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  • Title: Remote sensing of cyanobacterial blooms in a hypertrophic lagoon (Albufera of València, Eastern Iberian Peninsula) using multitemporal Sentinel-2 images.
    Author: Sòria-Perpinyà X, Vicente E, Urrego P, Pereira-Sandoval M, Ruíz-Verdú A, Delegido J, Soria JM, Moreno J.
    Journal: Sci Total Environ; 2020 Jan 01; 698():134305. PubMed ID: 31514039.
    Abstract:
    Eutrophy in Albufera of Valencia (Eastern Iberian Peninsula) is a quite old problem since after the intense eutrophication processes throughout the 1960s. The system shifted to a turbid stable state consolidated by the virtual disappearance of macrophytes by the early 1970s. The lagoon has been studied extensively since the 1980s, but efforts to revert the system to a clear state have not yielded the expected results because cultural eutrophication due to the growth of population in its area of influence and the effects of climate change, decreasing rainfall and increasing evaporation. This has driven to an increase in water retention times in the lagoon in recent years, resulting in a phytoplanktonic shift towards potentially toxic cyanobacteria species, often forming blooms. Cyanobacterial blooms severely affect water quality for human use, ranging from recreation and fishing to drinking water resources, as indicated in the surveillance protocol of World Health Organization (WHO). The current state of the lake requires constant monitoring and remote sensing is an optimal tool for the continuous monitoring of the whole water mass. This work is included in the ESAQS project (Ecological Status of AQuatic systems with Sentinel satellites), to establish a protocol for regular and frequent monitoring of the ecological status of reservoirs, lakes and lagoons. Algorithms are developed using the images provided by the Sentinel-2 (A and B), provided with a spatial resolution of 10 m and a temporal frequency of 5 days. In this work we demonstrate that using this new earth observation satellite is possible to develop a consistent and suitable algorithm to estimate the phycocyanin concentration [PC] and establish a protocol for regular and frequent monitoring. Calibrating (R2 = 0.841; n = 21; p < 0.001) and validating (R2 = 0.775; n = 55; p < 0.001; RMSE% = 40) the algorithm with field data are also demonstrated.
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