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Correcting GEDI Water Level Estimates for Inland Waterbodies Using Machine Learning

hal.structure.identifierTerritoires, Environnement, Télédétection et Information Spatiale [UMR TETIS]
dc.contributor.authorFAYAD, Ibrahim
hal.structure.identifierTerritoires, Environnement, Télédétection et Information Spatiale [UMR TETIS]
dc.contributor.authorBAGHDADI, Nicolas
hal.structure.identifierLaboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème [UMR LISAH]
hal.structure.identifierAgroParisTech
dc.contributor.authorBAILLY, Jean-Stéphane
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorFRAPPART, Frédéric
hal.structure.identifierTerritoires, Environnement, Télédétection et Information Spatiale [UMR TETIS]
dc.contributor.authorPANTALEONI RELUY, Núria
dc.date.accessioned2024-04-08T11:47:15Z
dc.date.available2024-04-08T11:47:15Z
dc.date.issued2022-05-13
dc.identifier.issn2072-4292
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195275
dc.description.abstractEnThe Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station has acquired more than 35 billion shots globally in the period between April 2019 and August 2021. The acquired shots could offer a significant database for the measure and monitoring of inland water levels over the Earth’s surface. Nonetheless, previous and current studies have shown that the provided GEDI elevation estimates are significantly less accurate than any available radar or LiDAR altimeter. Indeed, our analysis of GEDI’s altimetric capabilities to retrieve water levels over the five North American Great Lakes presented estimates with a bias that ranged between 0.26 and 0.35 m and a root mean squared error (RMSE) ranging between 0.54 and 0.68 m. Therefore, our objective in this study is to post-process the original GEDI water level estimates from an error model taking instrumental, atmospheric, and lakes surface state factors as proxies, which affect the physical shape of the waveforms, hence introducing uncertainties on the elevation estimates. The first tested model, namely a random forest regressor (RFICW) with the instrumental, atmospheric, and water surface state factors as inputs, was validatedtemporally (trained on a given year and validated on another) and spatially (trained on a given lake and validated on the remaining four). The results showed a significant decrease in elevation estimation errors both temporally and spatially. The temporally validated models showed an RMSE on the corrected elevation estimates of 0.18 m. Concerning the spatially validated model, the results varied based on the lake data used for training. Indeed, the most accurate spatially validated model showed an RMSE of 0.17 m, while the least accurate model showed an RMSE of 0.26 m. Finally, given that an elevation correction model using all the factors (instrumental, atmospheric, and water surface state factors) presents a best-case scenario, as water surface state factors are only available over a selected number of lakes globally, three additional models based on random forest were tested. The first, RFI , uses only instrumental factors as correction factors, RFIC uses both instrumental and atmospheric factors, while the third, RFIW, uses instrumental and water surface state factors. The temporal validation of these models showed that the model using instrumental factors, while less accurate than the remaining two models, was capable of correcting the original GEDI elevation estimates by a factor of two across the five lakes. On the other hand, the RFIC model was the most accurate between the three, with a slight degradation in comparison to the full model. Indeed, the RFIC model showed an RMSE on the estimation of water levels of 0.21 m.
dc.language.isoen
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enLiDAR
dc.subject.engreat lakes
dc.subject.enwater levels correction
dc.subject.enrandom forest
dc.title.enCorrecting GEDI Water Level Estimates for Inland Waterbodies Using Machine Learning
dc.typeArticle de revue
dc.identifier.doi10.3390/rs14102361
dc.subject.halSciences de l'environnement/Ingénierie de l'environnement
bordeaux.journalRemote Sensing
bordeaux.page2361
bordeaux.volume14
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.issue10
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03685718
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03685718v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing&rft.date=2022-05-13&rft.volume=14&rft.issue=10&rft.spage=2361&rft.epage=2361&rft.eissn=2072-4292&rft.issn=2072-4292&rft.au=FAYAD,%20Ibrahim&BAGHDADI,%20Nicolas&BAILLY,%20Jean-St%C3%A9phane&FRAPPART,%20Fr%C3%A9d%C3%A9ric&PANTALEONI%20RELUY,%20N%C3%BAria&rft.genre=article


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