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At least 55 records · Page 3

On-board Neural Processor Design for an Intelligent Multi-sensor Microspacecraft

A compact VLSI neural processor based on the Optimization Cellular Neural Network (OCNN)has been under development to provide a wide range of support for an intelligent remote sensing microspacecraft which requires both high bandwidth communication and high-performance computing for on-board data analysis, thematic data reduction, synergy of multiple types of sensors, and other smart-sensor functions. The OCNN architecture is a programmable multi-dimensional array of neurons which are locally connected with their local neurons. The OCNN operation theory, architecture, design and implementation, prototype chip, and system applications have been investigated in detail and presented in this paper.

array↗

Ocean Drifters Get the Facts

With the help of Small Business Innovation Research (SBIR) funding from NASA's Goddard Space Flight Center, of Greenbelt, Maryland, Clearwater Instrumentation, of Watertown, Massachusetts, created the ClearSat-Autonomous Drifting Ocean Station (ADOS). The multi-sensor array ocean drifting station was developed to support observations of Earth by NASA satellites. It is a low-cost device for gathering an assortment of data necessary to the integration of present and future satellite measurements of biological and physical processes. Clearwater Instrumentation developed its ADOS technology based on Goddard's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) project, but on a scale that is practical for commercial use. ADOS is used for the in situ measuring of ocean surface layer properties such as ocean color, surface thermal structure, and surface winds. Thus far, multiple ADOS units have been sold to The Scripps Institution of Oceanography, where they are being applied in the field of academic science research. Fisheries can also benefit, because ADOS can locate prime cultivation conditions for this fast-growing industry.

Source record↗

Hydrologic applications of SAR derived soil moisture

The MACHYDRO-90 was a multi-sensor aircraft campaign conducted to study drainage basin hydrology and the role of soil moisture in defining hydrologic characteristics and patterns. The results from the synthetic aperture radar (SAR) are presented. Data were collected over a period in which the soil conditions changed from dry to wet and then through a drying period which was close to ideal. Radar backscatter data are compared to detailed soil moisture samples taken to define soil moisture gradients within a watershed. The analysis also includes 40-MHz bandwidth SAR data, which provide very high spatial resolution. It is shown these data can be interpreted for hydrology and their application to hydrologic modeling is discussed.

Engman, Edwin T.↗

Estimation of cirrus cloud particle fallspeeds from vertically pointing Doppler radar

The First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment 2 (FIRE 2) was conducted in Coffeyville, Kansas in late 1991 to study the microphysical and radiative properties of cirrus clouds. A variety of active and passive remote sensors were employed, including an 8-mm-wavelength cloud-sensing Doppler radar developed at the Wave Propagation Laboratory (WPL). The radar, having excellent sensitivity to cloud particles (-30 dBZ at 10 km), good spatial resolution (37 m), and velocity precision (.05 ms -1), is an excellent tool for observing cirrus clouds. Having this radar directed toward the zenith for long periods of time during FIRE 2 permitted the reflectivity-weighted particle fallspeed to be related to reflectivity which allowed a separation of ice particle fallspeeds from vertical air motions. Additionally, such relationships proved useful in other multi-sensor techniques for determining vertical profiles of ice particle characteristic size and ice water content in cirrus clouds. The analysis method and the results of applying it to cirrus cloud reflectivity and velocity data collected during FIRE 2 are discussed.

Orr, Brad W.↗

TRMM Observations of Lightning and Rainfall

A multi-sensor algorithm is proposed that uses total lightning observations in conjunction,with conventional weather satellite imagery to develop proportionality relationships that can be used to improve space-time estimates of rainfall in data sparse regions. Previous studies have examined the relationships between rainfall and cloud-to-ground lightning only. The proposed algorithm is developed from relationships developed between total lightning and rainfall data collected at the TRMM ground validation site at Kennedy Space Center, Florida and elsewhere. The algorithm is evaluated throughout the tropics with data collected by the TRMM Lightning Imaging Sensor (LIS) and the other TRMM instruments. Based on earlier studies of relationships among total lightning, passive microwave ice scattering signatures, and cloud top height, this algorithm is expected to improve rainfall estimates from geosynchronous orbit. A lightning sensor is currently being designed for a future flight on the GOES satellite.

Goodman, S. J.↗

Geologic mapping in Death Valley, California/Nevada using NASA/JPL airborne systems (AVIRIS, TIMS, and AIRSAR)

A multi-sensor aircraft campaign called the Geologic Remote Sensing Field Experiment (GRSFE) conducted during 1989 resulted in acquisition of high quality multispectral images in the visible, near infrared, shortwave infrared, thermal infrared, and microwave regions of the electromagnetic spectrum. The airborne data sets include the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the Thermal Infrared Multispectral Scanner (TIMS), and the Airborne Synthetic Aperture Radar (SAR). Ancillary data include Landsat Thematic Mapper, laboratory and field spectral measurements, and traditional geologic mapping. The GRSFE data for a site in the northern Death Valley, (California and Nevada) region were calibrated to physical units and geometrically registered to a map base. Various aspects of this experiment are briefly discussed.

Kruse, Fred A.↗

Overview of the Oregon Transect Ecosystem Research Project

The Oregon Transect Ecosystem Research (OTTER) project is a study of ecosystem functions in coniferous forests using the methods of computer modeling, experimental and theoretical remote sensing, and ecological field and laboratory techniques. The study is focused on predicting the major fluxes of carbon, nitrogen, and water, and the factors that dynamically regulate them. The OTTER project was conceived to test two major questions: (1) Can a generalized ecosystem simulation model, designed to use mainly parameters available from remote sensing, predict the functioning of forests across an environmentally variable region? and (2) To what extent can the variables required by this model be derived from remotely sensed data? The scientific objectives and scope of the project demanded that a coordinated effort be made to link ground measurements with remote sensing and modeling requirements. OTTER was selected as a focus for a National Aeronautics and Space Administration (NASA)-sponsored Multi-sensor Aircraft Campaign (MAC; combining NASA aircraft and sensors with those of others) on the basis of experience gained in past ecosystem studies and remote-sensing projects, and the importance of the OTTER objectives to NASA's long-range science goals and plans. Having several independent approaches available, both on the ground and from various remote-sensing platforms, proved valuable in estimating and validating many of the critical variables. This experience and cross comparison should help simplify future studies of a similar nature. Edited data sets from the OTTER project are now available to the scientific community on optical disks or via on-line data banks at NASA (Washington, D.C., USA) and Oregon State University (Corvallis, Oregon, USA).

Peterson, David L.↗

Supervised fully polarimetric classification of the Black Forest test site: From MAESTROI to MAC Europe

A study on the performance of a supervised fully polarimetric maximum likelihood classifier for synthetic aperture radar (SAR) data when applied to a specific classification context: forest classification based on age classes and in the presence of a sloping terrain is presented. For the experimental part, the polarimetric AIRSAR data at P, L, and C-band, acquired over the German Black Forest near Freiburg in the frame of the 1989 MAESTRO-1 campaign and the 1991 MAC Europe campaign was used, MAESTRO-1 with an ESA/JRC sponsored campaign, and MAC Europe (Multi-sensor Aircraft Campaign); in both cases the multi-frequency polarimetric JPL Airborne Synthetic Aperture Radar (AIRSAR) radar was flown over a number of European test sites. The study is structured as follows. At first, the general characteristics of the classifier and the dependencies from some parameters, like frequency bands, feature vector, calibration, using test areas lying on a flat terrain are investigated. Once it is determined the optimal conditions for the classifier performance, we then move on to the study of the slope effect. The bulk of this work is performed using the Maestrol data set. Next the classifier performance with the MAC Europe data is considered. The study is divided into two stages: first some of the tests done on the Maestro data are repeated, to highlight the improvements due to the new processing scheme that delivers 16 look data. Second we experiment with multi images classification with two goals: to assess the possibility of using a training set measured from one image to classify areas in different images; and to classify areas on critical slopes using different viewing angles. The main points of the study are listed and some of the results obtained so far are highlighted.

Degrandi, G.↗

Extratropical Influence of Sea Surface Temperature and Wind on Water Recycling Rate Over Oceans and Coastal Lands

Water vapor and precipitation are two important parameters confining the hydrological cycle in the atmosphere and over the ocean surface. In the extratropical areas, due to variations of midlatitude storm tracks and subtropical jetstreams, water vapor and precipitation have large variability. Recently, a concept of water recycling rate defined previously by Chahine et al. (GEWEX NEWS, August, 1997) has drawn increasing attention. The recycling rate of moisture is calculated as the ratio of precipitation to total precipitable water (its inverse is the water residence time). In this paper, using multi-sensor spacebased measurements we will study the role of sea surface temperature and ocean surface wind in determining the water recycling rate over oceans and coastal lands. Response of water recycling rate in midlatitudes to the El Nino event will also be discussed. Sea surface temperature data are derived from satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) blended with in situ measurements, available for the period 1982-1998. Global sea surface wind observations are obtained from spaceborne scatterometers aboard on the European Remote-Sensing Satellite (ERS1 and 2), available for the period 1991-1998. Global total precipitable water provided by the NASA Water Vapor Project (NVAP) is available for the period 1988-1995. Global monthly mean precipitation provided by the Global Precipitation Climatology Project (GPCP) is available for the period 1987-1998.

Hu, Hua↗

Microwave Remote Sensing and the Cold Land Processes Field Experiment

The Cold Land Processes Field Experiment (CLPX) has been designed to advance our understanding of the terrestrial cryosphere. Developing a more complete understanding of fluxes, storage, and transformations of water and energy in cold land areas is a critical focus of the NASA Earth Science Enterprise Research Strategy, the NASA Global Water and Energy Cycle (GWEC) Initiative, the Global Energy and Water Cycle Experiment (GEWEX), and the GEWEX Americas Prediction Project (GAPP). The movement of water and energy through cold regions in turn plays a large role in ecological activity and biogeochemical cycles. Quantitative understanding of cold land processes over large areas will require synergistic advancements in 1) understanding how cold land processes, most comprehensively understood at local or hillslope scales, extend to larger scales, 2) improved representation of cold land processes in coupled and uncoupled land-surface models, and 3) a breakthrough in large-scale observation of hydrologic properties, including snow characteristics, soil moisture, the extent of frozen soils, and the transition between frozen and thawed soil conditions. The CLPX Plan has been developed through the efforts of over 60 interested scientists that have participated in the NASA Cold Land Processes Working Group (CLPWG). This group is charged with the task of assessing, planning and implementing the required background science, technology, and application infrastructure to support successful land surface hydrology remote sensing space missions. A major product of the experiment will be a comprehensive, legacy data set that will energize many aspects of cold land processes research. The CLPX will focus on developing the quantitative understanding, models, and measurements necessary to extend our local-scale understanding of water fluxes, storage, and transformations to regional and global scales. The experiment will particularly emphasize developing a strong synergism between process-oriented understanding, land surface models and microwave remote sensing. The experimental design is a multi-sensor, multi-scale (1-ha to 160,000 km ^ {2}) approach to providing the comprehensive data set necessary to address several experiment objectives. A description focusing on the microwave remote sensing components (ground, airborne, and spaceborne) of the experiment will be presented.

Kim, Edward J.↗

L band push broom microwave radiometer: Soil moisture verification and time series experiment Delmarva Peninsula

The verification of a multi-sensor aircraft system developed to study soil moisture applications is discussed. This system consisted of a three beam push broom L band microwave radiometer, a thermal infrared scanner, a multispectral scanner, video and photographic cameras and an onboard navigational instrument. Ten flights were made of agricultural sites in Maryland and Delaware with little or no vegetation cover. Comparisons of aircraft and ground measurements showed that the system was reliable and consistent. Time series analysis of microwave and evaporation data showed a strong similarity that indicates a potential direction for future research.

Jackson, T. J.↗

Space shuttle post-entry and landing analysis. Volume 1: Candidate system evaluations

The general purpose of this study is to aid in the evaluation and design of multi-sensor navigation schemes proposed for the orbiter. The scope of the effort is limited to the post-entry, energy management, and approach and landing mission phases. One candidate system based on conventional navigation aids is illustrated including two DME (Distance Measuring Equipment) stations and ILS (Instrument Landing System) glide slope and localizer antennas. Some key elements of the system not shown are the onboard IMUs (Inertial Measurement Units), altimeters, and a computer. The latter is programmed to mix together (filter) the IMU data and the externally-derived data. A completely automatic, all-weather landing capability is required. Since no air-breathing engines will be carried on orbital flights, there will be no chance to go around and try again following a missed approach.

Crawford, B. S.↗

Multi-Sensor Characterization of the Boreal Forest: Initial Findings

Results are presented in an initial apriori knowledge approach toward using complementary multi-sensor multi-temporal imagery in characterizing vegetated landscapes over a site in the Boreal Ecosystem-Atmosphere Study (BOREAS). Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data were segmented using multiple endmember spectral mixture analysis and binary decision tree approaches. Individual date/sensor land cover maps had overall accuracies between 55.0% - 69.8%. The best eight land cover layers from all dates and sensors correctly characterized 79.3% of the cover types. An overlay approach was used to create a final land cover map. An overall accuracy of 71.3% was achieved in this multi-sensor approach, a 1.5% improvement over our most accurate single scene technique, but 8% less than the original input. Black spruce was evaluated to be particularly undermapped in the final map possibly because it was also contained within jack pine and muskeg land coverages.

Reith, Ernest↗

A TRMM-Calibrated Infrared Rainfall Algorithm Over Brazil

An improved version of the Convective/Stratiform Technique (CST) and its application over Brazil are presented. Keeping the major components of traditional CST, we have modified the scheme for classing convective and stratiform rainfall and tested various schemes to eliminate non-raining cirrus clouds. The parameters of the technique are calibrated using Tropical Rainfall Measuring Mission (TRMM) multi-sensor observations, including TMI, PR and Visible Infrared Scanner (VIRS). This technique takes advantage of the high temporal sampling rate of geosynchronous infrared channel and the better instantaneous rain observation of TRMM Microwave Imager (TMI) and PR. Moreover, sparse TMI rain estimates are used to dynamically calibrate the parameters of the technique to improve its performance. The technique is applied to make rainfall estimates on various time scales and to study rainfall statistics such as the distributions of rain intensity and storm duration over a four-month period beginning January 1999. The study period coincides with the TRMM/Large-Scale Biosphere-Atmosphere experiment in Amazonia (LBA) ground validation experiment, and observations from the LBA radar are used to validate the technique. Results show the mean diurnal cycle of precipitation over the LBA area, and are compared to radar data from the Tropical Oceans and Global Atmosphere (TOGA) radar. When the larger geographic region is considered, the analysis of the mean hourly estimates revealed the pronounced effects of rivers, topography and local circulations on the rainfall.

Negri, Andrew J.↗

Measuring Geophysical Parameters of the Greenland Ice Sheet using Airborne Radar Altimetry

This paper presents radar-altimeter scattering models for each of the diagenetic zones of the Greenland ice sheet. AAFE radar- altimeter waveforms obtained during the 1991 and 1993 NASA multi-sensor airborne altimetry experiments over Greenland reveal that the Ku-band return pulse changes significantly with the different diagenetic zones. These changes are due to varying amounts of surface and volume scattering in the return waveform. In the ablation and soaked zones, where surface scattering dominates the AAFE return, geophysical parameters such as rms surface height and rms surface slope are obtained by fitting the waveforms to a surface-scattering model. Waveforms from the percolation zone show that the sub-surface ice features have a much more significant effect on the return pulse than the surrounding snowpack. Model percolation waveforms, created using a combined surface- and volume-scattering model and an ice-feature distribution obtained during the 1993 field season, agree well with actual AAFE waveforms taken in the same time period. Using a combined surface- and volume-scattering model for the dry-snow-zone return waveforms, the rms surface height and slope and the attenuation coefficient of the snowpack are obtained. These scattering models not only allow geophysical parameters of the ice sheet to be measured but also help in the understanding of satellite radar-altimeter data.

Ferraro, Ellen J.↗

A multi-sensor remote sensing approach for measuring primary production from space

It is proposed to develop a multi-sensor remote sensing method for computing marine primary productivity from space, based on the capability to measure the primary ocean variables which regulate photosynthesis. The three variables and the sensors which measure them are: (1) downwelling photosynthetically available irradiance, measured by the VISSR sensor on the GOES satellite, (2) sea-surface temperature from AVHRR on NOAA series satellites, and (3) chlorophyll-like pigment concentration from the Nimbus-7/CZCS sensor. These and other measured variables would be combined within empirical or analytical models to compute primary productivity. With this proposed capability of mapping primary productivity on a regional scale, we could begin realizing a more precise and accurate global assessment of its magnitude and variability. Applications would include supplementation and expansion on the horizontal scale of ship-acquired biological data, which is more accurate and which supplies the vertical components of the field, monitoring oceanic response to increased atmospheric carbon dioxide levels, correlation with observed sedimentation patterns and processes, and fisheries management.

Gautier, Catherine↗

Structure and Characteristics of Precipitation Systems Observed by TRMM

Multi-sensor measurements of total lightning (in-cloud plus cloud-to-ground) from the Lightning Imaging Sensor (LIS) aboard the TRMM satellite has been combined with passive microwave ice scattering and precipitation radar reflectivity profile information previously cataloged for precipitation features observed by TRMM. These precipitation features are defined by their surface rainfall or ice scattering signatures, with horizontal scales approx. 100 - 10000 sq km. Additionally, convective scale (approx. 100 sq km) storms are defined by the lightning data. Over the U.S. the national cloud-to-ground lightning observations are used to also determine the ratio of in-cloud to cloud-to-ground lightning, where it is hypothesized these ratios are related to the vertical profile of reflectivity and concentration of precipitation-sized ice. Combining the LIS, TRMM Microwave Imager (TMI), and Precipitation Radar (PR) for these storms allows investigation of the convective structures and convective regimes leading to observed lightning, scattering, and reflectivity signatures. Application of this approach to a global, multi-year data set (1998-2000) furthers our understanding of convective processes in different climatological regimes. The figure shows the distribution of high flash rate storms.

Goodman, Steve↗

Estimating Uncertainty in GPCP and TRMM Multi-Satellite Precipitation Estimates

One of the high-priority problems in satellite precipitation estimation is developing algorithms for estimating the errors in precipitation retrievals by individual sensors and subsequent multi-satellite combinations. Classically, we distinguish between "random" and "bias" errors, which do and do not, respectively average to zero over a "big enough" time/space sample. The current operational GPCP and TRMM multi-satellite algorithms are nearly unique in estimating random error for the monthly precipitation estimates from individual sensor systems (including gauge), following Huffman, and then making multi-sensor combinations. No routinely operational global precipitation produces estimates of bias error. Subsequently, a similar scheme has been followed to provide random error estimates for the Multi-satellite Precipitation Analysis (MPA) being computed in real time and after real time for TRMM. The Huffman algorithm for random error is briefly reviewed, including a discussion of the limitations imposed on the algorithm by standard monthly precipitation data sets. Starting from a very simple theoretical treatment of the histogram of precipitation samples in a month, an equation is developed that depends on the estimated average precipitation rate for the month, the number of samples in the month, and two constants. The constants are set separately for each source of precipitation estimate (such as "raingauge") by calibration at selected ground sites. We discuss recent work validating the random error estimates to highlight the successes and limitations of this first-generation approach. We then consider what information is needed from the individual sensor algorithms to facilitate additional accuracy in the estimation of random errors across the time/space span of climate regimes which a global estimation system must handle. In addition, the thorny issue of estimating bias is raised. Finally, the role of error estimates (and the qualitative errors!) in creating combinations of precipitation estimates from different individual sensors is discussed. This issue is particularly important when fine scales in space and time are being considered, say the 0.25 x 0.25-deg 3-hourly estimates in the MPA.

Huffman, G. J.↗