Browsing by Author "Bresciani, Mariano"

Now showing 1 - 4 of 4
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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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    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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    Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters
    (Elsevier, 2021-02) Pahlevan, Nima; Smith, Brandon; Binding, Caren; Gurlin, Daniela; Li, Lin; Bresciani, Mariano; Giardino, Claudia; Earth Sciences, School of Science
    Following more than two decades of research and developments made possible through various proof-of-concept hyperspectral remote sensing missions, it has been anticipated that hyperspectral imaging would enhance the accuracy of remotely sensed in-water products. This study investigates such expected improvements and demonstrates the utility of hyperspectral radiometric measurements for the retrieval of near-surface phytoplankton properties1, i.e., phytoplankton absorption spectra (aph) and biomass evaluated through examining the concentration of chlorophyll-a (Chla). Using hyperspectral data (409–800 nm at ~5 nm resolution) and a class of neural networks known as Mixture Density Networks (MDN) (Pahlevan et al., 2020), we show that the median error in aph retrievals is reduced two-to-three times (N = 722) compared to that from heritage ocean color algorithms. The median error associated with our aph retrieval across all the visible bands varies between 20 and 30%. Similarly, Chla retrievals exhibit significant improvements (i.e., more than two times; N = 1902), with respect to existing algorithms that rely on select spectral bands. Using an independent matchup dataset acquired near-concurrently with the acquisition of the Hyperspectral Imager for the Coastal Ocean (HICO) images, the models are found to perform well, but at reduced levels due to uncertainties in the atmospheric correction. The mapped spatial distribution of Chla maps and aph spectra for selected HICO swaths further solidify MDNs as promising machine-learning models that have the potential to generate highly accurate aquatic remote sensing products in inland and coastal waters. For aph retrieval to improve further, two immediate research avenues are recommended: a) the network architecture requires additional optimization to enable a simultaneous retrieval of multiple in-water parameters (e.g., aph, Chla, absorption by colored dissolved organic matter), and b) the training dataset should be extended to enhance model generalizability. This feasibility analysis using MDNs provides strong evidence that high-quality, global hyperspectral data will open new pathways toward a better understanding of biodiversity in aquatic ecosystems.
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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.