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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (~16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008).

Case, Jonathan L.↗
NASA NTRS2010

Characterization of Forest Opacity Using Multi-Angular Emission and Backscatter Data

This paper discusses the results from a series of field experiments using ground-based L-band microwave active/passive sensors. Three independent approaches are employed to the microwave data to determine vegetation opacity of coniferous trees. First, a zero-order radiative transfer model is fitted to multi-angular microwave emissivity data in a least-square sense to provide "effective" vegetation optical depth. Second, a ratio between radar backscatter measurements with the corner reflector under trees and in an open area is calculated to obtain "measured" tree propagation characteristics. Finally, the "theoretical" propagation constant is determined by forward scattering theorem using detailed measurements of size/angle distributions and dielectric constants of the tree constituents (trunk, branches, and needles). The results indicate that "effective" values underestimate attenuation values compared to both "theoretical" and "measured" values.

Kurum, Mehmet↗
NASA NTRS2018

Developing a Remotely Sensed Drought Monitoring Indicator for Morocco

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the socio-economy of Morocco, which is characterized both by low-average annual rainfall and high irregularity in the spatial distribution and timing of precipitation across the country. This work aims to develop a comprehensive and integrated method for drought monitoring based on remote sensing techniques. The main input parameters are derived monthly from satellite data at the national scale and are then combined to generate a composite drought index presenting different severity classes of drought. The input parameters are: Standardized Precipitation Index calculated from satellite-based precipitation data since 1981 (CHIRPS), anomalies in the day-night difference of Land Surface Temperature as a proxy for soil moisture, Normalized Difference Vegetation Index anomalies from Moderate Resolution Imaging Spectroradiometer (MODIS) data and Evapotranspiration anomalies from surface energy balance modeling. All of these satellite-based indices are being used to monitor vegetation condition, rainfall and land surface temperature. The weighted combination of these input parameters into one composite indicator takes into account the importance of the rainfall-based parameter (SPI). The composite drought index maps were generated during the growing seasons going back to 2003. These maps have been compared to both the historical, in situ precipitation data across Morocco and with the historical yield data across different provinces with information being available since 2000. The maps are disseminated monthly to several main stakeholders' groups including the Ministry of Agriculture and Department of Water in Morocco.

Drought monitoring↗
NASA NTRS1983

Measured microwave emission and scattering in vegetation canopies

Radiometric measurements of vegetation have been conducted by placing reflecting metal screens under three types of vegetation, and using a radiometer operating in the 11.1 and 5.88 cm wavelengths. A theoretical model involving a loss term and a scattering albedo value was fitted to the measured temperatures. Canopy absorption and scattering must be determined to assess the vegetation's effect on microwave sensor sensitivity to the dielectric properties of the underlying soil. Using the theoretical model and calculated absorption and scattering values derived from the screened fields, the effect of vegetation cover on soil emission was predicted. A comparison of predicted full canopy temperatures with those measured was then conducted.

Brunfeldt, D. R.↗
NASA NTRSDate not supplied

An Observation-Driven Framework for Modeling Post-Fire Hydrologic Response: Evaluation for Two Central California Case Studies

In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale for post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of fire impacts on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify wildfire impacts on runoff generation. We consider the vegetation and soil disturbances as the primary contributors to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty and consider model sensitivity, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We evaluated the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration. Analysis of two events in central California shows that 1) the LAI assimilation has a greater impact on water balance; and 2) the soil scheme captures a range of outcomes, with substantial impacts on surface runoff for some ensemble members, which could affect flood potential.

Timothy M Lahmers↗
NASA NTRS1986

The effect of leaf size on the microwave backscattering by corn

Attema and Ulaby (1978) proposed the cloud model to predict the microwave backscattering properties of vegetation. This paper describes a modification in which the biophysical properties and microwave properties of vegetation are related at the level of the individual scatterer (e.g., the leaf or the stalk) rather than at the level of the aggregated canopy (e.g., the green leaf area index). Assuming that the extinction cross section of an average leaf was proportional to its water content, that a power law relationship existed between the backscattering cross section of an average green corn leaf and its area, and that the backscattering coefficient of the surface was a linear function of its volumetric soil moisture content, it is found that the explicit inclusion of the effects of corn leaf size in the model led to an excellent fit between the observed and predicted backscattering coefficients. Also, an excellent power law relationship existed between the backscattering cross section of a corn leaf and its area.

Paris, J. F.↗
NASA NTRS1984

Remote Sensing of Earth Terrain

Theoretical models that are useful and practical in relating remote sensing data to the important physical parameters characterizing Earth terrain are developed. The development of models that are useful in data analysis and interpretation, scene simulation, and developing new remote sensing approaches and techniques is discussed. Numerous theoretical models that are applicable to the active and passive remote sensing of plowed fields, atmospheric precipitation, vegetation, and snow fields were developed. The radiative transfer theory is used to interpret the active and passive data as a function of rain rate. Both the random medium model and the discrete scatterer model is used to study the remote sensing of vegetation fields. Due to the non-spherical geometry of the scatterers there is strong azimuthal dependence in the observed data. Thus, the anisotropic random medium model and the discrete scatterer model with nonspherical particles was developed. In order to relate the remote sensing data to the actual physical parameters, the scattering of electromagnetic waves from randomly distributed dielectric scatterers was studied. Both the rigorous random discrete scatterer theory and the strong fluctuation theory are used to derive the backscattering cross section in terms of the actual physical parameters and the results agree well with the data obtained from the snow fields.

Kong, J. A.↗
NASA NTRS1993

The effect of topography on radar scattering from vegetated areas

This paper shows how the radar scattering from vegetated areas is affected by the topography of the surface underneath the vegetation. It is shown, using a discrete scatterer model, that the dominant scattering mechanism may change drastically when the ground surface is tilted relative to the horizontal. In the case of a horizontal ground surface, total scattering may be dominated by scattering off the tree trunks, followed by a reflection off the ground surface. For a relatively small tilt in the ground surface (about 2 deg from horizontal), the ground-trunk interaction term may be replaced by scattering from the branches alone as the dominant scattering mechanism. We also show that the effect of the topography is more pronounced for scattering by longer wavelengths, and discuss the implications on algorithms designed to infer forest woody biomass and soil and vegetation moisture using polarimetric SAR data. The effect of the topography on the scattering behavior from forested areas is illustrated with images acquired by the NASA/JPL three-frequency polarimetric SAR over the Black Forest in Germany.

Van Zyl, Jakob J.↗
NASA NTRS1994

Atmosphere-biosphere exchange flux of carbon dioxide in a tallgrass prairie modeled with satellite spectral data

The estimation of the rate of net CO2 uptake of vegetated land surfaces is essential for studies of global carbon cycle. The present paper demonstrates the use of spectral reflectance data from satellite remote sensing to model net CO2 flux (NCF) of a tallgrass canopy at the Konza prairie, Kansas. A bidirectional reflectance canopy model was used to estimate seasonal changes in canopy leaf area index (LAI) from surface reflectances remotely sensed by SPOT 1 and Landsat 5 satellites. The radiation model was also coupled with leaf conductance-photosynthesis models to scale up stomatal conductance and NCF from individual leaves to canopy level according to radiation distribution inside the canopy. The satellite-data-driven model was able to closely simulate the seasonal change in LAI as well as the short-term variation of canopy LAI caused by the dry period during late July and early August in the area. Modeled canopy stomatal conductance (g(sub c)) and NCF agree with measurements within 0.16 cm/s and 0.28 mg m(exp -2)/s, respectively, during the growth season from late May to late August. In October both measured and modeled NCF turned to small negative values as canopy photosynthesis diminished and predicted LAI approached zero. In addition to data scatter, some of the differences between modeled and measured g(sub c) and NCF may be attributed to uncertainties in seasonal changes of plant physiological status that were not detected by satellite data; some of the differences were caused by inadequate description of the dependence of nighttime CO2 flux of soil respiration on near-surface turbulent mixing.

Gao, W.↗
NASA NTRS2025

An Observation-Driven Framework for Modeling Post-Fire Hydrologic Response: Evaluation for Two Central California Case Studies

In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale of post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of the impacts of fire on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify the impacts of wildfire on runoff generation. We consider the disturbances in the vegetation and soil as the two main factors contributing to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We consider the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration for two case studies. These case studies demonstrate the general applicability of hydrophobicity formulation to serve as a guideline for exploring the range of hydrologic responses post-fire.

Fire Hydrology↗
NASA NTRS1988

Use of LANDSAT images of vegetation cover to estimate effective hydraulic properties of soils

This work focuses on the characterization of natural, spatially variable, semivegetated landscapes using a linear, stochastic, canopy-soil reflectance model. A first application of the model was the investigation of the effects of subpixel and regional variability of scenes on the shape and structure of red-infrared scattergrams. Additionally, the model was used to investigate the inverse problem, the estimation of subpixel vegetation cover, given only the scattergrams of simulated satellite scale multispectral scenes. The major aspects of that work, including recent field investigations, are summarized.

Eagleson, Peter S.↗
NASA NTRS1984

Effects of atmosphere and view and illumination geometry on visible and near infrared radiance data from the advanced very high resolution radiometer (AVHR)

The use of Dave's models to evaluate satellite off-nadir remote sensing of green vegetation cover types by simulating the visible and near-infrared advanced very high resolution radiometer (AVHRR) NOAA-6 and NOAA-7 radiances for three green-leaf biomass levels and bare soil. Ground measurements of surface reflectances were used. The simulations were done along a scan line at 30 deg latitude during the summer solstice, equinox, and winter solstice. The simulation models are described and the effect of atmosphere over moderately vegetated surfaces is discussed. The results show that sensor response to atmospheric path length can be substantial for the AVHRR visible and near-infrared channels and normalized difference values, but they can be minimized by high sun and clear atmospheric viewing. The results indicate that AVHRR data would be most useful for monitoring low green leaf biomas canopies.

Holben, B. N.↗
NASA NTRS1983

A mathematical characterization of vegetation effect on microwave remote sensing from the Earth

In passive microwave remote sensing of the earth, a theoretical model that utilizes the radiative transfer equations was developed to account for the volume scattering effects of the vegetation canopy. Vegetation canopies such as alfalfa, sorghum, and corn are simulated by a layer of ellipsoidal scatterers and cylindrical structures. The ellipsoidal scatterers represent the leaves of vegetation and are randomly positioned and oriented. The orientation of ellipsoids is characterized by a probability density function of Eulerian angles of rotation. The cylindrical structures represent the stalks of vegetation and their radii are assumed to be much smaller than their lengths. The underlying soil is represented by a half-space medium with a homogeneous permittivity and uniform temperature profile. The radiative transfer quations are solved by a numerical method using a Gaussian quadrature formula to compute both the vertical and horizontal polarized brightness temperature as a function of observation angle. The theory was applied to the interpretation of experimental data obtained from sorghum covered fields near College Station, Texas.

Choe, Y.↗
NASA NTRS2022

Arizona Water Resources Ii: Utilizing Aerial Imagery and Nasa Earth Observations to Assess Pinyon-Juniper Tree Mortality in Flagstaff, AZ

Pinyon-juniper woodlands (PJW) provide critical and resilient habitat for the local mammal and small bird species of Arizona's northern xeric environment. Drought in Arizona has been persistent for many decades, yet in 2021 PJW experienced a mass tree mortality event at the Wupatki National Monument (WNM) and in other areas across the American Southwest. Previously, researchers at the National Park Service (NPS) and the team from the NASA DEVELOP National Program attempted to quantify the extent of mortality in Northern Arizona between 2015 and 2021 using high resolution National Agricultural Imagery Program (NAIP) aerial photographs. This project aimed to improve the previous term’s methodology and expanded the comparison of the post-mortality event in 2021 to include tree cover assessments for 2017 and 2019. In this iteration, the team utilized NAIP imagery in conjunction with Landscape Fire and Resource Management Planning Tools (LANDFIRE) to calculate the total difference in PJW mortality using an unsupervised classification model trained from multi-date Modified Soil-Adjusted Vegetation Index (MSAVI) and the Visible Atmospherically Resistant Index (VARI) data for the study area. The research also assessed correlations between tree mortality and environmental factors using Western Land Data Assimilation System (WLDAS) modelled climate data. Average PJW mortality from 2015 to 2021 was 21.63% including 19.8% in WNM with the vast majority of dieback occurring between 2019 and 2021. The correlations were weak with the most correlated variables being bare soil evaporation (0.15), rainfall (0.14), groundwater storage (0.13), and wind speed (0.12), perhaps indicating drought as a likely driver of PJW mortality.

pinyon-juniper woodlands↗
NASA NTRS2011

Analysis of the Relationship Between Climate and NDVI Variability at Global Scales

interannual variability in modeled (CASA) C flux is in part caused by interannual variability in Normalized Difference Vegetation Index (NDVI) Fraction of Photosynthetically Active Radiation (FPAR). This study confirms a mechanism producing variability in modeled NPP: -- NDVI (FPAR) interannual variability is strongly driven by climate; -- The climate driven variability in NDVI (FPAR) can lead to much larger fluctuation in NPP vs. the NPP computed from FPAR climatology

Zeng, Fan-Wei↗
NASA NTRS1991

Transient water stress in a vegetation canopy - Simulations and measurements

Consideration is given to observational and modeling evidence of transient water stress, the effects of the transpiration plateau on the canopy radiometric temperature, and the factors responsible for the onset of the transpiration plateau, such as soil moisture. Attention is also given to the point at which the transient stress can be detected by remote measurement of surface temperature.

Carlson, Toby N.↗
NASA NTRS1984

Remote sensing of soil moisture

Four major objectives are proposed: (1) to study the sensitivity of active and passive microwave remote sensing approaches to soil moisture variations; (2) to investigate the effect of vegetation cover on microwave backscatter and emission; (3) to test theoretical models of microwave backscatter and emission from a natural terrain against the observations obtained from SIR-B and aircraft radiometer flights; and (4) to estimate vegetation biomass with airborne visible and infrared sensors.

Wang, J. R.↗
NASA NTRS2021

FORECASTING WHEAT YIELD USING REMOTE SENSING: THE ARYA FORECASTING SYSTEM

In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI)and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied toMODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.

B. Franch↗