GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

Moritz K. Lehmann; Daniela Gurlin; Nima Pahlevan; Krista Alikas; Janet Anstee; Sundarabalan V. Balasubramanian; Cláudio C.F. Barbosa; Caren Binding; Astrid Bracher; Mariano Bresciani; Ashley Burtner; Zhigang Cao; Arnold G. Dekker; Courtney Di Vittorio; Nathan Drayson; Reagan M. Errera; Virginia Fernandez; Dariusz Ficek; Cédric G. Fichot; Peter Gege; Claudia Giardino; Anatoly A. Gitelson; Steven R. Greb; Hayden Henderson; Hiroto Higa; Abolfazl Irani Rahaghi; Cédric Jamet; Dalin Jiang; Thomas Jordan; Kersti Kangro; Jeremy A. Kravitz; Arne S. Kristoffersen; Raphael Kudela; Lin Li; Martin Ligi; Hubert Loisel; Steven Lohrenz; Ronghua Ma; Daniel A. Maciel; Tim J. Malthus; Bunkei Matsushita; Mark Matthews; Camille Minaudo; Deepak R. Mishra; Sachidananda Mishra; Tim Moore; Wesley J. Moses; Hà Nguyễn; Evlyn M.L.M. Novo; Stéfani Novoa; Daniel Odermatt; David M. O’Donnell; Leif G. Olmanson; Michael Ondrusek; Natascha Oppelt; Sylvain Ouillon; Waterloo Pereira Filho; Stefan Plattner; Antonio Ruiz Verdú; Salem I. Salem; John F. Schalles; Stefan G.H. Simis; Eko Siswanto; Brandon Smith; Ian Somlai-Schweiger; Mariana A. Soppa; Evangelos Spyrakos; Elinor Tessin; Hendrik J. van der Woerd; Andrea Vander Woude; Ryan A. Vandermeulen; Vincent Vantrepotte; Marcel R. Wernand; Mortimer Werther; Kyana Young; Linwei Yue

doi:10.1038/s41597-023-01973-y

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

Moritz K. Lehmann, Daniela Gurlin, Nima Pahlevan, Krista Alikas, Janet Anstee, Sundarabalan V. Balasubramanian, Cláudio C.F. Barbosa, Caren Binding, Astrid Bracher, Mariano Bresciani, Ashley Burtner, Zhigang Cao, Arnold G. Dekker, Courtney Di Vittorio, Nathan Drayson, Reagan M. Errera, Virginia Fernandez, Dariusz Ficek, Cédric G. Fichot, Peter GegeClaudia Giardino, Anatoly A. Gitelson, Steven R. Greb, Hayden Henderson, Hiroto Higa, Abolfazl Irani Rahaghi, Cédric Jamet, Dalin Jiang, Thomas Jordan, Kersti Kangro, Jeremy A. Kravitz, Arne S. Kristoffersen, Raphael Kudela, Lin Li, Martin Ligi, Hubert Loisel, Steven Lohrenz, Ronghua Ma, Daniel A. Maciel, Tim J. Malthus, Bunkei Matsushita, Mark Matthews, Camille Minaudo, Deepak R. Mishra, Sachidananda Mishra, Tim Moore, Wesley J. Moses, Hà Nguyễn, Evlyn M.L.M. Novo, Stéfani Novoa, Daniel Odermatt, David M. O’Donnell, Leif G. Olmanson, Michael Ondrusek, Natascha Oppelt, Sylvain Ouillon, Waterloo Pereira Filho, Stefan Plattner, Antonio Ruiz Verdú, Salem I. Salem, John F. Schalles, Stefan G.H. Simis, Eko Siswanto, Brandon Smith, Ian Somlai-Schweiger, Mariana A. Soppa, Evangelos Spyrakos, Elinor Tessin, Hendrik J. van der Woerd, Andrea Vander Woude, Ryan A. Vandermeulen, Vincent Vantrepotte, Marcel R. Wernand, Mortimer Werther, Kyana Young, Linwei Yue

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Abstract

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Original language	English (US)
Article number	100
Journal	Scientific Data
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41597-023-01973-y
State	Published - Dec 2023

Bibliographical note

Funding Information:
Funding sources include: Estonian Ministry of Education and Research; European Commission FP7, H2020, FP7-ENV-2007-1-226224; Estonian Research Council; Helmholtz Infrastructure Initiative FRAM; BMBF 03G0218A; New Zealand Ministry for Business, Innovation & Employment grants UOWX1503, UOWX1802, KENTR1601, NASA ROSES grants 80HQTR19C0015, 80NSSC 21K0499, 80NSSC22K1389, and USGS Landsat Science Team Award 140G0118C0011, Vietnam National Foundation for Science and Technology Development (NAFOSTED), grant number 105.08-2019.329, Federal Ministry for Economic Affairs and Energy, Germany, Award: LAKESAT 50EE1340, EnMAP CalVal 50EE1923, TypSynSat 50EE1915.

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© 2023, The Author(s).

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Lehmann, M. K., Gurlin, D., Pahlevan, N., Alikas, K., Anstee, J., Balasubramanian, S. V., Barbosa, C. C. F., Binding, C., Bracher, A., Bresciani, M., Burtner, A., Cao, Z., Dekker, A. G., Di Vittorio, C., Drayson, N., Errera, R. M., Fernandez, V., Ficek, D., Fichot, C. G., ... Yue, L. (2023). GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality. Scientific Data, 10(1), Article 100. https://doi.org/10.1038/s41597-023-01973-y

Lehmann, MK, Gurlin, D, Pahlevan, N, Alikas, K, Anstee, J, Balasubramanian, SV, Barbosa, CCF, Binding, C, Bracher, A, Bresciani, M, Burtner, A, Cao, Z, Dekker, AG, Di Vittorio, C, Drayson, N, Errera, RM, Fernandez, V, Ficek, D, Fichot, CG, Gege, P, Giardino, C, Gitelson, AA, Greb, SR, Henderson, H, Higa, H, Rahaghi, AI, Jamet, C, Jiang, D, Jordan, T, Kangro, K, Kravitz, JA, Kristoffersen, AS, Kudela, R, Li, L, Ligi, M, Loisel, H, Lohrenz, S, Ma, R, Maciel, DA, Malthus, TJ, Matsushita, B, Matthews, M, Minaudo, C, Mishra, DR, Mishra, S, Moore, T, Moses, WJ, Nguyễn, H, Novo, EMLM, Novoa, S, Odermatt, D, O’Donnell, DM, Olmanson, LG, Ondrusek, M, Oppelt, N, Ouillon, S, Pereira Filho, W, Plattner, S, Verdú, AR, Salem, SI, Schalles, JF, Simis, SGH, Siswanto, E, Smith, B, Somlai-Schweiger, I, Soppa, MA, Spyrakos, E, Tessin, E, van der Woerd, HJ, Vander Woude, A, Vandermeulen, RA, Vantrepotte, V, Wernand, MR, Werther, M, Young, K & Yue, L 2023, 'GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality', Scientific Data, vol. 10, no. 1, 100. https://doi.org/10.1038/s41597-023-01973-y

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title = "GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality",

abstract = "The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.",

author = "Lehmann, {Moritz K.} and Daniela Gurlin and Nima Pahlevan and Krista Alikas and Janet Anstee and Balasubramanian, {Sundarabalan V.} and Barbosa, {Cl{\'a}udio C.F.} and Caren Binding and Astrid Bracher and Mariano Bresciani and Ashley Burtner and Zhigang Cao and Dekker, {Arnold G.} and {Di Vittorio}, Courtney and Nathan Drayson and Errera, {Reagan M.} and Virginia Fernandez and Dariusz Ficek and Fichot, {C{\'e}dric G.} and Peter Gege and Claudia Giardino and Gitelson, {Anatoly A.} and Greb, {Steven R.} and Hayden Henderson and Hiroto Higa and Rahaghi, {Abolfazl Irani} and C{\'e}dric Jamet and Dalin Jiang and Thomas Jordan and Kersti Kangro and Kravitz, {Jeremy A.} and Kristoffersen, {Arne S.} and Raphael Kudela and Lin Li and Martin Ligi and Hubert Loisel and Steven Lohrenz and Ronghua Ma and Maciel, {Daniel A.} and Malthus, {Tim J.} and Bunkei Matsushita and Mark Matthews and Camille Minaudo and Mishra, {Deepak R.} and Sachidananda Mishra and Tim Moore and Moses, {Wesley J.} and H{\`a} Nguyễn and Novo, {Evlyn M.L.M.} and St{\'e}fani Novoa and Daniel Odermatt and O{\textquoteright}Donnell, {David M.} and Olmanson, {Leif G.} and Michael Ondrusek and Natascha Oppelt and Sylvain Ouillon and {Pereira Filho}, Waterloo and Stefan Plattner and Verd{\'u}, {Antonio Ruiz} and Salem, {Salem I.} and Schalles, {John F.} and Simis, {Stefan G.H.} and Eko Siswanto and Brandon Smith and Ian Somlai-Schweiger and Soppa, {Mariana A.} and Evangelos Spyrakos and Elinor Tessin and {van der Woerd}, {Hendrik J.} and {Vander Woude}, Andrea and Vandermeulen, {Ryan A.} and Vincent Vantrepotte and Wernand, {Marcel R.} and Mortimer Werther and Kyana Young and Linwei Yue",

note = "Funding Information: Funding sources include: Estonian Ministry of Education and Research; European Commission FP7, H2020, FP7-ENV-2007-1-226224; Estonian Research Council; Helmholtz Infrastructure Initiative FRAM; BMBF 03G0218A; New Zealand Ministry for Business, Innovation & Employment grants UOWX1503, UOWX1802, KENTR1601, NASA ROSES grants 80HQTR19C0015, 80NSSC 21K0499, 80NSSC22K1389, and USGS Landsat Science Team Award 140G0118C0011, Vietnam National Foundation for Science and Technology Development (NAFOSTED), grant number 105.08-2019.329, Federal Ministry for Economic Affairs and Energy, Germany, Award: LAKESAT 50EE1340, EnMAP CalVal 50EE1923, TypSynSat 50EE1915. Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

month = dec,

doi = "10.1038/s41597-023-01973-y",

language = "English (US)",

volume = "10",

journal = "Scientific Data",

issn = "2052-4463",

publisher = "Nature Publishing Group",

number = "1",

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TY - JOUR

T1 - GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

AU - Lehmann, Moritz K.

AU - Gurlin, Daniela

AU - Pahlevan, Nima

AU - Alikas, Krista

AU - Anstee, Janet

AU - Balasubramanian, Sundarabalan V.

AU - Barbosa, Cláudio C.F.

AU - Binding, Caren

AU - Bracher, Astrid

AU - Bresciani, Mariano

AU - Burtner, Ashley

AU - Cao, Zhigang

AU - Dekker, Arnold G.

AU - Di Vittorio, Courtney

AU - Drayson, Nathan

AU - Errera, Reagan M.

AU - Fernandez, Virginia

AU - Ficek, Dariusz

AU - Fichot, Cédric G.

AU - Gege, Peter

AU - Giardino, Claudia

AU - Gitelson, Anatoly A.

AU - Greb, Steven R.

AU - Henderson, Hayden

AU - Higa, Hiroto

AU - Rahaghi, Abolfazl Irani

AU - Jamet, Cédric

AU - Jiang, Dalin

AU - Jordan, Thomas

AU - Kangro, Kersti

AU - Kravitz, Jeremy A.

AU - Kristoffersen, Arne S.

AU - Kudela, Raphael

AU - Li, Lin

AU - Ligi, Martin

AU - Loisel, Hubert

AU - Lohrenz, Steven

AU - Ma, Ronghua

AU - Maciel, Daniel A.

AU - Malthus, Tim J.

AU - Matsushita, Bunkei

AU - Matthews, Mark

AU - Minaudo, Camille

AU - Mishra, Deepak R.

AU - Mishra, Sachidananda

AU - Moore, Tim

AU - Moses, Wesley J.

AU - Nguyễn, Hà

AU - Novo, Evlyn M.L.M.

AU - Novoa, Stéfani

AU - Odermatt, Daniel

AU - O’Donnell, David M.

AU - Olmanson, Leif G.

AU - Ondrusek, Michael

AU - Oppelt, Natascha

AU - Ouillon, Sylvain

AU - Pereira Filho, Waterloo

AU - Plattner, Stefan

AU - Verdú, Antonio Ruiz

AU - Salem, Salem I.

AU - Schalles, John F.

AU - Simis, Stefan G.H.

AU - Siswanto, Eko

AU - Smith, Brandon

AU - Somlai-Schweiger, Ian

AU - Soppa, Mariana A.

AU - Spyrakos, Evangelos

AU - Tessin, Elinor

AU - van der Woerd, Hendrik J.

AU - Vander Woude, Andrea

AU - Vandermeulen, Ryan A.

AU - Vantrepotte, Vincent

AU - Wernand, Marcel R.

AU - Werther, Mortimer

AU - Young, Kyana

AU - Yue, Linwei

N1 - Funding Information: Funding sources include: Estonian Ministry of Education and Research; European Commission FP7, H2020, FP7-ENV-2007-1-226224; Estonian Research Council; Helmholtz Infrastructure Initiative FRAM; BMBF 03G0218A; New Zealand Ministry for Business, Innovation & Employment grants UOWX1503, UOWX1802, KENTR1601, NASA ROSES grants 80HQTR19C0015, 80NSSC 21K0499, 80NSSC22K1389, and USGS Landsat Science Team Award 140G0118C0011, Vietnam National Foundation for Science and Technology Development (NAFOSTED), grant number 105.08-2019.329, Federal Ministry for Economic Affairs and Energy, Germany, Award: LAKESAT 50EE1340, EnMAP CalVal 50EE1923, TypSynSat 50EE1915. Publisher Copyright: © 2023, The Author(s).

PY - 2023/12

Y1 - 2023/12

N2 - The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

AB - The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

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DO - 10.1038/s41597-023-01973-y

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SN - 2052-4463

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JO - Scientific Data

JF - Scientific Data

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ER -

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

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