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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves.

radiometers↗
NASA NTRS2016

Snow Grain Size Retrieval over the Polar Ice Sheets with the Ice, Cloud and Land Elevation Satellite (ICESat) Observations

Snow grain size is an important parameter for cryosphere studies. As a proof of concept, this paper presents an approach to retrieve this parameter over Greenland, East and West Antarctica ice sheets from surface reflectances observed with the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) at 1064 nanometers. Spaceborne lidar observations overcome many of the disadvantages in passive remote sensing, including difficulties in cloud screening and low sun angle limitations; hence tend to provide more accurate and stable retrievals. Results from the GLAS L2A campaign, which began on 25 September and lasted until 19 November, 2003, show that the mode of the grain size distribution over Greenland is the largest (approximately 300 microns) among the three, West Antarctica is the second (220 microns) and East Antarctica is the smallest (190 microns). Snow grain sizes are larger over the coastal regions compared to inland the ice sheets. These results are consistent with previous studies. Applying the broadband snow surface albedo parameterization scheme developed by Garder and Sharp (2010) to the retrieved snow grain size, ice sheet surface albedo is also derived. In the future, more accurate retrievals can be achieved with multiple wavelengths lidar observations.

ice↗
NASA NTRS2017

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

High Resolution↗
NASA NTRS2019

Picture this SELFI: Submillimeter Enceladus Life Fundamentals Instrument

The Submillimeter Enceladus Life Fundamentals Instrument (SELFI) is a passive remote sensing submillimeter heterodyne spectrometer being developed at NASA GSFC under NASA's Maturation of Instruments for Solar System Exploration (MatISSE) program. SELFI will advance submillimeter receiver technology by 1) investigating the chemical and isotopic compositions and corresponding densities of Enceladus' plume material, their vertical thermal structures, and the transport mechanisms within the plumes, and 2) characterizing the source regions from which the plumes emerge. The Enceladus plumes are important in the context of life and habitability of its subsurface ocean environment. SELFI remote sensing measurements will 1) measure the spatial and temporal variabilities in the plume chemical compositions, 2) provide insight in to Enceladus' subsurface ocean environment by monitoring H2O, HDO, d18O, and d17O, 3) constrain the oxidation state of the subsurface ocean using H2O2 and O3, and 4) utilize the SO2 and H2S spectral signatures to constrain the impact arising from both the sea-floor volcanoes and pre-biotic molecules. Moreover, the detection of the remaining molecular species (14 in total) is vital to improving the current state of knowledge of Enceladus' subsurface ocean habitability this also permits us to explore the chemical alteration processes arising from primordial volatiles that have been observed in comets. Lastly, SELFI's continuum observations enable the correlation between observed variations in plume activity with surface temperatures.SELFI is currently being developed under a technology maturation program that will advance the RF-to-digital electronics of a submillimeter-wave heterodyne spectrometer to simultaneously observe fourteen molecular species with resonances between 530 GHz and 600 GHz. SELFI will have fine radiometric resolution, high spectral resolution (resolving power R > 106), multiple continuum channels and a high dynamical range, necessary to map Enceladus across its 30 K to 250 K temperature range. Under the MatISSE program, SELFI will advance from TRL 4 to 6 four key technologies of the RF-to-digital subsystem, which are: 1) the RF low noise amplifier design; 2) the single-sideband (SSB) mixer and local oscillator; 3) the IF assembly down-converter that maps the fourteen species to 2 x 500 MHz bandwidth; and 4) the digital spectrometer electronics.

Racette, Paul↗
NASA NTRS2019

Detection of Residual “Hot Spots” in RFI-Filtered SMAP Data

Radio frequency interference (RFI) is a well-documented problem for passive remote sensing of the Earth at L-band even though the measurements are made in the protected band at 1.413 GHz. Consequently, filtering for RFI is an important early step in the processing of measurements made by the SMAP (Soil Moisture Active/Passive) radiometer. However, the filtered data still include regions with suspiciously high antenna temperatures. One possible cause of these “hot spots” is interference not fully detected during RFI filtering. This paper presents evidence supporting this hypothesis and describes an algorithm to identify these “hot spots” so that they can be removed from the measurements. The impact of removing these “hot spots” is generally small, but evidence is presented that the brightness temperature and soil moisture improve when the hot spots are removed.

Soldo, Yan↗
NASA NTRS2010

TPSAS-NF1676L-10206-DND

NASA is at fore-front in developing active and passive remote sensing technologies and unique capabilities towards space-based observations for understanding the complexities and interactions among Earth system components. The world is facing significant environmental challenges and a robust, integrated, and flexible system of observations and models are needed for understanding the short-and long term impact on the Earth system. A fundamental challenge for the coming decade is to ensure that space-based observations, analyses, better interpretive understanding, enhanced predictive models, broadened international community participation, and improved means for information assimilation and disseminations are well coordinated to realize the full economic, societal, and security benefit of Earth science. This presentation will provide an overview of NASA?s future vision for Earth science missions for global observations and the challenges associated in applying them for societal benefit.

Upendra N Singh↗
NASA NTRSDate not supplied

TPSAS-NF1676L-23266-DND

Currently, near-surface air quality information (e.g. PM_2.5) must be inferred from column-integrated quantities (i.e. Aerosol Optical Thickness – AOT) obtained by passive remote sensing from downward-looking satellite instruments. Such retrievals must address the following questions: What do we use for the height of the aerosols? Mixed Layer (ML) height? Can we assume that near-surface aerosol extinction is about the same as the mean aerosol extinction in the ML? How well is near-surface extinction related to surface PM_2.5? How well can column AOT be used to infer near-surface aerosol extinction and PM_2.5?

Richard Ferrare↗
NASA NTRS2020

Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

EPIC↗
NASA NTRSDate not supplied

EPIC Cloud Observations with Oxygen Bands Over Snow, Ice and Sunglint Regions

Cloud detections over snow/ice surfaces and sunglint regions are challenging with passive remote sensing instrument due to lack of contract between cold/bright surfaces in the former and glint reflectance that could exceed that of cloudy sky in the latter. We developed novel cloud detection algorithms for these regions utilizing EPIC’s unique oxygen-bands. Over the snow and ice surfaces, a dynamic threshold scheme based on the ratios of two pairs of EPIC’s oxygen bands are developed to significantly improve the existing algorithm in these regions. Over the ocean, we improved the EPIC’s ocean cloud mask algorithm by implementing a dynamic reflectance threshold and supplemental A-band ratio test for cloud detection in the sun glint regions. The new ocean cloud mask algorithm improves the diurnal cycles of cloud fraction over ocean by reducing the artificial peak at local noon time in the glint center latitudes and reducing early morning and afternoon cloud fraction in most oceanic regions.

EPIC↗
NASA NTRS2008

Overview of terrain relative navigation approaches for Precise Lunar Landing

The driving precision landing requirement for the Autonomous Landing and Hazard Avoidance Technology project is to autonomously land within 100m of a predetermined location on the lunar surface. Traditional lunar landing approaches based on inertial sensing do not have the navigational precision to meet this requirement. The purpose of Terrain Relative Navigation (TRN) is to augment inertial navigation by providing position or bearing measurements relative to known surface landmarks. From these measurements, the navigational precision can be reduced to a level that meets the 100m requirement. There are three different TRN functions: global position estimation, local position estimation and velocity estimation. These functions can be achieved with active range sensing or passive imaging. This paper gives a survey of many TRN approaches and then presents some high fidelity simulation results for contour matching and area correlation approaches to TRN using active sensors. Since TRN requires an a-priori reference map, the paper concludes by describing past and future lunar imaging and digital elevation map data sets available for this purpose.

Montgomery, James F.↗
NASA NTRS2018

Differential Absorption Radar at 170 GHz for Atmospheric Boundary Layer Water Vapor Profiling

We are developing a frequency-modulated continuous-wave (FMCW) radar between 167 and 174.8 GHz to measure differential absorption due to water vapor within the atmospheric boundary layer. In this work, we report on single-frequency measurements performed within this band in the presence of precipitating clouds. Despite the relatively low transmit power of 6-10 dBm, the high transmit/receive isolation and low noise figure of the system enables detection of radar echos from rain or clouds with high signal-to-noise ratio (SNR) out to about one kilometer. Near future upgrades to the system that will lengthen significantly the detectable range include a 15 to 20 dB increase in output power and 20 dB increase in antenna gain. This work builds on technology developed and measurements performed in our group in the 183.5 to 193 GHz band, which is subject to transmission restrictions due to passive remote sensing platforms that rely on those frequencies.

Rodriguez Monje, Raquel↗
NASA NTRS2022

Novel Parameterization of Ice Cloud Effective Diameter from Collocated CALIOP-IIR and CloudSat Retrievals

Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter D(e), that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud D(e) has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of D(e) as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA’s A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds.

Ice Cloud↗
NASA NTRSDate not supplied

Sensitivity of Single-Scattering Properties to Precipitation Particle Meltwater Geometry for GPM Passive Microwave and Radar Remote Sensing Applications

The objective of the current work is to describe the microwave single-scattering properties of partially melted ice-phase precipitation particles using physically-based computational methods, taking into full account the varied geometries of the initially “dry” ice particles and the distributions of liquid water that develop during the melting process. Ultimately, the bulk properties of ensembles of these partially-melted particles will be included in “scattering tables” to support radar and combined radar-radiometer remote sensing of precipitation. A meshless Lagrangian melting procedure (snowMELT) based on smoothed-particle hydrodynamics is applied to both spherical and finely-structured ice particle models to describe the full evolution of the particles from dry ice particles to liquid drops. The melting of spherical ice particles using snowMELT is compared to an alternative continuum physics model to validate melt times and internal thermodynamics. The discrete dipole approximation is then utilized to calculate the single-scattering properties of different mixed-phase particles throughout the melting process. The sensitivities of particle single-scattering properties to thermodynamic and hydrodynamic assumptions in snowMELT are explored, and the implications for combined radar-radiometer precipitation remote sensing from GPM are discussed.

William S Olson↗
NASA NTRSDate not supplied

The Stratospheric Aerosol & Gas Experiment III (SAGE III) on the International Space Station (ISS): Challenges and Successes of Operating an Optical Payload on ISS

The Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) has been in operations since March of 2017. The primary objective of this passive remote sensing payload is to measure the vertical distribution of aerosol, ozone and other trace gases in the upper atmosphere. This measurement can be used to improve our understanding of the Earth’s ozone recovery and how climate change is affecting the stratosphere. Presented here are challenges of operating an optical Earth observation payload on the highly dynamic International Space Station platform during the extended mission phase of the SAGE III on ISS mission. SAGE III/ISS has continued to maintain its coverage of low and mid-latitude observations while working around the expansion of the ISS and the increase in visiting vehicles that has occurred over the past year. With the addition of the Multi-purpose Laboratory Module (MLM), the first expansion of ISS in over a decade, the SAGE III/ISS payload used its ability to measure contamination to determine how this would affect the optical components on our payload. The addition of the MLM also modified the yaw, pitch, and roll of the ISS which the SAGE III/ISS operations team have been able to use to our advantage as this has put the ISS in the optimal expected pitch range for SAGE III/ISS data acquisition. The SAGE III/ISS operations team will continue to work with the dynamic platform while striving to maintain the mission observation goals as the ISS moves into the new phase of its operational life. Highlights from the first 5-yrs of SAGE III/ISS observations are presented showcasing abrupt changes to the stratosphere by volcanic eruptions and extreme wildfires.

Jamie Nehrir↗
NASA NTRSDate not supplied

Delay Tolerant, Radio Frequency Identification (RFID )-enabled Sensing

Radio Frequency Identification (RFID) technology offers a completely passive method to transmit fixed data sequences from an RFID tag, which typically doesn’t have its own power supply, to an interrogator. Radio frequency (RF) energy harvested from the interrogator is rectified by the tag and used to charge an integrated circuit (IC). The IC then modulates the received signal with the data stored on the tag and reflects the energy back to the interrogator. RFID has seen great proliferation in terrestrial inventory management applications, and it has recently made the jump to spaceflight applications onboard the International Space Station, augmenting an existing optical bar‐code infrastructure for tracking supplies. A number of advanced automated logistics management (ALM) concepts employing RFID are currently being developed and evaluated, including so‐called “smart” shelves, cubbies, and trash receptacles using low‐power, embeddable RFID interrogators. An infrastructure where both crew members and robotic assistants, such as autonomous free flyers, are similarly equipped with small RFID interrogators seems likely. It therefore behooves us to consider extending this infrastructure beyond ALM to applications such as low power, embedded sensing. Typically, data on an RFID tag can only be written by an RFID interrogator, using interrogator energy. In recent years, however, a few efforts have focused on using that energy to drive data acquisition from the tag IC, allowing the tag to modify its stored data sequence with sensor data before replying to an interrogator. In this way, the tag can act as a completely passive sensing device. One problem exists with this approach, however: the tag cannot gather data when an interrogator is not present. Thus, strictly passive RFID sensing tags cannot gather data at regular intervals, in the manner of a typical wireless sensor network, without careful, and impractical, planning of mobile interrogator movements. To address this shortcoming, we look to a recent advance in RFID technology which allows an external microprocessor to power the tag IC and write directly into its RFID memory using a wired serial interface. In this paradigm, data gathering is driven by a small, on‐board power supply (using batteries or harvested energy), and data transfer is provided passively through the RFID interrogation service. Since communication typically consumes the lion’s share of power in WSNs, such a technique has the potential to enable extremely long‐lived, embedded wireless sensing when used with extremely low‐current microcontrollers. Since the communication channel is only open when an interrogator is present and actively interrogating the RFID sensing tag, transport of periodically‐sampled sensor data presents itself as a delay/disruption‐tolerant networking (DTN) problem. In this paper, we present the design of a DTN‐like overlay on the common EPC Global, Class 1, Generation 2 RFID standard. This overlay allows seamless, guaranteed data transfer using the EPC Global protocol, supporting extremely low‐power, embedded sensing using an infrastructure likely to be already in place for ALM applications. We evaluate a prototype implementation of a complete end‐to‐end system using a robotic RFID interrogation agent, and we present future directions for the development of this sensing technique.

Raymond S. Wagner↗
NASA NTRS2005

Global Survey and Statistics of Radio-Frequency Interference in AMSR-E Land Observations

Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz AMSR-E channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RFI at 6.9 GHz is most densely concentrated in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is concentrated mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing.

microwave remote sensing↗
NASA NTRS1986

Radar scene generation for tactical decision aids

The Mueller matrix and polarization covariance matrix for polarimetric radar systems was studied. The clutter was modeled by a layer of random permittivity, described by a three-dimensional correlation function, with variance, and horizontal and vertical correlation lengths. This model was applied, using the wave theory with Born approximations carried to the second order, to find the backscattering elements of the polarimetric matrices. Theoretical predictions are matched with experimental data for vegetation fields. The strong fluctuation theory was used to derive the backscattering cross sections. A two-layer anisotropic random medium model was developed for the active and passive microwave remote sensing of ice fields. A three-layer random medium model was adopted to study the volume scattering effects for the active and passive microwave remote sensing of snow-covered ice fields.

Kong, J. A.↗