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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume IV-1
https://doi.org/10.5194/isprs-annals-IV-1-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-IV-1-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
26 Sep 2018
PREFACE
B. Jutzi
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany
M. Weinmann
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany
S. Hinz
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 125–132, https://doi.org/10.5194/isprs-annals-IV-1-125-2018, https://doi.org/10.5194/isprs-annals-IV-1-125-2018, 2018
M. Weinmann, P. M. Maier, J. Florath, and U. Weidner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 155–162, https://doi.org/10.5194/isprs-annals-IV-1-155-2018, https://doi.org/10.5194/isprs-annals-IV-1-155-2018, 2018
B. Jutzi, M. Weinmann, and S. Hinz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 1–3, https://doi.org/10.5194/isprs-archives-XLII-1-1-2018, https://doi.org/10.5194/isprs-archives-XLII-1-1-2018, 2018
M. Boldt, A. Thiele, K. Schulz, and S. Hinz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 59–64, https://doi.org/10.5194/isprs-archives-XLII-1-59-2018, https://doi.org/10.5194/isprs-archives-XLII-1-59-2018, 2018
P. Hübner, M. Weinmann, and S. Wursthorn
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 195–202, https://doi.org/10.5194/isprs-archives-XLII-1-195-2018, https://doi.org/10.5194/isprs-archives-XLII-1-195-2018, 2018
P. Hübner, M. Weinmann, M. Hillemann, B. Jutzi, and S. Wursthorn
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 455–462, https://doi.org/10.5194/isprs-archives-XLII-2-455-2018, https://doi.org/10.5194/isprs-archives-XLII-2-455-2018, 2018
K. Chen, M. Weinmann, X. Gao, M. Yan, S. Hinz, B. Jutzi, and M. Weinmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 65–72, https://doi.org/10.5194/isprs-annals-IV-2-65-2018, https://doi.org/10.5194/isprs-annals-IV-2-65-2018, 2018
R. Ilehag, M. Weinmann, A. Schenk, S. Keller, B. Jutzi, and S. Hinz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 65–71, https://doi.org/10.5194/isprs-archives-XLII-3-W3-65-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-65-2017, 2017
M. Breunig, A. Borrmann, E. Rank, S. Hinz, T. Kolbe, M. Schilcher, R.-P. Mundani, J. R. Jubierre, M. Flurl, A. Thomsen, A. Donaubauer, Y. Ji, S. Urban, S. Laun, S. Vilgertshofer, B. Willenborg, M. Menninghaus, H. Steuer, S. Wursthorn, J. Leitloff, M. Al-Doori, and N Mazroobsemnani
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W4, 341–352, https://doi.org/10.5194/isprs-archives-XLII-4-W4-341-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W4-341-2017, 2017
R. Blomley and M. Weinmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 43–50, https://doi.org/10.5194/isprs-annals-IV-2-W4-43-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-43-2017, 2017
J. Kern, M. Weinmann, and S. Wursthorn
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 83–90, https://doi.org/10.5194/isprs-annals-IV-2-W4-83-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-83-2017, 2017
B. Ruf, B. Erdnuess, and M. Weinmann
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 325–332, https://doi.org/10.5194/isprs-archives-XLII-2-W6-325-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W6-325-2017, 2017
M. Weinmann, M. S. Müller, M. Hillemann, N. Reydel, S. Hinz, and B. Jutzi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 399–406, https://doi.org/10.5194/isprs-archives-XLII-2-W6-399-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W6-399-2017, 2017
R. Ilehag, A. Schenk, and S. Hinz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 145–151, https://doi.org/10.5194/isprs-archives-XLII-2-W6-145-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W6-145-2017, 2017
M. S. Müller, S. Urban, and B. Jutzi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W3, 49–57, https://doi.org/10.5194/isprs-annals-IV-2-W3-49-2017, https://doi.org/10.5194/isprs-annals-IV-2-W3-49-2017, 2017
R. Niessner, H. Schilling, and B. Jutzi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1-W1, 115–123, https://doi.org/10.5194/isprs-annals-IV-1-W1-115-2017, https://doi.org/10.5194/isprs-annals-IV-1-W1-115-2017, 2017
M. Weinmann, B. Jutzi, C. Mallet, and M. Weinmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1-W1, 157–164, https://doi.org/10.5194/isprs-annals-IV-1-W1-157-2017, https://doi.org/10.5194/isprs-annals-IV-1-W1-157-2017, 2017
K. O. Dierenbach, M. Weinmann, and B. Jutzi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 11–19, https://doi.org/10.5194/isprs-archives-XLI-B3-11-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-11-2016, 2016
F. Alshawaf, B. Fersch, S. Hinz, H. Kunstmann, M. Mayer, and F. J. Meyer
Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015, https://doi.org/10.5194/hess-19-4747-2015, 2015
Short summary
Short summary
This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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