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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Retrieval, Inter-Comparison, and Validation of Above-Cloud Aerosol Optical Depth from A-train Sensors

Absorbing aerosols produced from biomass burning and dust outbreaks are often found to overlay lower level cloud decks and pose greater potentials of exerting positive radiative effects (warming) whose magnitude directly depends on the aerosol loading above cloud, optical properties of clouds and aerosols, and cloud fraction. Recent development of a 'color ratio' (CR) algorithm applied to observations made by the Aura/OMI and Aqua/MODIS constitutes a major breakthrough and has provided unprecedented maps of above-cloud aerosol optical depth (ACAOD). The CR technique employs reflectance measurements at TOA in two channels (354 and 388 nm for OMI; 470 and 860 nm for MODIS) to retrieve ACAOD in near-UV and visible regions and aerosol-corrected cloud optical depth, simultaneously. An inter-satellite comparison of ACAOD retrieved from NASA's A-train sensors reveals a good level of agreement between the passive sensors over the homogeneous cloud fields. Direct measurements of ACA such as carried out by the NASA Ames Airborne Tracking Sunphotometer (AATS) and Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) can be of immense help in validating ACA retrievals. We validate the ACA optical depth retrieved using the CR method applied to the MODIS cloudy-sky reflectance against the airborne AATS and 4STAR measurements. A thorough search of the historic AATS-4STAR database collected during different field campaigns revealed five events where biomass burning, dust, and wildfire-emitted aerosols were found to overlay lower level cloud decks observed during SAFARI-2000, ACE-ASIA 2001, and SEAC4RS- 2013, respectively. The co-located satellite-airborne measurements revealed a good agreement (RMSE less than 0.1 for AOD at 500 nm) with most matchups falling within the estimated uncertainties in the MODIS retrievals. An extensive validation of satellite-based ACA retrievals requires equivalent field measurements particularly over the regions where ACA are often observed from satellites, i.e., south-eastern Atlantic Ocean, tropical Atlantic Ocean, northern Arabian Sea, South-East and North-East Asia.

validation↗
NASA NTRS2022

A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

machine learning/artificial intelligence↗
NASA NTRS2024

3D Cloud Masking Across A Broad Swath Using Multi-Angle Polarimetry and Deep Learning

Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, publicly available dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA’s Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.

clouds↗
NASA NTRS2017

TPSAS-NF1676L-26965-DND

A comprehensive understanding of the spatial and temporal distributions of clouds on a global scale can be best achieved when the vertical distributions and multi-layer occurrence frequencies obtained from active remote sensors are fully integrated with the horizontal distributions currently provided by passive sensors. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft was specially designed to acquire aerosol and cloud profiles with unprecedented high vertical resolution and accuracy. As a part of the A-Train satellite constellation, CALIPSO has been operating routinely for more than 10 years and continues to provide a wealth of cloud observations to describe the mean state and inter-annual variability. Recently a suite of level 3 (L3) cloud products has been under development by the CALIPSO lidar science working group at the NASA Langley Research Center. These products describe 3-dimensional (3D) cloud occurrence and 3D ice cloud extinction coefficients and ice water content. Future evolution of the products will add observations from the Imaging Infrared Radiometer onboard CALIPSO. Here we present a brief introduction and provide results from a product prototype. We will characterize the inter-annual vertical variability of zonal cloud occurrence and ice water content during the last 10 years. Suggestions and comments are welcome to help us design and provide better cloud climatology products using CALIOP observations for our cloud community.

Xia Cai↗
NASA NTRS1988

Simulation and analysis of image-based navigation system for rotorcraft low-altitude flight

The automation of helicopter NOE flight entails the modification of nominal trajectories on the basis of the detection and location of obstacles by means of onboard sensors. This process is presently studied using a sequence of images from a passive sensor mounted at the helicopter's center of gravity and oriented with the viewing axis along the rotorcraft's longitudinal body-axis. Three different Kalman filters are used to estimate the location of an object on the ground during the course of various simulated helicopter maneuvers; two of the three filters are found to yield good object location estimates.

Sridhar, Banavar↗
NASA NTRS2010

Progress in Measurement of Carbon Dioxide Using a Broadband LIDAR

Innovative active system using advanced source technology development will enable precise daytime or nighttime measurements of column CO2. Directly responds to NRC DS ASCENDS mission. Number of lasers is reduced compared to competing technologies which reduces the complexity of sensor and thus the cost and risk of failure. Knowledge gained from previously developed passive sensor decreases the risk and cost of the present lidar system development. The instrument can play a significant role as an intercomparison instrument for OCO (Orbiting Carbon Observatory) if it is rebuilt and launched as well as other laser based instruments under development for participation in ASCENDS. It can play a role as an airborne instrument in its own right in addressing the problems of scale arising from differences between point observations by the existing ground based CO2 network and wider area measurements obtained by satellites. Developed 2.0 micron broadband system as well and will compare performance of both systems to choose optimal approach for ASCENDS. Have begun development of approach that uses array detectors instead of APD. This approach will have lower noise than APD and may simplify design of the detector optical train.

Heaps, William S.↗
NASA NTRS2000

Optimization of Microelectronic Devices for Sensor Applications

The NASA/JPL goal to reduce payload in future space missions while increasing mission capability demands miniaturization of active and passive sensors, analytical instruments and communication systems among others. Currently, typical system requirements include the detection of particular spectral lines, associated data processing, and communication of the acquired data to other systems. Advances in lithography and deposition methods result in more advanced devices for space application, while the sub-micron resolution currently available opens a vast design space. Though an experimental exploration of this widening design space-searching for optimized performance by repeated fabrication efforts-is unfeasible, it does motivate the development of reliable software design tools. These tools necessitate models based on fundamental physics and mathematics of the device to accurately model effects such as diffraction and scattering in opto-electronic devices, or bandstructure and scattering in heterostructure devices. The software tools must have convenient turn-around times and interfaces that allow effective usage. The first issue is addressed by the application of high-performance computers and the second by the development of graphical user interfaces driven by properly developed data structures. These tools can then be integrated into an optimization environment, and with the available memory capacity and computational speed of high performance parallel platforms, simulation of optimized components can proceed. In this paper, specific applications of the electromagnetic modeling of infrared filtering, as well as heterostructure device design will be presented using genetic algorithm global optimization methods.

Cwik, Tom↗
NASA NTRS1987

An operational multispectral scanner for bathymetric surveys - The ABS NORDA scanner

The Naval Ocean Research and Development Activity (NORDA) is developing the Airborne Bathymetric Survey (ABS) system, which will take shallow water depth soundings from a Navy P-3 aircraft. The system combines active and passive sensors to obtain optical measurements of water depth. The ABS NORDA Scanner is the systems passive multispectral scanner whose design goal is to provide 100 percent coverage of the seafloor, to depths of 20 m in average coastal waters. The ABS NORDA Scanner hardware and operational environment is discussed in detail. The optical model providing the basis for depth extraction is reviewed and the proposed data processing routine discussed.

Haimbach, Stephen P.↗
NASA NTRS2005

A novel tiered sensor fusion approach for terrain characterization and safe landing assessment

This paper presents a novel tiered sensor fusion methodology for real-time terrain safety assessment. A combination of active and passive sensors, specifically, radar, lidar, and camera, operate in three tiers according to their inherent ranges of operation. Low-level terrain features (e.g. slope, roughness) and high-level terrain features (e.g. hills, craters) are integrated using principles of reasoning under uncertainty. Three methodologies are used to infer landing safety: Fuzzy Reasoning, Probabilistic Reasoning, and Evidential Reasoning. The safe landing predictions from the three fusion engines are consolidated in a subsequent decision fusion stage aimed at combining the strengths of each fusion methodology. Results from simulated spacecraft descents are presented and discussed.

fuzzy reasoning↗
NASA NTRS1979

Measurement of chlorophyll a fluorescence with an airborne fluorosensor

Phytoplankton biomass and diversity among various algal species are important for marine productivity assessments. The spatial heterogeneity of phytoplankton in coastal and estuarine environments complicates estimates of total biomass using conventional surface sampling techniques. Since synoptic or near-synoptic data can be quite useful in these studies, this area is a natural focal point for development of remote sensors. However, it is very difficult to sense phytoplankton density and diversity with spacecraft-borne passive sensors primarily because modulation in the signal due to phytoplankton is of the same order as that of atmospheric effects. The same sensors mounted on aircraft may be able to detect and quantify high concentrations of phytoplankton (blooms), but the current lack of knowledge about the spectral reflectance signatures of the major phytoplankton color groups rules out any diversity measurements by this type of sensor. An active fluorosensor mounted on a low-flying aircraft or helicopter is not limited by any of these constraints. A brief survey of the four currently active systems is presented.

Jarrett, O., Jr.↗
NASA NTRS1990

Velocity filtering applied to optical flow calculations

Optical flow is a method by which a stream of two-dimensional images obtained from a forward-looking passive sensor is used to map the three-dimensional volume in front of a moving vehicle. Passive ranging via optical flow is applied here to the helicopter obstacle-avoidance problem. Velocity filtering is used as a field-based method to determine range to all pixels in the initial image. The theoretical understanding and performance analysis of velocity filtering as applied to optical flow is expanded and experimental results are presented.

Barniv, Yair↗
NASA NTRS1992

Inertial navigation sensor integrated motion analysis for autonomous vehicle navigation

Recent work on INS integrated motion analysis is described. Results were obtained with a maximally passive system of obstacle detection (OD) for ground-based vehicles and rotorcraft. The OD approach involves motion analysis of imagery acquired by a passive sensor in the course of vehicle travel to generate range measurements to world points within the sensor FOV. INS data and scene analysis results are used to enhance interest point selection, the matching of the interest points, and the subsequent motion-based computations, tracking, and OD. The most important lesson learned from the research described here is that the incorporation of inertial data into the motion analysis program greatly improves the analysis and makes the process more robust.

Roberts, Barry↗
NASA NTRS2022

Cloud-radiation Interactions and Cloud-climate Feedbacks from an Active-sensor Satellite Perspective

Clouds are ubiquitous in the troposphere. Their interactions with radiation may result in either a warming or a cooling of the Earth system and generate diverse climate feedbacks. The vertical structure of the radiative effects of clouds as well as the response of clouds to global warming (i.e., the cloud feedbacks) are inadequately constrained within the diversity of current climate models, which limits our ability to project the magnitude of future warming. In this chapter we show how relatively recent active-sensor spaceborne observations have narrowed constraints on cloud feedbacks. The value added beyond what can be retrieved from passive sensors is only just beginning to be exploited.

Cloud radiation↗
NASA NTRS1989

Sensor fusion: Spatial reasoning and scene interpretation; Proceedings of the Meeting, Cambridge, MA, Nov. 7-9, 1988

The present conference discusses topics in the fusion of active and passive sensors, object estimation and verification, three-dimensional representation and knowledge integration, three-dimensional perception from multisensor data, the representation of uncertainty in multisensor fusion, and sensor calibration and registration. Also discussed are the areas of multisensor target detection and classification, multisensor processing architectures, knowledge structures and spatial reasoning, sensory interfaces to telerobotic systems, and navigation with spatial data bases.

Schenker, Paul S.↗
NASA NTRS1991

Vision based techniques for rotorcraft low altitude flight

An overview of research in obstacle detection at NASA Ames Research Center is presented. The research applies techniques from computer vision to automation of rotorcraft navigation. The development of a methodology for detecting the range to obstacles based on the maximum utilization of passive sensors is emphasized. The development of a flight and image data base for verification of vision-based algorithms, and a passive ranging methodology tailored to the needs of helicopter flight are discussed. Preliminary results indicate that it is possible to obtain adequate range estimates except at regions close to the FOE. Closer to the FOE, the error in range increases since the magnitude of the disparity gets smaller, resulting in a low SNR.

Sridhar, Banavar↗
NASA NTRS2012

Detecting Falling Snow from Space

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earth's surface in order to fully capture the atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms includes determining the thresholds of detection for various active and passive sensor channel configurations, snow event cloud structures and microphysics, snowflake particle electromagnetic properties, and surface types. In this work, cloud resolving model simulations of a lake effect and synoptic snow event were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W -band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) Ku and Ka band, and the GPM Microwave Imager (GMI) channels from 10 to 183 plus or minus 7 GHz. Eleven different snowflake shapes were used to compute radar reflectivities and passive brightness temperatures. Notable results include: (1) the W-Band radar has detection thresholds more than an order of magnitude lower than the future GPM sensors, (2) the cloud structure macrophysics influences the thresholds of detection for passive channels, (3) the snowflake microphysics plays a large role in the detection threshold for active and passive instruments, (4) with reasonable assumptions, "the passive 166 GHz channel has detection threshold values comparable to the GPM DPR Ku and Ka band radars with approximately 0.05 g per cubic meter detected at the surface, or an approximately 0.5-1 millimeter per hr. melted snow rate (equivalent to 0.5-2 centimeters per hr. solid fluffy snowflake rate). With detection levels of falling snow known, we can focus current and future retrieval efforts on detectable storms and concentrate advances on achievable results. We will also have an understanding of the light snowfall events missed by the sensors and not captured in the global estimates.

Gail Skofronick-Jackson↗
NASA NTRS1984

Crop moisture estimation over the southern Great Plains with dual polarization 1.66 centimeter passive microwave data from Nimbus 7

Moisture content of snow-free, unfrozen soil is inferred using passive microwave brightness temperatures from the scanning multichannel microwave radiometer (SMMR) on Nimbus-7. Investigation is restricted to the two polarizations of the 1.66 cm wavelength sensor. Passive microwave estimates of soil moisture are of two basic categories; those based upon soil emissivity and those based upon the polarization of soil emission. The two methods are compared and contrasted through the investigation of 54 potential functions of polarized brightness temperatures and, in some cases, ground-based temperature measurements. Of these indices, three are selected for the estimated emissivity, the difference between polarized brightness temperatures, and the normalized polarization difference. Each of these indices is about equally effective for monitoring soil moisture. Using an antecedent precipitation index (API) as ground control data, temporal and spatial analyses show that emissivity data consistently give slightly better soil moisture estimates than depolarization data. The difference, however, is not statistically significant. It is concluded that polarization data alone can provide estimates of soil moisture in areas where the emissivity cannot be inferred due to nonavailability of surface temperature data.

Mcfarland, M. J.↗
NASA NTRS1979

Robotic vision

Robotic vision, involving the use of a vision system to control a process, is discussed. Design and selection of active sensors employing radiation of radio waves, sound waves, and laser light, respectively, to light up unobservable features in the scene are considered, as are design and selection of passive sensors, which rely on external sources of illumination. The segmentation technique by which an image is separated into different collections of contiguous picture elements having such common characteristics as color, brightness, or texture is examined, with emphasis on the edge detection technique. The IMFEX (image feature extractor) system performing edge detection and thresholding at 30 frames/sec television frame rates is described. The template matching and discrimination approach to recognize objects are noted. Applications of robotic vision in industry for tasks too monotonous or too dangerous for the workers are mentioned.

Williams, D. S.↗