The document analyzes suspended sediments in the Po River prodelta using Landsat-8 imagery from 2013-2016. It finds that Landsat-8 data has the potential to observe turbidity patterns at sub-mesoscale spatial resolutions due to its 30m pixel size. Statistical analysis revealed spatial correlation between turbidity and hydrometeorological data like river discharge and wind speed. While the 7-9 day revisit time of Landsat-8 may not fully capture temporal variability, the analysis provided insight into geostatistical patterns and sensitive areas impacted by hydrodynamic forcings. The launch of Sentinel-2 satellites will improve temporal monitoring and observation of processes at different spatiotemporal scales.

1. 5. Results
Mul ‐temporal analysis of suspended sediments in the Po River prodelta by
means of Landsat‐8 OLI data
The mul ‐temporal analysis of Landsat‐8‐derived products was per‐
formed to inves gate the suspended sediment dynamics in the Po River
prodelta and the adjacent coastal zone (Northern Adria c Sea). Under‐
standing the spa al and temporal variability of the Po river plume is of
primary importance for the study of northern Adria c Sea hydrology.
Landsat‐8 OLI imagery, with finer spa al resolu on and high quality of ra‐
diometric resolu on, is suitable to inves gate mid to small scale turbulent
structures of the buoyant flow at the surface. For turbidity retrieving in
the period 2013‐2016, OLI data were converted into water‐leaving radi‐
ance reflectance (ρw) with ACOLITE (Vanhellemont and Ruddick, 2014—
2015). The ρw data were then converted into turbidity [FNU] following Do‐
glio et al., (2015). With the aid of in situ data (Braga et al., 2015), a qua‐
lita ve interpreta on of the factors controlling these pa erns through ‐
me and space was proposed (e.g. interac on among hydro‐
meteorological forcing, coastal currents and prodelta morphology). The
OLI spa al resolu on (30 m) has shown its poten ality for synop c obser‐
va ons of SPM and turbidity pa erns at sub‐mesoscale.
1. Abstract
The Po River prodelta is a
complex environment,
where its five major distrib‐
utaries contribute to the
freshwater input in the
northern Adria c Sea, ex‐
hibi ng different and varia‐
ble par oning of water
discharge and sediment
load. This coastal system is
dominated by riverine in‐
puts and hydrodynamic
forcing. Their interac on in‐
fluences the physical and
biogeochemical processes
of the whole basin.
The results highlight the capability of OLI data to analyse the Po River prodelta in terms of
spa al analysis and sta s cal correla on with hydrometereological data at the sub‐
mesoscale. The 7‐9 days frequency of OLI data might not be adequate for capturing mul ‐
temporal analysis of interannual and seasonal variability in the NAS, however the analysis
provided informa on on the geosta s cal pa erns and the highest sensi ve area due to
hydrodynamic forcings (fig. 7—8). The recent launch of the ESA’s satellites, Sen nel‐2A
and the forthcoming launch of Sen nel‐2B, would improve the temporal analysis reducing
the revisit me and obtaining me series with reliable advantages to observe and under‐
stand processes opera ng on different space‐ me scales.
6. Conclusion
2. Study area
Fig. 1 The Po river Prodelta with 5 distributaries. The triangles are the meteo sta ons, the blue
circle is the hydrologic sta on of Pontelagoscuro.
3. Data
Landsat 8 OLI imagery Hydrometereological data
Foce Po
Acqua Alta Oceanographic Tower (AAOT)
Pontelagoscuro hydrologic sta on
4. Method
Fig. 2 Time series 02/07/2013—25/01/2016 (50 observa ons).
Path 191 and 192, Row 029—for day me overpasses. Spa al
resolu on 30 m
1 measure per hour ‐> resampled to me series (10 + 24 h before the overpass)
7. References
8. Acknowledgments
NNE Foce
S1
Pila
NNE Foce S1
Fig. 5 Coefficient of Turbidity varia on (/) map in the study area. The dots represent the
correla on of turbidity values retrieved in each loca on with pt3.
Fig. 6 Temporal varia on of sea surface temperature, turbidity
in 3 loca ons (Pila, NNE Foce, S1) vs water discharge meas‐
ured in Pontelagoscuro Sta on.
Fig. 3 The Hydrometereologic dataset in 3 different sta ons.
PILA
1
3
2
1 2 3 Coefficient Varia on Map of Turbidity
Spa al and temporal analysis
Correla on of Q values with Turbidity
Braga F., Manzo C., Brando V., Giardino C., Bresciani M., et al., (2015) Mapping total suspended sediments in the Po River pro‐
delta with mul ‐temporal andsat‐8 OLI data ECSA 2015
Doglio , A., Ruddick, K., Nechad, B., Doxaran, D., Knaeps, E., 2015. A single algorithm to retrieve turbidity from remotely‐
sensed data in all coastal and estuarine waters. Remote Sens. Environ. 156, 157–168.
Vanhellemont, Q., Ruddick, K., 2014. Turbid wakes associated with offshore wind turbines observed with Landsat 8. Remote
Sensing of Environment, 145, 105‐115. doi:10.1016/j.rse.2014.01.009
Vanhellemont, Q., Ruddick, K., 2015. Advantages of high quality SWIR bands for ocean colour processing: Examples from
Landsat‐8. Remote Sensing of Environment, 161, 89‐106. doi:10.1016/j.rse.2015.02.007
Landsat‐8 data available from the U.S. Geological Survey. Po discharge data were provided by ARPA‐ER. Wind measurements
were provided by ISPRA‐VE. We are grateful to RBINS for making ACOLITE publicly and freely available.
>75%
Ciro Manzo*1
, Federica Braga2
, Luca Zaggia2
, Vi orio Brando4
, Claudia Giardino3
, Mariano Bresciani3
, Debora Bellafiore2
,
Francesco Riminucci2,5
, Mariangela Ravaioli2
, Cris ana Bassani1
1
Na onal Research Council of Italy, Ins tute for Atmospheric Pollu on Research (IIA‐CNR), Rome, Italy; 2
Na onal Research Council of Italy, Ins tute of Marine Sciences (ISMAR‐CNR), Italy;
3
Na onal Research Council of Italy, Ins tute for Electromagne c Sensing of the Environment (IREA‐CNR), Italy; 4
Na onal Research Council of Italy – Is tuto di Scienze dell’Atmosfera e del Cli‐
ma (CNR‐ISAC), GOS Team, Via Fosso del Cavaliere 1, 00133 Rome, Italy; 5
ProAmbiente S.c.r.l., Emilia‐Romagna High Technology Network in Bologna, Italy
Fig. 4 Calcula on procedure performed with R sta s c, IDL and GDAL.
2
3
1
25 km
17 km
Variance 203
NE Wind
FNU VARIOGRAM MAP
High spa al anyso‐
tropy
11.5 km
Variance 12328
Flood
Lower spa al any‐
sotropy
5.6 km
14 km
Variance 490
High spa al anyso‐
tropy
SE Wind
Rose Diagram measured at Foce Po
Correla on of wind speed with Turbidity
Correla on pa erns with hydrometreological data
The analysis for spa al correla on analysis was
performed considering the correla on between
each pixel of the map and hydrometereologic
data measured in specific sta on.
Fig. 8 Correla on maps of turbidity with water discharge measured in Pontelagoscuro and wind speed measured in
Foce Po Sta on. On the bo om le the rose diagram of wind direc ons and speed in the temporal range.
Fig. 7 (le ) Turbidity in extreme events due to different hydrodynamic forcing.
(right) Variogram map with semivariance in every compass direc on with ani‐
sotropy ellipses (in km min and max autocorrela on distances). The x and y
axes are separa on distances in E‐W and N‐S direc ons, respec vely.
3 extreme events
Temperature Turbidity