Mapping turbidity patterns in the Po river prodelta using multi-temporal Landsat 8 imagery. (5th November 2017)

• Record Type: Journal Article
• Authors: Braga, Federica; Zaggia, Luca; Bellafiore, Debora; Bresciani, Mariano; Giardino, Claudia; Lorenzetti, Giuliano; Maicu, Francesco; Manzo, Ciro; Riminucci, Francesco; Ravaioli, Mariangela; Brando, Vittorio Ernesto
• Is Part Of: Estuarine, coastal and shelf science. Volume 198:Part B(2017)
• Page Start: 555
• Page End: 567
• Publication Date: 2017-11-05
• Subjects: Turbidity -- Landsat 8 satellite data -- Po river prodelta -- River plume -- Sediment resuspension -- Driving forcings; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2016.11.003
• Languages: English
• ISSNs: 0272-7714

Abstract

Abstract: Thirty-meters resolution turbidity maps derived from Landsat 8 (L8) images were used to investigate spatial and temporal variations of suspended matter patterns and distribution in the area of Po River prodelta (Italy) in the period from April 2013 to October 2015. The main focus of the work was the study of small and sub-mesoscale structures, linking them to the main forcings that control the fate of suspended sediments in the northern Adriatic Sea. A number of hydrologic and meteorological events of different extent and duration was captured by L8 data, quantifying how river discharge and meteo-marine conditions modulate the distribution of turbidity on- and off-shore. At sub-mesoscale, peculiar patterns and smaller structures, as multiple plumes and sand bars, were identified thanks to the unprecedented spatial and radiometric resolution of L8 sensor. The use of these satellite-derived products provides interesting information, particularly on turbidity distribution among the different delta distributaries in specific fluvial regimes that fills the knowledge gap of traditional studies based only on in situ data. A novel approach using satellite data within model implementation is then suggested.