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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-3
https://doi.org/10.5194/isprs-archives-XLII-3-1467-2018
© 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-archives-XLII-3-1467-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 Apr 2018
| 30 Apr 2018
MONITORING URBAN HEAT ISLAND THROUGH GOOGLE EARTH ENGINE: POTENTIALITIES AND DIFFICULTIES IN DIFFERENT CITIES OF THE UNITED STATES
R. Ravanelli
Geodesy and Geomatics Division, DICEA - University of Rome “La Sapienza”, Rome, Italy
A. Nascetti
Geodesy and Geomatics Division, DICEA - University of Rome “La Sapienza”, Rome, Italy
Geoinformatics, KTH Royal Institute of Technology, Stockholm, Sweden
R. V. Cirigliano
Geodesy and Geomatics Division, DICEA - University of Rome “La Sapienza”, Rome, Italy
C. Di Rico
Geodesy and Geomatics Division, DICEA - University of Rome “La Sapienza”, Rome, Italy
P. Monti
Department of Civil, Building and Environmental Engineering (DICEA) - University of Rome “La Sapienza”, Rome, Italy
M. Crespi
Geodesy and Geomatics Division, DICEA - University of Rome “La Sapienza”, Rome, Italy
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Short summary
Short summary
The estimation of precipitable water vapour (W) content is of great interest in both meteorological and climatological studies. Sun photometers allowed the development of W automatic estimations with high temporal resolution. A new methodology, based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance are dependent on vertical profiles of temperature, air pressure and moisture typical of each measurement site, has been presented providing good results.
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M. Di Rita, A. Nascetti, and M. Crespi
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R. Ravanelli, A. Nascetti, M. Di Rita, L. Nigro, D. Montanari, F. Spagnoli, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 589–592, https://doi.org/10.5194/isprs-archives-XLII-5-W1-589-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-589-2017, 2017
A. Nascetti, F. Nocchi, A. Camplani, C. Di Rico, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 783–788, https://doi.org/10.5194/isprs-archives-XLI-B7-783-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-783-2016, 2016
F. Fratarcangeli, A. Nascetti, P. Capaldo, A. Mazzoni, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 815–820, https://doi.org/10.5194/isprs-archives-XLI-B7-815-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-815-2016, 2016
M. Di Rita, A. Nascetti, F. Fratarcangeli, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 821–825, https://doi.org/10.5194/isprs-archives-XLI-B7-821-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-821-2016, 2016
R. Ravanelli, A. Nascetti, and M. Crespi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 699–702, https://doi.org/10.5194/isprs-archives-XLI-B5-699-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-699-2016, 2016
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