Articles | Volume 11, issue 4
https://doi.org/10.5194/tc-11-1575-2017
https://doi.org/10.5194/tc-11-1575-2017
Research article
 | 
04 Jul 2017
Research article |  | 04 Jul 2017

Unmanned aerial system nadir reflectance and MODIS nadir BRDF-adjusted surface reflectances intercompared over Greenland

John Faulkner Burkhart, Arve Kylling, Crystal B. Schaaf, Zhuosen Wang, Wiley Bogren, Rune Storvold, Stian Solbø, Christina A. Pedersen, and Sebastian Gerland

Related authors

Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology
John F. Burkhart, Felix N. Matt, Sigbjørn Helset, Yisak Sultan Abdella, Ola Skavhaug, and Olga Silantyeva
Geosci. Model Dev., 14, 821–842, https://doi.org/10.5194/gmd-14-821-2021,https://doi.org/10.5194/gmd-14-821-2021, 2021
Short summary
Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model
Aynom T. Teweldebrhan, Thomas V. Schuler, John F. Burkhart, and Morten Hjorth-Jensen
Hydrol. Earth Syst. Sci., 24, 4641–4658, https://doi.org/10.5194/hess-24-4641-2020,https://doi.org/10.5194/hess-24-4641-2020, 2020
The Lagrangian particle dispersion model FLEXPART version 10.4
Ignacio Pisso, Espen Sollum, Henrik Grythe, Nina I. Kristiansen, Massimo Cassiani, Sabine Eckhardt, Delia Arnold, Don Morton, Rona L. Thompson, Christine D. Groot Zwaaftink, Nikolaos Evangeliou, Harald Sodemann, Leopold Haimberger, Stephan Henne, Dominik Brunner, John F. Burkhart, Anne Fouilloux, Jerome Brioude, Anne Philipp, Petra Seibert, and Andreas Stohl
Geosci. Model Dev., 12, 4955–4997, https://doi.org/10.5194/gmd-12-4955-2019,https://doi.org/10.5194/gmd-12-4955-2019, 2019
Short summary
Simulations of black carbon (BC) aerosol impact over Hindu Kush Himalayan sites: validation, sources, and implications on glacier runoff
Sauvik Santra, Shubha Verma, Koji Fujita, Indrajit Chakraborty, Olivier Boucher, Toshihiko Takemura, John F. Burkhart, Felix Matt, and Mukesh Sharma
Atmos. Chem. Phys., 19, 2441–2460, https://doi.org/10.5194/acp-19-2441-2019,https://doi.org/10.5194/acp-19-2441-2019, 2019
Short summary
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches
Aynom T. Teweldebrhan, John F. Burkhart, and Thomas V. Schuler
Hydrol. Earth Syst. Sci., 22, 5021–5039, https://doi.org/10.5194/hess-22-5021-2018,https://doi.org/10.5194/hess-22-5021-2018, 2018

Related subject area

Remote Sensing
Out-of-the-box calving-front detection method using deep learning
Oskar Herrmann, Nora Gourmelon, Thorsten Seehaus, Andreas Maier, Johannes J. Fürst, Matthias H. Braun, and Vincent Christlein
The Cryosphere, 17, 4957–4977, https://doi.org/10.5194/tc-17-4957-2023,https://doi.org/10.5194/tc-17-4957-2023, 2023
Short summary
Mapping the extent of giant Antarctic icebergs with deep learning
Anne Braakmann-Folgmann, Andrew Shepherd, David Hogg, and Ella Redmond
The Cryosphere, 17, 4675–4690, https://doi.org/10.5194/tc-17-4675-2023,https://doi.org/10.5194/tc-17-4675-2023, 2023
Short summary
Allometric scaling of retrogressive thaw slumps
Jurjen van der Sluijs, Steven V. Kokelj, and Jon F. Tunnicliffe
The Cryosphere, 17, 4511–4533, https://doi.org/10.5194/tc-17-4511-2023,https://doi.org/10.5194/tc-17-4511-2023, 2023
Short summary
Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery
Trystan Surawy-Stepney, Anna E. Hogg, Stephen L. Cornford, and David C. Hogg
The Cryosphere, 17, 4421–4445, https://doi.org/10.5194/tc-17-4421-2023,https://doi.org/10.5194/tc-17-4421-2023, 2023
Short summary
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023,https://doi.org/10.5194/tc-17-4343-2023, 2023
Short summary

Cited articles

Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., and Shettle, E. P.: AFGL Atmospheric Constituent Profiles (0.120 km) Environmental research papers, Accession Number: ADA175173, AIR FORCE GEOPHYSICS LAB HANSCOM AFB MA, Defense Technical Information Center, available at: http://www.dtic.mil/docs/citations/ADA175173 (last access: 28 June 2017), 1986.
Ascher, D., Dubois, P. F., Hinsen, K., Hugunin, J., and Oliphant, T.: Numerical Python, Lawrence Livermore National Laboratory, Livermore, CA, ucrl-ma-128569 Edn., 1999.
Bais, A. F., Kazadzis, S., Balis, D., Zerefos, C. S., and Blumthaler, M.: Correcting Global Solar Ultraviolet Spectra Recorded by a Brewer Spectroradiometer for its Angular Response Error, Appl. Optics, 37, 6339, https://doi.org/10.1364/AO.37.006339, 1998.
Bates, T. S., Quinn, P. K., Johnson, J. E., Corless, A., Brechtel, F. J., Stalin, S. E., Meinig, C., and Burkhart, J. F.: Measurements of atmospheric aerosol vertical distributions above Svalbard, Norway, using unmanned aerial systems (UAS), Atmos. Meas. Tech., 6, 2115–2120, https://doi.org/10.5194/amt-6-2115-2013, 2013.
Bennartz, R., Shupe, M. D., Turner, D. D., Walden, V. P., Steffen, K., Cox, C. J., Kulie, M. S., Miller, N. B., and Pettersen, C.: July 2012 Greenland melt extent enhanced by low-level liquid clouds, Nature, 496, 83–86, https://doi.org/10.1038/nature12002, 2013.
Download
Short summary
We present the first use of spectrometer measurements from a drone to assess reflectance and albedo over the Greenland Ice Sheet. In order to measure albedo – a critical parameter in the earth's energy balance – a drone was flown along 200 km transects coincident with Terra and Aqua satellites flying MODIS. We present a direct comparison of UAV-measured reflectance with satellite data over Greenland and provide a new method to study cryospheric surfaces using UAV with spectral instruments.