dc.contributor.author | Bakken, Sivert | |
dc.contributor.author | Luis, Kelly | |
dc.contributor.author | Johnsen, Geir | |
dc.contributor.author | Johansen, Tor Arne | |
dc.date.accessioned | 2024-04-16T07:49:40Z | |
dc.date.available | 2024-04-16T07:49:40Z | |
dc.date.created | 2024-01-02T15:40:10Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 2158-6276 | |
dc.identifier.uri | https://hdl.handle.net/11250/3126671 | |
dc.description.abstract | This work compares different regression models combined with hybrid modeling to estimate water clarity using hyper-spectral remote sensing data. The Secchi depth, a proxy of water clarity, can be modeled using first principles bio-optical modeling and other static pre-processing steps are used to generate four different feature sets. The different feature sets and regression models are evaluated using cross-validation on the recently published GLORIA dataset, representing a vast set of Secchi depth measurements from various aquatic environments (N = 3914). The best-performing feature generation and regression model combination can provide promising Secchi depth inference from hyperspectral data (RMSE = 1.543, AP D = 39.419, R 2 = 0.636). The study demonstrates the potential of hyperspectral remote sensing data for monitoring and managing aquatic ecosystems. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Evaluating hyperspectral Secchi depth retrieval through hybrid modeling and regression | en_US |
dc.title.alternative | Evaluating hyperspectral Secchi depth retrieval through hybrid modeling and regression | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.journal | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing | en_US |
dc.identifier.doi | 10.1109/WHISPERS61460.2023.10431165 | |
dc.identifier.cristin | 2219213 | |
dc.relation.project | Norges forskningsråd: 223254 | en_US |
dc.relation.project | Norges forskningsråd: 325961 | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |