| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/4A3ANT8 | |||
| Repository | sid.inpe.br/mtc-m21d/2023/10.17.19.40 (restricted access) | |||
| Last Update | 2023:10.17.19.40.34 (UTC) self-uploading-INPE-MCTI-GOV-BR | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2023/10.17.19.40.34 | |||
| Metadata Last Update | 2024:01.02.17.16.49 (UTC) administrator | |||
| DOI | 10.1016/j.isprsjprs.2023.09.019 | |||
| ISSN | 0924-2716 | |||
| Label | self-archiving-INPE-MCTIC-GOV-BR | |||
| Citation Key | BegliominiBMNPMLOPL:2023:MaLeCy | |||
| Title | Machine learning for cyanobacteria mapping on tropical urban reservoirs using PRISMA hyperspectral data  | |||
| Year | 2023 | |||
| Month | Oct. | |||
| Access Date | 2024, May 18 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 12745 KiB | |||
| 2. Context | ||||
| Author | 1 Begliomini, Felipe N. 2 Barbosa, Cláudio Clemente Faria 3 Martins, Vitor S. 4 Novo, Evlyn Márcia Leão de Moraes 5 Paulino, Rejane de Souza 6 Maciel, Daniel Andrade 7 Lima, Thainara Munhoz Alexandre de 8 O'Shea, Ryan E. 9 Pahlevan, Nima 10 Lamparelli, Marta C. | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JGSB 3 4 8JMKD3MGP5W/3C9JH39 | |||
| Group | 1 2 DIOTG-CGCT-INPE-MCTI-GOV-BR 3 4 DIOTG-CGCT-INPE-MCTI-GOV-BR 5 6 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 7 SER-SRE-DIPGR-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 University of Cambridge 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Mississippi State University (MSU) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 Instituto Nacional de Pesquisas Espaciais (INPE) 7 Instituto Nacional de Pesquisas Espaciais (INPE) 8 NASA Goddard Space Flight Center 9 NASA Goddard Space Flight Center 10 Environmental Company of the State of São Paulo (CETESB) | |||
| Author e-Mail Address | 1 fnb25@cam.ac.uk 2 claudio.barbosa@inpe.br 3 4 evlyn.novo@inpe.br | |||
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing | |||
| Volume | 204 | |||
| Pages | 378-396 | |||
| Secondary Mark | A1_GEOCIÊNCIAS A2_INTERDISCIPLINAR A2_CIÊNCIAS_AMBIENTAIS B1_ENGENHARIAS_IV B1_BIODIVERSIDADE C_CIÊNCIAS_AGRÁRIAS_I | |||
| History (UTC) | 2023-10-17 19:40:34 :: simone -> administrator :: 2023-10-17 19:40:36 :: administrator -> simone :: 2023 2023-10-17 19:41:54 :: simone -> administrator :: 2023 2023-12-18 23:44:47 :: administrator -> self-uploading-INPE-MCTI-GOV-BR :: 2023 2023-12-19 01:54:41 :: self-uploading-INPE-MCTI-GOV-BR -> administrator :: 2023 2024-01-02 17:16:49 :: administrator -> simone :: 2023 | |||
| 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 | C-Phycocyanin Cyanobacteria Inland water Remote sensing Urban reservoir Water quality | |||
| Abstract | Urban reservoirs are important for drinking water services and urban living. However, potentially toxic cyanobacteria blooms are frequently present due to human pollution and might threaten the urban water supply. Conveniently, cyanobacteria can be monitored by remote sensing-based approaches based on the spectral features of C-Phycocyanin (PC). Furthermore, methods leveraging Machine Learning Algorithms (MLA) for PC estimation from hyperspectral data have highlighted the potential to estimate PC more accurately - even at low concentrations. Since relatively few methodologies for PC retrieval in tropical environments have been developed or validated, this research evaluated PRISMA hyperspectral data processed with three MLA (Random Forest, Extreme Gradient Boost, and Support Vector Machines) to estimate PC concentrations in the Billings reservoir, Brazil. The same MLA were used to generate PC models using Wordview-3 and Landsat-8/OLI simulated data to assess the potential gain of using hyperspectral over multispectral data. A PRISMA image was processed with three atmospheric correction methods and validated with co-located in-situ data, where the best atmospherically corrected product was used to generate synthetic Landsat-8/OLI and Worldview-3 images. The PC models were calibrated and validated through Monte Carlo simulation using field radiometric and biological data (Chlorophyll-a, PC, and phytoplankton taxonomy) collected in eight field campaigns (N = 115). The PRISMA and the synthetic multispectral images were used for a second round of models validation using co-located PC measurements (match-up window ± 4 h). The global PC Mixture Density Network was also applied to the PRISMA data, and the estimates were compared with the other MLA. The results showed that the standard PRISMA surface reflectance product provided the best atmospheric correction (MAE < 20% for the 500700 nm bands), while ACOLITE and 6SV underperformed it from two to more than ten-fold. Cyanobacteria species were abundant in 96% of the taxonomical samples, even though relatively low PC concentrations were found (PC from 0 to 301.81 μg/L and median PC = 2.9 μg/L). The global Mixture Density Network sharply overestimated PC (MAE = 280% and Bias = 280%), potentially due to Billings reservoir's low PC:Chlorophyll-a ratio relative to the original training dataset. PRISMA/Random Forest (MAE = 45%) achieved the lowest error for orbital PC estimate, while Extreme Gradient Boost outperformed the other MLA using Worldview-3 (MAE = 49%) and Landsat-8 (MAE = 74%) synthetic imagery. Therefore, the results suggest hyperspectral and multispectral orbital data aligned with MLA are feasible for monitoring PC, even for waters containing low PC concentrations and reduced PC:Chlorophyll-a ratios. | |||
| Area | SRE | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Machine learning for... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > LabISA > Machine learning for... | |||
| Arrangement 3 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Machine learning for... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
| |||
| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | 1-s2.0-S0924271623002617-main.pdf | |||
| User Group | simone | |||
| Reader Group | administrator self-uploading-INPE-MCTI-GOV-BR simone | |||
| Visibility | shown | |||
| Archiving Policy | denypublisher denyfinaldraft24 | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/439EAFB 8JMKD3MGPCW/46KUATE | |||
| Citing Item List | sid.inpe.br/bibdigital/2022/04.03.22.23 2 sid.inpe.br/bibdigital/2020/09.18.00.06 2 sid.inpe.br/mtc-m21/2012/07.13.14.43.57 2 | |||
| Dissemination | WEBSCI; PORTALCAPES; COMPENDEX; SCOPUS. | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||