Day 4

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Paper title Correcting GEDI’s water surface elevation estimates using Instrumental, atmospheric, and water state variables
Authors
  1. Ibrahim Fayad INRAE Speaker
  2. Nicolas Baghdadi INRAE
  3. Jean-Stéphane Bailly AgroParisTech / UMR Lisah
Form of presentation Poster
Topics
  • A7. Hydrology and Water Cycle
    • A7.01 Inland Water Storage and Runoff: Modeling, In Situ Data and Remote Sensing
Abstract text In the last couple of decades, active remote sensing technologies, such as radar or LiDAR based sensors, became an essential source of information for the monitoring of inland water body levels. This is due to their validated high accuracies [1], and as a way to fill-in for the ever-decreasing water-level gauge stations that is reported worldwide [2,3].
In this study, we are interested in evaluating the accuracy, and correcting, water level estimates from the recently launched Global Ecosystem Dynamics Investigation (GEDI) full waveform (FW) LiDAR sensor on board the International Space Station (ISS). GEDI, which became operational in 2019, is equipped with three 1064 nm lasers with a pulse repetition frequency (PRF) of 242 Hz. One of the lasers’ power is split in two while the remaining two operate at full power. These four lasers are equipped with beam dithering units (BDUs) that rapidly deflect the light by 1.5 mrads in order to produce eight tracks of data. The acquired footprints along the eight tracks are separated by 600 m across track, and 60 m along the track, with a footprint diameter of 25 m.
Since the launch of GEDI, there have been few studies that assessed its accuracy for the estimation of in-land water levels [4–6]. The first study conducted by Fayad et al. [4], used the first two months of GEDI acquisitions (mid-April to mid-June 2019) to assess the accuracy of GEDI altimetry over eight lakes in Switzerland. For these two months, they reported a mean difference between GEDI and in situ gauge water elevations (bias) ranging from -13.8 cm (under estimation) to +9.8 cm (over estimation) with a standard deviation (SD) of the bias ranging from 14.5 to 31.6 cm. The study conducted by Xiang et al. [6] over the five great lakes of north America (Superior, Michigan, Huron, Erie and Ontario) using five months of GEDI acquisitions (April to August 2019) found a bias ranging from -32 cm (under estimation) to 11 cm (over estimation) with a SD that ranged from 15 to 34 cm. Finally in the study of Frappart et al. [5] which assessed the accuracy of GEDI data over ten Swiss lakes using acquisitions spread over seven months (April to October 2019) found a bias that ranged from -15 cm (under estimation) to +21 cm (over estimation) with a SD ranging from 10 cm to 30 cm.
The factors influencing the physical shape of the waveform and therefore the accuracy of LiDAR’s altemetric capabilities can be grouped into three categories: (1) Instrumental factors (e.g. viewing angle, signal over noise ratio), (2) water surface variations factors (e.g. wave heights and period, wave type), and (3) Atmospheric factors (e.g. cloud presence and cloud composition). For example, the viewing angle at acquisition time was demonstrated to increase elevation errors for ICESat-1 GLAS when the viewing angle deviates from nadir due to precision attitude determination [7]. Water specular reflection is also another potential source of errors due to the saturation of the detector [8]. Finally, clouds and their composition are major factors that affect the quality of LiDAR acquisitions [9,10]. Indeed, and while opaque clouds attenuates the LiDAR signal thus the receiver only captures noise, less opaque clouds allow the LiDAR to make a full round trip, but could potentially increase the photon path length due to forward scattering (atmospheric path delay), thus resulting in biases in elevation measurements [11]. Moreover, GEDI’s return signal strength will greatly vary between cloud-free shots and clouded acquisitions [9].
The objective of this study is therefore two folds. First, the performance of GEDI’s altimetric capabilities using filtered (i.e. removal of noisy acquisitions) GEDI waveforms across the five great lakes (Lakes Erie, Huron, Ontario, Michigan, and Superior) was assessed. Next, a random forest regression model was trained in order to estimate the calculated difference between GEDI acquisitions and in situ water level records using the instrumental, water surface variations and atmospheric variables as predictors to this model. The output of this model, which is namely the estimated difference between each GEDI acquisition and its corresponding in situ reference, was subtracted from each GEDI’s acquisition elevation in order to produce corrected elevation estimates.
Results showed that uncorrected GEDI estimated have on average a bias of 0.3 m (ranged between 0.25 and 0.42 m) and a root mean squared error (RMSE) of 0.58 m (ranged between 0.54 and 0.67 m). After the application of our model, the bias was mostly eliminated (ranged between -0.07 and 0.01 m), and the average RMSE decreased to 0.17 m (ranged between 0.14 and 0.21 m).

References

1. Birkett, C.; Reynolds, C.; Beckley, B.; Doorn, B. From Research to Operations: The USDA Global Reservoir and Lake Monitor. In Coastal Altimetry; Vignudelli, S., Kostianoy, A.G., Cipollini, P., Benveniste, J., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2011; pp. 19–50 ISBN 978-3-642-12795-3.
2. Shiklomanov, A.I.; Lammers, R.B.; Vörösmarty, C.J. Widespread Decline in Hydrological Monitoring Threatens Pan-Arctic Research. Eos Trans. AGU 2002, 83, 13, doi:10.1029/2002EO000007.
3. Hannah, D.M.; Demuth, S.; van Lanen, H.A.J.; Looser, U.; Prudhomme, C.; Rees, G.; Stahl, K.; Tallaksen, L.M. Large-Scale River Flow Archives: Importance, Current Status and Future Needs. Hydrol. Process. 2011, 25, 1191–1200, doi:10.1002/hyp.7794.
4. Fayad, I.; Baghdadi, N.; Bailly, J.S.; Frappart, F.; Zribi, M. Analysis of GEDI Elevation Data Accuracy for Inland Waterbodies Altimetry. Remote Sensing 2020, 12, 2714, doi:10.3390/rs12172714.
5. 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, 2196, doi:10.3390/rs13112196.
6. Xiang, J.; Li, H.; Zhao, J.; Cai, X.; Li, P. Inland Water Level Measurement from Spaceborne Laser Altimetry: Validation and Comparison of Three Missions over the Great Lakes and Lower Mississippi River. Journal of Hydrology 2021, 597, 126312, doi:10.1016/j.jhydrol.2021.126312.
7. Urban, T.J.; Schutz, B.E.; Neuenschwander, A.L. A Survey of ICESat Coastal Altimetry Applications: Continental Coast, Open Ocean Island, and Inland River. Terrestrial Atmospheric and Oceanic Sciences 2008, 19, 1–19.
8. Lehner, B.; Döll, P. Development and Validation of a Global Database of Lakes, Reservoirs and Wetlands. Journal of hydrology 2004, 296, 1–22.
9. Fayad, I.; Baghdadi, N.; Riedi, J. Quality Assessment of Acquired GEDI Waveforms: Case Study over France, Tunisia and French Guiana. Remote Sensing 2021, 13, 3144, doi:10.3390/rs13163144.
10. Shu, S.; Liu, H.; Frappart, F.; Kang, E.L.; Yang, B.; Xu, M.; Huang, Y.; Wu, B.; Yu, B.; Wang, S.; et al. Improving Satellite Waveform Altimetry Measurements With a Probabilistic Relaxation Algorithm. IEEE Trans. Geosci. Remote Sensing 2021, 59, 4733–4748, doi:10.1109/TGRS.2020.3010184.
11. Yuekui Yang; Marshak, A.; Palm, S.P.; Varnai, T.; Wiscombe, W.J. Cloud Impact on Surface Altimetry From a Spaceborne 532-Nm Micropulse Photon-Counting Lidar: System Modeling for Cloudy and Clear Atmospheres. IEEE Trans. Geosci. Remote Sensing 2011, 49, 4910–4919, doi:10.1109/TGRS.2011.2153860.