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NASA NTRS2017

A Massive Shell of Supernova-Formed Dust in SNR G54.1+0.3

While theoretical models of dust condensation predict that most refractory elements produced in core-collapsesupernovae (SNe) efficiently condense into dust, a large quantity of dust has so far only been observed inSN1987A. We present an analysis of observations from the Spitzer Space Telescope, Herschel SpaceObservatory, Stratospheric Observatory for Infrared Astronomy, and AKARI of the infrared shell surrounding thepulsar wind nebula in the supernova remnant G54.1+0.3. We attribute a distinctive spectral feature at 21 m to amagnesium silicate grain species that has been invoked in modeling the ejecta-condensed dust in Cas A, whichexhibits the same spectral signature. If this species is responsible for producing the observed spectral feature andaccounts for a significant fraction of the observed infrared continuum, we find that it would be the dominantconstituent of the dust in G54.1+0.3, with possible secondary contributions from other compositions, such ascarbon, silicate, or alumina grains. The total mass of SN-formed dust required by this model is at least 0.3Me. Wediscuss how these results may be affected by varying dust grain properties and self-consistent grain heating models.The spatial distribution of the dust mass and temperature in G54.1+0.3 confirms the scenario in which the SNformeddust has not yet been processed by the SN reverse shock and is being heated by stars belonging to a clusterin which the SN progenitor exploded. The dust mass and composition suggest a progenitor mass of 1627Me andimply a high dust condensation efficiency, similar to that found for Cas A and SN1987A. The study providesanother example of significant dust formation in a Type IIP SN explosion and sheds light on the properties ofpristine SN-condensed dust.

Temim, Tea↗
NASA NTRS2018

Correlated Amino Acid and Mineralogical Analyses of Milligram and Submilligram Samples of Carbonaceous Chondrite Lonewolf Nunataks 94101

Amino acids, the building blocks of proteins, have been found to be indigenous in the eight carbonaceous chondrite groups. The abundances, structural, enantiomeric and isotopic compositions of amino acids differ significantly among meteorites of different groups and petrologic types. These results suggest parent-body conditions (thermal or aqueous alteration), mineralogy, and the preservation of amino acids are linked. Previously, elucidating specific relationships between amino acids and mineralogy was not possible because the samples analyzed for amino acids were much larger than the scale at which petrologic heterogeneity is observed (sub mm-scale differences corresponding to sub-mg samples); for example, Pizzarello and coworkers measured amino acid abundances and performed X-ray diffraction (XRD) on several samples of the Murchison meteorite, but these analyses were performed on bulk samples that were 500 mg or larger. Advances in the sensitivity of amino acid measurements by liquid chromatography with fluorescence detection/time-of-flight mass spectrometry (LC-FD/TOF-MS), and application of techniques such as high resolution X-ray diffraction (HR-XRD) and scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) for mineralogical characterizations have now enabled coordinated analyses on the scale at which mineral heterogeneity is observed. In this work, we have analyzed samples of the Lonewolf Nunataks (LON) 94101 CM2 carbonaceous chondrite. We are investigating the link(s) between parent body processes, mineralogical context, and amino acid compositions in meteorites on bulk samples (approx. 20mg) and mineral separates (< or = 3mg) from several of spatial locations within our allocated samples. Preliminary results of these analyses are presented here.

LONEWOLF NUNATAKS 94101↗
NASA NTRS2018

Spatial Variations in Crop Growing Seasons Pivotal to Reproduce Global Fluctuations in Maize and Wheat Yields

Testing our understanding of crop yield responses to weather fluctuations at global scale is notoriously hampered by limited information about underlying management conditions, such as cultivar selection or fertilizer application. Here, we demonstrate that accounting for observed spatial variations in growing seasons increases the variance in reported national maize and wheat yield anomalies that can be explained by process-based model simulations from 34 to 58% and 47 to 54% across the 10 most weather-sensitive main producers, respectively. For maize, the increase in explanatory power is similar to the increase achieved by accounting for water stress, as compared to simulations assuming perfect water supply in both rainfed and irrigated agriculture. Representing water availability constraints in irrigation is of second-order importance. We improve the model’s explanatory power by better representing crops’ exposure to observed weather conditions, without modifying the weather response itself. This growing season adjustment now allows for a close reproduction of heat wave and drought impacts on crop yields.

Maize↗
NASA NTRS2021

A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks

Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDN(A)). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDN(A) model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDN(A) model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDN(B)) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρ(s)). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDN(B) models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing.

Brandon Smith↗
NASA NTRSDate not supplied

Use of Aerosol Observations From Aircraft in Satellite Remote Sensing and Modelling Comparisons

Aerosol extensive and intensive properties vary throughout the world and remain one of the largest uncertainties in determining future climate both because of difficulties in their measurement and their modelled behaviors. Here we highlight the contributions of airborne sampling by the sunphotometer 4STAR (Spectrometers for Sky-Scanning Sun Tracking Atmospheric Research) from two different parts of the world; Korean peninsula and region during the KORUS-AQ experiment (KORean-US Air Quality in May-June 2016) and over the South East Atlantic during ORACLES (ObseRvations of CLouds above Aerosols and their intEractionS in August-September-October 2016, 2017, and 2018) field campaign. The high temporal and spatial resolution, afforded by airborne sampling during KORUS-AQ, are used to determine the variability of aerosol intensive and extensive properties, which is a metric than can be compared in between multiple different observations and models, even over varying surfaces and spanning multiple different aerosol emission sources. We show the consistency over spatial scales of the AOD (Aerosol Optical Depth), and the aerosol intensive properties (Angstrom exponent - AE, fine mode fraction - FMF) observed by 4STAR, GOCI (Geostationary Ocean Color Imager Yonsei aerosol retrieval v2), MERRA-2 reanalysis (Modern-Era Retrospective Analysis for Research and Applications, v2), and from airborne in situ aerosol optical measurements by LARGE (NASA Langley Aerosol Research Group Experiment). The majority of AODs due to fine mode aerosol is observed at altitudes lower than 2 km and is dependent on the prevailing meteorological conditions. AE and FMF are found to be more spatially variable than AOD during all of KORUS-AQ, even when accounting for potential sampling biases. This may indicate that microphysical processes like aerosol particle formation, growth, and coagulation impact the dominant aerosol size at shorter scales than their combined effect on the aerosol optical depth by the aerosol emission, transport, and removal. Averaging between measurements and model, the distance at which the correlation to itself is reduced by 15% is 65 km for AOD, and 22.7 km for AE. Vertically resolved measurement from an aircraft enable the direct measurements of aerosols overlying clouds. When aerosol overlie bright clouds, their radiative impact on the incident light can be either positive or negative. This difference in radiative impact promotes difficulties in remote sensing aerosol properties, which have shown biases in AOD retrievals when clouds are underneath. The AOD above clouds measured by 4STAR during ORACLES are used to build and improve upon the current Near-Real-Time MODACAERO algorithm for above cloud AOD from MODIS, into a continuity product for NASA EOS/SNPP/JPSS.

Aerosol↗
NASA NTRS1991

The metal abundance and specific energy of intracluster gas

The hot gas in the cores of rich galaxy clusters is metal-rich with nearly solar abundances of metals. It is not clear whether the metals were shed from galaxies via protogalactic winds or via ram-pressure stripping. It has been suggested that if metals were injected via centrally concentrated stripping, the overall abundances could be much less than those observed in cluster cores, diminishing the degree of stellar processing required. The observed energetics of intracluster gas can be used to deduce the metal injection mechanism, which in turn may allow the global metal abundance uncertainty to be resolved in the absence of spatially resolved X-ray spectra. Existing X-ray spectral and surface brightness data for galaxy clusters indicate that the gas in cool clusters has substantially greater specific energy than could have been gained through cluster collapse. Supernovae-driven protogalactic winds can provide this extra energy, while ram-pressure stripping cannot. Such protogalactic winds will distribute metals fairly homogeneously. Much processing of gas through stars is then required, with protogalaxies losing perhaps one-half of their initial luminous mass in metal-rich winds. Furthermore, the oxygen-to-iron ratio observed in two clusters indicates that the bulk of the iron in cluster gas was produced by Type II supernovae, not Type I supernovae, as is usually supposed.

White, Raymond E., III↗
NASA NTRS2003

Microelectromechanical System (MEMS) Device Being Developed for Active Cooling and Temperature Control

High-capacity cooling options remain limited for many small-scale applications such as microelectronic components, miniature sensors, and microsystems. A microelectromechanical system (MEMS) using a Stirling thermodynamic cycle to provide cooling or heating directly to a thermally loaded surface is being developed at the NASA Glenn Research Center to meet this need. The device can be used strictly in the cooling mode or can be switched between cooling and heating modes in milliseconds for precise temperature control. Fabrication and assembly employ techniques routinely used in the semiconductor processing industry. Benefits of the MEMS cooler include scalability to fractions of a millimeter, modularity for increased capacity and staging to low temperatures, simple interfaces, limited failure modes, and minimal induced vibration. The MEMS cooler has potential applications across a broad range of industries such as the biomedical, computer, automotive, and aerospace industries. The basic capabilities it provides can be categorized into four key areas: 1) Extended environmental temperature range in harsh environments; 2) Lower operating temperatures for electronics and other components; 3) Precision spatial and temporal thermal control for temperature-sensitive devices; and 4) The enabling of microsystem devices that require active cooling and/or temperature control. The rapidly expanding capabilities of semiconductor processing in general, and microsystems packaging in particular, present a new opportunity to extend Stirling-cycle cooling to the MEMS domain. The comparatively high capacity and efficiency possible with a MEMS Stirling cooler provides a level of active cooling that is impossible at the microscale with current state-of-the-art techniques. The MEMS cooler technology builds on decades of research at Glenn on Stirling-cycle machines, and capitalizes on Glenn s emerging microsystems capabilities.

Beach, Duane E.↗
NASA NTRS2009

Geologic Mapping along the Arabia Terra Dichotomy Boundary: Mawrth Vallis and Nili Fossae, Mars

Geologic mapping studies at the 1:1M-scale are being used to assess geologic materials and processes that shape the highlands along the Arabia Terra dichotomy boundary. In particular, this mapping will evaluate the distribution, stratigraphic position, and lateral continuity of compositionally distinct outcrops in Mawrth Vallis and Nili Fossae as identified by spectral instruments currently in orbit. Placing these landscapes, their material units, structural features, and unique compositional outcrops into spatial and temporal context with the remainder of the Arabia Terra dichotomy boundary may provide constraints on: 1) origin of the dichotomy boundary, 2) paleo-environments and climate conditions, and 3) various fluvial-nival modification processes related to past and present volatile distribution and their putative reservoirs (aquifers, lakes and oceans, surface and ground ice) and the influences of nearby volcanic and tectonic features on hydrologic processes in these regions. The results of this work will include two 1:1M scale geologic maps of twelve MTM quadrangles (Mawrth Vallis - 20022, 20017, 20012, 25022, 25017, and 25012; and Nili Fossae - 20287, 20282, 25287, 25282, 30287, 30282).

Bleamaster, Leslie F., III↗
NASA NTRSDate not supplied

WIS and WIGOS Metadata as the Foundation for a Sustainable Framework for Global Greenhouse Gas Watch Data Exchange

Metadata (data about data) is a critical component of data discovery, description, evaluation, documentation, and preservation. Developing and propagating metadata standards has been a longstanding area of activity in WMO and beyond. The WIS2 and WIGOS metadata models are being actively developed and maintained by dedicated task teams, established under the WMO Expert Team on Metadata. The metadata representations and vocabularies are governed by well-established processes within WMO. These standards are being used in a number of metadata/data exchange activities (e.g., WMO Information System 2.0 (WIS2), WIGOS (WMDR), Climate Data Management Systems (CMDS), etc.). It should also be noted that the application of the WIS2 and WIGOS standards fully support the WMO Unified Data Policy and open data policy as well as greatly enhance the value of observations by fostering data F.A.I.R.ness. Furthermore, the WMO metadata standards can serve as the foundation for a framework that will facilitate metadata mapping between the existing schemas used in well-established data centres, e.g., WMO WDCGG (World Data Centre for Greenhouse Gases) and NOAA ObsPack (Observation Package Data Products) and to automate metadata exchange between data centres as well as with WMO. These activities will play a central role in integrating measurements sponsored by various member countries and organizations to provide a more comprehensive characterization of the temporal and spatial distribution of the greenhouse gases. At the same time, this metadata exchange can lead to member countries and partner organizations improving their current metadata collection process for data discoverability, interoperability, and (re)usability. This presentation will describe metadata activities in the context of WIS2 and WIGOS and how they apply to GGGW data integration via metadata mapping and exchange.

Gao Chen↗
NASA NTRS1987

Edge detection - Image-plane versus digital processing

To optimize edge detection with the familiar Laplacian-of-Gaussian operator, it has become common to implement this operator with a large digital convolution mask followed by some interpolation of the processed data to determine the zero crossings that locate edges. It is generally recognized that this large mask causes substantial blurring of fine detail. It is shown that the spatial detail can be improved by a factor of about four with either the Wiener-Laplacian-of-Gaussian filter or an image-plane processor. The Wiener-Laplacian-of-Gaussian filter minimizes the image-gathering degradations if the scene statistics are at least approximately known and also serves as an interpolator to determine the desired zero crossings directly. The image-plane processor forms the Laplacian-of-Gaussian response by properly combining the optical design of the image-gathering system with a minimal three-by-three lateral-inhibitory processing mask. This approach, which is suggested by Marr's model of early processing in human vision, also reduces data processing by about two orders of magnitude and data transmission by up to an order of magnitude.

Huck, Friedrich O.↗
NASA NTRS2022

Imaging the End-to-End Dynamics of the Global Solar Wind-Magnetosphere Interaction

Much of what we know about the solar wind’s interaction with the Earth’s magnetosphere has been gained from isolated in situ measurements by single or multiple spacecraft. Based on their observations, we know that reconnection, whether on the dayside magnetopause or deep within the Earth’s magnetotail, controls the flow of solar wind energy into and through the global system. We know that nightside activity provides the energized particles that power geomagnetic storms. But by their very nature these isolated in situ measurements cannot provide an instantaneous global view of the entire system or its cross-scale dynamics. As a result, we don’t know which mode of reconnection prevails on the dayside magnetopause or within the magnetotail as a function of solar wind and geomagnetic conditions. We don’t know which mode or modes of nightside activity supply the most energized particles to the ring current. Nor do we know the dominant loss mode for ring current decay: precipitation, magnetopause outflow, or charge exchange with neutrals. Nor do we know how processes deep within the magnetosphere provide feedback to those happening in the outer magnetosphere. The answers to these questions could have an impact far beyond magnetospheric physics, since magnetic reconnection, particle acceleration, and charge-exchange are fundamental plasma processes that operate at other planets and throughout the universe. Comprehensive end-to-end global imaging of the key micro, meso-, and macro-scale plasma structures that comprise the magnetosphere will provide the answers to these questions via observations with a spatial resolution that exceeds anything possible with in situ measurements. Each proposed interaction mechanism generates a diagnostic plasma structure or boundary signature. Global, end-to-end, imaging provides the pathway to understanding the system as a whole, its constituent parts, and its cross-scale processes on a continuous basis, as needed to quantify the flow of solar wind energy through the global magnetospheric system. The significance of each mechanism is the product of its amplitude and occurrence rate. This white paper describes how a comprehensively-instrumented single spacecraft in a high-latitude circular polar orbit can provide the essential observations needed to track and quantify the flow of solar wind energy through the magnetosphere, including the solar wind plasma and magnetic field input, the magnetopause location in soft X-rays, the auroral oval in far ultraviolet, the ring current in energetic neutrals, the plasmasphere in extreme ultraviolet, the exosphere in Lyman-, the microstructure of the nightside auroral oval in ground-based all sky cameras, and the magnetic perturbations of ionospheric current patterns seen by ground-based magnetometers.

D G Sibeck↗
NASA NTRS2003

Multi-Decadal Pathfinder Data Sets of Global Land Biophysical Variables from AVHRR and MODIS and their Use in GCM Studies of Biogeophysics and Biogeochemistry

The problem of how the scale, or spatial resolution, of reflectance data impacts retrievals of vegetation leaf area index (LAI) and fraction absorbed photosynthetically active radiation (PAR) has been investigated. We define the goal of scaling as the process by which it is established that LAI and FPAR values derived from coarse resolution sensor data equal the arithmetic average of values derived independently from fine resolution sensor data. The increasing probability of land cover mixtures with decreasing resolution is defined as heterogeneity, which is a key concept in scaling studies. The effect of pixel heterogeneity on spectral reflectances and LAI/FPAR retrievals is investigated with 1 km Advanced Very High Resolution Radiometer (AVHRR) data aggregated to different coarse spatial resolutions. It is shown that LAI retrieval errors at coarse resolution are inversely related to the proportion of the dominant land cover in such pixel. Further, large errors in LAI retrievals are incurred when forests are minority biomes in non-forest pixels compared to when forest biomes are mixed with one another, and vice-versa. A physically based technique for scaling with explicit spatial resolution dependent radiative transfer formulation is developed. The successful application of this theory to scaling LAI retrievals from AVHRR data of different resolutions is demonstrated

Myneni, Ranga↗
NASA NTRS2016

Combining Livestock Production Information in a Process-Based Vegetation Model to Reconstruct the History of Grassland Management

Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the greenhouse gas balance and surface energy budget of this biome, both at field scale and at large spatial scale. However, global gridded historical information on grassland management intensity is not available. Combining modelled grass-biomass productivity with statistics of the grass-biomass demand by livestock, we reconstruct gridded maps of grassland management intensity from 1901 to 2012. These maps include the minimum area of managed vs. maximum area of unmanaged grasslands and the fraction of mown vs. grazed area at a resolution of 0.5deg by 0.5deg. The grass-biomass demand is derived from a livestock dataset for 2000, extended to cover the period 19012012. The grass-biomass supply (i.e. forage grass from mown grassland and biomass grazed) is simulated by the process-based model ORCHIDEE-GM driven by historical climate change, risingCO2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6.1 x 10(exp 6) km(exp 2) in 1901 to 12.3 x 10(exp 6) kmI(exp 2) in 2000, although the expansion pathway varies between different regions. ORCHIDEE-GM also simulated augmentation in global mean productivity and herbage-use efficiency over managed grassland during the 20th century, indicating a general intensification of grassland management at global scale but with regional differences. The gridded grassland management intensity maps are model dependent because they depend on modelled productivity. Thus specific attention was given to the evaluation of modelled productivity against a series of observations from site-level net primary productivity (NPP) measurements to two global satellite products of gross primary productivity (GPP) (MODIS-GPP and SIF data). Generally, ORCHIDEE-GM captures the spatial pattern, seasonal cycle, and inter-annual variability of grassland productivity at global scale well and thus is appropriate for global applications presented here.

Livestock dataset↗
NASA NTRSDate not supplied

PolarMERRA: A Polar-Focused Global Reanalysis Project for Scientific and Stakeholder Needs

The adequate modeling of physical processes in the Arctic and Antarctic is vital for developing prediction capabilities and for obtaining an understanding of rapidly evolving polar conditions, including their potential global impacts. These processes are often poorly represented in global models and reanalyses, owing to a legacy modeling focus on midlatitude processes, as well as a scarcity of observations needed for model development in polar regions. The polarMERRA initiative, a joint effort between NASA’s Cryospheric Sciences and Modeling and Prediction programs, seeks to improve the representation of cryospheric and polar atmospheric processes and to develop an open-source framework for a quantitively evaluation of polar-relevant variables against current and future satellite and in-situ observations, models, and reanalyses. Here, we evaluate the impacts of spatial resolution and modifications to sea ice, ice sheet, and atmospheric parameterizations on the representation of high latitude conditions by using a quantitative scorecard approach that leverages NASA’s extensive satellite record. Investigations are conducted using the NASA Goddard Earth Observing System model (GEOS) and its data assimilation system (GEOS DAS). Through a quantitative identification of process deficiencies, bias reductions in key surface variables, including temperature and precipitation over cryospheric surfaces, may be achieved. The polarMERRA project additionally seeks to identify additional data sources for use in the GEOS DAS, and to incorporate data and parameterization improvements into future model and reanalysis products for scientific and stakeholder use.

Lauren C Andrews↗
NASA NTRS1994

Assessment of climate variability of the Greenland Ice Sheet: Integration of in situ and satellite data

The proposed research involves the application of multispectral satellite data in combination with ground truth measurements to monitor surface properties of the Greenland Ice Sheet which are essential for describing the energy and mass of the ice sheet. Several key components of the energy balance are parameterized using satellite data and in situ measurements. The analysis will be done for a ten year time period in order to get statistics on the seasonal and interannual variations of the surface processes and the climatology. Our goal is to investigate to what accuracy and over what geographic areas large scale snow properties and radiative fluxes can be derived based upon a combination of available remote sensing and meteorological data sets. Operational satellite sensors are calibrated based on ground measurements and atmospheric modeling prior to large scale analysis to ensure the quality of the satellite data. Further, several satellite sensors of different spatial and spectral resolution are intercompared to access the parameter accuracy. Proposed parameterization schemes to derive key component of the energy balance from satellite data are validated. For the understanding of the surface processes a field program was designed to collect information on spectral albedo, specular reflectance, soot content, grain size and the physical properties of different snow types. Further, the radiative and turbulent fluxes at the ice/snow surface are monitored for the parameterization and interpretation of the satellite data. The expected results include several baseline data sets of albedo, surface temperature, radiative fluxes, and different snow types of the entire Greenland Ice Sheet. These climatological data sets will be of potential use for climate sensitivity studies in the context of future climate change.

Steffen, K.↗
NASA NTRS2013

3D Radiative Transfer Effects in Multi-Angle/Multi-Spectral Radio-Polarimetric Signals from a Mixture of Clouds and Aerosols Viewed by a Non-Imaging Sensor

When observing a spatially complex mix of aerosols and clouds in a single relatively large field-of-view, nature entangles their signals non-linearly through polarized radiation transport processes that unfold in the 3D position and direction spaces. In contrast, any practical forward model in a retrieval algorithm will use only 1D vector radiative transfer (vRT) in a linear mixing technique. We assess the difference between the observed and predicted signals using synthetic data from a high-fidelity 3D vRT model with clouds generated using a Large Eddy Simulation model and an aerosol climatology. We find that this difference is signal--not noise--for the Aerosol Polarimetry Sensor (APS), an instrument developed by NASA. Moreover, the worst case scenario is also the most interesting case, namely, when the aerosol burden is large, hence hase the most impact on the cloud microphysics and dynamics. Based on our findings, we formulate a mitigation strategy for these unresolved cloud adjacency effects assuming that some spatial information is available about the structure of the clouds at higher resolution from "context" cameras, as was planned for NASA's ill-fated Glory mission that was to carry the APS but failed to reach orbit. Application to POLDER (POLarization and Directionality of Earth Reflectances) data from the period when PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) was in the A-train is briefly discussed.

APS↗
NASA NTRS2021

Sensing the Ocean Biological Carbon Pump from Space: A Review of Capabilities, Concepts, Research Gaps and Future Developments

The element carbon plays a central role in climate and life on Earth. It is capable of moving among the geosphere, cryosphere, atmosphere, biosphere and hydrosphere. This flow of carbon is referred to as the Earth's carbon cycle. It is also intimately linked to the cycling of other elements and compounds. The ocean plays a fundamental role in Earth's carbon cycle, helping to regulate atmospheric CO2 concentration. The ocean biological carbon pump (OBCP), defined as a set of processes that transfer organic carbon from the surface to the deep ocean, is at the heart of the ocean carbon cycle. Monitoring the OBCP is critical to understanding how the Earth's carbon cycle is changing. At present, satellite remote sensing is the only tool available for viewing the entire surface ocean at high temporal and spatial scales. In this paper, we review methods for monitoring the OBCP with a focus on satellites. We begin by providing an overview of the OBCP, defining and describing the pools of carbon in the ocean, and the processes controlling fluxes of carbon between the pools, from the surface to the deep ocean, and among ocean, land and atmosphere. We then examine how field measurements, from ship and autonomous platforms, complement satellite observations, provide validation points for satellite products and lead to a more complete view of the OBCP than would be possible from satellite observations alone. A thorough analysis is then provided on methods used for monitoring the OBCP from satellite platforms, covering current capabilities, concepts and gaps, and the requirement for uncertainties in satellite products. We finish by discussing the potential for producing a satellite-based carbon budget for the oceans, the advantages of integrating satellite-based observations with ecosystem models and field measurements, and future opportunities in space, all with a view towards bringing satellite observations into the limelight of ocean carbon research.

Robert J. W. Brewin↗
NASA NTRS1991

Spatial acquisition and tracking for deep-space optical communication packages

The problem of acquiring and tracking a sun-lit earth, which poses a rather stringent demand on the design of the pointing acquisition and tracking subsystem for a deep-space optical communication package, is addressed. The acquisition subsystem must be capable of locating and tracking the receiver location based on a sun-lit earth image that is blurred by the point spread function of the imaging optics and spatially quantized by the detector pixels. Spatial tracking must be performed at bandwidths in excess of 1 kHz to effectively reject platform jitters. A simple solution of locating the earth limb shows a great potential for providing the necessary acquisition and tracking accuracy. By using separate acquisition and tracking detectors, the system can be implemented without extensive technology development. The slow bandwidth required for the acquisition process can be readily accomplished with an available CCD array, and an analog tracking loop can be implemented using a quadrant tracker and calibrated centroid tracking without the excessive computing overhead of the array detector-based spatial tracker.

Chen, Chien-Chung↗