Lunar surface strength estimate from Orbiter II photograph.
Lunar surface bearing strength estimate from Orbiter II photograph, noting Sabine D crater measurement as potential landing site for Surveyor and Apollo
LUNAR LANDING SITE↗
SEARCH · Engineering Papers
Results for “Surface estimation”
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.
Application of Artificial Intelligence for Surface PM2.5 Estimations from Geostationary Satellite and Atmospheric Numerical Model Data
PM2.5, particulate matter (PM) with a diameter less than or equal to 2.5 μm, is emitted from anthropogenic fuel combustion and forest fires. Due to their small size, PM2.5 can penetrate into respiratory systems and cause or exacerbate serious illness. The US Environmental Protection Agency (EPA) regulates the levels of surface PM2.5 but surface monitoring has spatial and temporal limitations. The Aerosol Optical Depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) missions and meteorological factors can be utilized as an alternative technique to estimate surface PM2.5 levels at a higher spatial and temporal resolution compared to surface monitors. Traditional estimation approaches rely on linear regression techniques and have limitations modeling the nonlinear relationship between the meteorological factors, AOD retrievals, and surface PM2.5. We compare different machine learning techniques and identify the best-suited model that can represent the nonlinearity between the factors affecting PM2.5 levels
Manisha Khatri↗
Feasibility of estimation of surface air temperature from meteorological satellite data test plan
A conceptual system designed to estimate daily surface air temperatures utilizing radiometric data obtained from polar orbiting meteorological satellites is discussed in this memorandum. The Surface Air Temperature Estimation System is an outgrowth of previous developmental and operational systems. The system represents an effort to integrate both satellite and surface meteorological observations into an operational framework which would be usable worldwide.
Phinney, D. E.↗
Synergism of active and passive microwave data for estimating bare surface soil moisture
Active and passive microwave sensors were applied effectively to the problem of estimating the surface soil moisture in a variety of environmental conditions. Research to date has shown that both types of sensors are also sensitive to the surface roughness and the vegetation cover. In estimating the soil moisture, the effect of the vegetation and roughness are often corrected either by acquiring multi-configuration (frequency and polarization) data or by adjusting the surface parameters in order to match the model predictions to the measured data. Due to the limitations on multi-configuration spaceborne data and the lack of a priori knowledge of the surface characteristics for parameter adjustments, it was suggested that the synergistic use of the sensors may improve the estimation of the soil moisture over the extreme range of naturally occurring soil and vegetation conditions. To investigate this problem, the backscattering and emission from a bare soil surface using the classical rough surface scattering theory were modeled. The model combines the small perturbation and the Kirchhoff approximations in conjunction with the Peak formulation to cover a wide range of surface roughness parameters with respect to frequency for both active and passive measurements. In this approach, the same analytical method was used to calculate the backscattering and emissivity. Therefore, the active and passive simulations can be combined at various polarizations and frequencies in order to estimate the soil moisture more actively. As a result, it is shown that (1) the emissivity is less dependent on the surface correlation length, (2) the ratio of the backscattering coefficient (HH) over the surface reflectivity (H) is almost independent of the soil moisture for a wide range of surface roughness, and (3) this ratio can be approximated as a linear function of the surface rms height. The results were compared with the data obtained by a multi-frequency radiometer-scatterometer system working at frequencies between 3.0 GHz to 11.0 GHz. The data were acquired over bare soil surfaces with moisture variations due to freezing and thawing and roughness changes due to rain and erosion. The model predictions are shown to be in reasonable agreement with the data. In addition, it was shown that the same ratio when calculated from the data shows almost no dependence on the soil moisture. Finally, a simple technique which combines the backscattering coefficient at HH polarization (active sensing) with the emissivity at H polarization (passive sensing) is suggested for retrieving the soil moisture from bare soil surfaces.
Saatchi, Sasan S.↗
The Estimation of Surface Latent Heat Flux Over the Ocean and its Relationship to Marine Atmospheric Boundary Layer (MABL) Structure
A new technique combining active and passive remote sensing instruments for the estimation of surface latent heat flux over the ocean is presented. This synergistic method uses aerosol lidar backscatter data, multi-channel infrared radiometer data and microwave scatterometer data acquired onboard the NASA P-3B research aircraft during an extended field campaign over the Atlantic ocean in support of the Lidar In-space Technology Experiment (LITE) in September of 1994. The 10 meter wind speed derived from the scatterometers and the lidar-radiometer inferred near-surface moisture are used to obtain an estimate of the surface flux of moisture via bulk aerodynamic formulae. The results are compared with the Special Sensor Microwave Imager (SSM/I) daily average latent heat flux and show reasonable agreement with an rms error and bias of about 50 and 25 W per square meters, respectively. In addition, the MABL height, entrainment zone thickness and integrated lidar backscatter intensity are computed from the lidar data and compared with the magnitude of the surface fluxes. The results show that the surface latent heat flux is most strongly correlated with entrainment zone top, bottom and the integrated MABL lidar backscatter, with corresponding correlation coefficients of 0.62, 0.67 and 0.61, respectively.
Palm, Stephen P.↗
The Estimation of Surface Latent Heat Flux over the Ocean and its Relationship to Marine Atmospheric Boundary Layer (MABL) Structure
A new technique combining active and passive remote sensing instruments for the estimation of surface latent heat flux over the ocean is presented. This synergistic method utilizes aerosol lidar backscatter data, multi-channel infrared radiometer data, and microwave scatterometer data acquired onboard the NASA P-313 research aircraft during an extended field campaign over the Atlantic ocean in support of the Lidar In-space Technology Experiment (LITE) in September of 1994. The 10 meter wind speed derived from scatterometers and lidar-radiometer inferred near-surface moisture are used to obtain an estimate of the surface flux of moisture via a bulk aerodynamic formula. The results are compared with the Special Sensor Microwave Imager (SSM/I) daily average latent heat flux and show reasonable agreement. However, the SSM/I values are biased low by about 15 W/sq m. In addition, the Marine Atmospheric Boundary Layer (MABL) height, entrainment zone thickness and integrated lidar backscatter intensity are computed from the lidar data and compared with the magnitude of the surface fluxes. The results show that the surface latent heat flux is most strongly correlated with entrainment zone depth, MABL height and the integrated MABL lidar backscatter, with corresponding correlation coefficients of 0.39, 0.43 and 0.71, respectively.
Palm, Stephen P.↗
Estimation of Surface Air Temperature from MODIS 1km Resolution Land Surface Temperature Over Northern China
Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and land surface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed land surface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed land surface temperature are statistically significant. The relationships between the maximum air temperature and daytime land surface temperature depends significantly on land surface types and vegetation index, but the minimum air temperature and nighttime land surface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS land surface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS land surface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C).
Shen, Suhung↗
Two Surface Temperature Retrieval Methods Compared Over Agricultural Lands
Accurate, spatially distributed surface temperatures are required for modeling evapotranspiration (ET) over agricultural fields under wide ranging conditions, including stressed and unstressed vegetation. Modeling approaches that use surface temperature observations, however, have the burden of estimating surface emissivities. Emissivity estimation, the subject of much recent research, is facilitated by observations in multiple thermal infrared bands. But it is nevertheless a difficult task. Using observations from a multiband thermal sensor, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), estimated surface emissivities and temperatures are retrieved in two different ways: the temperature emissivity separation approach (TES) and the normalized emissivity approach (NEM). Both rely upon empirical relationships, but the assumed relationships are different. TES relies upon a relationship between the minimum spectral emissivity and the range of observed emissivities. NEM relies upon an assumption that at least one thermal band has a pre-determined emissivity (close to 1.0). The benefits and consequences of each approach will be demonstrated for two different landscapes: one in central Oklahoma, USA and another in southern New Mexico.
French, Andrew N.↗
Evaluation of the maximum cross-correlation method of estimating sea surface velocities from sequential satellite images
The maximum cross correlation (MCC) method of Leese et al. (1971) for estimating sea surface velocities from sequences of satellite images is evaluated by comparing the MCC fields obtained from sequences of AVHRR and CZCS with in situ data and velocity fields calculated with a high-resolution quasi-geostrophic model. The rms differences and vector correlations between the velocity field produced by the MCC method and the model's field are presented. It is shown that much of the difference between the MCC fields and either the in situ data or the model velocity fields can be accounted for by considering physical and biological processes not included in the MCC method. The conditions under which the method is likely to be most successful are discussed.
Tokmakian, Robin↗
Assessment of the global monthly mean surface insolation estimated from satellite measurements using global energy balance archive data
Global sets of surface radiation budget (SRB) have been obtained from satellite programs. These satellite-based estimates need validation with ground-truth observations. This study validates the estimates of monthly mean surface insolation contained in two satellite-based SRB datasets with the surface measurements made at worldwide radiation stations from the Global Energy Balance Archive (GEBA). One dataset was developed from the Earth Radiation Budget Experiment (ERBE) using the algorithm of Li et al. (ERBE/SRB), and the other from the International Satellite Cloud Climatology Project (ISCCP) using the algorithm of Pinker and Laszlo and that of Staylor (GEWEX/SRB). Since the ERBE/SRB data contain the surface net solar radiation only, the values of surface insolation were derived by making use of the surface albedo data contained GEWEX/SRB product. The resulting surface insolation has a bias error near zero and a root-mean-square error (RMSE) between 8 and 28 W/sq m. The RMSE is mainly associated with poor representation of surface observations within a grid cell. When the number of surface observations are sufficient, the random error is estimated to be about 5 W/sq m with present satellite-based estimates. In addition to demonstrating the strength of the retrieving method, the small random error demonstrates how well the ERBE derives from the monthly mean fluxes at the top of the atmosphere (TOA). A larger scatter is found for the comparison of transmissivity than for that of insolation. Month to month comparison of insolation reveals a weak seasonal trend in bias error with an amplitude of about 3 W/sq m. As for the insolation data from the GEWEX/SRB, larger bias errors of 5-10 W/sq m are evident with stronger seasonal trends and almost identical RMSEs.
Li, Zhanqing↗
Surface Temperature Estimation Over Land Using Satellite Microwave Radiometry
Surface temperature is an important parameter of the physical processes that govern the fluxes of energy and water at the biosphere-atmosphere interface. The energy and water fluxes have major influences on the Earth's weather and climate.
Microwave radiometry temperature tropics infrared ↗
Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Modeling Performance in a Real-Time Air Quality Estimation System
Aerosol optical depth (AOD), derived from satellite measurements using Moderate Resolution Imaging Spectrometer (MODIS), offers indirect estimates of particle matter. Research shows a significant positive correlation between satellite-based measurements of AOD and ground-based measurements of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5). In addition, satellite observations have also shown great promise in improving estimates of PM2.5 air quality surface. Research shows that correlations between AOD and ground PM2.5 are affected by a combination of many factors such as inherent characteristics of satellite observations, terrain, cloud cover, height of the mixing layer, and weather conditions, and thus might vary widely in different regions, different seasons, and even different days in a same location. Analysis of correlating AOD with ground measured PM2.5 on a day-to-day basis suggests the temporal scale, a number of immediate latest days for a given run's day, for their correlations needs to be considered to improve air quality surface estimates, especially when satellite observations are used in a real-time pollution system. The second reason is that correlation coefficients between AOD and ground PM2.5 cannot be predetermined and needs to be calculated for each day's run for a real-time system because the coefficients can vary over space and time. Few studies have been conducted to explore the optimal way to apply AOD data to improve model accuracies of PM2.5 surface estimation in a real-time air quality system. This paper discusses the best temporal scale to calculate the correlation of AOD and ground particle matter data to improve the results of pollution models in real-time system.
Li,Hui↗
Using near–surface temperature data to vicariously calibrate high-resolution thermal infrared imagery and estimate physical surface properties
Explore the source record for details and available documents.
Timothy N. Titus↗
Assessing Bare-Ice Albedo Simulated By MAR Over the Greenland Ice Sheet (2000–2021) and Implications for Meltwater Production Estimates
Surface mass loss from the Greenland ice sheet (GrIS) has accelerated over the past decades, mainly due to enhanced surface melting and liquid water runoff in response to atmospheric warming. A large portion of runoff from the GrIS originates from exposure of the darker bare ice in the ablation zone when the overlying snow melts, where surface albedo plays a critical role in modulating the energy available for melting. In this regard, it is imperative to understand the processes governing albedo variability to accurately project future mass loss from the GrIS. Bare-ice albedo is spatially and temporally variable and contingent on non-linear feedbacks and the presence of light-absorbing constituents. An assessment of models aiming at simulating albedo variability and associated impacts on meltwater production is crucial for improving our understanding of the processes governing these feedbacks and, in turn, surface mass loss from Greenland. Here, we report the results of a comparison of the bare-ice extent and albedo simulated by the regional climate model Modèle Atmosphérique Régional (MAR) with satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the GrIS below 70∘ N. Our findings suggest that MAR overestimates bare-ice albedo by 22.8 % on average in this area during the 2000–2021 period with respect to the estimates obtained from MODIS. Using an energy balance model to parameterize meltwater production, we find this bare-ice albedo bias can lead to an underestimation of total meltwater production from the bare-ice zone below 70∘ N of 42.8 % during the summers of 2000–2021.
albedo↗
Estimation of surface temperature in remote pollution measurement experiments
A simple algorithm has been developed for estimating the actual surface temperature by applying corrections to the effective brightness temperature measured by radiometers mounted on remote sensing platforms. Corrections to effective brightness temperature are computed using an accurate radiative transfer model for the 'basic atmosphere' and several modifications of this caused by deviations of the various atmospheric and surface parameters from their base model values. Model calculations are employed to establish simple analytical relations between the deviations of these parameters and the additional temperature corrections required to compensate for them. Effects of simultaneous variation of two parameters are also examined. Use of these analytical relations instead of detailed radiative transfer calculations for routine data analysis results in a severalfold reduction in computation costs.
Gupta, S. K.↗
Multi-Axis Identifiability Using Single-Surface Parameter Estimation Maneuvers on the X-48B Blended Wing Body
The problem of parameter estimation on hybrid-wing-body type aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aero- dynamic control effectors that act in coplanar motion. This fact adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of system inputs must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, asymmetric, single-surface maneuvers are used to excite multiple axes of aircraft motion simultaneously. Time history reconstructions of the moment coefficients computed by the solved regression models are then compared to each other in order to assess relative model accuracy. The reduced flight-test time required for inner surface parameter estimation using multi-axis methods was found to come at the cost of slightly reduced accuracy and statistical confidence for linear regression methods. Since the multi-axis maneuvers captured parameter estimates similar to both longitudinal and lateral-directional maneuvers combined, the number of test points required for the inner, aileron-like surfaces could in theory have been reduced by 50%. While trends were similar, however, individual parameters as estimated by a multi-axis model were typically different by an average absolute difference of roughly 15-20%, with decreased statistical significance, than those estimated by a single-axis model. The multi-axis model exhibited an increase in overall fit error of roughly 1-5% for the linear regression estimates with respect to the single-axis model, when applied to flight data designed for each, respectively.
Ratnayake, Nalin A.↗
Deriving Soil Moisture with the Combined L-Band Radar and Radiometer Measurements
In this study, we develop a combined active/passive technique to estimate surface soil moisture with the focus on the short vegetated surfaces. We first simulated a database for both active and passive signals under SMAP s sensor configurations using the radiative transfer model with a wide range of conditions for surface soil moisture, roughness and vegetation properties that we considered as the random orientated disks and cylinders. Using this database, we developed 1) the techniques to estimate surface backscattering and emission components and 2) the technique to estimate soil moisture with the estimated surface backscattering and emission components. We will demonstrate these techniques with the model simulated data and its validation with the airborne PALS image data from the soil moisture SGP 99 and SMEX 02 experiments.
Shi, Jiancheng↗
Satellite Estimates of Surface Short-wave Fluxes: Issues of Implementation
Surface solar radiation reaching the Earth's surface is the primary forcing function of the land surface energy and water cycle. Therefore, there is a need for information on this parameter, preferably, at global scale. Satellite based estimates are now available at accuracies that meet the demands of many scientific objectives. Selection of an approach to estimate such fluxes requires consideration of trade-offs between the use of multi-spectral observations of cloud optical properties that are more difficult to implement at large scales, and methods that are simplified but easier to implement. In this study, an evaluation of such trade-offs will be performed. The University of Maryland Surface Radiation Model (UMD/SRB) has been used to reprocess five years of GOES-8 satellite observations over the United States to ensure updated calibration and improved cloud detection over snow. The UMD/SRB model was subsequently modified to allow input of information on aerosol and cloud optical depth with information from independent satellite sources. Specifically, the cloud properties from the Atmospheric Radiation Measurement (ARM) Satellite Data Analysis Program (Minnis et al., 1995) are used to drive the modified version of the model to estimate surface short-wave fluxes over the Southern Great Plain ARM sites for a twelve month period. The auxiliary data needed as model inputs such as aerosol optical depth, spectral surface albedo, water vapor and total column ozone amount were kept the same for both versions of the model. The estimated shortwave fluxes are evaluated against ground observations at the ARM Central Facility and four satellite ARM sites. During summer, the estimated fluxes based on cloud properties derived from the multi-spectral approach were in better agreement with ground measurements than those derived from the UMD/SRB model. However, in winter, the fluxes derived with the UMD/SRB model were in better agreement with ground observations than those estimated from cloud properties provided by the ARM Satellite Data Analysis Program. During the transition periods, the results were comparable.
Wang, H.↗