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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Exploring Mission Design for Imaging Spectroscopy Retrievals for Land and Aquatic Ecosystems

The retrieval algorithms used for optical remote sensing satellite data to estimate Earth's geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal-to-noise ratio (SNR) performance to meet biogeoscience objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at >10 nm spectral resolution, >30 m spatial resolution, and >8-day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit, and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.

A. M. Raiho↗
NASA NTRSDate not supplied

Using Dual-Regression to Produce 16-Day Average AIRS Soundings

Temperature and humidity profiles are needed to estimate surface radiation budget. The Clouds and the Earth’s Radiant Energy System (CERES) team uses temperature and humidity profiles from a reanalysis product produced by NASA’s Global Modeling and Assimilation Office for surface irradiance computations. Biases and drifts in temperature and humidity profiles in the reanalysis product result in biases and drifts in surface irradiances computed with them. One approach to correct biases and drifts in temperature and humidity profile is to use satellite observations, similar to assimilating instantaneous spectral radiances to correct modeled temperature and humidity profiles. In this work, we use mean spectral radiances to test the possibility of understanding biases in reanalysis mean temperature and humidity profiles. Specifically, we use 16-day mean Atmospheric Infrared Sounder (AIRS) radiances and only use clear-sky spectral radiances with a viewing zenith angle nadir to near-nadir. We use the dual-regression method (Smith et al. 2012) to test whether temperature and humidity profiles retrieved from the 16-day mean spectral radiances agree with the average of temperature and humidity profiles derived from instantaneous spectral radiances. When daytime and nighttime spectral radiances are averaged separately and daytime and nighttime retrievals are performed separately, temperature and humidity profiles derived from the mean spectral radiances agree well with mean temperature and humidity profiles derived from instantaneous spectral radiances. The agreement improves when clouds are further screened to compute 16-day mean clear-sky spectral radiances.

Anthony DiNorscia↗
NASA NTRS2011

Global Intercomparison of 12 Land Surface Heat Flux Estimates

A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993-1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/ Mosaic/Noah). An intercomparison of the global latent heat flux (Q(sub le)) annual means shows a spread of approx 20 W/sq m (all-product global average of approx 45 W/sq m). A similar spread is observed for the sensible (Q(sub h)) and net radiative (R(sub n)) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Q(sub le)and Q(sub h) absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Q(sub le) differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Q(sub le) and R(sub n) were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Jimenez, C.↗
NASA NTRS2019

Evaluating a Regional Climate Model Simulation of Greenland Ice Sheet Snow and Firn Density for Improved Surface Mass Balance Estimates

Modeling vertical profiles of snow and firn density near the surface of the Greenland ice sheet (GrIS) is key to estimating GrIS mass balance, and by extension, global sea level change. To understand sources of error in simulated GrIS density, we compare GrIS density profiles from a leading regional climate model with coincident in situ measurements. We identify key contributors to model density and mass balance biases, including underestimated simulated fresh snow density (which leads to underestimation of density in the top 1 m of snow by ~10%). In areas undergoing frequent melting, positive density biases (of 7% in the top 1 m, and 10% between 1 and 10 m) are likely associated with errors in representing meltwater production, retention and refreezing. The results highlight the importance of accurately capturing fresh snow density and meltwater processes in models used to estimate GrIS mass balance change.

P M Alexander↗
NASA NTRS1993

Sea ice-atmospheric interaction: Application of multispectral satellite data in polar surface energy flux estimates

This is the third annual report on: Sea Ice-Atmosphere Interaction - Application of Multispectral Satellite Data in Polar Surface Energy Flux Estimates. The main emphasis during the past year was on: radiative flux estimates from satellite data; intercomparison of satellite and ground-based cloud amounts; radiative cloud forcing; calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible channels and comparison of two satellite derived albedo data sets; and on flux modeling for leads. Major topics covered are arctic clouds and radiation; snow and ice albedo, and leads and modeling.

Steffen, Konrad↗
NASA NTRS1992

How well can regional fluxes be derived from smaller-scale estimates?

Regional surface fluxes are essential lower boundary conditions for large scale numerical weather and climate models and are the elements of global budgets of important trace gases. Surface properties affecting the exchange of heat, moisture, momentum and trace gases vary with length scales from one meter to hundreds of km. A classical difficulty is that fluxes have been measured directly only at points or along lines. The process of scaling up observations limited in space and/or time to represent larger areas was done by assigning properties to surface classes and combining estimated or calculated fluxes using an area weighted average. It is not clear that a simple area weighted average is sufficient to produce the large scale from the small scale, chiefly due to the effect of internal boundary layers, nor is it known how important the uncertainty is to large scale model outcomes. Simultaneous aircraft and tower data obtained in the relatively simple terrain of the western Alaska tundra were used to determine the extent to which surface type variation can be related to fluxes of heat, moisture, and other properties. Surface type was classified as lake or land with aircraft borne infrared thermometer, and flight level heat and moisture fluxes were related to surface type. The magnitude and variety of sampling errors inherent in eddy correlation flux estimation place limits on how well any flux can be known even in simple geometries.

Moore, Kathleen E.↗
NASA NTRS1993

How well can regional fluxes be derived from smaller-scale estimates?

Regional surface fluxes are essential lower boundary conditions for large scale numerical weather and climate models and are the elements of global budgets of important trace gases. Surface properties affecting the exchange of heat, moisture, momentum and trace gases vary with length scales from one meter to hundreds of km. A classical difficulty is that fluxes have been measured directly only at points or along lines. The process of scaling up observations limited in space and/or time to represent larger areas was done by assigning properties to surface classes and combining estimated or calculated fluxes using an area weighted average. It is not clear that a simple area weighted average is sufficient to produce the large scale from the small scale, chiefly due to the effect of internal boundary layers, nor is it known how important the uncertainty is to large scale model outcomes. Simultaneous aircraft and tower data obtained in the relatively simple terrain of the western Alaska tundra were used to determine the extent to which surface type variation can be related to fluxes of heat, moisture, and other properties. Surface type was classified as lake or land with aircraft borne infrared thermometer, and flight level heat and moisture fluxes were related to surface type. The magnitude and variety of sampling errors inherent in eddy correlation flux estimation place limits on how well any flux can be known even in simple geometries.

Moore, Kathleen E.↗
NASA NTRS1980

Thermography for estimating near-surface soil moisture under developing crop canopies

Previous investigations of thermal infrared techniques using remote sensors (thermography) for estimating soil water content have been limited primarily to bare soil. Ground-based and aircraft investigations were conducted to evaluate the potential for extending the thermography approach to developing crop canopies. A significant exponential relationship was found between the volumetric soil water content in the 0-4 cm soil layer and the diurnal difference between surface soil temperature measured at 0230 and 1330 LST (satellite overpass times of NASA's Heat Capacity Mapping Mission - HCMM). Surface soil temperatures were estimated using minimum air temperature, percent cover of the canopy and remote measurements of canopy temperature. Results of the investigation demonstrated that thermography can potentially be used to estimate soil temperature and soil moisture throughout a complete growing season for a number of different crops and soils.

Heilman, J. L.↗
NASA NTRS1976

Development of weight and cost estimates for lifting surfaces with active controls

Equations and methodology were developed for estimating the weight and cost incrementals due to active controls added to the wing and horizontal tail of a subsonic transport airplane. The methods are sufficiently generalized to be suitable for preliminary design. Supporting methodology and input specifications for the weight and cost equations are provided. The weight and cost equations are structured to be flexible in terms of the active control technology (ACT) flight control system specification. In order to present a self-contained package, methodology is also presented for generating ACT flight control system characteristics for the weight and cost equations. Use of the methodology is illustrated.

Anderson, R. D.↗
NASA NTRS1991

Sea ice - atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates

In the past six months, work has continued on energy flux sensitivity studies, ice surface temperature retrievals, corrections to Advanced Very High Resolution Radiometer (AVHRR) thermal infrared data, modelling of cloud fraction retrievals, and radiation climatologies. We tentatively conclude that the SSM/I may not provide accurate enough estimates of ice concentration and type to improve our shorter term energy flux estimates. SSM/I derived parameters may still be applicable in longer term climatological flux characterizations. We hold promise for a system coupling observation to a ice deformation model. Such a model may provide information on ice distribution which can be used in energy flux calculations. Considerable variation was found in modelled energy flux estimates when bulk transfer coefficients are modulated by lead fetch. It is still unclear what the optimum formulation is and this will be the subject of further work. Data sets for ice surface temperature retrievals were assembled and preliminary data analysis was started. Finally, construction of a conceptual framework for further modelling of the Arctic radiation flux climatology was started.

Steffen, Konrad↗
NASA NTRS1997

Estimation of Bare Surface Soil Moisture and Surface Roughness Parameter Using L-Band SAR Image Data

An algorithm based on a fit of the single-scattering Integral Equation Method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well- managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.

Shi, Jian-Cheng↗
NASA NTRS1994

On the use of radiative surface temperature to estimate sensible heat flux over sparse shrubs in Nevada

The accurate partitioning of available energy into sensible and latent heat flux is crucial to the understanding of surface atmosphere interactions. This issue is more complicated in arid and semi arid regions where the relative contribution to surface fluxes from the soil and vegetation may vary significantly throughout the day and throughout the season. A three component model to estimate sensible heat flux over heterogeneous surfaces is presented. The surface was represented with two adjacent compartments. The first compartment is made up of two components, shrubs and shaded soil, the second of open 'illuminated' soil. Data collected at two different sites in Nevada (U.S.) during the Summers of 1991 and 1992 were used to evaluate model performance. The results show that the present model is sufficiently general to yield satisfactory results for both sites.

Chehbouni, A.↗
NASA NTRS1997

Estimation of Bare Surface Soil Moisture and Surface Roughness Parameter Using L-Band SAR Image Data

An algorithm based on a fit of the single-scattering Integral Equation Method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quasi-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.

Shi, Jian-Cheng↗
NASA NTRS1987

Estimation of the average surface heat flux over an inhomogeneous terrain from the vertical velocity variance

An indirect method of estimating the surface heat flux from observations of vertical velocity variance at the lower mid-levels of the convective atmospheric boundary layer is described. Comparison of surface heat flux estimates with those from boundary-layer heating rates is good, and this method seems to be especially suitable for inhomogeneous terrain for which the surface-layer profile method cannot be used.

Eilts, M. D.↗
NASA NTRS1998

Multi-Satellite Estimates of Land-Surface Properties for Determination of Energy and Water Budgets

Using the WETNET database, existing methods for the estimation of surface wetness from SSM/I data have been assessed and further developed. A physical-statistical method for optimal estimation of daily surface heat flux and Bowen ratio on the mesoscale has been developed and tested. This method is based on observations of daytime planetary boundary layer (PBL) growth from operational ravansonde and daytime land-surface temperature amplitude from Geostationary Operational Environmental (GOES) satellites. The mesoscale patterns of these heat fluxes have been compared with an AVHRR-based vegetation index and surface wetness (separately estimated from SSM/I and in situ observations). Cases of the 1988 Midwest drought and a surface/atmosphere moisture gradient (dry-line) in the southern Plains were studied. The analyses revealed significant variations in sensible heat flux (S(sub 0), and Bowen ratio, B(sub 0)) associated with vegetation cover and antecedent precipitation. Relationships for surface heat flux (and Bowen ratio) from antecedent precipitation and vegetation index have been developed and compared to other findings. Results from this project are reported in the following reviewed literature.

Menzel, W. Paul↗
NASA NTRS1997

Correction of Sampling Errors in Ocean Surface Cross-Sectional Estimates from Nadir-Looking Weather Radar

The return from the ocean surface has a number of uses for airborne meteorological radar. The normalized surface cross section has been used for radar system calibration, estimation of surface winds, and in algorithms for estimating the path-integrated attenuation in rain. However, meteorological radars are normally optimized for observation of distributed targets that fill the resolution volume, and so a point target such as the surface can be poorly sampled, particularly at near-nadir look angles. Sampling the nadir surface return at an insufficient rate results in a negative bias of the estimated cross section. This error is found to be as large as 4 dB using observations from a high-altitude airborne radar. An algorithm for mitigating the error is developed that is based upon the shape of the surface echo and uses the returned signal at the three range gates nearest the peak surface echo.

Caylor, I. Jeff↗
NASA NTRS1991

Estimation of emittances and surface temperatures from AVHRR data

An approach to the estimation of surface temperatures and emissivities using AVHRR (Advanced Very High Resolution Radiometer) data acquired near noon is presented. Thermal data in channels 3, 4, and 5 were used to estimate the reflective component in 3. For three water bodies in Oregon the average emittances found for channels 3 and 4 were 0.979 and 0.991, respectively. Also studied were agricultural sites and forests, comprising both primary (PF) and secondary (SF) types. For the agricultural sites, the average emittances were 0.919 and 0.943 for channels 3 and 4. For the PF and SF the results were 0.976 and 0.983 for channel 3 and 0.988 and 0.995 for channel 4.

Schutt, John B.↗