{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T14:21:53Z","timestamp":1770906113653,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spaceborne LiDAR altimetry has been demonstrated to be an essential source of data for the estimation and monitoring of inland water level variations. In this study, water level estimates from the Global Ecosystem Dynamics Investigation (GEDI) were validated against in situ gauge station records over Lake Geneva for the period between April 2019 and September 2020. The performances of the first and second releases (V1 and V2, respectively) of the GEDI data products were compared, and the effects on the accuracy of the instrumental and environmental factors were analyzed in order to discern the most accurate GEDI acquisitions. The respective influences of five parameters were analyzed in this study: (1) the signal-over-noise ratio (SNR); (2) the width of the water surface peak within the waveform (gwidth); (3) the amplitude of the water surface peak within the waveform (A); (4) the viewing angle of GEDI (VA); and (5) the acquiring beam. Results indicated that all these factors, except the acquiring beam, had an effect on the accuracy of GEDI elevations. Nonetheless, using VA as a filtering criterion was demonstrated to be the best compromise between retained shot count and water level estimation accuracy. Indeed, by choosing the shots with a VA \u2264 3.5\u00b0, 74.6% of the shots (after an initial filter) were retained with accuracies similar to choosing A &gt; 400 (46.2% retained shots), SNR &gt; 15 dB (63.3% retained shots), or gwidth &lt; 10 bins (46.5% of retained shots). Finally, the comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant, but higher, bias and fewer deviations to the in situ data than V1. Indeed, by choosing GEDI shots with VA \u2264 3.5\u00b0, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r = 0.66) and 42.8 cm with V1 (r = 0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA \u2264 3.5\u00b0). Regarding the bias, a higher value was observed with V2, but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number and strong dependence on GEDI dates.<\/jats:p>","DOI":"10.3390\/rs14020340","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T09:10:36Z","timestamp":1641978636000},"page":"340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Comparative Analysis of GEDI\u2019s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies"],"prefix":"10.3390","volume":"14","author":[{"given":"Ibrahim","family":"Fayad","sequence":"first","affiliation":[{"name":"TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, 34090 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, 34090 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-8274","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9ric","family":"Frappart","sequence":"additional","affiliation":[{"name":"LEGOS, University of Toulouse, CNES, CNRS, IRD, UPS, 14 avenue Edouard Belin, 31400 Toulouse, France"},{"name":"ISPA, INRAE, Bordeaux Sciences Agro, 33140 Villenave d\u2019Ornon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vignudelli, S., Kostianoy, A.G., Cipollini, P., and Benveniste, J. (2011). From Research to Operations: The USDA Global Reservoir and Lake Monitor. Coastal Altimetry, Springer.","DOI":"10.1007\/978-3-642-12796-0"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"015002","DOI":"10.1088\/1748-9326\/10\/1\/015002","article-title":"Global Surveys of Reservoirs and Lakes from Satellites and Regional Application to the Syrdarya River Basin","volume":"10","author":"Biancamaria","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.isprsjprs.2011.09.002","article-title":"Improving the Assessment of ICESat Water Altimetry Accuracy Accounting for Autocorrelation","volume":"66","author":"Abdallah","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., Bailly, J.S., Frappart, F., and Zribi, M. (2020). Analysis of GEDI Elevation Data Accuracy for Inland Waterbodies Altimetry. Remote Sens., 12.","DOI":"10.3390\/rs12172714"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Normandin, C., Frappart, F., Diepkil\u00e9, A.T., Marieu, V., Mougin, E., Blarel, F., Lubac, B., Braquet, N., and Ba, A. (2018). Evolution of the Performances of Radar Altimetry Missions from ERS-2 to Sentinel-3A over the Inner Niger Delta. Remote Sens., 10.","DOI":"10.3390\/rs10060833"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.5194\/hess-25-1643-2021","article-title":"Evaluation of Historic and Operational Satellite Radar Altimetry Missions for Constructing Consistent Long-Term Lake Water Level Records","volume":"25","author":"Shu","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1029\/2002EO000007","article-title":"Widespread Decline in Hydrological Monitoring Threatens Pan-Arctic Research","volume":"83","author":"Shiklomanov","year":"2002","journal-title":"Eos Trans. AGU"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1002\/hyp.7794","article-title":"Large-Scale River Flow Archives: Importance, Current Status and Future Needs","volume":"25","author":"Hannah","year":"2011","journal-title":"Hydrol. Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.srs.2020.100002","article-title":"The Global Ecosystem Dynamics Investigation: High-Resolution Laser Ranging of the Earth\u2019s Forests and Topography","volume":"1","author":"Dubayah","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ryan, J.C., Smith, L.C., Cooley, S.W., Pitcher, L.H., and Pavelsky, T.M. (2020). Global Characterization of Inland Water Reservoirs Using ICESat-2 Altimetry and Climate Reanalysis. Geophys. Res. Lett., 47.","DOI":"10.1029\/2020GL088543"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Madson, A., and Sheng, Y. (2021). Automated Water Level Monitoring at the Continental Scale from ICESat-2 Photons. Remote Sens., 13.","DOI":"10.3390\/rs13183631"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Frappart, F., Blarel, F., Fayad, I., Berg\u00e9-Nguyen, M., Cr\u00e9taux, J.-F., Shu, S., Schregenberger, J., and 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 Sens., 13.","DOI":"10.3390\/rs13112196"},{"key":"ref_13","first-page":"3","article-title":"Analysis of Lake Level Changes in Nam Co in Central Tibet Utilizing Synergistic Satellite Altimetry and Optical Imagery","volume":"17","author":"Braun","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"126312","DOI":"10.1016\/j.jhydrol.2021.126312","article-title":"Inland Water Level Measurement from Spaceborne Laser Altimetry: Validation and Comparison of Three Missions over the Great Lakes and Lower Mississippi River","volume":"597","author":"Xiang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3319\/TAO.2008.19.1-2.1(SA)","article-title":"A Survey of ICESat Coastal Altimetry Applications: Continental Coast, Open Ocean Island, and Inland River","volume":"19","author":"Urban","year":"2008","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jhydrol.2004.03.028","article-title":"Development and Validation of a Global Database of Lakes, Reservoirs and Wetlands","volume":"296","author":"Lehner","year":"2004","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., and Riedi, J. (2021). Quality Assessment of Acquired GEDI Waveforms: Case Study over France, Tunisia and French Guiana. Remote Sens., 13.","DOI":"10.3390\/rs13163144"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4733","DOI":"10.1109\/TGRS.2020.3010184","article-title":"Improving Satellite Waveform Altimetry Measurements With a Probabilistic Relaxation Algorithm","volume":"59","author":"Shu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4910","DOI":"10.1109\/TGRS.2011.2153860","article-title":"Cloud Impact on Surface Altimetry From a Spaceborne 532-Nm Micropulse Photon-Counting Lidar: System Modeling for Cloudy and Clear Atmospheres","volume":"49","author":"Yang","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","unstructured":"Dubayah, R., Luthcke, S., Blair, J., Hofton, M., Armston, J., and Tang, H. (2022, January 10). GEDI L1B Geolocated Waveform Data Global Footprint Level V001. 2020. distributed by NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/GEDI\/GEDI01_B.001."},{"key":"ref_21","unstructured":"Dubayah, R., Hofton, M., Blair, J., Armston, J., Tang, H., and Luthcke, S. (2022, January 10). GEDI L2A Elevation and Height Metrics Data Global Footprint Level V001. 2020. distributed by NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/GEDI\/GEDI02_A.001."},{"key":"ref_22","unstructured":"Dubayah, R., Luthcke, S., Blair, J., Hofton, M., Armston, J., and Tang, H. (2022, January 10). GEDI L1B Geolocated Waveform Data Global Footprint Level V002. 2021. distributed by NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/GEDI\/GEDI01_B.002."},{"key":"ref_23","unstructured":"Dubayah, R., Hofton, M., Blair, J., Armston, J., Tang, H., and Luthcke, S. (2022, January 10). GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002. 2021. distributed by NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/GEDI\/GEDI02_A.002."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103104","DOI":"10.1117\/1.OE.53.10.103104","article-title":"Signal-to-Noise Ratio\u2013Based Quality Assessment Method for ICESat\/GLAS Waveform Data","volume":"53","author":"Nie","year":"2014","journal-title":"Opt. Eng"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5785","DOI":"10.1080\/01431160802132786","article-title":"Multi-scale Thermal Pattern Monitoring of a Large Lake (Lake Geneva) Using a Multi-sensor Approach","volume":"29","author":"Oesch","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1007\/s00585-996-1207-z","article-title":"Summertime Winds and Direct Cyclonic Circulation: Observations from Lake Geneva","volume":"14","author":"Lemmin","year":"1996","journal-title":"Ann. Geophys."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/340\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:24Z","timestamp":1760362044000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,12]]},"references-count":26,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020340"],"URL":"https:\/\/doi.org\/10.3390\/rs14020340","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,12]]}}}