Future research priorities that improve the exploitation of airborne imagery for
water quality purposes might include: (1) a need to investigate the improvements
gained from the synergistic use of different sensors in shallow waters (e.g. the use
of aerial LiDAR bathymetry adds data on geomorphic features for the benthic
habitat mapping achieved by imaging spectrometry); (2) a need of advanced
spectral inversion techniques to simultaneously retrieve the concentrations of
phytoplankton pigments (e.g. chl-a, phycocyanin and phycoerythrin); (3) a need
of unmanned aerial vehicles equipped with hyperspectral sensors to make the tool
more cost-effective and thence routinely used technique for water quality at local
scale.
Acknowledgements MIVIS data presented in this chapter were provided by A. Cavazzini,
G. Pizzaferri (Blom CGR Parma), C. Defrancesco (APPA Trento) and A. Martinelli (ARPA
Lazio). This study would have not been possible without contributions provided by M. Bartoli
and R. Bolpagni (University of Parma), M. Musanti (CNR-IREA) and G.L. Fila (CRA Municipality of Sirmione) during these years. This study was co-funded by European Union (FP7-People
Co-funding of Regional, National and International Programmes, GA n. 600407) and the CNR
RITMARE Flagship Project.
References
1. Bukata RP, Jerome JH, Kondratyev KY, Pozdnyakov DV (1995) Optical properties and
remote sensing of inland coastal waters. CRC, Boca Raton, p 384
2. Morel A, Prieur L (1977) Analysis of variations in ocean color. Limnol Oceanogr 22(4):
709–722
3. Schalles JF (2006) Optical remote sensing techniques to estimate phytoplankton chlorophyll a
concentrations in coastal waters with varying suspended matter and CDOM concentrations. In:
Richardson LL, LeDrew EF (eds) Remote sensing of aquatic coastal ecosystem processes:
science and management applications. Springer, Dordrecht, pp 27–79
4. Viaroli P, Lasserre P, Campostrini P (2007) Lagoons and coastal wetlands in the global change
context: impacts and management issues. Hydrobiologia 577:1–168
5. Moses WJ, Gitelson AA, Perk RL, Gurlin D, Rundquist DC, Leavitt BC, Barrow TM,
Barkhage P (2012) Estimation of chlorophyll-a concentration in turbid productive waters
using airborne hyperspectral data. Water Res 46(4):993–1004
6. Gower J, King S (2007) Validation of chlorophyll fluorescence derived from MERIS on the
west coast of Canada. Int J Remote Sens 28:625–635
7. Song K, Li L, Li Z, Tedesco L, Hall B, Shi K (2013) Remote detection of cyanobacteria
through phycocyanin for water supply source using three-band model. Ecol Inform 15:22–33
8. Simis SGH, Peters SWM, Gons HJ (2005) Remote sensing of the cyanobacterial pigment
phycocyanin in turbid inland water. Limnol Oceanogr 50:237–245
9. Knaeps E, Dogliotti AI, Raymaekers D, Ruddick K, Sterckx S (2012) In situ evidence of
non-zero reflectance in the OLCI 1020 nm band for a turbid estuary. Remote Sens Environ
120:133–144
10. Eleveld MA (2012) Wind-induced resuspension in a shallow lake from Medium Resolution
Imaging Spectrometer (MERIS) full-resolution reflectances. Water Resour Res. 48(4).
doi:10.1029/2011WR011121
78
C. Giardino et al.
water quality purposes might include: (1) a need to investigate the improvements
gained from the synergistic use of different sensors in shallow waters (e.g. the use
of aerial LiDAR bathymetry adds data on geomorphic features for the benthic
habitat mapping achieved by imaging spectrometry); (2) a need of advanced
spectral inversion techniques to simultaneously retrieve the concentrations of
phytoplankton pigments (e.g. chl-a, phycocyanin and phycoerythrin); (3) a need
of unmanned aerial vehicles equipped with hyperspectral sensors to make the tool
more cost-effective and thence routinely used technique for water quality at local
scale.
Acknowledgements MIVIS data presented in this chapter were provided by A. Cavazzini,
G. Pizzaferri (Blom CGR Parma), C. Defrancesco (APPA Trento) and A. Martinelli (ARPA
Lazio). This study would have not been possible without contributions provided by M. Bartoli
and R. Bolpagni (University of Parma), M. Musanti (CNR-IREA) and G.L. Fila (CRA Municipality of Sirmione) during these years. This study was co-funded by European Union (FP7-People
Co-funding of Regional, National and International Programmes, GA n. 600407) and the CNR
RITMARE Flagship Project.
References
1. Bukata RP, Jerome JH, Kondratyev KY, Pozdnyakov DV (1995) Optical properties and
remote sensing of inland coastal waters. CRC, Boca Raton, p 384
2. Morel A, Prieur L (1977) Analysis of variations in ocean color. Limnol Oceanogr 22(4):
709–722
3. Schalles JF (2006) Optical remote sensing techniques to estimate phytoplankton chlorophyll a
concentrations in coastal waters with varying suspended matter and CDOM concentrations. In:
Richardson LL, LeDrew EF (eds) Remote sensing of aquatic coastal ecosystem processes:
science and management applications. Springer, Dordrecht, pp 27–79
4. Viaroli P, Lasserre P, Campostrini P (2007) Lagoons and coastal wetlands in the global change
context: impacts and management issues. Hydrobiologia 577:1–168
5. Moses WJ, Gitelson AA, Perk RL, Gurlin D, Rundquist DC, Leavitt BC, Barrow TM,
Barkhage P (2012) Estimation of chlorophyll-a concentration in turbid productive waters
using airborne hyperspectral data. Water Res 46(4):993–1004
6. Gower J, King S (2007) Validation of chlorophyll fluorescence derived from MERIS on the
west coast of Canada. Int J Remote Sens 28:625–635
7. Song K, Li L, Li Z, Tedesco L, Hall B, Shi K (2013) Remote detection of cyanobacteria
through phycocyanin for water supply source using three-band model. Ecol Inform 15:22–33
8. Simis SGH, Peters SWM, Gons HJ (2005) Remote sensing of the cyanobacterial pigment
phycocyanin in turbid inland water. Limnol Oceanogr 50:237–245
9. Knaeps E, Dogliotti AI, Raymaekers D, Ruddick K, Sterckx S (2012) In situ evidence of
non-zero reflectance in the OLCI 1020 nm band for a turbid estuary. Remote Sens Environ
120:133–144
10. Eleveld MA (2012) Wind-induced resuspension in a shallow lake from Medium Resolution
Imaging Spectrometer (MERIS) full-resolution reflectances. Water Resour Res. 48(4).
doi:10.1029/2011WR011121
78
C. Giardino et al.