| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/45PLRRL | |||
| Repository | sid.inpe.br/mtc-m21d/2021/11.11.18.21 (restricted access) | |||
| Last Update | 2021:11.11.18.21.13 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2021/11.11.18.21.13 | |||
| Metadata Last Update | 2022:04.03.23.14.05 (UTC) administrator | |||
| DOI | 10.1016/j.isprsjprs.2021.10.009 | |||
| ISSN | 0924-2716 | |||
| Citation Key | MacielBarNovFloBeg:2021:WaClBr | |||
| Title | Water clarity in Brazilian water assessed using Sentinel-2 and machine learning methods  | |||
| Year | 2021 | |||
| Month | Dec. | |||
| Access Date | 2024, May 18 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 16583 KiB | |||
| 2. Context | ||||
| Author | 1 Maciel, Daniel Andrade 2 Barbosa, Cláudio Clemente Faria 3 Novo, Evlyn Márcia Leão de Moraes 4 Flores Júnior, Rogério 5 Begliomini, Felipe Nincao | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JGSB 3 8JMKD3MGP5W/3C9JH39 | |||
| Group | 1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 2 DIOTG-CGCT-INPE-MCTI-GOV-BR 3 DIOTG-CGCT-INPE-MCTI-GOV-BR 4 CAP-COMP-DIPGR-INPE-MCTI-GOV-BR 5 SER-SRE-DIPGR-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 damaciel_maciel@hotmail.com 2 claudio.barbosa@inpe.br 3 evlyn.leao@gmail.com 4 rogerio.floresjr@gmail.com 5 fnincao@hotmail.com | |||
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing | |||
| Volume | 182 | |||
| Pages | 134-152 | |||
| Secondary Mark | A1_GEOCIÊNCIAS A2_INTERDISCIPLINAR A2_CIÊNCIAS_AMBIENTAIS B1_ENGENHARIAS_IV B1_BIODIVERSIDADE C_CIÊNCIAS_AGRÁRIAS_I | |||
| History (UTC) | 2021-11-11 18:21:13 :: simone -> administrator :: 2021-11-11 18:21:15 :: administrator -> simone :: 2021 2021-11-11 18:21:23 :: simone -> administrator :: 2021 2022-04-03 23:14:05 :: 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 | Atmospheric correction Google earth engine Remote sensing Secchi disk depth Water quality Water transparency | |||
| Abstract | Secchi Disk Depth (Zsd) is one of the widely used water quality measurements. Controlled by variations in Optically Active Constituents, it is a key index of overall water quality. In-situ measurements of Zsd lacks spatiotemporal coverage which could be solved using remote sensing data, such as from the Sentinel-2/MSI. However, inland waters have highly variable optical properties, and that is still a challenge for the state-of-art algorithms of Zsd retrieval. One of the most promising approaches for dealing with this challenge is the use of Machine Learning methods. Moreover, predicting Zsd for large areas using high-resolution remote sensing imagery requires a high computational effort, which could be solved using Cloud-Computing platforms. Therefore, this study evaluates the use of Machine Learning (Random Forest, Extreme Gradient Boosting, and Support Vector Machines) and Semi-Analytical algorithms (SAA) for Zsd retrieval focused on Sentinel-2 imageries available in the Google Earth Engine platform to assess the clarity of the Brazilian inland waters. Machine Learning methods were calibrated and validated using a comprehensive dataset (N = 1492) collected in the last 20 years in Brazil. The results were compared with semi-analytical approaches. After evaluation with in-situ data, the best algorithm was implemented in the Google Earth Engine platform to generate Zsd maps. The calibration with in-situ data demonstrated that the Machine Learning methods outperform the SAA, with the Random Forest presenting the best results (errors lower than 22%). The results showed that when SAA were applied to the environment in which they were calibrated, the results were closer to that of machine learning methods, indicating that SAA could also be used for Zsd retrieval. The application of Random Forest to the Sentinel-2 atmospherically corrected imagery had errors of 28%, demonstrating the feasibility of the algorithm and atmospheric correction methods for predicting Zsd. | |||
| Area | SRE | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > Water clarity in... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Water clarity in... | |||
| Arrangement 3 | urlib.net > BDMCI > Fonds > LabISA > Water clarity in... | |||
| Arrangement 4 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Water clarity in... | |||
| Arrangement 5 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Water clarity in... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
| |||
| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | maciel_water.pdf | |||
| User Group | simone | |||
| Reader Group | administrator 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/3F2PHGS 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/439EAFB 8JMKD3MGPCW/46KUATE 8JMKD3MGPCW/46KUES5 | |||
| Citing Item List | sid.inpe.br/bibdigital/2013/10.12.22.16 10 sid.inpe.br/bibdigital/2020/09.18.00.06 6 sid.inpe.br/mtc-m21/2012/07.13.14.43.57 3 | |||
| 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 label 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 | ||||