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The Annual Report of the Optical Data Storage Center of the University of Arizona is presented. Summary reports on continuing projects are presented. Research areas include: magneto-optic media, optical heads, and signal processing.
The High Resolution Imager (HRI) observation of the CTB1 region requested in the original proposal was performed and data received. The primary target, the candidate neutron star, was detected at the predicted intensity. As proposed, both timing and spatial extent analysis has been performed on the extracted photons. The preliminary timing analysis showed no evidence for the faint pulsations seen in the PSPC data. As the pulse fraction was rather small, this was not an unexpected result. We have, however, recently developed more sophisticated timing analysis capabilities and are in the process of reanalyzing the data to more accurately constrain the pulse fraction, if indeed any evidence for pulsation is found. We have supplemented the HRI observations with optical observations. Filter images, in BVR & I, have been obtained as well as spectra for candidate counterparts. As of this writing, the original analysis holds, i.e. the x-ray flux is not consistent with the L(sub x)/L(sub opt) ratios expected from the candidate stars within the 3 sigma positional error circles. HRI confirmation of the flux as well as the refinement of the position have allowed us to limit the candidate list and refine the original L(sub x)/L(sub opt) analysis.
A robotic system includes a humanoid robot with robotic joints each moveable using an actuator(s), and a distributed controller for controlling the movement of each of the robotic joints. The controller includes a visual perception module (VPM) for visually identifying and tracking an object in the field of view of the robot under threshold lighting conditions. The VPM includes optical devices for collecting an image of the object, a positional extraction device, and a host machine having an algorithm for processing the image and positional information. The algorithm visually identifies and tracks the object, and automatically adapts an exposure time of the optical devices to prevent feature data loss of the image under the threshold lighting conditions. A method of identifying and tracking the object includes collecting the image, extracting positional information of the object, and automatically adapting the exposure time to thereby prevent feature data loss of the image.
This software implements digital control of a WGM (whispering-gallerymode) resonator temperature based on the dual-mode approach. It comprises one acquisition (dual-channel) and three control modules. The interaction of the proportional-integral loops is designed in the original way, preventing the loops from fighting. The data processing is organized in parallel with the acquisition, which allows the computational overhead time to be suppressed or often completely avoided. WGM resonators potentially provide excellent optical references for metrology, clocks, spectroscopy, and other applications. However, extremely accurate (below micro-Kelvin) temperature stabilization is required. This software allows one specifically advantageous method of such stabilization to be implemented, which is immune to a variety of effects that mask the temperature variation. WGM Temperature Tracker 2.3 (see figure) is a LabVIEW code developed for dual-mode temperature stabilization of WGM resonators. It has allowed for the temperature stabilization at the level of 200 nK with one-second integration time, and 6 nK with 10,000-second integration time, with the above room-temperature set point. This software, in conjunction with the appropriate hardware, can be used as a noncryogenic temperature sensor/ controller with sub-micro-Kelvin sensitivity, which at the time of this reporting considerably outperforms the state of the art.
This presentation presents an overview of the aerosol data assimilation work performed at GMAO. The GMAO Forward Processing system and the biomass burning emissions from QFED are first presented. Then, the current assimilation of Aerosol Optical Depth (AOD), performed by means of the analysis splitting method is briefly described, followed by some results on the quality control of observations using a Neural Network trained using AERONET AOD. Some applications are shown such as the Mount Pinatubo eruption in 1991 using the MERRA-2 aerosol dataset. Finally preliminary results on the EnKF implementation for aerosol assimilation are presented.
The Geoscience Laser Altimeter System (GLAS) will be placed into orbit in 2001 aboard the Ice, Cloud and Land Elevation Satellite (ICESat). From its nearly polar orbit (94 degree inclination), GLAS will provide continuous global measurements of the vertical distribution of clouds and aerosols while simultaneously providing high accuracy topographic profiling of surface features. During the mission, which is slated to last 3 to 5 years, the data collected by GLAS will be in near-real time to produce level 1 and 2 data products at the NASA GLAS Science Computing Facility (SCF) at Goddard Space Flight Center in Greenbelt, Maryland. The atmospheric products include cloud and aerosol layer heights, planetary boundary layer depth, polar stratospheric clouds and thin cloud and aerosol optical depth. These products will be made available to the science community within days of their creation. The processing algorithms must be robust, adaptive, efficient, and clever enough to run autonomously for the widely varying atmospheric conditions that will be encountered. This paper presents an overview of the GLAS atmospheric data products and briefly discusses the design of the processing algorithms.
The NASA Lewis Research Center recently codeveloped a state-of-the-art x-ray CT facility (designated SMS SMARTSCAN model 100-112 CITA by Scientific Measurement Systems, Inc., Austin, Texas). This multipurpose, modularized, digital x-ray facility includes an imaging system for digital radiography, CT, and computed laminography. The system consists of a 160-kV microfocus x-ray source, a solid-state charge-coupled device (CCD) area detector, a five-axis object-positioning subassembly, and a Sun SPARCstation-based computer system that controls data acquisition and image processing. The x-ray source provides a beam spot size down to 3 microns. The area detector system consists of a 50- by 50- by 3-mm-thick terbium-doped glass fiber-optic scintillation screen, a right-angle mirror, and a scientific-grade, digital CCD camera with a resolution of 1000 by 1018 pixels and 10-bit digitization at ambient cooling. The digital output is recorded with a high-speed, 16-bit frame grabber that allows data to be binned. The detector can be configured to provide a small field-of-view, approximately 45 by 45 mm in cross section, or a larger field-of-view, approximately 60 by 60 mm in cross section. Whenever the highest spatial resolution is desired, the small field-of-view is used, and for larger samples with some reduction in spatial resolution, the larger field-of-view is used.
The post-launch Cal/Val phase of the SMAP mission is guided by two primary objectives for each science product team: 1) to calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the SMAP Level-1 mission science requirements. Algorithm science and product maintenance activities during the SMAP extended mission phase have also involved periodic algorithm calibration and product refinements to maintain or enhance product consistency and performance as well as science utility. This report provides an assessment of the latest (Version 5) SMAP Level 4 Carbon (L4_C) product. The L4_C Version 5 (v5) global record now spans more than six years (March 2015 – present) of SMAP operations and has benefited from five major reprocessing updates to the operational product. These reprocessing events and L4_C product release updates have incorporated various algorithm refinements and calibration adjustments to account for similar refinements to the upstream GEOS land model assimilation system, SMAP brightness temperatures, and MODIS vegetation inputs used for L4_C processing. The SMAP L4_C algorithms utilize a terrestrial carbon flux model informed by daily surface and root zone soil moisture information contributed from the SMAP Level 4 Soil Moisture (L4_SM) product along with optical remote sensing-based (e.g. MODIS-based) land cover and canopy fractional photosynthetic active radiation (fPAR), and other ancillary biophysical data. The carbon flux model estimates global daily net ecosystem CO2 exchange (NEE) and the component carbon fluxes, namely, vegetation gross primary production (GPP) and soil heterotrophic respiration (Rh). Other L4_C product elements include surface (~0-5 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture-related controls on GPP and ecosystem respiration (Kimball et al. 2014, Jones et al. 2017). The L4_C product addresses SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture-related constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity. The SMAP L4_C algorithms and operational product are mature and at a CEOS Validation Stage 4 level (Jackson et al. 2012) based on extensive validation of the multi-year record against a diverse array of independent benchmarks, well characterized global performance, and systematic refinements gained from five major reprocessing events. There are no Level-1 mission science requirements for the L4_C product; however, self-imposed requirements have been established focusing on NEE as the primary product field for validation, and on demonstrating L4_C accuracy and success in meeting product science requirements (Jackson et al. 2012). The other L4_C product fields also have strong utility for carbon science applications (e.g., Liu et al. 2019, Endsley et al. 2020); however, analysis of these other fields is considered secondary relative to primary validation activities focusing on NEE. The L4_C targeted accuracy requirements are to meet or exceed a mean unbiased root-mean-square error (ubRMSE, or standard deviation of the error) for NEE of 1.6 g C m-2 d-1 and 30 g C m-2 yr-1, emphasizing northern (≥45°N) boreal and arctic ecosystems; this accuracy is similar to that of tower eddy covariance measurement-based observations (Baldocchi 2008). Methods used for the latest v5 L4_C product performance and validation assessment have been established from the SMAP Cal/Val plan and previous studies (Jackson et al. 2012, Jones et al. 2017) and include: 1) consistency evaluations of the product fields against earlier product releases (version 4 or earlier); 2) comparisons of daily carbon flux estimates with independent tower eddy 4covariance measurement-based daily carbon (CO2) flux observations from core tower validation sites (CVS); and 3) consistency checks against other global carbon products, including soil carbon inventory records, global GPP records derived from tower observation upscaling methods, and satellite-based observations of canopy solar induced chlorophyll fluorescence (SIF) as a surrogate for GPP. Metrics used to evaluate relative agreement between L4_C product fields and observational benchmarks include correlation (r-value), RMSE differences, bias and model sensitivity diagnostics. Following these validation criteria, the present report provides a validation assessment of the latest L4_C product release (v5). Detailed descriptions of the L4_C algorithm and additional global product accuracy and performance results are given elsewhere (Jones et al. 2017, Endsley et al. 2020). The v5 L4_C product replaces earlier product versions and continues to show: (i) accuracy and performance levels meeting or exceeding SMAP L4_C science requirements; (ii) improvement over the previous product version (version 4); and (iii) suitability for a diversity of science applications. Example L4_C applications from the recent literature include clarifying environmental trends and controls on the northern terrestrial carbon sink (Liu et al. 2019), diagnosing drought-related impacts on ecosystem productivity (Li et al. 2020), and regional monitoring of cropland conditions for projecting annual yields (Wurster et al. 2020). 2EXPECTED L4_C ALGORITHM AND PRODUCTPERFORMANCE The L4_C algorithm performance, including variance and uncertainty estimates of model outputs, was determined during the mission pre-launch phase through spatially explicit model sensitivity studies using available model inputs similar to those currently being used for operational production and evaluating the resulting model simulations over the observed range of northern (≥45 °N) and global conditions (Kimball et al. 2012, Entekhabi et al. 2014). The L4_C algorithm options were also evaluated during the mission prelaunch phase, including deriving canopy fPAR from lower order NDVI (Normalized Difference Vegetation Index) inputs in lieu of using MODIS (MOD15) fPAR; and including an explicit model representation of boreal fire disturbance recovery impacts. These results indicated that the L4_C accuracy requirements (i.e., NEE ubRMSE ≤ 30 g C m-2 yr-1or ≤ 1.6 g C m-2 d-1) could be met from the baseline algorithms over more than 82% and 89% of global and northern vegetated land areas, respectively (Yi et al. 2013, Kimball et al. 2014). The global L4_C algorithm error budget for NEE derived during the mission prelaunch phase indicated that the estimated NEE ubRMSE uncertainty is proportional to GPP and is therefore larger in higher biomass productivity areas, including forests and croplands (Kimball et al. 2014). Likewise, NEE ubRMSE uncertainty is expected to be lower in less-productive areas, including grasslands and shrublands. Expected model NEE ubRMSE levels were also generally within targeted accuracy levels for characteristically less-productive boreal and Arctic biomes, even though relative model error as a proportion of total productivity (NEE RMSE / GPP) may be large in these areas. The estimated NEE uncertainty was lower than expected in some warmer tropical high biomass productivity areas (e.g. Amazon rainforest) because of reduced low temperature and moisture constraints to the L4_C respiration calculations so that the bulk of model uncertainty is contributed by GPP in these areas. Model NEE uncertainty in the African Congo was estimated to
The BOREAS RSS-7 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of Leaf Area Index (LAI) from remotely sensed images. Ground measurements of LAI and Fraction of Photosynthetically Active Radiation (FPAR) absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-Aug-1993 to 19-Sep-1994. The measurements were intensive at the NSA and SSA tower sites, but were made just once or twice at auxiliary sites. The final processed LAI and FPAR data set is contained in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884).
Hyperspectral data offers unique capabilities for characterizing the ocean environment. The spectral characterization of the composition of ocean waters can be organized into biological and terrigenous components. Biological photosynthetic pigments in ocean waters have unique spectral ocean color signatures which can be associated with different biological species. Additionally, suspended sediment has different scattering coefficients which result in ocean color signatures. Measuring the spatial distributions of these components in the maritime environments provides important tools for understanding and monitoring the ocean environment. These tools have significant applications in pollution, carbon cycle, current and water mass detection, location of fronts and eddies, sewage discharge and fate etc. Ocean color was used from satellite for describing the spatial variability of chlorophyll, water clarity (K(sub 490)), suspended sediment concentration, currents etc. Additionally, with improved atmospheric correction methods, ocean color results produced global products of spectral water leaving radiance (L(sub W)). Ocean color results clearly indicated strong applications for characterizing the spatial and temporal variability of bio-optical oceanography. These studies were largely the results of advanced atmospheric correction techniques applied to multispectral imagery. The atmosphere contributes approximately 80 percent - 90 percent of the satellite received radiance in the blue-green portion of the spectrum. In deep ocean waters, maximum transmission of visible radiance is achieved at 490nm. Conversely, nearly all of the light is absorbed by the water at wavelengths greater than about 650nm and thus appears black. These spectral ocean properties are exploited by algorithms developed for the atmospheric correction used in satellite ocean color processing. The objective was to apply atmospheric correction techniques that were used for procesing satellite Coastal Zone Color Scanner (CZCS) data to AVIRIS data. Quantitative measures of L(sub W) from AVIRIS are compared with ship ground truth data and input into bio-optical models.
The elimination of redundancy in data to maximize processing speed and minimize storage requirements were objectives in a feasibility study of a microprocessor based oculometer system that would be portable in size and flexible in use. The appropriate architectural design of the signal processor, improved optics, and the reduction of size, weight, and power to the system were investigated. A flow chart is presented showing the strategy of the design. The simulation for developing microroutines for the high speed algorithmic processor subsystem is discussed as well as the Karhunen-Loeve transform technique for data compression.
Flood and surface water mapping is becoming increasingly necessary, as extreme flooding events worldwide can damage crop yields and contribute to billions of dollars economic damages as well as social effects including fatalities and destroyed communities (Xaio et al. 2004; Kwak et al. 2015; Mueller et al. 2016).Utilizing earth observing satellite data to map standing water from space is indispensable to flood mapping for disaster response, mitigation, prevention, and warning (McFeeters 1996; Brakenridge and Anderson 2006). Since the early 1970s(Landsat, USGS 2013), researchers have been able to remotely sense surface processes such as extreme flood events to help offset some of these problems. Researchers have demonstrated countless methods and modifications of those methods to help increase knowledge of areas at risk and areas that are flooded using remote sensing data from optical and radar systems, as well as free publically available and costly commercial datasets.
Although the STARLINK project was set up to provide image processing facilities to UK astronomers, it has grown over the last 12 years to the extent that it now provides most of the data analysis facilities for UK astronomers. One aspect of the growth of the STARLINK network is that it now has to cater for astronomers working in a diverse range of wavelengths. Since a given individual may be working with data obtained in a variety of wavelengths, it is most convenient if the data can be stored in a common format and the programs that analyze the data have a similar 'look and feel'. What is known as 'STARLINK software' is obtained from many sources: STARLINK funded programmers; astronomers; foreign projects such as AIPS; generally available shareware; and commercial sources when this proves cost effective. This means that the ideal situation of a completely integrated system cannot be realized in practice. Nevertheless, many of the major packages written by STARLINK application programmers and by astronomers do use a common data format, based on the Hierarchical Data System, so that interchange of data between packages designed separately from each other is simply a matter of using the same file names. For example, as astronomer might use KAPPA to read some optical spectra off a FITS tape, then use CCDPACK to debias and flat field the data (it is easy to set up an overnight batch job to do this if there is a lot of data), then use KAPPA to have a quick look at the data and then use Figaro to reduce the spectra. It is useful to divide data analysis packages into wavelength specific packages, or even instrument specific packages, and general purpose ones. Once the instrumental signature has been removed from some data, any appropriate general purpose package can be used to analyze te data. For example, the ASTERIX package deals with x-ray data reduction, but after dealing with all of the x-ray specific processing, an astronomer may well want to find the brightness of objects in a given frame. Since ASTERIX uses the standard STARLINK data format, the astronomer can use PHOTOM or DAOPHOT 2 to measure the brightness of the objects. Although DAOPHOT was written with optical astronomy in mind, it is useful for analyzing data from several wavelengths. The ability of DAOPHOT 2 to handle non-standard point spread functions can be especially useful in many areas of astronomy.
The infrared array referred to as Big Mac (for Marshall Array Camera), was designed for ground based astronomical observations in the wavelength range 5 to 35 microns. It contains 20 discrete gallium-doped germanium bolometer detectors at a temperature of 1.4K. Each bolometer is irradiated by a square field mirror constituting a single pixel of the array. The mirrors are arranged contiguously in four columns and five rows, thus defining the array configuration. Big Mac utilized cold reimaging optics and an up looking dewar. The total Big Mac system also contains a telescope interface tube for mounting the dewar and a computer for data acquisition and processing. Initial astronomical observations at a major infrared observatory indicate that Big Mac performance is excellent, having achieved the design specifications and making this instrument an outstanding tool for astrophysics.
The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) is presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. The independent analysis results corresponding to the Orbiter Guidance, Navigation, and Control (GNC) Subsystem hardware are documented. The function of the GNC hardware is to respond to guidance, navigation, and control software commands to effect vehicle control and to provide sensor and controller data to GNC software. Some of the GNC hardware for which failure modes analysis was performed includes: hand controllers; Rudder Pedal Transducer Assembly (RPTA); Speed Brake Thrust Controller (SBTC); Inertial Measurement Unit (IMU); Star Tracker (ST); Crew Optical Alignment Site (COAS); Air Data Transducer Assembly (ADTA); Rate Gyro Assemblies; Accelerometer Assembly (AA); Aerosurface Servo Amplifier (ASA); and Ascent Thrust Vector Control (ATVC). The IOA analysis process utilized available GNC hardware drawings, workbooks, specifications, schematics, and systems briefs for defining hardware assemblies, components, and circuits. Each hardware item was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode.
Large-aperture phased array antennas operating at millimeter wave frequencies are designed for space-based communications and imaging. Array elements are comprised of active transmit/receive (T/R) modules which are linked to the central processing unit through a high-speed fiberoptic network. This paper demonstrates optical control of active modules for satellite communication at 24 GHz. An approach called T/R level data mixing, which utilizes fiberoptic transmission of a data signal to individual T/R modules to be upconverted by an optically synchronized local oscillator, is demonstrated at 24 GHz. A free-running HEMT oscillator, used as local oscillator at 24 GHz, is synchronized using indirect subharmonic optical injection locking over a locking range of 14 MHz. Results of data link performance over 500-1000 MHz is also reported in terms of gain-bandwidth, linearity and third-order intercept, sensitivity, and dynamic range.
Discussion with the RXTE team at GSFC showed that a sufficiently accurate background subtraction procedure had now, been derived for sources at the flux level of PKS 2126-158. However this solution does not apply to observations carried out before April 1997, including our observation. The prospect of an improved solution becoming available soon is slim. As a result the RXTE team agreed to re-observe PKS2126-158. The new observation was carried out in April 1999. Quasi-simultaneous optical observations were obtained, as Service observing., at the 4-meter Anglo-Australian Telescope, and ftp-ed from the AAT on 22April. The RXTE data was processed in late June, arriving at SAO in early July. Coincidentally, our collaborative Beppo-SAX observation of PKS2126-158 was made later in 1999, and a GTO Chandra observation (with which we are involved) was made on November 16. Since this gives us a unique monitoring data for a high redshift quasar over a broad pass-band we are now combining all three observations into a single comprehensive study Final publication of the RXTE data will thus take place under another grant.
A test facility has been constructed to demonstrate the capability of differential ensemble photometry to detect transits of Earth-size planets orbiting solar-like stars. The main objective is to determine the effects of various noise sources on the capability of a CCD photometer to maintain a system relative precision of 1 x $10^(-5)$ for mv = 12 stars in the presence of system-induced noise sources. The facility includes a simulated star field, fast optics to simulate the telescope, a thinned back-illuminated CCD similar to those to be used on the spacecraft and computers to perform the onboard control, data processing and extraction. The test structure is thermally and mechanically isolated so that each source of noise can be introduced in a controlled fashion and evaluated for its contribution to the total noise budget. The effects of pointing errors or a changing thermal environment are imposed by piezo-electric devices. Transits are injected by heating small wires crossing apertures in the star plate. Signals as small as those from terrestrial-size transits of solar-like stars are introduced to demonstrate that such planets can be detected under realistic noise conditions. Examples of imposing several noise sources and the resulting detectabilities are presented. These show that a differential ensemble photometric approach CCD photometer can readily detect signals associated with Earth-size transits.