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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Topographic Signatures in Aquarius Radiometer/Scatterometer Response: Initial Results

The effect of topography on remote sensing at L-band is examined using the co-located Aquarius radiometer and scatterometer observations over land. A correlation with slope standard deviation is demonstrated for both the radiometer and scatterometer at topographic scales. Although the goal of Aquarius is remote sensing of sea surface salinity, the radiometer and scatterometer are on continuously and collect data for remote sensing research over land. Research is reported here using the data over land to determine if topography could have impact on the passive remote sensing at L-band. In this study, we report observations from two study regions: North Africa between 15 deg and 30 deg Northern latitudes and Australia less the Tasmania Island. Common to these two regions are the semi-arid climate and low population density; both favorable conditions to isolate the effect of topography from other sources of scatter and emission such as vegetation and urban areas. Over these study regions, topographic scale slopes within each Aquarius pixel are computed and their standard deviations are compared with Aquarius scatterometer and radiometer observations over a 36 day period between days 275 and 311 of 2011.

Utku, C.↗
NASA NTRS2017

A Piezoelectric Passive Wireless Sensor for Monitoring Strain

Interest in passive wireless sensing has grown over the past few decades to meet demands in structural health monitoring.(Deivasigamani et al., 2013; Wilson and Juarez, 2014) This work describes a passive wireless sensor for monitoring strain, which does not have an embedded battery or chip. Without an embedded battery, the passive wireless sensor has the potential to maintain its functionality over long periods in remote/harsh environments. This work also focuses on monitoring small strain (less than 1000 micro-ɛ). The wireless sensing system includes a reader unit, a coil-like transponder, and a sensing unit. It operates in the Megahertz (MHz) frequency range, which allows for a few centimeters of separation between the reader and sensing unit during measurements. The sensing unit is a strain-sensitive piezoelectric resonator that maximizes the energy efficiency at the resonance frequency, so it converts nanoscale mechanical variations to detectable differences in electrical signal. In response to an external loading, the piezoelectric sensor breaks from its original electromechanical equilibrium, and the resonant frequency shifts as the system reaches a new balanced equilibrium. In this work, the fixture of the sensing unit is a small, sticker-like package that converts the surface strain of a test material to measurable shifts in resonant frequencies. Furthermore, electromechanical modeling provides a lumped-parameter model of the system to describe and predict the measured wireless signals of the sensor. Detailed characterization demonstrates how this wireless sensor has resolution comparable to that of conventional wired strain sensors for monitoring small strain.

Zou, Xiyue↗
NASA NTRSDate not supplied

Assessing Carbon Properties in Coastal Waters with a New Observing System Testbed

Large rainfall events over land can lead to a substantial flux of carbon and nutrients to estuaries and the coastal ocean. In the mid-Atlantic on the east coast of North America (35° - 42° N), these events often happen due to tropical storm activity as well as less predictable anomalously large midlatitude storms or abrupt spring snow melt runoff. Storms can directly impact the coastal carbon cycle via export of carbon from land to sea, while also stimulating phytoplankton production due to the influx of nutrients to the coastal ocean. To assess the impact of high precipitation and river flow events on the coastal carbon cycle, we have integrated multiple observing platforms in an analytical framework to dynamically observe carbon-related properties. The new observing system testbed (NOS-T) allows for an estimate of phytoplankton and organic carbon stocks in the surface ocean, with a goal of providing near real time analytical capability. A case study of high river discharge in the mid-Atlantic in the summer and fall of 2018 and 2021 were used to examine how riverine carbon manifests along the land-estuary-ocean continuum particularly in Chesapeake Bay. Chesapeake Bay was chosen as the study site because of long term monitoring by the Chesapeake Bay Program and a robust and well-developed regional biogeochemical modeling system, the Chesapeake Bay Environmental Forecast System (CBEFS), that is publicly available. There is also new capability in Chesapeake Bay to combine hyperspectral radiometric data from the Aerosol Robotic Network site that is online in 2022 with in-water observations of optical properties. Precipitation data from the Global Precipitation Measurement IMERG data set was used to establish triggering criteria for storm carbon flux observation. USGS and in-water carbon data were used to establish statistical models to estimate the mass flux of organic carbon into Chesapeake Bay using measured river discharge. The year 2018 was examined as there was record rainfall and near record river flow in the late summer. The CBEFS modeling was compared with in-water estimates of dissolved organic carbon (DOC) to assess the model’s ability to capture storm fluxes of carbon to the ecosystem. Multiple satellite platforms were also used to assess how remote sensing using passive Earth orbiting sensors can be used to observe carbon in this complex coastal region. A set of recommendations have been established to improve sensing capability to measure aquatic carbon during storm events. A unique challenge in these dynamic inland waters is how rapidly carbon fluxes can evolve in space and time, with many sources contributing to the water leaving reflectance that satellites can observe. To improve our ability to quantify carbon stocks and fluxes in near real time, a suite of satellite sensors and high-resolution modeling capability is needed, all supplemented by in situ monitoring. Future inclusion of in-water observations that would be deployed when the system reaches triggering criteria, as well as taskable orbital instruments, will improve estimates of ocean carbon properties and provide the ability to calculate major carbon stocks and fluxes in near real time following episodic storm events.

land-ocean continuum↗
NASA NTRS2016

RFI Detection and Mitigation using Independent Component Analysis as a Pre-Processor

Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

independent component analysis↗
NASA NTRS2002

NASA's New Laser Risk Reduction Program For Future Space Lidar Missions

NASA has been performing ground, airborne, and space-based scientific measurements since it was formed in 1958. Initial ground and airborne measurements were made with in situ instruments. By necessity, initial earth observation space-based missions were accomplished with passive remote sensing. Active microwave radar was added to the sensor repertoire in the late 1970s. A few key measurements important to NASA remain unaccomplished, however, despite the passive and radar successes. These critical measurements include space-based altimetry; and high spatial resolution profiling of aerosol properties, wind velocity, clouds, and molecular concentrations. Fortunately, a new technology, active optical radar or laser radar or lidar, has matured to the point that the last decade has seen a growing consideration of lidar for space missions. Part of the surge in consideration of lidar has been the tremendous progress in solid-state lasers fueled by advances in crystal growth quality and pump laser diode technology.

Michael J. Kavaya↗
NASA NTRS2012

Space-Based Lidar Systems

An overview of space-based lidar systems is presented. from the first laser altimeter on APOLLO 15 mission in 1971 to the Mercury Laser Altimeter on MESSENGER mission currently in orbit, and those currently under development. Lidar, which stands for Light Detection And Ranging, is a powerful tool in remote sensing from space. Compared to radars, lidars operate at a much shorter wavelength with a much narrower beam and much smaller transmitter and receiver. Compared to passive remote sensing instruments. lidars carry their own light sources and can continue measuring day and night. and over polar regions. There are mainly two types of lidars depending on the types of measurements. lidars that are designed to measure the distance and properties of hard targets are often called laser rangers or laser altimeters. They are used to obtain the surface elevation and global shape of a planet from the laser pulse time-of-night and the spacecraft orbit position. lidars that are designed to measure the backscattering and absorption of a volume scatter, such as clouds and aerosols, are often just called lidars and categorized by their measurements. such as cloud and aerosol lidar, wind lidar, CO2 lidar, and so on. The advantages of space-based lidar systems over ground based lidars are the abilities of global coverage and continuous measurements.

Sun, Xiaoli↗
NASA NTRS1979

Microwave remote sensing laboratory design

Application of active and passive microwave remote sensing to the study of ocean pollution is discussed. Previous research efforts, both in the field and in the laboratory were surveyed to derive guidance for the design of a laboratory program of research. The essential issues include: choice of radar or radiometry as the observational technique; choice of laboratory or field as the research site; choice of operating frequency; tank sizes and material; techniques for wave generation and appropriate wavelength spectrum; methods for controlling and disposing of pollutants used in the research; and pollutants other than oil which could or should be studied.

Friedman, E.↗
NASA NTRS1985

Remote sensing of earth terrain

Progress on the investigation of the anisotropy of the terrain media, such as vegetation canopy and sea ice, and the study of the fluctuation-dissipation theorem in conjunction with the application of strong fluctuation theory for passive remote sensing of snowpacks is reported. The Feynman diagrammatic technique is used to derive the Dyson equation for the mean field and the Bethe-Salpeter equation for the correlation or the covariance of the field for electromagnetic wave propagation and scattering in an anisotropic random medium. With the random permittivity expressed in a general form, the bilocal and the nonlinear approximations are employed to solve the Dyson equation and the ladder approximation to the Bethe-Salpeter equation. The mean dyadic Green's function for a two layer anisotropic random medium with arbitrary three dimensional correlation function was investigated with the zeroth-order solutions to the Dyson equation under the four characteristic waves associated with the coherent vector fields propagating in an anisotropic random medium layer, which are the ordinary and extraordinary waves with upward and downward propagating vectors.

Kong, J. A.↗
NASA NTRS2010

Estimating Glaciation Temperature of Deep Convective Clouds with Remote Sensing Data

Major uncertainties exist for observing and modeling ice content inside deep convective clouds (DCC). One of the difficulties has been the lack of characterization of vertical profiles of cloud hydrometeor phase. Here we propose a technique to estimate the DCC glaciation temperature using passive remote sensing data. It is based on a conceptual model of vertical hydrometeor size profiles inside DCCs. Estimates from the technique agree well with our general understanding of the problem. Furthermore, the link between vertical profiles of cloud particle size and hydrometeor thermodynamic phase is confirmed by a 3-13 cloud retrieval technique. The technique is applied to aircraft measurements of cloud side reflectance and the result was compared favorably with an independent retrieval of thermodynamic phase based on different refractive indices at 2.13 micron and 2.25 micron. Possible applications of the technique are discussed.

Yuan, Tianle↗
NASA NTRS2012

Boundary Layer Remote Sensing with Combined Active and Passive Techniques: GPS Radio Occultation and High-Resolution Stereo Imaging (WindCam) Small Satellite Concept

Objective: significant progress in understanding low-cloud boundary layer processes. This is the Single largest uncertainty in climate projections. Radio occultation has unique features suited to boundary layer remote sensing (1) Cloud penetrating (2) Very high vertical resolution (approximately 50m-100m) (3) Sensitivity to thermodynamic variables

stereo camera↗
NASA NTRS1976

Passive microwave radiometric observations of the atmosphere

Passive microwave sensing techniques can be used to obtain the temperature of the atmosphere, and to infer liquid water, water vapor and other molecular species over ocean or above the troposphere. The use of satellite-borne passive microwave radiometers is discussed along with the future use of the instrument on Nimbus-G and on the space shuttle.

Cassel, A. L.↗
NASA NTRS2003

RFI at L-band in Synthetic Aperture Radiometers

The spectral window at 1.413 GHz (L-band), set aside for passive use only, is critical for passive remote sensing of the earth from space. It is the largest spectral window available in the long wavelength end of the microwave spectrum where measurements are needed to monitor parameters of the surface such as soil moisture and sea surface salinity. The sensitivity to these parameters is rapidly lost at higher frequencies and is compromised by the ionosphere and antenna size at lower frequencies. Instruments for remote sensing from space in this spectral window are being developed by NASA (Aquarius) and ESA (SMOS) and are expected to be in orbit in a few years (2006). Although the band at 1.413 GHz is protected for passive use, RFI is a common problem. For example, the synthetic aperture radiometer, ESTAR (L-band, Horizontal polarization), has frequently experienced problems with RFI. During the Southern Great Plains Experiments (1997 and 1999), ESTAR experienced RFI significant enough to warrant changes in flight lines. The largest sources of RFI were identified as originating in airports and a likely source is air traffic control radar. In experiments in the vicinity of Richmond, VA, RFI in the form of periodic spikes was recorded, again suggestive of radar. However, in most cases the sources of the RFI are unknown. RFI is a sufficiently common problem that the first step in processing ESTAR data is a screening for RFI (a filter is used to detect large, rapid changes in brightness). Recently, measurements have been made with a new synthetic aperture radiometer, 2D-STAR. Examples of RFI observed simultaneously with ESTAR and the new synthetic aperture radiometer will be presented. 2D-STAR is an airborne instrument designed to develop the technology of aperture synthesis in two dimensions. It employs dual polarized patch antennas arranged in a cross configuration (+). Synthesis in two dimensions offers the potential for optimal thinning, but because of the wide field of view of the individual antennas it is potentially more susceptible to RFI. The 2D-STAR instrument was flown together with ESTAR in a series of test flights on the NASA P-3 aircraft near the Wallops Flight Facility during the summer of 2002. Both instruments experienced RFI over common locations. Examples will be presented and a comparison between instruments presented.

LeVine, David M.↗
NASA NTRS2005

Impact of the Sun on Remote Sensing of Sea Surface Salinity from Space

The sun is a sufficiently strong source of radiation at L-band to be an important source of interference for radiometers on future satellite missions such as SMOS, Aquarius, and Hydros designed to monitor soil moisture and sea surface salinity. Radiation from the sun can impact passive remote sensing systems in several ways, including line-of-sight radiation that comes directly from the sun and enters through antenna side lobes and radiation that is reflected from the surface to the radiometer. Examples are presented in the case of Aquarius, a pushbroom radiometer with three beams designed to monitor sea surface salinity. Near solar minimum, solar contamination is not a problem unless the sun enters near the main beam. But near solar maximum, contamination from the sun equivalent to a change of salinity on the order of 0.1 psu can occur even when the signal enters in sidelobes far from the main beam.

COMPONENT REPORTS↗
NASA NTRS2006

Two-micron Detector Development using Sb-based Material Systems

NASA Langley Research Center (LaRC), in partnership with the University of Delaware (UD), developed AlGaAsSb/InGaAsSb custom-designed phototransistors in the 0.6-2.5 micron wavelength range for applications to laser remote sensing. The phototransistor s performance greatly exceeds the previously reported results at this wavelength range in the literature. The performances of the custom-designed phototransistor, such as responsivity, detectivity, and gain, are improved significantly as compared to the previously published detectors as well as commercial detectors. Detection in the 0.6- to 2.5- micron broadband with a single phototransistor will result in reduction or elimination of heavy and complex optical components now required for multiple wavelength detection in atmospheric remote sensors resulting in smaller, lighter, simpler instruments with higher performance. This high performance broadband phototransistor will eliminate the need for high power laser for active remote sensing and also the Si (1.0- micron cutoff) and InGaAs (extended 2.3- micron cutoff) detectors. The developed broadband phototransistor will be applicable for the next generation of space-based Earth observations and other planetary instruments for active and passive remote sensing with substantial reduction in size, complexity, and weight to measure water vapor, methane, and carbon dioxide in planetary atmospheres as well as aerosol, cloud, water vapor, O2, CO, and CO2 for a broad range of applications to Earth and Space Science Missions under Science Mission Directorate (SMD) research programs.

Abedin, M. N.↗
NASA NTRS2012

Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and Lidar Sampling Error

The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission (+/-20 Mg/ha or 20%, the greater of the two, for at least 80% of grid cells). A data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional-global extents. A general framework for mapping above ground live forest biomass density (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify contiguous areas (>0.5 ha) that are relatively homogenous in remote sensing metrics (forest patches). We used this image-derived data with simulated spaceborne LiDAR derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (LVIS) to compute AGB and estimate LiDAR sampling error for forest patches and 100 m, 250 m, 500 m, and 1 km grid cells. At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. First, this data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting. Second, a data fusion approach for estimating AGB using simulated spaceborne LiDAR with SAR and passive optical image combinations reduced forest AGB sampling errors 12%-38% from those where LiDAR is used with SAR or passive optical alone. In absolute terms, sampling errors were reduced from 14-40 Mg/ha to 11-28 Mg/ha across all grid scales and prediction methods, where minimum sampling errors were 11, 15, 18, and 22 Mg/ha for 1 km, 500 m, 250 m, and 100 m grid scales, respectively. Third, spaceborne global scale accuracy requirements were achieved whereby at least 80% of the grid cells at 100 m, 250 m, 500 m, and 1 km grid levels met AGB accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500 m and 250 m grid level, respectively..

LiDAR↗
NASA NTRS2016

Spaceborne Remote Sensing of Aerosol Type: Global Distribution, Model Evaluation and Translation into Chemical Speciation

It is essential to evaluate and refine aerosol classification methods applied to passive satellite remote sensing. We have developed an aerosol classification algorithm (called Specified Clustering and Mahalanobis Classification, SCMC) that assigns an aerosol type to multi-parameter retrievals by spaceborne, airborne or ground-based passive remote sensing instruments [1]. The aerosol types identified by our scheme are pure dust, polluted dust, urban-industrialdeveloped economy, urban-industrialdeveloping economy, dark biomass smoke, light biomass smoke and pure marine. We apply the SCMC method to inversions from the ground-based AErosol RObotic NETwork (AERONET [2]) and retrievals from the space-borne Polarization and Directionality of Earths Reflectances instrument (POLDER, [3]). The POLDER retrievals that we use differ from the standard POLDER retrievals [4] as they make full use of multi-angle, multispectral polarimetric data [5]. We analyze agreement in the aerosol types inferred from both AERONET and POLDER and evaluate GEOS-Chem [6] simulations over the globe. Finally, we use in-situ observations from the SEAC4RS airborne field experiment to bridge the gap between remote sensing-inferred qualitative SCMC aerosol types and their corresponding quantitative chemical speciation. We apply the SCMC method to airborne in-situ observations from the NASA Langley Aerosol Research Group Experiment (LARGE, [7]) and the Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP, [8]) instruments; we then relate each coarsely defined SCMC type to a sum of percentage of individual aerosol species, using in-situ observations from the Particle Analysis by Laser Mass Spectrometry (PALMS, [9]), the Soluble Acidic Gases and Aerosol (SAGA, [10]), and the High - Resolution Time - of - Flight Aerosol Mass Spectrometer (HR ToF AMS, [11]).

airborne↗
NASA NTRS2004

Comparison of Model Prediction with Measurements of Galactic Background Noise at L-Band

The spectral window at L-band (1.413 GHz) is important for passive remote sensing of surface parameters such as soil moisture and sea surface salinity that are needed to understand the hydrological cycle and ocean circulation. Radiation from celestial (mostly galactic) sources is strong in this window and an accurate accounting for this background radiation is often needed for calibration. Modem radio astronomy measurements in this spectral window have been converted into a brightness temperature map of the celestial sky at L-band suitable for use in correcting passive measurements. This paper presents a comparison of the background radiation predicted by this map with measurements made with several modem L-band remote sensing radiometers. The agreement validates the map and the procedure for locating the source of down-welling radiation.

LeVine, David M.↗