Articles | Volume 13, issue 4
https://doi.org/10.5194/essd-13-1653-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-1653-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Apr 2021
Data description paper |
| 21 Apr 2021
| 21 Apr 2021
Open access to regional geoid models: the International Service for the Geoid
Mirko Reguzzoni
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Daniela Carrion
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Alberta Albertella
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Lorenzo Rossi
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Giovanna Sona
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Khulan Batsukh
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Juan Fernando Toro Herrera
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Kirsten Elger
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Riccardo Barzaghi
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Fernando Sansó
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
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L. Rossi, C. I. De Gaetani, D. Pagliari, E. Realini, M. Reguzzoni, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 991–998, https://doi.org/10.5194/isprs-archives-XLII-2-991-2018, https://doi.org/10.5194/isprs-archives-XLII-2-991-2018, 2018
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S. Conversi, D. Carrion, A. Norcini, and M. Riva
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J. F. Toro Herrera, D. Carrion, and M. A. Brovelli
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C. Gerosa, M. Bresciani, G. Luciani, C. A. Biraghi, D. Carrion, M. Rogora, and M. A. Brovelli
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L. Rossi, F. Ioli, E. Capizzi, L. Pinto, and M. Reguzzoni
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Friederike Koerting, Nicole Koellner, Agnieszka Kuras, Nina Kristin Boesche, Christian Rogass, Christian Mielke, Kirsten Elger, and Uwe Altenberger
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Mineral resource exploration and mining is an essential part of today's high-tech industry. Modern remote-sensing exploration techniques from multiple platforms (e.g., satellite) to detect the spectral characteristics of the surface require spectral libraries as an essential reference. To enable remote mapping, the spectral libraries for rare-earth-bearing minerals, copper-bearing minerals and surface samples from a copper mine are presented here with their corresponding geochemical validation.
V. Yordanov, M. A. Brovelli, D. Carrion, L. Barazzetti, L. J. A. Francisco, H. R. Comia, and M. I. Caravela
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J. F. Toro, D. Carrion, M. A. Brovelli, and M. Percoco
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 197–203, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-197-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-197-2020, 2020
C. A. Biraghi, E. Pessina, D. Carrion, and M. A. Brovelli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 237–244, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-237-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-237-2020, 2020
D. Carrion, E. Pessina, C. A. Biraghi, and G. Bratic
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 245–251, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-245-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-245-2020, 2020
K. Spasenovic, D. Carrion, and F. Migliaccio
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 291–297, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-291-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-291-2020, 2020
Sabah Ramouz, Yosra Afrasteh, Mirko Reguzzoni, and Abdolreza Safari
Adv. Geosci., 50, 65–75, https://doi.org/10.5194/adgeo-50-65-2020, https://doi.org/10.5194/adgeo-50-65-2020, 2020
S. Jovanovic, D. Carrion, and M. A. Brovelli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W14, 119–126, https://doi.org/10.5194/isprs-archives-XLII-4-W14-119-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W14-119-2019, 2019
K. Spasenovic, D. Carrion, F. Migliaccio, and B. Pernici
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W14, 221–225, https://doi.org/10.5194/isprs-archives-XLII-4-W14-221-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W14-221-2019, 2019
J. F. Toro, D. Carrion, A. Albertella, and M. A. Brovelli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W14, 233–238, https://doi.org/10.5194/isprs-archives-XLII-4-W14-233-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W14-233-2019, 2019
K. Spasenovic and D. Carrion
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1263–1268, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1263-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1263-2019, 2019
F. Migliaccio, D. Carrion, and F. Ferrario
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1321–1326, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1321-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1321-2019, 2019
E. Sinem Ince, Franz Barthelmes, Sven Reißland, Kirsten Elger, Christoph Förste, Frank Flechtner, and Harald Schuh
Earth Syst. Sci. Data, 11, 647–674, https://doi.org/10.5194/essd-11-647-2019, https://doi.org/10.5194/essd-11-647-2019, 2019
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R. Barzaghi, M. Reguzzoni, C. I. De Gaetani, S. Caldera, and L. Rossi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 209–216, https://doi.org/10.5194/isprs-archives-XLII-2-W11-209-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-209-2019, 2019
C. Cappelletti, M. Boniardi, A. Casaroli, C. I. De Gaetani, D. Passoni, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W9, 227–234, https://doi.org/10.5194/isprs-archives-XLII-2-W9-227-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W9-227-2019, 2019
G. Ronchetti, D. Pagliari, and G. Sona
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 983–989, https://doi.org/10.5194/isprs-archives-XLII-2-983-2018, https://doi.org/10.5194/isprs-archives-XLII-2-983-2018, 2018
L. Rossi, C. I. De Gaetani, D. Pagliari, E. Realini, M. Reguzzoni, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 991–998, https://doi.org/10.5194/isprs-archives-XLII-2-991-2018, https://doi.org/10.5194/isprs-archives-XLII-2-991-2018, 2018
A. Albertella, M. A. Brovelli, and D. Gonzalez Ferreiro
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W2, 11–17, https://doi.org/10.5194/isprs-archives-XLII-4-W2-11-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W2-11-2017, 2017
Francesco Avanzi, Alberto Bianchi, Alberto Cina, Carlo De Michele, Paolo Maschio, Diana Pagliari, Daniele Passoni, Livio Pinto, Marco Piras, and Lorenzo Rossi
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-57, https://doi.org/10.5194/tc-2017-57, 2017
Revised manuscript not accepted
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We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
Giovanna Sona, Daniele Passoni, Livio Pinto, Diana Pagliari, Daniele Masseroni, Bianca Ortuani, and Arianna Facchi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 1023–1029, https://doi.org/10.5194/isprs-archives-XLI-B1-1023-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-1023-2016, 2016
Carlo De Michele, Francesco Avanzi, Daniele Passoni, Riccardo Barzaghi, Livio Pinto, Paolo Dosso, Antonio Ghezzi, Roberto Gianatti, and Giacomo Della Vedova
The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, https://doi.org/10.5194/tc-10-511-2016, 2016
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We investigate snow depth distribution at peak accumulation over a small Alpine area using photogrammetry-based surveys with a fixed wing unmanned aerial system. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to -0.073 m. Moreover, in this case study snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m.
B. K. Biskaborn, J.-P. Lanckman, H. Lantuit, K. Elger, D. A. Streletskiy, W. L. Cable, and V. E. Romanovsky
Earth Syst. Sci. Data, 7, 245–259, https://doi.org/10.5194/essd-7-245-2015, https://doi.org/10.5194/essd-7-245-2015, 2015
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This paper introduces the new database of the Global Terrestrial Network for Permafrost (GTN-P) on permafrost temperature and active layer thickness data. It describes the operability of the Data Management System and the data quality. By applying statistics on GTN-P metadata, we analyze the spatial sample representation of permafrost monitoring sites. Comparison with environmental variables and climate projection data enable identification of potential future research locations.
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Simon Deggim, Annette Eicker, Lennart Schawohl, Helena Gerdener, Kerstin Schulze, Olga Engels, Jürgen Kusche, Anita T. Saraswati, Tonie van Dam, Laura Ellenbeck, Denise Dettmering, Christian Schwatke, Stefan Mayr, Igor Klein, and Laurent Longuevergne
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Andreas Kvas, Jan Martin Brockmann, Sandro Krauss, Till Schubert, Thomas Gruber, Ulrich Meyer, Torsten Mayer-Gürr, Wolf-Dieter Schuh, Adrian Jäggi, and Roland Pail
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Short summary
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João Teixeira da Encarnação, Pieter Visser, Daniel Arnold, Aleš Bezdek, Eelco Doornbos, Matthias Ellmer, Junyi Guo, Jose van den IJssel, Elisabetta Iorfida, Adrian Jäggi, Jaroslav Klokocník, Sandro Krauss, Xinyuan Mao, Torsten Mayer-Gürr, Ulrich Meyer, Josef Sebera, C. K. Shum, Chaoyang Zhang, Yu Zhang, and Christoph Dahle
Earth Syst. Sci. Data, 12, 1385–1417, https://doi.org/10.5194/essd-12-1385-2020, https://doi.org/10.5194/essd-12-1385-2020, 2020
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Although not the primary mission of the Swarm three-satellite constellation, the sensors on these satellites are accurate enough to measure the melting and accumulation of Earth’s ice reservoirs, precipitation cycles, floods, and droughts, amongst others. Swarm sees these changes well compared to the dedicated GRACE satellites at spatial scales of roughly 1500 km. Swarm confirms most GRACE observations, such as the large ice melting in Greenland and the wet and dry seasons in the Amazon.
E. Sinem Ince, Franz Barthelmes, Sven Reißland, Kirsten Elger, Christoph Förste, Frank Flechtner, and Harald Schuh
Earth Syst. Sci. Data, 11, 647–674, https://doi.org/10.5194/essd-11-647-2019, https://doi.org/10.5194/essd-11-647-2019, 2019
Short summary
Short summary
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Short summary
The International Service for the Geoid provides free access to a repository of geoid models. The most important ones are freely available to perform analyses on the evolution of the geoid computation research field. Furthermore, the ISG performs research taking advantage of its archive and organizes specific training courses on geoid determination. This paper aims at describing the service and showing the added value of the archive of geoid models for the scientific community and technicians.
The International Service for the Geoid provides free access to a repository of geoid models....
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