Maciel, Daniel A; Pahlevan, Nima; Barbosa, Cláudio C F (2023): Water quality and remote sensing reflectance data for global inland, coastal and ocean waters [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.961720
Always quote citation above when using data! You can download the citation in several formats below.
Abstract:
This dataset represents the remote sensing and Secchi Disk depth data that were used to calibrate and validate a global Mixture Density Network algorithm. Remote sensing reflectance, water quality, and satellite data associated with this dataset were compiled from several providers and institutions in global waters between 1990 and 2022. Data were compiled to support water quality remote sensing applications and the development of a Secchi Disk Depth algorithm. Information about the methods used in each dataset and how they were collected is available in the referred publications.
Keyword(s):
Supplement to:
Maciel, Daniel A; Pahlevan, Nima; Barbosa, Cláudio C F; Martins, Vitor S; Smith, Brandon; O'Shea, Ryan E; Balasubramanian, Sundarabalan V; Saranathan, Arun M; Novo, Evlyn M L M (2023): Towards global long-term water transparency products from the Landsat archive. Remote Sensing of Environment, 229, 113889, https://doi.org/10.1016/j.rse.2023.113889
Related to:
Barbosa, Cláudio C F; Novo, E; Ferreira, R; Carvalho, L; Cairo, C; Lopes, F; Stech, J; Alcantara, E (2015): Brazilian inland water bio-optical dataset to support carbon budget studies in reservoirs as well as anthropogenic impacts in Amazon floodplain lakes: Preliminary results. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3, 1439-1446, https://doi.org/10.5194/isprsarchives-XL-7-W3-1439-2015
Lehmann, Moritz K; Gurlin, Daniela; Pahlevan, Nima; Alikas, Krista; Anstee, Janet M; Balasubramanian, Sundarabalan V; Barbosa, Cláudio C F; Binding, Caren; Bracher, Astrid; Bresciani, Mariano; Burtner, Ashley; Cao, Zhigang; Dekker, Arnold G; Di Vittorio, Courtney; Drayson, Nathan; Errera, Reagan M; Fernandez, Virginia; Ficek, Dariusz; Fichot, Cédric G; Gege, Peter; Giardino, Claudia; Gitelson, Anatoly A; Greb, Steven R; Henderson, Hayden; Higa, Hiroto; Irani Rahaghi, Abolfazl; Jamet, Cédric; Jiang, Dalin; Jordan, Thomas; Kangro, Kersti; Kravitz, Jeremy A; Kristoffersen, Arne S; Kudela, Raphael; Li, Lin; Ligi, Martin; Loisel, Hubert; Lohrenz, Steven; Ma, Ronghua; Maciel, Daniel A; Malthus, Tim J; Matsushita, Bunkei; Matthews, Mark; Minaudo, Camille; Mishra, Deepak R; Mishra, Sachidananda; Moore, Tim; Moses, Wesley J; Nguyen, Hà; Novo, Evlyn M L M; Novoa, Stéfani; Odermatt, Daniel; O'Donnell, David M; Olmanson, Leif G; Ondrusek, Michael; Oppelt, Natascha; Ouillon, Sylvain; Pereira Filho, Waterloo; Plattner, Stefan; Ruiz Verdú, Antonio; Salem, Salem I; Schalles, John F; Simis, Stefan G H; Siswanto, Eko; Smith, Brandon; Somlai-Schweiger, Ian; Soppa, Mariana A; Spyrakos, Evangelos; Tessin, Elinor; van der Woerd, Hendrik J; Vander Woude, Andrea J; Vandermeulen, Ryan A; Vantrepotte, Vincent; Wernand, Marcel Robert; Werther, Mortimer; Young, Kyana; Yue, Linwei (2023): GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality. Scientific Data, 10(1), 100, https://doi.org/10.1038/s41597-023-01973-y
Maciel, Daniel A; Barbosa, Cláudio C F; Novo, Evlyn M L M; Flores Júnior, Rogério; Begliomini, Felipe Nincao (2021): Water clarity in Brazilian water assessed using Sentinel-2 and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing, 182, 134-152, https://doi.org/10.1016/j.isprsjprs.2021.10.009
Pahlevan, Nima; Smith, Brandon; Alikas, Krista; Anstee, Janet M; Barbosa, Cláudio C F; Binding, Caren; Bresciani, Mariano; Cremella, Bruno; Giardino, Claudia; Gurlin, Daniela; Fernandez, Virginia; Jamet, Cédric; Kangro, Kersti; Lehmann, Moritz K; Loisel, Hubert; Matsushita, Bunkei; Hà, Nguyên; Olmanson, Leif G; Potvin, Geneviève; Simis, Stefan G H; Vanderwoude, Andrea J; Vantrepotte, Vincent; Ruiz Verdú, Antonio (2022): Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment, 270, 112860, https://doi.org/10.1016/j.rse.2021.112860
Wang, Shenglei; Lee, Zhongping; Shang, Shaoling; Li, Junsheng; Zhang, Bing; Lin, Gong (2019): Deriving inherent optical properties from classical water color measurements: Forel-Ule index and Secchi disk depth. Optics Express, 27(5), 7642, https://doi.org/10.1364/OE.27.007642
Comment:
Files in the Simulated Dataset.zip folder (rrs_tm_v3.xlsx, rrs_etm_v3.xlsx, rrs_oli_v3.xlsx, and rrs_hyper_v3.xlsx) represent the in situ datasets. The first three sheets are the simulated Rrs for the specific sensor (e.g., TM, ETM+, or OLI) and the last one (rrs_hyper_v3.xlsx) is hyperspectral Rrs. Band simulation was performed using the bandSimulation R package available here: https://github.com/dmaciel123/BandSimulation. Detailed metadata for these files are available in the simulated dataset metadata.XLSX file.
For the satellite matchups (ACOLITE_Matchups.ZIP), the files tm_acolite_maciel_v3.xlsx, etm_acolite_maciel_v3.xlsx, and oli_acolite_maciel_v3.xlsx are the matchups between in situ measured Zsd and Landsat data for TM, ETM+, and OLI, respectively. Detailed metadata description for this dataset is available in acolite metadata.xlsx.
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | File content | Content | Maciel, Daniel A | |||
| 2 | Binary Object | Binary | Maciel, Daniel A | |||
| 3 | Binary Object (Media Type) | Binary (Type) | Maciel, Daniel A | |||
| 4 | Binary Object (File Size) | Binary (Size) | Bytes | Maciel, Daniel A |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Status:
Curation Level: Basic curation (CurationLevelB)
Size:
4 data points