Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-75-2024
© Author(s) 2024. 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-16-75-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Jan 2024
Data description paper |
| 08 Jan 2024
| 08 Jan 2024
AltiMaP: altimetry mapping procedure for hydrography data
Menaka Revel
CORRESPONDING AUTHOR
Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
now at: Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Canada
Xudong Zhou
Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
Prakat Modi
Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
Department of Civil Engineering, Shibaura Institute of Technology, Tokyo, Japan
Jean-François Cretaux
Laboratoire d'Études en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, IRD, CNES, CNRS, UPS, Toulouse, France
Stephane Calmant
Laboratoire d'Études en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, IRD, CNES, CNRS, UPS, Toulouse, France
Dai Yamazaki
Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
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Short summary
As satellite technology advances, there is an incredible amount of remotely sensed data for observing terrestrial water. Satellite altimetry observations of water heights can be utilized to calibrate and validate large-scale hydrodynamic models. However, because large-scale models are discontinuous, comparing satellite altimetry to predicted water surface elevation is difficult. We developed a satellite altimetry mapping procedure for high-resolution river network data.
As satellite technology advances, there is an incredible amount of remotely sensed data for...
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