Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets
2.2.1. Radar Altimetry Data
- Ten-day repeat orbit period missions: Jason-1, Jason-2, and Jason-3
- 2.
- Thirty-five-day repeat orbit period missions: ERS-2, ENVISAT, and SARAL
- 3.
- Twenty-seven-day repeat orbit period missions: SENTINEL-3A and SENTINEL-3B
2.2.2. Lidar Altimetry Data
- ICESat-2
- 2.
- GEDI
2.2.3. In Situ Water Levels
2.2.4. CHGeo2004 Geoid Model
2.3. Altimetry-Based Water Levels
2.3.1. Measurement Principle
2.3.2. Generating the Time-Series of Water Levels from Radar Altimetry Data Using AlTiS
- Read radar altimetry data from ERS-2, ENVISAT, JASON-1/2/3, SARAL, and SENTINEL-3A and 3B radar altimetry missions.
- Display the different variables contained in the Geophysical Data Records (GDR) of each mission including H, R0, the different corrections applied to R0, h, automatically computed from (2) when reading the data, as well as several other variables such as the backscattering coefficients and the pulse peakiness [62] at the different microwave frequencies, the brightness temperatures at the different frequencies measured by the radiometer on-board the satellite platform, and the normalized index defined by CTOH to help for the statistical analysis [63], with Landsat True color image supplied by the Global Imagery Browse Services (GIBS from NASA’s Earth observations [64]) as background.
- Manually select the valid data/remove the invalid data contouring them using the mouse.
- Generating the time series of water levels computing the median and mean values and the associated median absolute deviation and standard deviation for each cycle. Note that the different altimeter tracks are processed individually. In this study, median values and associated median absolute deviations computed each cycle are used to minimize the potential impact of residual outliers on small number of observations due to the moderate width of the lakes under the altimeter tracks (see Table 5).
2.3.3. Generating the Time-Series of Water Levels from ICESat-2 Lidar Data
2.3.4. Generating the Time-Series of Water Levels from GEDI Lidar Data
2.3.5. Levelling of the Different Water Level Datasets
2.3.6. Validation of Altimetry-Based Lake Water Levels
3. Results
3.1. Validation of Radar Altimetry-Based Water Levels
3.2. Validation of Lidar Altimetry-Based Water Levels
3.2.1. Validation of ICESat-2-Based Lake Water Levels
3.2.2. Validation of GEDI-Based Lake Water Levels
4. Discussion
4.1. Availability and Accuracy of Radar Altimetry-Based Water Levels in Mountainous Areas
4.2. Availability and Accuracy of Lidar Altimetry-Based Water Levels in Mountainous Areas
- RMSE and R were computed over a larger number of observations using RA data than using GEDI ones. In addition, as Lakes Geneva and Neuchâtel are characterized by a low seasonal amplitude (below 1 m), this could explain the lower statistical results obtained using the GEDI data, especially as GEDI sampling period is limited to 6 months.
- The footprint of the GEDI mission is much smaller (25 m of diameter) than the ones from the RA missions (a few kilometers of radius decreasing as the frequency increases from Ku to Ka bands for LRM missions, a surface of a few kilometers of length by a couple of hundreds of m of width SAR missions). GEDI waveform are more impacted by both geophysical and anthropogenic factors affecting the lake surface.
- Contrary to RA measurements, lidar observations are strongly impacted by the presence of water in the atmosphere, degrading the accuracy of the height retrievals.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Altimetry Mission | Jason-1 | Jason-2 | Jason-3 | ERS-2 | ENVISAT | SARAL | SENTINEL-3 |
---|---|---|---|---|---|---|---|
Start | 01/2002 | 07/2008 | 02/2019 | 05/1995 | 05/2002 | 03/2013 | 02/2016 (A) 04/2018 (B) |
End | 01/2009 | 10/2016 | On-going | 11/2003 | 10/2010 | 07/2016 | On-going (A and B) |
GDR | E | D | D | CTOH [40] | v2.1 | T | |
Along-track sampling | 20 Hz | 20 Hz | 20 Hz | 20 Hz | 18 Hz | 40 Hz | 20 Hz |
Retracker | ICE | ICE | ICE | ICE-1 | ICE-1 | ICE-1 | OCOG |
ΔRiono | global ionospheric map (GIM) [41]-based 1 | ||||||
ΔRdry | European Centre for Medium-Range Weather Forecasts (ECMWF)-based using Digital Elevation Model (DEM) | ECMWF-based using h | ECMWF-based using DEM | ||||
ΔRwet | ECMWF-based using DEM | ||||||
ΔRsolid Earth | Based on Cartwright et al. [42] | ||||||
ΔRpole | Based on Wahr et al. [43] |
Lake | Dates |
---|---|
Geneva | 3 December 2018; 29 December 2018; 27 January 2019; 2 February 2019; 25 February 2020; 30 March 2019; 28 April 2019; 3 August 2019; 25 August 2019; 27 September 2019; 24 November 2019; 23 December 2019 |
Neuchâtel | 3 March 2019; 26 March 2019; 2 June 2019; 25 June 2019; 23 December 2019 |
Thun | 29 November 2018; 27 February 2019; 22 March 2019; 26 September 2019; 18 October 2019 |
Lucerne | 17 December 2018; 23 February 2019; 18 March 2019; 25 May 2019; 17 June 2019; 24 August 2019; 15 September 2019; 25 October 2019 |
Zug | 25 May 2019; 15 March 2020; 22 May 2020 |
Zürich | 02/05; 04/05; 11/05; 08/06; 04/07; 01/09; 23/09 |
Lake | Number of Shots | Dates |
---|---|---|
Geneva | 12,195 | 20/04; 04/05; 28/05; 20/06; 01/07; 04/07; 16/07; 18/07; 29/08; 02/09; 21/09; 23/09; 13/10 |
Neuchâtel | 8522 | 21/04; 28/04; 29/05; 24/06; 20/07; 03/08; 18/08; 19/09; 28/09; 16/10; 25/10 |
Thun | 1774 | 20/04; 21/04; 18/07; 18/08; 27/09; 25/10 |
Lucerne | 1813 | 20/04; 22/05; 23/06; 28/06; 18/07; 05/09; 27/09 |
Zug | 964 | 04/05; 17/06; 04/07; 13/07; 14/09; 23/09 |
Zürich | 2433 | 02/05; 04/05; 11/05; 08/06; 04/07; 01/09; 23/09 |
Walensee | 1083 | 20/04; 02/05; 23/06; 27/09; |
Sempach | 239 | 04/05; 22/05; 08/06; 01/09 |
Sarnersee | 203 | 20/04; 18/07; 23/07; |
Lake | Mean Area (km2) [51] | Station | Station ID | Longitude 1 (°) | Latitude 1 (°) | h0 (m) | N (m) | Validation Period |
---|---|---|---|---|---|---|---|---|
Geneva | 580 | St-Prex | 2027 | 6.4611379 | 46.4827956 | 421.92 | 49.93 | 01/1995–01/2020 |
Geneva | 580 | Sécheron | 2028 | 6.1523679 | 46.218622 | 421.82 | 49.83 | 01/1995–01/2020 |
Neuchâtel | 215 | Grandson | 2154 | 6.6423606 | 46.8057745 | 478.82 | 49.86 | 01/1995–01/2020 |
Thun | 48 | Spiez, Kraftwerk BKW | 2093 | 7.6645587 | 46.6967403 | 608.09 | 50.09 | 09/2018–01/2020 |
Lucerne | 114 | Lucerne | 2207 | 8.3198266 | 47.0548813 | 482.03 | 48.08 | 01/1995–01/2020 |
Lucerne | 114 | Brunnen | 2025 | 8.6037922 | 46.9934707 | 482.49 | 48.52 | 01/1995–01/2020 |
Zug | 38 | Zug | 2017 | 8.5143326 | 47.1678740 | 451.62 | 47.59 | 01/2018–01/2020 |
Zürich | 68 | Zürich | 2209 | 8.5504684 | 47.3547707 | 453.21 | 47.32 | 02/2016–01/2020 |
Zürich (Obersee) | 20 | Schmerikon | 2014 | 8.9400917 | 47.2247744 | 457.25 | 47.32 | 04/2018–01/2020 |
Walensee | 24 | Murg | 2118 | 9.2100321 | 47.1133767 | 467.07 | 48.13 | 01/1995–01/2020 |
Sempach | 14 | Sempach | 2168 | 8.1891830 | 47.1342944 | 551.97 | 48.03 | 09/2018–01/2020 |
Sarnen | 8 | Sarnen | 2088 | 8.2424260 | 46.8877257 | 520.36 | 49.37 | 09/2018–01/2020 |
Lake | Altimetry Missions | Track | Repeat Orbit (Days) | Distance over the Lake (km) | Valid Data |
---|---|---|---|---|---|
Geneva | ERS-2/ENVISAT/SARAL | 0846 | 35 | 9.4 | Yes/No/Yes |
Geneva | ERS-2/ENVISAT/SARAL | 0887 | 35 | 5.6 | Few/Yes/Yes |
Geneva | JASON-1/2/3 | 044 | 10 | 5.6 | No/Yes/Yes |
Geneva | SENTINEL-3A | 0358 | 27 | 12.2 | Yes |
Geneva | SENTINEL-3A | 0741 | 27 | 4.7 | Yes |
Geneva | SENTINEL-3B | 0741 | 27 | 11.2 | Yes |
Neuchâtel | ERS-2/ENVISAT/SARAL | 0343 | 35 | 5.6 | Yes/No/No |
Neuchâtel | ERS-2/ENVISAT/SARAL | 0846 | 35 | 12.9 | No/Few/No |
Neuchâtel | SENTINEL-3A | 0358 | 27 | 3.9 | Yes |
Neuchâtel | SENTINEL-3A | 0741 | 27 | 6.1 | Yes |
Thun | SENTINEL-3A | 0085 | 27 | 5.5 | Yes |
Lucerne | ERS-2/ENVISAT/SARAL | 0257 | 35 | 2.4/3.3/2.4 1 | Few/Few/Few |
Lucerne | ERS-2/ENVISAT/SARAL | 0760 | 35 | 5.8 | Yes/Yes/Yes |
Lucerne | SENTINEL-3A | 0199 | 27 | 1.3/0.8 1 | Yes |
Lucerne | SENTINEL-3A | 0586 | 27 | 8.00 | Yes |
Zürichsee | SENTINEL-3A | 0586 | 27 | 3.1 | Yes |
Obersee (Zürich) | SENTINEL-3B | 0313 | 27 | 1.5 | Yes |
Walensee | ERS-2/ENVISAT/SARAL | 0760 | 35 | 1.5 | Few/No/No |
Walensee | SENTINEL-3B | 0700 | 27 | 1.1 | Yes |
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Frappart, F.; Blarel, F.; Fayad, I.; Bergé-Nguyen, M.; Crétaux, J.-F.; Shu, S.; Schregenberger, J.; Baghdadi, N. Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes. Remote Sens. 2021, 13, 2196. https://doi.org/10.3390/rs13112196
Frappart F, Blarel F, Fayad I, Bergé-Nguyen M, Crétaux J-F, Shu S, Schregenberger J, Baghdadi N. Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes. Remote Sensing. 2021; 13(11):2196. https://doi.org/10.3390/rs13112196
Chicago/Turabian StyleFrappart, Frédéric, Fabien Blarel, Ibrahim Fayad, Muriel Bergé-Nguyen, Jean-François Crétaux, Song Shu, Joël Schregenberger, and Nicolas Baghdadi. 2021. "Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes" Remote Sensing 13, no. 11: 2196. https://doi.org/10.3390/rs13112196
APA StyleFrappart, F., Blarel, F., Fayad, I., Bergé-Nguyen, M., Crétaux, J. -F., Shu, S., Schregenberger, J., & Baghdadi, N. (2021). Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes. Remote Sensing, 13(11), 2196. https://doi.org/10.3390/rs13112196