Ice Area Losses near , From Landsat Christopher A. Shuman1, Compton J. Tucker2, Katherine A. Melocik3, and Rikke D. Jepsen3 1UMBC Joint Center for Earth Technology; 2NASA Goddard Space Flight Center; and 3Science Systems and Applications, Inc. Landsat 2 Landsat 4 Landsat 5 A Landsat 2 MSS West Multispectral Scanner Multispectral Scanner Thematic Mapper image taken on Northwall 8 Aug. 1980 9 Sep. 1982 3 Nov. 1988 6.34 km2 6.07 km2 4.67 km2 th the 8 of August Firn East Northwall 1980 begins a Firn time series that now extends to a Meren Southwall Landsat 8 OLI Hanging Firn Carstensz image from the Glacier th Landsat 5 Landsat 7 Landsat 5 11 of March Thematic Mapper Enhanced Thematic Mapper+ Thematic Mapper 2018. These nine 17 Nov. 1993 9 Oct. 1999 14 Oct. 2004 3.36 km2 2.74 km2 1.88 km2 images detail ongoing losses from the last glacier area in tropical Asia. Each scene is Landsat 5 Landsat 8 Landsat 8 labeled with the Thematic Mapper Operational Land Imager Operational Land Imager sensor, date, 28 Oct. 2009 13 Oct. 2015 11 Mar. 2018 1.29 km2 0.56 km2 0.47 km2 and the total ice area remaining.

Average area loss over 37.588 years is 0.156 km2 per year Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Christopher A. Shuman, Cryospheric Sciences, NASA GSFC and UMBC JCET E-mail: [email protected], [email protected] Phone: 301-614-5706 References: in the Tropics, but Not for Long, 14 Feb. 2018: https://earthobservatory.nasa.gov/IOTD/view.php?id=91716 Decline of the Last Glaciers in the Eastern Tropics,14 Dec. 2016: https://landsat.gsfc.nasa.gov/decline-of-the-last-glaciers-in-the-eastern-tropics/ Klein, A.G. and J.L. Kincaid, Retreat of glaciers on Puncak Jaya, Irian Jaya, determined from 2000 and 2002 IKONOS satellite images, Journal of Glaciology, vol. 52/176 pp. 65-79, https://doi.org/10.3189/172756506781828818 Glaciers of Irian Jaya, Indonesia, US Geological Survey Professional Paper 1386-H-1 https://pubs.usgs.gov/pp/p1386h/indonesia/intoc.html Hope G.S., Peterson J.A. Radok U. and Allison I., 1976. The equatorial glaciers of – results of the 1971–1973 Australian Universities’ expeditions to Irian Jaya: survey, glaciology, meteorology, biology and paleoenvironments. Rotterdam , Balkema A.A. (see older oblique aerial and ground- based photographs in Chapter 3 here: http://papuaweb.org/dlib/bk/hope1976/index.html).

Data Sources: Landsat 2, 4 Multispectral Scanner (MSS), Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper + (ETM+), and Landsat 8 Operational Land Imager (OLI) multispectral were used to create this time series of ice losses in this mountain region since 1980. A slightly larger area has been prepared for the hyperwall to encompass the and a tiny ice remnant near Ngga Pilimsit to the west of the mine. Ice Losses in the Tropics: https://svs.gsfc.nasa.gov/30938

Technical Description of Figure 1: The figure shows nine subsets of individual false-color multispectral Landsat scenes selected for low/no snow or cloud cover and fairly high southern hemisphere sun angle. A number of the images selected show dark blue bare ice while others show that some whiter snow or possibly firn from a previous year was still visible, typically at higher elevations. The 1980 MSS scene indicates one pixel may contain a melt pond. The ETM+ image appears to be a bit saturated. Each scene area is 9.18 x 7.62 km. The time series shown here shows that the ice loss is due to thinning that reveals bedrock knobs or ridges and leads to fragmentation of the remaining ice.

The full Landsat archive was investigated for useable images and the available image series shows that the Meren Glacier, in a valley between the two highest parts of the mountainous terrain, was lost by 1998. The Southwall Hanging Firn, in the shadow of the 4884 m peak of Puncak Jaya was lost by 2005 and the shrinking West Northwall Firn area was gone by 2012. As discussed in Klein and Kincaid (2006), two small glaciers at the eastern end of the were indistinguishable from it by the turn of this century. Because the remaining East Northwall Firn is near the second and third highest peaks in the area, Sumantri and (4870 and 4863 m respectively), it may last longer than the Carstensz Glacier remnant and both may be gone before 2030. An earlier Landsat 1 MSS scene from 1974 April 27 was used in USGS 1386-H-1 and shows the three main eastern glacial ice masses were probably continuous at that time but that image is only available on film now.

Image processing was done with PCI-Geomatica and area estimates were generated by unsupervised classification in that program by two operators (CAS and RDJ). Further image processing was done in Adobe Photoshop with final annotations added in PowerPoint. The 1982 image was repositioned due to its initial poor geolocation processing by James Storey (SGT) here at GSFC. All MSS images are currently undergoing reprocessing by the USGS.

Scientific significance, societal relevance, and relationships to future missions: The continuing observations from the Landsat series of sensors in areas of dramatic cryospheric change provides compelling visuals for the public, scientific colleagues, and policymakers. The ability of Landsat’s sensors to detail glaciological features is very well shown in this ~38 year time series.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics From microscopy to spectroscopy: unravelling spatio-temporal phytoplankton distributions from space A.R. Neeley1,2 1Ocean Ecology Laboratory (616) NASA GSFC 2University of Maryland, UMCES (b) Arctic Ocean (a) (c)

Nanophytoplankton Microphytoplankton Picophytoplankton

Chukchi Sea

Alaska

Figure 1 Figure 2 Fractional concentration

The number of satellite algorithms for deriving phytoplankton functional types (PFTs) and size classes (PSCs) has grown dramatically, as has the demand for such products for applications from assessing climate change impacts on marine ecosystems. The purpose of this study is to use water column measurements of phytoplankton taxonomy, such as from microscopy, and optical properties (remote sensing reflectances, absorption and scattering) to ground-truth multiple PFT and PSC algorithms applied to the Chukchi Sea Region.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Aimee R. Neeley, Ocean Ecology Laboratory (616), NASA GSFC E-mail: [email protected] Phone: 301-614-5778

References: Hirata, T. et al., 2011. Synoptic relationships between surface chlorophyll-a and diagnostic pigments specific to phytoplankton functional types. Biogeosciences , Volume 8, p. 311–327. Overland, J. E., & Wang, M. (2013). When will the summer Arctic be nearly sea ice free? Geophysical Research Letters, 40(10), 2097-2101. Mathis, J. T., Grebmeier, J. M., Hansell, D. A., Hopcroft, R. R., Kirchman, D. L., Lee, S. H., et al. (2014). Carbon biogeochemistry of the western Arctic: Primary production, carbon export and the controls on ocean acidification. In The Pacific Arctic Region (pp. 223-268): Springer. Tremblay, J.-É., Simpson, K., Martin, J., Miller, L., Gratton, Y., Barber, D., & Price, N. M. (2008). Vertical stability and the annual dynamics of nutrients and chlorophyll fluorescence in the coastal, southeast Beaufort Sea. Journal of Geophysical Research: Oceans, 113(C7), doi:10.1029/2007JC004547. Data Sources: MODIS Aqua by the Ocean Biology Processing Group, Ocean Ecology Laboratory (616), NASA Goddard Space Flight Center. Figures were created using NASA’s SeaDAS software.

Technical Description of Figures: th Figure 1: True color image of the Chukchi Sea and Arctic Ocean from MODIS Aqua on July 19 , 2011. Figure 2: Fractional contribution of (a) large (b) medium and (c) small phytoplankton to the phytoplankton community derived from a chlorophyll-based PSC algorithm.

Scientific significance, societal relevance, and relationships to future missions: Sea ice extent in the Arctic Ocean is declining at an alarming rate, leading to predictions that the Arctic will be ice-free in the summer as early as 2020. The expected consequences of ice-free summers are longer open water duration and an extended growth season for pelagic phytoplankton. It is timely to consider how phytoplankton communities will respond to longer periods of seasonally open water. Time series ocean color data are important for understanding phytoplankton bloom dynamics. It is important to evaluate and improve the accuracy of PFT algorithm output so that these algorithms can be used to evaluate shifts in phytoplankton communities in both size and functional groups in the Chukchi Sea as well as the global ocean. Application of PFT algorithms in this manner will also serve as a proof of concept for future efforts to derive greater insight into phytoplankton ecology from satellite based monitoring.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics The Operational Inland Water Height Data Product for ICESat-2: Algorithm Development and Testing Michael Jasinski1, Jeremy Stoll 1,2 1Hydrological Sciences Laboratory/617, 2SSAI, Inc.

A global, operational, Inland Water Height Data Product has been developed for ICESat-2. The principal product consists of along track surface water height statistics for each of six ICESat-2 beams, including mean, standard deviation and slope, for up to 300,000 water bodies greater than ~ 3 km2, to be traversed during the ICESat-2 mission.

Earth Sciences Division – Hydrosphere, Biosphere and Geophysics Name: Michael Jasinski, Hydrological Sciences Lab/617, NASA GSFC E-mail: [email protected] Phone: 301-614-5782

References: Jasinski, M.; Stoll, J.; Cook, W.; Ondrusek, M.; Stengel, E., and Brunt, K., 2016. Inland and Near-Shore Water Profiles Derived from the High-Altitude Multiple Altimeter Beam Experimental Lidar (MABEL). Journal of Coastal Research, SI No. 76, pp. 44-55. https://doi.org/10.2112/SI76-005.

Markus, T., T. Neumann, A. Martino, W. Abdalati, K. Brunt, B. Csatho, S. Farrell, H. Fricker, A. Gardner, D. Harding, M. Jasinski, R. Kwok, L. Magruder, D. Lubin, S. Luthcke, J. Morison, R. Nelson, A. Neuenschwander, S. Palm, S. Popescu, C. Shum, B. E. Schutz, B. Smith, Y. Yang, and J. Zwally. 2017. "The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation." Remote Sensing of Environment 190 260-273 10.1016/j.rse.2016.12.029.

Data Sources: ICESat-2’s sole instrument is the Advanced Topographic Laser Altimeter System (ATLAS). It possesses high frequency (10kHz) micropulse technology with single photon detection at 532 nm. ATLAS will have a 500 km, non sun-sync orbit, with 91-day repeat in the sub-polar and ocean regions, and a mapping, non-repeat orbit pattern over the . The Inland Water Height data product was tested using high-altitude, above troposphere, MABEL observations flown aboard ER-2 and Proteus aircraft during 2012 through 2015.

Additional Contributors: C. Arp, C. Birkett, K. Brunt, C. Carabajal, M. Carroll, W. Cook, D. Harding, C. Hiemstra, B. Jones, T. Markus, T. Neumann, M. Ondrusek, T. Pavelsky, E. Stengel, J. Robbins, D. Hancock, J. Guerber, J. Nattala.

Technical Description of Figures:

Figure 1: The global operational Inland Water Height Data Product, or ATL13, has been developed for ICESat-2. The principal product consists of along track surface water height statistics for each ICESat-2 beam including height, standard deviation and slope. The ICESat-2 Inland Water Body Height Algorithm processes ATLAS data in 100-photon variable segment lengths, from about 50-300m, depending on atmospheric and water conditions.

Figure 2: A global inland water body mask, that includes lakes, reservoirs, estuaries and bays, and the near shore, was constructed based on several available shapefile databases to help identify lakes traversed by ICESat-2. Shown is the composited ATL13 Inland Water Body Mask for only North America. Each database is represented by different colors [E.g. Black - Lehner & Doll (2004) L1; Green - Lehner & Doll (2004) L2; Lavender – Wessel and Smith (2017)].

Figure 3: The operational ATL13 data product has been tested using high altitude MABEL observations during 2012-15. Each green point represents a photon that was transmitted by and reflected back to MABEL. Photon clouds are then processed to provide statistics for water surface segments shown in Figure 3 and also schematically in Figure 1.

Scientific significance, societal relevance, and relationships to future missions: The ICESat-2 ATL13 Inland Water Body Height Data Product will provide unprecedented accuracy in estimating inland water surface statistics compared to previous satellite altimetry. It will be useful for numerous global hydrologic studies including water balance, flooding, drought monitoring, near shore bathymetry, and operational water resources management. The positive results of the MABEL analyses confirm the feasibility of ICESat-2 operational retrievals, even in partly cloudy conditions, with a mean height precision of approximately 5-10 cm per 100m segment length, depending on water and atmospheric conditions.

Earth Sciences Division – Hydrosphere, Biosphere and Geophysics Ground Reflectance Training Course At Railroad Valley Playa Kurtis Thome1 and Brian Wenny2 1Biospheric Sciences Lab, NASA GSFC , 2SSAI 0.4 0.35 0.3 0.25 Student-based field spectrometer results 0.2 Field transfer radiometer 0.15 Surface Reflectance 0.1 350 450 550 650 750 850 950 Wavelength (nm) 1.05 Ratio of transfer radiometer to field spectrometer 1.03

1.01

0.99

ReflectanceRatio 0.97

0.95 350 550 750 950 Wavelength (nm) Figure 1 Figure 2 Calibration and validation ensures high data quality and comparability between sensor systems. The Geoscience Spaceborne Imaging Spectroscopy Technical Committee (GSIS TC) conducted a training activity in July 2017 at the vicarious calibration test site at Railroad Valley, NV. The course, organized by C. Ong (Commonwealth Scientific and Industrial Research Organisation in Perth) and K. Thome (NASA/GSFC) included demonstration of measurement protocols for surface and atmospheric parameters that minimize uncertainties as part of the vicarious calibration of on-orbit sensors. The course also allowed evaluation of a field portable transfer radiometer and sampling approaches to surface reflectance retrievals.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Kurt Thome, Biospheric Science Lab, NASA GSFC E-mail: [email protected] Phone: 301-614-6671

References: C. Ong, K. Thome, U. Heiden, J. Czapla-Myers, and A. Mueller, “Reflectance-based, imaging spectrometer error budget field practicum at the Rail Road Valley Test Site, Nevada USA”, submitted to IEEE GRSS Magazine.

Data Sources: Results will be used to understand the uncertainties for vicarious calibration of earth imagers operating in the solar reflective including ASTER, ETM+, MISR, MODIS, OLI, VIIRS, and current and planned commercial imagers.

Technical Description of Figures:

Figure 1: Top photograph shows training course participants at Lunar Lake Playa where a discussion of test site selection and impacts of site inhomogeneity took place. Lower photographs show the training participants measuring the diffuser references used to convert the site measurements to reflectance and coincident site measurements being made with a field spectrometer as well as Goddard’s Calibration Test Site SI-Traceable Transfer Radiometer (CaTSSITTR).

Figure 2: Top graph shows results of the surface reflectance of a 50 m long path of the playa surface used for training and intercomparions tests. Results from a student-based collection of the playa and coincident transfer radiometer data are shown.

Bottom graph shows the ratios of the transfer radiometer and field spectrometer surface reflectance results. The ratios are well within the combined uncertainties of the two measurements.

Scientific significance, societal relevance, and relationships to future missions: The field training exercise conducted at Railroad Valley provided valuable training for personnel working across various earth observation areas, from engineers developing future sensors to calibration scientists actively working in the field. A comprehensive set of valuable data were acquired as part of the training which will be useful to answer numerous science questions including: 1) understanding the spatial and spectral homogeneity of the site being measured; 2) understanding the optimal sampling to characterize the site; and, 3) optimization of sampling techniques, including looking into automation of some aspects of the measurement protocols. The comparisons between the diffuser-based retrievals of reflectance and the radiance-based retrievals will offer the lessons needed for the calibration and validation of missions such as Landsat and CLARREO Pathfinder and those projects identified in the recent Decadal Survey. The training course provided valuable experience to those scientists that will be performing the validation of these new missions as well as to the trainers that were fortunate enough to participate.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Archeomagnetic measurements have a positive Impact on modern day geomagnetic forecasts Andrew Tangborn1,2 , Weijia Kuang1 1Geodesy and Geophysics Lab, NASA GSFC , 2JCET, UMBC

RMS O−F Difference in magnetic field Difference in velocity field 0.35 L = 2 assim L = 3 0.3 assim L = 4 assim 1590 0.25 F

− 0.2

0.15 RMS O

0.1 1990 0.05

0 0 500 1000 1500 2000 Year

Figure 1 Figure 2

Archeomagnetic measurements from nearly two thousand years ago are shown to have a positive impact on geomagnetic forecasts in 1990, using a geodynamo model. Figure 1 shows the RMS errors (compared with field models) for forecasts with different degrees included for the early archeomagnetic observations. Figure 2 shows the difference in the internal geomagnetic (a,c) and velocity (b,d) in 1590 and 1990. These differences are carried forward in time for hundreds of years and are seen in the forecast accuracies.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics Name: Andrew Tangborn, Geodesy and Geophysics Lab, NASA GSFC E-mail: [email protected] Phone: 301-614-6643

References:

Tangborn, A. and W. Kuang, 2018, Impact of archeomagnetic field model data on modern era geomagnetic forecasts, Physics of Earth and Planetary Interiors, 276, pp 2-9.

Data Sources: Geomagnetic field model data from CALS3k.4 (10 – 1590 CE), gufm1 (1590-1960) , CM4 (1960-1990).

Technical Description of Figures:

Figure 1: RMS difference between the observations and forecasts (O-F) every 20 years.

Figure 2: Difference between two experiments with different archeomagnetic observations assimilated (10 – 1590 CE), for meridional magnetic and velocity fields in 1590 and 1990.

Figure 1 is from a 2000 year assimilation of field model data, including archeomagnetic data, CALS3k.4 (10-1590), sailing ship and observatory data, gufm1 (1590-1960), observatory and satellite data, CM4 (1960-1990). The large drop in errors in 1590 are the result of smaller errors in the direct magnetic field measurements in gufm1. Figure 2 shows how changing assimilation parameters from the archeomagnetic data continues to affect both the magnetic and velocity fields in 1990.

Scientific significance, societal relevance, and relationships to future missions: Processes in the Earth’s core occur over a wide range of time scales (decades to millions of years), so that events that took place thousands of years ago continue to impact the present day magnetic field. This means that accurate prediction of future changes to the magnetic field require good knowledge of the field thousands of years ago. It also implies that we can use the highly accurate and dense satellite data (such as SWARM) to test assumptions about the accuracy of the much earlier archeo- and paleomagnetic geomagnetic data.

Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics