0000000000465130

AUTHOR

Mariano Bresciani

showing 2 related works from this author

Optical types of inland and coastal waters

2017

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 condi…

Earth observationBiogeochemical cycle010504 meteorology & atmospheric sciencesAquatic ecosystem0211 other engineering and technologiesHyperspectral imagingBiosphere02 engineering and technology15. Life on landAquatic ScienceOceanography01 natural sciences6. Clean water13. Climate actionEnvironmental scienceEcosystem14. Life underwaterWater qualityCluster analysis021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingLimnology and Oceanography
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Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery

2021

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 (?R) 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 concentrat…

Biomass (ecology)Aquatic remote sensingcyanoHABsHICOMultispectral imageAtmospheric correctionPhycocyaninSoil ScienceHyperspectral imagingGeologyPRISMASpectral bandsCyanobacteriacyanobacteria ; phycocyanin ; machine learning ; mixture density network ; aquatic remote sensing ; cyanoHABs ; HICO ; PRISMAMachine learningMixture density networkEnvironmental scienceRadiometrySatelliteNoise (video)Computers in Earth SciencesRemote sensingRemote Sensing of Environment
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