Browsing by Author "Simis, Stefan G. H."

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    Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery
    (Elsevier, 2021-12) O'Shea, Ryan E.; Pahlevan, Nima; Smith, Brandon; Bresciani, Mariano; Egerton, Todd; Giardino, Claudia; Li, Lin; Moore, Tim; Ruiz-Verdu, Antonio; Ruberg, Steve; Simis, Stefan G. H.; Stumpf, Richard; Vaičiūtė, Diana; Earth Sciences, School of Science
    Retrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆Rrs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrations (Chla), PCs, and remote sensing reflectance (Rrs) measurements to estimate PC from all relevant spectral bands. The performance of the developed model is demonstrated via PC maps produced from select images of the Hyperspectral Imager for the Coastal Ocean (HICO) and Italian Space Agency's PRecursore IperSpettrale della Missione Applicativa (PRISMA) using a matchup dataset. As input to the MDN, we incorporate a combination of widely used band ratios (BRs) and line heights (LHs) taken from existing multispectral algorithms, that have been proven for both Chla and PC estimation, as well as novel BRs and LHs to increase the overall cyanobacteria biomass estimation accuracy and reduce the sensitivity to ∆Rrs. When trained on a random half of the dataset, the MDN achieves uncertainties of 44.3%, which is less than half of the uncertainties of all viable optimized multispectral PC algorithms. The MDN is notably better than multispectral algorithms at preventing overestimation on low (<10 mg m−3) PC. Visibly, HICO and PRISMA PC maps show the wider dynamic range that can be represented by the MDN. The available in situ and satellite-derived Rrs matchups and measured in situ PC demonstrate the robustness of the MDN for estimating low (<10 mg m−3) PC and the reduced impact of ∆Rrs on medium-to-high in situ PC (>10 mg m−3). According to our extensive assessments, the developed model is anticipated to enable practical PC products from PRISMA and HICO, therefore the model is promising for planned hyperspectral missions, such as the Plankton Aerosol and Cloud Ecosystem (PACE). This advancement will enhance the complementary roles of hyperspectral radiometry from satellite and low-altitude platforms for quantifying and monitoring cyanobacteria harmful algal blooms at both large and local spatial scales.
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    Errata to “Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment”
    (IEEE, 2023-08) Zolfaghari, Kiana; Pahlevan, Nima; Binding, Caren; Gurlin, Daniela; Simis, Stefan G. H.; Verdú, Antonio Ruiz; Li, Lin; Crawford, Christopher J.; VanderWoude, Andrea; Errera, Reagan; Zastepa, Arthur; Duguay, Claude R.; Earth and Environmental Sciences, School of Science
    In the above article [1], the references in the following paragraph should be corrected as shown below. This excerpt appears in the first column on page 4 of the above article.
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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.
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    Optical types of inland and coastal waters
    (Wiley, 2018-03-01) Spyrakos, Evangelos; O'Donnell, Ruth; Hunter, Peter D.; Miller, Claire; Scott, Marian; Simis, Stefan G. H.; Neil, Claire; Barbosa, Claudio C. F.; Binding, Caren E.; Bradt, Shane; Bresciani, Mariano; Dall'Olmo, Giorgio; Giardino, Claudia; Gitelson, Anatoly A.; Kutser, Tiit; Li, Lin; Matsushita, Bunkei; Martinez‐Vicente, Victor; Matthews, Mark W.; Ogashawara, Igor; Ruiz‐Verdú, Antonio; Schalles, John F.; Tebbs, Emma; Zhang, Yunlin; Tyler, Andrew N.; Earth Sciences, School of Science
    Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.