Browsing by Author "Ruiz Verdú, Antonio"

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    GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality
    (Nature, 2023-02) Lehmann, Moritz K.; Gurlin, Daniela; Pahlevan, Nima; Alikas, Krista; Conroy, Ted; Anstee, Janet; 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; Rahaghi, Abolfazl Irani; 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.; Nguyễn, Hà; Novo, Evlyn M. L. M.; Novoa, Stéfani; Odermatt, Daniel; O'Donnell, David M.; Olmanson, Leif G.; Ondrusek, Michael; Oppelt, Natascha; Ouillon, Sylvain; Filho, Waterloo Pereira; 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; Vandermeulen, Ryan A.; Vantrepotte, Vincent; Wernand, Marcel R.; Werther, Mortimer; Young, Kyana; Yue, Linwei; Earth and Environmental Sciences, School of Science
    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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    Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment
    (IEEE, 2022-10) Zolfaghari, Kiana; Pahlevan, Nima; Binding, Caren; Gurlin, Daniela; Simis, Stefan G. H.; Ruiz Verdú, Antonio; Li, Lin; Crawford, Christopher J.; VanderWoude, Andrea; Errera, Reagan; Zastepa, Arthur; Duguay, Claude R.; Earth Sciences, School of Science
    Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large ( N=905 ) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( Rrs ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to Rrs spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ∼73 % and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.