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

1Na onal Research Council of Italy, Ins tute for Atmospheric Pollu on Research (IIA‐CNR), Rome, Italy; 2Na onal Research Council of Italy, Ins tute of Marine Sciences (ISMAR‐CNR), Italy; 3Na onal Research Council of Italy, Ins tute for Electromagne c Sensing of the Environment (IREA‐CNR), Italy; 4Na 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; 5ProAmbiente 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