Landsat 8/9 Performance and Calibration Update OLI-2 On

Landsat 8/9 Performance and Calibration Update B. Markham1, J. McCorkel1, J. Barsi2, R. Morfitt3, USGS; E. Knight4, E. Donley4, N. Raqueno5, A. Gerace5, M. Montanaro5, S. Hook6 1Biospheric Sciences, NASA GSFC, 2SSAI, NASA GSFC, 3USGS, 4Ball Aerospace, 5RIT, 6JPL

Average Vicarious Cal Calibration Band Calibration Samples RMSE [K] Bias [K] B10 368 -0.3 0.5 B11 368 0.3 0.8 Figure 2

Figure 1 Figure 3

OLI-2 on Landsat-9 will have up to 25% higher Signal-to-Noise than Landsat-8 OLI at typical radiances (Ltyp) by virtue of retaining all 14 bits versus 12 for OLI. The stray light correction for TIRS on Landsat-8 has successfully reduced the calibration error to near the performance of previous Landsat thermal bands. Stray light correction for Landsat-9 TIRS-2 has been modeled, designed and implemented. In preparation for full spectral testing of the Landsat-9 OLI-2 with Goddard Laser for Absolute Measurement of Radiance (GLAMR), a OLI focal plane module was tested with GLAMR at GSFC.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Brian L. Markham, Biospheric Sciences, NASA GSFC E-mail: Brian.L.Markham@nasa.gov Phone: 301-614-6608

References: 2017 Barsi, J., Markham, B., Montanaro, M., Morfitt, R., Hook, S., Schott, J., Raqueno, N., Gerace, A., Landsat-8 TIRS thermal radiometric calibration status, Proc. SPIE 10402, Earth Observing Systems XXII, 104021G; doi: 10.1117/12.2276045 2017 Gerace, A., Montanaro, M., Derivation and validation of the stray light correction algorithm for the thermal infrared sensor onboard Landsat 8, Remote Sensing of Environment, 191, 246-257.

Technical Description of Images: Figure 1. Predictions of the Landsat-9 OLI-2 Signal to Noise Ratio performance versus Landsat-8 OLI; Inherently the two instruments have comparable performance; transmitting two additional bits to the ground on Landsat-9 increases performance significantly in many bands at low radiance levels. (Ball Aerospace graph from Mission Preliminary Design Review)

Figure 2. The Landsat-8 TIRS Vicarious Calibration Results after stray light correction (collection-1 processing). After stray light correction as implemented in the operational Landsat Product Generation System, comparison of production data to ground measurements indicated some small biases still remaining (should be correctable) and RMSE error comparable to previous Landsat thermal bands, a significant improvement over the pre stray light correction results. Initial results and predictions indicate that the Landsat-9 TIRS-2 will have significantly improved stray light performance.

Figure 3. The GLAMR integrating sphere fed by lasers provides monochromatic light for the spectral testing of an OLI Focal Plane Module at GSFC. GLAMR will be used for instrument level testing of the OLI-2 spectral bands (all bands, all detectors) compared to the ~10% of detectors tested with a monochromator-based system for the OLI on Landsat-8.

Scientific significance, societal relevance and relationship to Decadal Survey: The radiometric performance of the Landsat-8 OLI instrument has opened up new applications, particularly in water quality due to its improved signal-to-noise performance; Landsat-9 OLI-2 will enhance this performance at low signal levels typical for water bodies. The Landsat-8 TIRS performance was significantly limited for surface temperature retrieval due to its stray light; the implemented algorithm now provides data comparable data to Landsat-7 and earlier sensors. The improved spectral characterization of OLI-2 will also assist in retrievals of water parameters, accounting for the spectral variation across the focal plane.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Quantifying air-snow processes and impurities in the cryosphere K. A. Casey1,2, C. Polashenski3,4, M. Tedesco5,6, S. Kaspari7, S.M. Skiles8, K. Kreutz9, et al. 1Cryospheric Sciences Lab, NASA GSFC , 2Land Remote Sensing, USGS, 3US Army Corp of Engineers, 4Dartmouth College, 5Columbia University, 6NASA GISS, 7Central Washington University, 8University of Utah, 9University of Maine

MCD43A3, B3 MCD43A3, B1 459-479 nm 620-670 nm clean snow

polluted snow

MCD43A3, B2 MCD43A3, B5 Snow Albedo 841-876 nm 1230-1250 nm

MODIS Spectral Bands 3 1 2 5

Wavelength (nm) Figure 1 Figure 2

Light-absorbing impurities including dust, black carbon, algae and other particulates reduce the albedo of Earth’s snow, land ice and sea ice. Remote sensing and in situ data analysis has yielded quantified results on particulate transport, deposition, composition and radiative impacts of light- absorbing impurities in several cryospheric regions.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Kimberly Casey, Cryospheric Science Lab, NASA GSFC; Land Remote Sensing, USGS E-mail: [email protected] Phone: 301-614-6098

References: Casey, K. A., C. Polashenski, J. Chen, and M. Tedesco, 2017, Impact of MODIS sensor calibration updates on Greenland Ice Sheet surface reflectance and albedo trends, The Cryosphere , 11: 1781-1795, doi:10.5194/tc-11-1781-2017 Casey, K. A., S. Kaspari, S. M. Skiles, K. Kreutz, M. Handley, 2017, The spectral and chemical measurement of pollutant impacts on snow near South Pole, Antarctica, Journal of Geophysical Research Atmospheres, doi:10.1002/2016JD026418 Thomas, J. L., C. M. Polashenski, A. J. Soja, L. Marelle, K. A. Casey, H. D. Choi, J. C. Raut, C. Wiedinmyer, L. K. Emmons, J. Fast, J. Pelon, K. S. Law, M. G. Flanner, J. E. Dibb, 2017, Quantifying black carbon deposition over the Greenland ice sheet from forest fires in Canada, Geophysical Research Letters, doi:10.1002/2017GL073701 Wang, Z., A. M. Erb, C. B. Schaaf, Q. Sun, Y. Liu, Y. Yang, Y. Shuai, K. A. Casey, M. O. Román, 2016, Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data, Remote Sensing of Environment, doi:10.1016/j.rse.2016.02.059

Data Sources: MODIS, Landsat, CALIPSO albedo, reflectance and aerosol remote sensing data are compared with field collected spectrometry as well as snow and ice sample geochemical and microscopy data. Earth system and climate modeling tools are used to understand particulate flow and radiative impacts of light-absorbing impurities.

Technical Description of Figures: The images show capabilities of optical sensors to yield insight on cryospheric impurities and physical processes. Improved understanding of light-absorbing impurities over snow and ice is crucial to expanding our understanding of air-snow interactions and reducing uncertainties in projecting ice mass balance, sea level rise and Earth’s radiative energy balance.

Figure 1: MODIS Terra+Aqua 2003-2016 decadal trend albedo maps of Greenland for selected spectral bands. (See Casey et al., The Cryosphere, 2017)

Figure 2: Field collected high spectral resolution data of Antarctic snow and ice 2014-2015 allow more precise quantification of pollutant type, concentration and radiative forcing. (See Casey et al., Journal of Geophysical Research-Atmospheres, 2017)

Scientific significance, societal relevance, and relationships to future missions: While the downward trends in mass of Greenland ice and many of Earth’s glaciers are measured with a variety of remote sensing and field data sets, the processes driving the mass loss are less well understood. Satellite multi-spectral remote sensing instruments provide global cryospheric data since the Landsat missions began in 1972 and MODIS sensors in 2000. But neither of these missions offer full spectral coverage which would allow process understanding and exact quantification of air and surface characteristics, composition and radiative impacts. Recent satellite technological demonstration missions (NASA’s Hyperion, CALIPSO and ESA’s CHRIS, SCIAMACHY) and airborne imaging campaigns (NASA’s AVIRIS-NG, HyTES) have provided key datasets for air and surface composition quantification. Future missions are recommended with high visible-thermal infrared spectral resolution in order to precisely quantify not only atmospheric and cryospheric processes, but also ecologic, geologic and aquatic processes on a global scale.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Radio Frequency Interference Detection with SMAP Yan Soldo1,2, D. M. Le Vine1 and Paolo de Matthaeis1,2 1Cryospheric Sciences Lab, NASA GSFC , 2GESTAR

Figure 1 Figure 2

A new technique for identifying and geographically localizing sources of radio frequency interference (RFI) has been developed using the unique features of the SMAP L-band radiometer. The source locations are being updated weekly and reported by the SMAP project to spectrum management authorities. The new technique uses rapid scan geometry and advanced detection algorithm of the SMAP radiometer to localize sources. A clustering algorithm helps identifying source locations with an accuracy much finer than the single look footprint of the radiometer.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: David Le Vine and Yan Soldo, Cryospheric Science Lab, NASA GSFC E-mail: [email protected] Phone: 301-614-5640

References: Soldo, Y., D. M. Le Vine, P. de Matthaeis, P. Richaume et al, “L-band RFI Detected by SMOS and Aquarius”, IEEE Transactions on Geoscience and Remote Sensing, 2017, Vol: 55, Issue: 7, Pages: 4220 – 4235. Digital Object Identifier 10.1109/TGRS.2017.269040 Soldo, Y, F. Cabot, A. Khazaal, et al, “Localization of RFI Sources for the SMOS Mission: A Means of Assessing SMOS Pointing Performance”, IEEE J. Selected Topics in Earth Observation and Remote Sensing (JSTARS), Vol 8 (#2), pp 617-627, 2015, DOI 10.1109/JSTARS.2014.2336988 Soldo, Y, D. M. Le Vine, A. Bringer, P. de Matthaeis, J.T. Johnson, P.N. Mohammed, J.R. Piepmeier, “Location of RFI Using the SMAP L-Band Radiometer”, submitted, IEEE Trans. Geoscience and Remote Sensing, September, 2017.

Data Sources: Passive microwave measurements from NASA SMAP mission. The RFI localization algorithm uses the SMAP RFI flag to identify potential sources of RFI. SMAP employs a polarimetric radiometer with both full-band and spectral decomposition to detect RFI.

Technical Description of Figures: The images show RFI localized by SMAP. The raw data from SMAP has a spatial resolution on the order of 40 km which is much too coarse to identify the physical location of potential radiators. The localization algorithm has an accuracy on the order of 1-2 km which makes it feasible for spectrum authorities to investigate a specific site.

Figure 1: The global distribution of RFI sources at L-band localized for the period June-August 2015. The sources localized by the European Space Agency (ESA) SMOS mission (“+”) are shown for comparison. The prevalence of RFI in many portions of the globe impacts the quality of remote sensing of soil moisture.

Figure 2: RFI during a single SMAP orbit over the Kamchatka Peninsula. The color code indicates the level of signal (brightness temperature) which should be about 100 K over ocean (blue) and about 250 K (yellow) over land. Larger signals are RFI and in this case most likely due to the very bright source in the center of the image (52˚ North, 160˚ East). The RFI localization algorithm inputs data such as this and gives the location of the most likely source (red dot in this case).

Scientific significance, societal relevance, and relationships to future missions: Passive microwave remote sensing from space involves detecting the very weak natural radiation from the Earth. This requires spectrum free from man-made transmissions such as TV and radar. Remote sensing of soil moisture and ocean salinity would not be possible were it not for the small spectral window at 1.413 GHz (L-band) protected from man-made use. But even though this band is protected by international regulations, interference (RFI) still exists in this band (Figure 1). Similar problems exist in other bands used in passive microwave remote sensing (e.g. for parameters such as sea surface temperature, ocean winds and atmospheric temperature profiles). The radiometer on SMAP demonstrates an advanced step toward improvement in detecting RFI and the localization algorithm is an additional step that will help identify sources of RFI and provide information to mitigate their effects on the retrieved science parameters.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics SnowEx: An Unprecedented Snow Campaign Edward Kim1, Charles Gatebe2,3, Dorothy Hall4,5 & the SnowEx Team 1Hydrological Sciences Lab, NASA GSFC , 2Climate and Radiation Lab, NASA GSFC , 3USRA , 4Cryospheric Sciences Lab, NASA GSFC , 5ESSIC Univ. Maryland

Figure 1

Figure 2 Figure 3

SnowEx is a multi-year airborne campaign to collect multi-sensor observations along with extensive ground truth measurements, to guide trade studies for a future global snow satellite mission. Forest cover— affecting about half of global terrestrial snow-covered areas—was used in Year 1 (2016-17) to challenge the sensing techniques. 9 sensors on 5 aircraft were flown; over 30 ground-based sensors were deployed; and 100 participants collected ground truth in western Colorado.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Ed Kim, Hydrological Sciences Lab, NASA GSFC E-mail: [email protected] Phone: 301-614-5653

References: Kim, E., et al, 2017. NASA’s SnowEx Year 1 Campaign. in review, EOS, American Geophysical Union, 2017.

Data Sources: All of the remote sensing and in situ data from SnowEx are being archived by the NASA National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) [http://nsidc.org/data/snowex] to ensure that SnowEx data are available to a broad user community. Quality control and data delivery to NSIDC are currently ongoing.

Technical Description of Figures: The images show the three major types of field activities during SnowEx year 1—airborne sensing, ground-based sensing, and ground truth data collection.

Figure 1: A P-3 aircraft from the Naval Research Lab prepares to fly 4 of the 9 airborne sensors—the SnowSAR (X & Ku band synthetic aperture radar) from the European Space Agency, Goddard’s Cloud Absorption Radiometer (CAR) bi-directional reflectance function multispectral sensor and the Quantum Well Infrared Photodetector (QWIP) thermal infrared (IR) imager, and the University of Washington’s KT-15 82D thermal IR radiometer. Figure 2: A boom truck from the University of Michigan observes snow in a forested location with a multi-band suite of passive microwave sensors. Figure 3: A few of the 100 participants who collected vital snow ground truth measurements.

Scientific significance, societal relevance, and relationships to future missions: Seasonal snow cover is the largest component of the terrestrial cryosphere in areal extent (up to 46M km2 of the Earth's surface (31% of land areas)), and has major societal impacts on water resources, natural hazards, water security, and weather and climate. In addition to its importance in the Water Cycle, snow’s albedo also drives the land surface energy balance on scales from local to planetary. Though mapping snow cover area is mature, measurement of snow volume or snow water equivalent (SWE) still has unacceptably-large uncertainties, and is currently a gap in NASA’s Earth observing system. No single sensing technique works for measurement of SWE across the wide range of global snow conditions. Multiple techniques, from the visible to the microwave, are sensitive to SWE, thus, a combination of sensing techniques coupled with in-situ measurements and modeling, is needed to monitor SWE globally. SnowEx is collecting new and unique multi-sensor observations to enable trade studies to determine optimum combinations along with models for a future global snow mission.

For more information, see [http://snow.nasa.gov/snowex]

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Rivers and floodplains as key components of global terrestrial water storage variability

Augusto Getirana1,2, Matthew Rodell1, Sujay Kumar1 and Manuela Girotto3,4 1Hydrological Sciences Laboratory, NASA GSFC, 2ESSIC, UMD, 3GMAO, NASA GSFC, 4GESTAR, USRA

1 0.5 0 SWS impact [−] Figure 1

Rivers and floodplains store 2,860 km3 globally and contribute to 8% of global terrestrial water storage (TWS) change, but that contribution differs widely among climate zones. Changes in surface water storage (SWS) are a principal component of TWS variability in the tropics, where major rivers flow over arid regions, and at high latitudes. SWS accounts for ~22-27% of TWS variability in both the Amazon and Nile basins.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Augusto Getirana, Hydrological Sciences Laboratory, NASA GSFC E-mail: [email protected] Phone: 301-614-5783

References: Getirana, A., Kumar, S., Girotto, M., Rodell, M., 2017. Rivers and floodplains as key components of global terrestrial water storage variability. Geophysical Research Letters, 44. DOI: 10.1002/2017GL074684

Data Sources: The Gravity Recovery and Climate Experiment (GRACE) and Tropical Rainfall Measurement Mission (TRMM), the Modern Era Retrospective analysis for Research and Applications 2 (MERRA-2), NOAA’s Global Data Assimilation System (GDAS), the European Centre for Medium-range Weather Forecasts (ECMWF), and the Princeton Meteorological Forcing Dataset. Simulations were performed using the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme and Noah-MP land surface model in the NASA Land Information System (LIS).

Technical Description of Figures: The image shows a global spatial distribution of the surface water storage (SWS) impact on terrestrial water storage (TWS) variability (Antarctica and Greenland excluded). In the color bar, 1 means a full dependency of TWS on SWS.

Based on a proposed index, and using global HyMAP and Noah-MP outputs at 1-degree spatial resolution for the 2003-2014 period, we determined the relative impacts of four major TWS components: groundwater, soil moisture, snow water equivalent, and SWS. In order to account for forcing uncertainties, four global scale meteorological datasets and two additional precipitation datasets were used to force the modeling system, resulting in a 12-member ensemble. The ensemble mean is used as the reference run.

Scientific significance, societal relevance, and relationships to future missions: The vast majority of investigations on TWS decomposition systematically neglect SWS by assuming that its contribution to TWS is trivial. Based on the current incomplete understanding of the contribution of SWS changes to TWS variability around the world, the objective here is to quantify that contribution within a modeling framework. These results are valuable for future studies to determine the importance of (i) integrating river routing schemes into land surface models, (ii) considering SWS when composing or decomposing TWS, and (iii) assimilating TWS and new variables within a multivariate DA framework in hydrology, based on the impact of each water storage component. Those considerations are important as scientists attempt to assimilate TWS (from GRACE and GRACE Follow On) and surface water level from existing (e.g., Jason- 3 and SARAL/AltiKa) and future (SWOT) sensors simultaneously within integrated global-scale modeling systems, which has great potential to improve our understanding of the spatial and temporal variability of terrestrial water storage and its components.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Chesapeake Bay as a source of dissolved organic carbon Sergio R. Signorini1, Antonio Mannino1, Marjorie A. M. Friedrichs2, Pierre St. Laurent2, and John Wilkin3 1Ocean Ecology, NASA GSFC, 2Institute of Marine Science, College of William & Mary, 3Rutgers University Top and bottom salinity, water flux, and DOC

(a) (b) (c ) DOC and DOC flux transects at seaward end of CB

(a) (b)

Figure 2

Figure 1

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Sergio R. Signorini, Ocean Ecology Lab, NASA GSFC and SAIC E-mail: [email protected] Phone: 301-286-9891

References: Feng, Y., M. A. M. Friedrichs, et al. (2015), Chesapeake Bay nitrogen fluxes derived from a land-estuarine ocean biogeochemical modeling system: Model description, evaluation,and nitrogen budgets, J. Geophys. Res. Biogeosci., doi:10.1002/2015JG002931. Mannino, A., M. Novak, S. Hooker, K. Hyde, and D. Aurin (2014), Algorithm development and validation of CDOM properties for Estuarine and continental shelf waters along the northeastern U.S. coast, Remote Sens. Environ., 152, 576–602, doi:10.1016/j.rse.2014.06.027. Mannino, A., S. R. Signorini, M. G. Novak, J. Wilkin, M. A. M. Friedrichs, and R. G. Najjar (2016), Dissolved organic carbon fluxes in the Middle Atlantic Bight: An integrated approach based on satellite data and ocean model products, J. Geophys. Res. Biogeo10.1002/2015JG003031. Signorini, S. R., A. Mannino, et al. (2013), Surface ocean pCO2 seasonality and sea-air CO2 flux estimates for the North American east coast, J. Geophys. Res., 118, 5439–5460, doi:10.1002/jgrc.20369.

Data Sources: Ocean color data (L2 reflectances) from SeaWiFS and MODIS, and field observations (DOC, salinity, temperature, and in-water radiometry). Also temperature, salinity, and water flux from a physical circulation model (PCM, Feng et al. 2015). Satellite data originates from the Ocean Biogeochemical Processing Group (OBPG) and the field observations from the Ocean Ecology Lab’s Field Support Group.

Technical Description of Figures: Figure 1: Monthly mean (March 2010 as an example) top and bottom salinity and water flux from the Chesapeake Bay Physical Circulation Model (PCM) and DOC from MODIS algorithm (Mannino et al., 2014; Mannino et al. 2016). Figure 2: Monthly mean (March 2010) transects (slice across horizontal distance and depth) of DOC and DOC flux at the seaward end of Chesapeake Bay (CB mouth). This study quantified DOC concentrations and the CB export of DOC to the Mid-Atlantic ocean waters using an integrated tracer flux approach (ITFA), applying field and satellite data and output from the PCM. Figure 1 shows an example of inputs to the ITFA and Figure 2 is the prediction of DOC concentration and DOC flux at CB mouth.

Scientific significance, societal relevance, and relationships to future missions: These high frequency estimates of DOC flux from CB can help us understand the estuarine organic carbon subsidy to continental shelves for use by the microbial community as well as improving carbon budgets for the estuaries and continental shelf. In addition, the method of using the PCM flow fields enables us to make a thorough estimate of the flow, and thus the DOC flux at the mouths of the estuaries, which cannot be accomplished with in situ direct current measurements due to the logistical challenges that it imposes. Future missions (hyperspectral PACE sensor, for example) will enable improved DOC algorithms that will reduce uncertainties on DOC and DOC export assessments.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Galactic Aberration from VLBI Observations D. S. MacMillan NVI, Inc. and the Geodesy and Geophysics Laboratory, NASA GSFC

Figure 1

Galactic aberration is due to the motion of the solar system around the center of the Milky Way Galaxy. It results in an apparent secular variation in the positions of radio sources observed by VLBI (Very Long Baseline Interferometry). The apparent source aberration proper motions (here estimated from a Goddard VLBI solution) has a characteristic dipolar pattern. As it is a systematic effect, a galactic aberration model will be applied in the generation of the next realization of the International Celestial Reference Frame (ICRF3).

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Dan MacMillan, NVI Inc. and the Geodesy and Geophysics Laboratory, NASA GSFC E-mail: [email protected] Phone: 301-614-6118

References: MacMillan, D. S., 2014. Determination of galactic aberration from VLBI measurements and its effect on VLBI reference frames and Earth orientation parameters. American Geophysical Union Meeting, San Francisco, CA.

Data Sources:. A typical VLBI terrestrial/celestial reference frame solution uses data taken since 1980. Through 2014 this amounted to 5425 24-hr sessions with a total of 8.7 million group delay observations.

Technical Description of Figure: Figure 1: The dipole variation characteristic of aberration is shown for the aberration proper motion vectors at the positions of the ICRF2 defining sources. They stream toward the center of the Milky Way Galaxy (and away from the anti-center). This plot is based on the galactic acceleration vector with an amplitude of 5.3 µas/year estimated in a solution from MacMillan (2014). Here the vectors are directed toward a pole at a declination of δ = -11º and right ascension of α = 267º (17.8 hours), which reasonably close to the Galactic center δ = -29° and α = 266° (17.7 hours). A more recent solution using data from 1980-2016 consisting of 10.8 million group delay observations from 5938 24-hr sessions, yielded an amplitude of 5.7 µas/year directed toward δ = -22º and α = 273º (18.2 hours).

Scientific significance, societal relevance, and relationships to future missions: A precise VLBI celestial reference frame is essential to determine a precise terrestrial reference frame and the corresponding Earth orientation parameters that connect the frames. Improving the ICRF is important for better UT1 and precession/nutation estimation. The ICRF is also important for spacecraft navigation and alignment of planetary ephemerides. The ICRF is defined by positions of defining radio sources that are assumed to have no measurable proper motion. However, radio sources do exhibit apparent motion, which is primarily due to quasar source structure changes which are difficult to model consistently. On the other hand, galactic aberration is a clear systematic effect that can easily be modeled. A model of galactic aberration will be applied in the generation of the next ICRF in 2018 (ICRF3), which will replace ICRF2. In terms of future micro-arcsecond astrometry, the effect of aberration with an amplitude of 5 µas/year is not negligible in comparison to the expected noise floor of 30 µas of ICRF3. The geodetic VLBI estimates of the galactic center component of the aberration vector are close to those inferred from independent VLBI astrometric measurements of the distance to the center of the galaxy and the rotation velocity of the solar system about the galactic center. The IVS Working Group on Galactic Aberration (chaired by D. MacMillan) made a recommendation on the model to be applied for ICRF3. It is based on a combination of several IVS geodetic VLBI estimates and estimates inferred from galactic astronomical measurements.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics