Analysing Spatiotemporal Variability of Chlorophyll-a Concentration and Water Surface Temperature in Coastal Lagoons of the Ebro Delta (NW Mediterranean Sea, Spain)

Resumen

Coastal lagoons are highly productive transitional water bodies threatened by human factors and vulnerable to global climate change effects. Monitoring biophysical parameters in these ecosystems is crucial for their preservation. In this work, we used Sentinel-2 and Landsat imagery combined with in situ data to (1) develop preliminary algorithms for retrieving the Chl-a concentration and water surface temperature of six lagoons located in the Ebro Delta (NE Mediterranean Sea, Spain), and to (2) compute maps and trend lines for analysing their spatiotemporal evolution from 2015 to 2022. Our findings showed that the algorithms' accuracy ranged from 72% to 78% and had limited potential under high Chl-a concentration regimes. Even so, they revealed the lagoons' trophic status, usual fluctuations, and deviations of both parameters attributed to seasonal (i.e., light and temperature) and short-term physical (i.e., winds) forcing, as well as valuable spatial patterns potentially useful for conservation efforts and land use planning. Future work will focus on the acquisition of a larger in situ data sample under a range of environmental conditions to improve the algorithms' robustness, which in turn will allow the investigation of natural and human factors controlling the dynamics of the two investigated parameters. ), and to (2) compute maps and trend lines for analysing their spatiotemporal evolution from 2015 to 2022. Our findings showed that the algorithms' accuracy ranged from 72% to 78% and had limited potential under high Chl-a concentration regimes. Even so, they revealed the lagoons' trophic status, usual fluctuations, and deviations of both parameters attributed to seasonal (i.e., light and temperature) and short-term physical (i.e., winds) forcing, as well as valuable spatial patterns potentially useful for conservation efforts and land use planning. Future work will focus on the acquisition of a larger in situ data sample under a range of environmental conditions to improve the algorithms' robustness, which in turn will allow the investigation of natural and human factors controlling the dynamics of the two investigated parameters.