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Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery
by
Fabbretto, Alice
, Bresciani, Mariano
, Giardino, Claudia
, Pinardi, Monica
, Mangano, Salvatore
, Padula, Rosalba
, Alikas, Krista
, Pellegrino, Andrea
in
Algorithms
/ Aquatic ecosystems
/ Aquatic plants
/ Artificial satellites in remote sensing
/ Biomass
/ chlorophyll
/ Climate change
/ Creeks & streams
/ Cyanobacteria
/ DESIS
/ EnMAP
/ Environmental monitoring
/ hyperspectral imagery
/ Hyperspectral imaging
/ In situ measurement
/ Italy
/ Lakes
/ Littoral environments
/ littoral zone
/ Machine learning
/ Macrophytes
/ Methods
/ Phycocyanin
/ phytoplankton
/ PRISMA
/ radiometry
/ rain
/ Rainfall
/ reflectance
/ Remote sensing
/ satellites
/ shallow lake
/ Spectroscopy
/ Spectrum analysis
/ summer
/ Suspended matter
/ Tourism
/ value added
/ Vegetation
/ Water quality
/ Water quality assessments
/ water reflectance
/ Water resources management
/ Water shortages
/ Water utilities
2024
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Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery
by
Fabbretto, Alice
, Bresciani, Mariano
, Giardino, Claudia
, Pinardi, Monica
, Mangano, Salvatore
, Padula, Rosalba
, Alikas, Krista
, Pellegrino, Andrea
in
Algorithms
/ Aquatic ecosystems
/ Aquatic plants
/ Artificial satellites in remote sensing
/ Biomass
/ chlorophyll
/ Climate change
/ Creeks & streams
/ Cyanobacteria
/ DESIS
/ EnMAP
/ Environmental monitoring
/ hyperspectral imagery
/ Hyperspectral imaging
/ In situ measurement
/ Italy
/ Lakes
/ Littoral environments
/ littoral zone
/ Machine learning
/ Macrophytes
/ Methods
/ Phycocyanin
/ phytoplankton
/ PRISMA
/ radiometry
/ rain
/ Rainfall
/ reflectance
/ Remote sensing
/ satellites
/ shallow lake
/ Spectroscopy
/ Spectrum analysis
/ summer
/ Suspended matter
/ Tourism
/ value added
/ Vegetation
/ Water quality
/ Water quality assessments
/ water reflectance
/ Water resources management
/ Water shortages
/ Water utilities
2024
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Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery
by
Fabbretto, Alice
, Bresciani, Mariano
, Giardino, Claudia
, Pinardi, Monica
, Mangano, Salvatore
, Padula, Rosalba
, Alikas, Krista
, Pellegrino, Andrea
in
Algorithms
/ Aquatic ecosystems
/ Aquatic plants
/ Artificial satellites in remote sensing
/ Biomass
/ chlorophyll
/ Climate change
/ Creeks & streams
/ Cyanobacteria
/ DESIS
/ EnMAP
/ Environmental monitoring
/ hyperspectral imagery
/ Hyperspectral imaging
/ In situ measurement
/ Italy
/ Lakes
/ Littoral environments
/ littoral zone
/ Machine learning
/ Macrophytes
/ Methods
/ Phycocyanin
/ phytoplankton
/ PRISMA
/ radiometry
/ rain
/ Rainfall
/ reflectance
/ Remote sensing
/ satellites
/ shallow lake
/ Spectroscopy
/ Spectrum analysis
/ summer
/ Suspended matter
/ Tourism
/ value added
/ Vegetation
/ Water quality
/ Water quality assessments
/ water reflectance
/ Water resources management
/ Water shortages
/ Water utilities
2024
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Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery
Journal Article
Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery
2024
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Overview
This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 > 0.9, a Spectral Angle < 10° and water quality mapping errors < 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.
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