| 1. Identity statement | |
| Reference Type | Journal Article |
| Site | plutao.sid.inpe.br |
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identifier | 8JMKD3MGP3W/44SJTKE |
| Repository | sid.inpe.br/plutao/2021/06.16.16.50 (restricted access) |
| Last Update | 2021:06.17.13.17.25 (UTC) lattes |
| Metadata Repository | sid.inpe.br/plutao/2021/06.16.16.50.57 |
| Metadata Last Update | 2024:04.17.08.12.13 (UTC) administrator |
| DOI | 10.3389/frsen.2020.623678 |
| ISSN | 2673-6187 |
| Label | lattes: 1596449770636962 9 SmithPSREMGBBMFAK:2021:ChAlLa |
| Citation Key | SmithPSREMGBBMFAK:2021:ChAlLa |
| Title | A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks  |
| Year | 2021 |
| Access Date | 2024, May 19 |
| Type of Work | journal article |
| Secondary Type | PRE PI |
| Number of Files | 1 |
| Size | 4876 KiB |
| 2. Context | |
| Author | 1 Smith, Brandon 2 Pahlevan, Nima 3 Schalles, John 4 Ruberg, Steve 5 Errera, Reagan 6 Ma, Ronghua 7 Giardino, Claudia 8 Bresciani, Mariano 9 Barbosa, Cláudio Clemente Faria 10 Moore, Tim 11 Fernández, Virginia 12 Alikas, Krista 13 Kangaro, Kersti |
| Resume Identifier | 1 2 3 4 5 6 7 8 9 8JMKD3MGP5W/3C9JGSB |
| Group | 1 2 3 4 5 6 7 8 9 DIOTG-CGCT-INPE-MCTI-GOV-BR |
| Affiliation | 1 NASA Goddard Space Flight Center 2 NASA Goddard Space Flight Center 3 Creighton University 4 NOAA 5 NOAA 6 Chinese Academy of Science 7 National Research Council of Italy 8 National Research Council of Italy 9 Instituto Nacional de Pesquisas Espaciais (INPE) 10 Florida Atlantic University 11 University of the Republic 12 University of Tartu 13 University of Tartu |
| Author e-Mail Address | 1 2 3 4 5 6 7 8 9 claudio.barbosa@inpe.br |
| Journal | Frontiers in Remote Sensing |
| Volume | 1 |
| Pages | e623678 |
| History (UTC) | 2021-06-17 13:17:25 :: lattes -> administrator :: 2021 2024-04-17 08:12:13 :: administrator -> simone :: 2021 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Content Type | External Contribution |
| Version Type | publisher |
| Keywords | machine learning Landsat-8 Chlorophyll-a Inland Waters aquatic remote sensing |
| Abstract | 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 algorithms 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 heldout 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 TermsChlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing. |
| Area | SRE |
| Arrangement 1 | urlib.net > BDMCI > Fonds > LabISA > A Chlorophyll-a Algorithm... |
| Arrangement 2 | urlib.net > Produção a partir de 2021 > CGCT > A Chlorophyll-a Algorithm... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| Language | en |
| Target File | smith_chlorophyll.pdf |
| User Group | lattes |
| Reader Group | administrator lattes |
| Visibility | shown |
| Read Permission | deny from all and allow from 150.163 |
| Update Permission | not transferred |
| 5. Allied materials | |
| Next Higher Units | 8JMKD3MGPCW/439EAFB 8JMKD3MGPCW/46KUATE |
| Citing Item List | sid.inpe.br/bibdigital/2020/09.18.00.06 3 sid.inpe.br/mtc-m21/2012/07.13.14.43.57 2 |
| Host Collection | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notes | |
| Notes | Setores de Atividade: Pesquisa e desenvolvimento científico. |
| Empty Fields | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress format isbn lineage mark mirrorrepository month nextedition number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url |
| 7. Description control | |
| e-Mail (login) | simone |
| update | |