| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/466DE92 | |||
| Repository | sid.inpe.br/mtc-m21d/2022/01.10.13.57 (restricted access) | |||
| Last Update | 2022:01.10.13.57.37 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2022/01.10.13.57.37 | |||
| Metadata Last Update | 2024:04.17.08.11.58 (UTC) administrator | |||
| DOI | 10.1016/j.rse.2021.112860 | |||
| ISSN | 0034-4257 | |||
| Citation Key | PahlevanSAABBBCGGFJKLLMHOPSVVR:2022:SiReSe | |||
| Title | Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3  | |||
| Year | 2022 | |||
| Month | Mar. | |||
| Access Date | 2024, May 18 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 13703 KiB | |||
| 2. Context | ||||
| Author | 1 Pahlevan, Nima 2 Smith, Brandon 3 Alikas, Krista 4 Anstee, Janet 5 Barbosa, Cláudio Clemente Faria 6 Binding, Caren 7 Bresciani, Mariano 8 Cremelha, Bruno 9 Giardino, Claudia 10 Gurlin, Daniela 11 Fernandez, Virginia 12 Jamet, Cédric 13 Kangro, Kersti 14 Lehmann, Moritz K. 15 Loisel, Hubert 16 Matshushita, Bunkei 17 Hà, Nguyên 18 Olmanson, Leif 19 Potvin, Geneviève 20 Simis, Stefan G. H. 21 VanderWoude, Andrea 22 Vantrepotte, Vincent 23 Ruiz-Verdù, Antonio | |||
| Resume Identifier | 1 2 3 4 5 8JMKD3MGP5W/3C9JGSB | |||
| Group | 1 2 3 4 5 DIOTG-CGCT-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 NASA Goddard Space Flight Center 2 NASA Goddard Space Flight Center 3 University of Tartu 4 Commonwealth Scientific and Industrial Research Organization (CSIRO) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 Environment and Climate Change Canada 7 National Research Council of Italy 8 University of Sherbrooke 9 National Research Council of Italy 10 Wisconsin Department of Natural Resources 11 Universidad la Republica 12 Univ. Littoral Côte d’Opale 13 University of Tartu 14 Xerra Earth Observation Institute and the University of Waikato 15 Univ. Littoral Côte d’Opale 16 University of Tsukuba 17 Vietnam National University 18 University of Minnesota 19 University of Sherbrooke 20 Plymouth Marine Laboratory 21 National Oceanic and Atmospheric Administration 22 Univ. Littoral Côte d’Opale 23 University of Valencia | |||
| Author e-Mail Address | 1 nima.pahlevan@nasa.gov 2 3 4 5 claudio.barbosa@inpe.br | |||
| Journal | Remote Sensing of Environment | |||
| Volume | 270 | |||
| Pages | e112860 | |||
| Secondary Mark | A1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_I A1_CIÊNCIAS_BIOLÓGICAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_BIODIVERSIDADE | |||
| History (UTC) | 2022-01-10 13:57:37 :: simone -> administrator :: 2022-01-10 13:57:37 :: administrator -> simone :: 2022 2022-01-10 14:02:01 :: simone -> administrator :: 2022 2022-07-08 16:51:11 :: administrator -> simone :: 2022 2022-12-20 13:32:11 :: simone -> administrator :: 2022 2024-04-17 08:11:58 :: administrator -> simone :: 2022 | |||
| 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 | Inland and coastal waters Machine learning MSI OLCI OLI Water quality | |||
| Abstract | Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model. | |||
| Area | SRE | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > LabISA > Simultaneous retrieval of... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Simultaneous retrieval of... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | pahlevan_2022.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | denypublisher allowfinaldraft24 | |||
| 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 2 sid.inpe.br/mtc-m21/2012/07.13.14.43.57 2 sid.inpe.br/bibdigital/2022/04.03.22.23 1 | |||
| Dissemination | WEBSCI; PORTALCAPES; MGA; 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 | ||||