A chlorophyll-a algorithm for Landsat-8 based on mixture density networks
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| Altres autors: | , , , , , , , , , , , |
| Format: | article |
| Idioma: | anglès |
| Publicat: |
2021
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| Accés en línia: | https://hdl.handle.net/20.500.12008/41002 |
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| _version_ | 1868890012234285056 |
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| author | Smith, Brandon |
| author2 | Pahlevan, Nima Schalles, John Ruberg, Steve Errera, Reagan Ma, Ronghua Giardino, Claudia Bresciani, Mariano Barbosa, Claudio Moore, Tim Fernández Ramos, Virginia Myriam Alikas, Krista Kangro, Kersti |
| author2_role | author author author author author author author author author author author author |
| author_browse | Alikas, Krista Barbosa, Claudio Bresciani, Mariano Errera, Reagan Fernández Ramos, Virginia Myriam Giardino, Claudia Kangro, Kersti Ma, Ronghua Moore, Tim Pahlevan, Nima Ruberg, Steve Schalles, John Smith, Brandon |
| author_facet | Smith, Brandon Pahlevan, Nima Schalles, John Ruberg, Steve Errera, Reagan Ma, Ronghua Giardino, Claudia Bresciani, Mariano Barbosa, Claudio Moore, Tim Fernández Ramos, Virginia Myriam Alikas, Krista Kangro, Kersti |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.none.fl_str_mv | Smith Brandon Pahlevan Nima Schalles John Ruberg Steve Errera Reagan Ma Ronghua Giardino Claudia Bresciani Mariano Barbosa Claudio Moore Tim Fernández Ramos Virginia Myriam, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Ciencias Geológicas. Alikas Krista Kangro Kersti |
| dc.creator.none.fl_str_mv | Smith, Brandon Pahlevan, Nima Schalles, John Ruberg, Steve Errera, Reagan Ma, Ronghua Giardino, Claudia Bresciani, Mariano Barbosa, Claudio Moore, Tim Fernández Ramos, Virginia Myriam Alikas, Krista Kangro, Kersti |
| dc.date.none.fl_str_mv | 2021 2023-11-08T15:04:13Z 2023-11-08T15:04:13Z |
| dc.format.none.fl_str_mv | 17 h. application/pdf |
| dc.identifier.none.fl_str_mv | Smith, B, Pahlevan, N, Schalles, J [y otros autores]. "A chlorophyll-a algorithm for Landsat-8 based on mixture density networks". Frontiers in Remote Sensing. [en línea] 2020, 1: 623678. 17 h. DOI: 10.3389/frsen.2020.623678. 2673-6187 https://hdl.handle.net/20.500.12008/41002 10.3389/frsen.2020.623678 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Frontiers |
| dc.relation.none.fl_str_mv | Frontiers in Remote Sensing, 2021, 1: 623678. |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Landsat Machin learning Aquatic remote sensing Coastal Lakes Chlorophyll-a |
| dc.title.none.fl_str_mv | A chlorophyll-a algorithm for Landsat-8 based on mixture density networks |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Material suplementario disponible en: |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_3a0be3aea03b152fe45dc45cfa463bf1 |
| identifier_str_mv | Smith, B, Pahlevan, N, Schalles, J [y otros autores]. "A chlorophyll-a algorithm for Landsat-8 based on mixture density networks". Frontiers in Remote Sensing. [en línea] 2020, 1: 623678. 17 h. DOI: 10.3389/frsen.2020.623678. 2673-6187 10.3389/frsen.2020.623678 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41002 |
| publishDate | 2021 |
| publishDateSort | 2021 |
| publisher.none.fl_str_mv | Frontiers |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | A chlorophyll-a algorithm for Landsat-8 based on mixture density networksSmith, BrandonPahlevan, NimaSchalles, JohnRuberg, SteveErrera, ReaganMa, RonghuaGiardino, ClaudiaBresciani, MarianoBarbosa, ClaudioMoore, TimFernández Ramos, Virginia MyriamAlikas, KristaKangro, KerstiLandsatMachin learningAquatic remote sensingCoastalLakesChlorophyll-aMaterial suplementario disponible en:Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDNA). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDNA model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDNA model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDNB) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρs ). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDNB models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing.FrontiersSmith BrandonPahlevan NimaSchalles JohnRuberg SteveErrera ReaganMa RonghuaGiardino ClaudiaBresciani MarianoBarbosa ClaudioMoore TimFernández Ramos Virginia Myriam, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Ciencias Geológicas.Alikas KristaKangro Kersti2023-11-08T15:04:13Z2023-11-08T15:04:13Z2021Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion17 h.application/pdfSmith, B, Pahlevan, N, Schalles, J [y otros autores]. "A chlorophyll-a algorithm for Landsat-8 based on mixture density networks". Frontiers in Remote Sensing. [en línea] 2020, 1: 623678. 17 h. DOI: 10.3389/frsen.2020.623678.2673-6187https://hdl.handle.net/20.500.12008/4100210.3389/frsen.2020.623678reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengFrontiers in Remote Sensing, 2021, 1: 623678.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)oai:colibri.udelar.edu.uy:20.500.12008/410022026-04-14T10:10:18Z |
| spellingShingle | A chlorophyll-a algorithm for Landsat-8 based on mixture density networks Smith, Brandon Landsat Machin learning Aquatic remote sensing Coastal Lakes Chlorophyll-a |
| status_str | publishedVersion |
| title | A chlorophyll-a algorithm for Landsat-8 based on mixture density networks |
| title_full | A chlorophyll-a algorithm for Landsat-8 based on mixture density networks |
| title_fullStr | A chlorophyll-a algorithm for Landsat-8 based on mixture density networks |
| title_full_unstemmed | A chlorophyll-a algorithm for Landsat-8 based on mixture density networks |
| title_short | A chlorophyll-a algorithm for Landsat-8 based on mixture density networks |
| title_sort | A chlorophyll-a algorithm for Landsat-8 based on mixture density networks |
| topic | Landsat Machin learning Aquatic remote sensing Coastal Lakes Chlorophyll-a |
| url | https://hdl.handle.net/20.500.12008/41002 |