from a flux inversion system. Using these fluxes as a prior in an assimilation system, e.g. one incorporating retrievals of column CO2 from the Orbiting Carbon Observatory 2 (OCO-2), allows the analysis to capture variability in CO2 on scales that would be missed otherwise. This approach supports NASA's capability to forecast atmospheric CO2 up to two weeks in advance by leveraging a GEOS system used to produce quasi-operational weather analyses and forecasts, providing a valuable new tool to the carbon monitoring research and applications communities.

Weir, B.↗
NASA NTRS1992

Modeling energy and mass fluxes from prairie canopies

The main emphasis of this research project is on partitioning of mass and energy fluxes between vegetation and soil at the FIFE site, preparation of data from the FIFE Information System for an international thermal data set comparison, and studying the relation between surface temperatures observed from satellites and in situ measurements of surface temperature.

Norman, John M.↗
NASA NTRS1996

Remotely Sensed Index of Deforestation/Urbanization for use in Climate Models

The purpose of this investigation is to use a new method for deriving land surface parameters from a combination of thermal infrared and vegetation index measurements from satellites (Landsat-TM, and NOAA-AVHRR) and to integrate these parameters with more conventional data bases. We have completed an investigation of urbanization in the State College, PA area and have begun work in Chester County, PA, and Costa Rica. Our basic hypothesis is that changes in land use, including deforestation, exert a profound influence on local microclimates whose effects may greatly exceed in importance those occurring on larger scales.

Carlson, Toby N.↗
NASA NTRS2014

Seasonal Parameterizations of the Tau-Omega Model Using the ComRAD Ground-Based SMAP Simulator

NASA's Soil Moisture Active Passive (SMAP) mission is scheduled for launch in November 2014. In the prelaunch time frame, the SMAP team has focused on improving retrieval algorithms for the various SMAP baseline data products. The SMAP passive-only soil moisture product depends on accurate parameterization of the tau-omega model to achieve the required accuracy in soil moisture retrieval. During a field experiment (APEX12) conducted in the summer of 2012 under dry conditions in Maryland, the Combined Radar/Radiometer (ComRAD) truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40 degrees over corn and soybeans throughout the crop growth cycle. A similar experiment was conducted only over corn in 2002 under normal moist conditions. Data from these two experiments will be analyzed and compared to evaluate how changes in vegetation conditions throughout the growing season in both a drought and normal year can affect parameterizations in the tau-omega model for more accurate soil moisture retrieval.

SMAP↗
DOE OSTI2022

Predicting spring green-up across diverse North American grasslands

Vegetation phenology influences many ecosystem and climate processes, such as carbon uptake and energy and water cycles. Thus, understanding drivers of vegetation phenology is crucial for predicting current and future impacts of climate change on ecological systems. Existing models can accurately predict the date of spring green-up in temperate forests but tend to perform poorly in grassland systems. We hypothesize this is because most do not incorporate water availability, a primary limiting factor for grassland plants. In this study, we used long-term datasets of digital imagery from the PhenoCam Network of 43 diverse North American grassland sites (195 site-years) to test existing spring phenology models, as well as develop several new models that incorporate precipitation or soil moisture (53 models). As a result, most of the new models performed substantially better, with the best model requiring sufficient accumulated precipitation followed by warm temperatures to trigger spring onset (root mean square error, RMSE, between predicted and observed dates = 16.0 days). Importantly, the best model performed well across all grassland types using a single set of parameters, from temperate to arid grasslands. Since plants are adapted to their local climates, model performance was further improved when parameters were independently optimized for four separate climate regions (RMSE = 10.4 days). Therefore, both sufficient precipitation and temperature are required for grassland green-up, but optimal thresholds vary by region. Running the top model with projected climate data (representative concentration pathway 8.5) suggests that, depending on the climate region, spring onset will occur up to 12 days earlier within 100 years in temperature-limited sites, but the trend is unclear for precipitation-limited sites (3.5 ± 8.0 days later). This new phenology model improves our ability to understand and predict grassland dynamics, with implications for both current and future ecosystem processes related to carbon and water cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Microbial carbon use efficiency promotes global soil carbon storage

Soils store more carbon than other terrestrial ecosystems. How soil organic carbon (SOC) forms and persists remains uncertain, which makes it challenging to understand how it will respond to climatic change. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Processed Surface Imagery & Raster Imagery taken onboard TigerShark (U3) UAS

Land-surface orthomosaic imagery derived from the images collected using the Micasense Altum Imager on board the UAS TigerShark (U3) on March 11th, 2021 at an altitude of 2000 ft. The Altum takes images of the land surface across five visible bands and one long wave infrared thermal band. The photogrametry software Agisoft PhotoScan v 1.4 is used to align and stitch the images into a larger composet image using the technique of structure from motion image capture to construct a dense cloud and 3-D model of the surface, which is used to produce a digital elevation model of the terraine surveyed and orthomosaic imagery. This dataset contains the raster calculated orthomosaics of various vegetative indicies, which are used to indicate plant health and land surface characteristics, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Water Index (NDWI), surface temperature, and Enhanced_vegetation_index (EVI). These orthomosaics were collected west of the Starkville, MS, airport, and include natural, agricultural, and industrial land coverage. The images have a resolution of 0.2 m/pix.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Urban-Scale Heat Wave Visualization: WRF-UCM modeling in Los Angeles, California

This visualization includes an E3SM-based animation of temperatures in North America during the July 2018 heat wave, followed by a higher resolution focus over Los Angeles using the WRF-UCM model to illustrate neighborhood-scale temperatures that account for urban features like buildings, waste heat, and vegetation, as well as sea breeze impacts. Finally, we show the potential impact of cool roofs to reduce temperatures.

Vahmani, Pouya↗
NASA NTRS1974

The economic impact of remote sensing data as the source of nonpoint pollution monitoring and control

Nonpoint pollution of streams with sediment as a result of runoff from alternative uses of land has become a socially unacceptable product of economic activity. This report describes a research approach to economically achieve correction of the nonpoint pollution problem. The research approach integrates the economic model with those data which may be obtainable from remotely sensed sources. The economic problem involves measurement of the direct benefits and costs associated with the changes in land management activities necessary to reduce the level of nonpoint pollution. Remotely sensed data from ERTS-1 may provide some of the information required for the economic model which indicates efficient solutions to the nonpoint pollution problem. Three classes of data (i.e., soil categories, vegetative cover, and water turbidity) have the potential to be measured by ERTS-1 systems. There is substantial research which indicates the ability of ERTS-1 to measure these classes of data under selected conditions.

Miller, W. L.↗
NASA NTRS1979

Radiometric resolution for monitoring vegetation: How many bits are needed?

The significance of the various number of radiometric quantizing levels required for satellite monitoring of vegetation resources was evaluated by using in situ collected spectral reflectance data, an atmospheric radiative transfer simulation model, and a satellite sensor simulation model. Reflectance data were converted to radiance data; passed through a model atmosphere to an altitude of 706 km; and subsequently quantized at 16, 32, 64, 128, 256, and 512 digital count levels for Thematic Mapper bands TM3(0.63 - 0.69 microns) and TM4(0.76 - 0.90 microns). The simulated digital count data were regressed against the in situ biological data to quantify the relationship between quantizing levels.

Tucker, C. J.↗
NASA NTRS1980

Radiometric resolution for monitoring vegetation - how many bits are needed

The number of radiometric quantizing levels required for satellite monitoring of vegetation resources was evaluated by using in situ collected spectral reflectance data, an atmospheric radiative transfer simulation model, and a satellite sensor simulation model. Reflectance data were converted to radiance data, passed through a model atmosphere to an altitude of 706 km, and subsequently quantized at 16, 32, 64, 128, 256 and 512 digital count levels for Thematic Mapper bands TM3 (0.63-0.69 microns) and TM4 (0.76-0.90 microns). The simulated digital count data were regressed against in situ biological data to quantify the relationship between quantizing levels. Results of the analysis demonstrated that solar zenith angle has an effect on the quantization equivalent change in reflectance, that 256 quantizing levels gave a 1-3% improvement per channel over 64 quantizing levels, and that 256 quantizing levels gave a 1% improvement per channel over 128 quantizing levels. No improvements were found for 256 versus 512 quantizing levels.

Tucker, C. J.↗
NASA NTRS1985

Spectral estimation of green leaf area index of oats

Green leaf area index (LAI) is a measure of vegetative growth and development and is frequently used as an input parameter in yield estimation and evapotranspiration models. Extensive destructive sampling is usually required to achieve accurate estimates of green LAI in natural situations. In this investigation, a statistical modeling approach was used to predict the green LAI of oats from bidirectional reflectance data collected with multiband radiometers. Stepwise multiple regression models based on two sets of spectral reflectance factors accounted for 73 percent and 65 percent of the variance in green LAI of oats. Exponential models of spectral data transformations of greenness, normalized difference, and near-infrared/red ratio accounted for more of the variance in green LAI than the multiple regression models.

Best, R. G.↗
NASA NTRS1986

The first International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment - FIFE

The International Satellite Land Surface Climatology Project (ISLSCP) will verify the use of satellite data for the estimation of land-surface properties through field experiments using point measurements on the ground and areal measurements from aircraft overflights. It will study approaches for obtaining areal averages of the radiation, moisture, and heat fluxes made using remotely sensed data, by combining the surface point measurements of the fluxes with the aircraft areal observations using a surface energy balance model. Surface parameters to be estimated from aircraft observations include: surface radiation temperature, albedo, land cover or vegetation index, and surface soil moisture. The latter will be obtained using passive and active microwave approaches. The area-averages of the surface properties will be compared with satellite data. The First ISLSCP Field Experiment is planned for a site having relatively uniform vegetation cover in the central great plains of the U.S.A.

Schmugge, T. J.↗
NASA NTRS1991

Analyzing remote sensing geobotanical trends in Quetico Provincial Park, Ontario, Canada, using digital elevation data

A 10 x 13-km area in Quetico Provincial Park, Canada has been studied using a digital elevation model to separate different drainage classes and to examine the influence of site factors and lithology on vegetation. Landsat Thematic Mapper data have been classified into six forest classes of varying deciduous-coniferous cover through nPDF, a procedure based on probability density functions. It is shown that forests growing on mafic lithologies are enriched in deciduous species, compared to those growing on granites. Of the forest classes found on mafics, the highest coniferous component was on north facing slopes, and the highest deciduous component on south facing slopes. Granites showed no substantial variation between site classes. The digital elevation derived site data is considered to be an important tool in geobotanical investigations.

Warner, Timothy A.↗
NASA NTRS1993

Surface energy fluxes at Central Florida during the convection and precipitation electrification experiment

One of the objectives of CaPE is to better understand the convective process in central and south Florida during the warm season. The energy and moisture exchanges between the surface and the atmosphere are closely related to this process. Some recent studies have shown that the surface energy balance plays an important role in the climatic fields (Shukla and Mintz, 1982; Sud and Smith, 1985; Sato et. al, 1989). Surface energy fluxes and related surface processes such as evapotranspiration and sensible heat transfer directly effect the temperature, humidity, cloud formation and precipitation. For example, mesoscale circulation around a discontinuity in vegetation type were shown to be stronger with wet soil than with dry soil using an evapotranspiration model (Pinty et. al, 1989). In order to better describe the processes in the atmosphere at various scales and improve our ability of modeling and predicting weather related events, it is crucial to understand the mechanism of surface energy transfer in relation to atmospheric events. Surface energy flux measurements are required to fully understand the interactions between the atmosphere and the surface.

Nie, D.↗
NASA NTRS2006

Seawater/Saline Agriculture for Energy, Warming, Water, Rainfall, Land, Food and Minerals

The combination of the incipient demise of cheap oil and increasing evidence of Global Warming due to anthropogenic fossil carbon release has reinvigorated the need for and efforts on Renewable energy sources, especially for transportation applications. Biomass/Bio-diesel appears to have many benefits compared to Hydrogen, the only other major renewable transportation fuel candidate. Biomass Production is currently limited by available arable land and fresh water. Halophyte Plants and seawater irrigation proffer a wholly new biomass production mantra using wastelands and very plentiful seawater. Such an approach addresses many-to-most of the major emerging Societal Problems including Land, Water, Food, Warming and Energy. For many reasons, including seawater agriculture, portions of the Sahara appear to be viable candidates for future Biomass Production. The apparent nonlinearity between vegetation cover and atmospheric conditions over North Africa necessitates serious coupled boundary layer Meteorology and Global Circulation Modeling to ensure that this form of Terra Forming is Favorable and to avoid adverse Unintended Consequences.

Bushnell, Dennis↗
DOE OSTI2023

Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling

Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The data is published via PNNL DataHub.

Li, Lingcheng↗