Spatiotemporal monitoring of low water turbidity in Moroccan coastal lagoon using Sentinel-2 data

Water turbidity is a key index used to evaluate coastal water quality conditions. To determine the turbidity in large areas, remote sensing technologies have the potential to provide a cost-effective alternative to experimental methods that require extensive time for field data collection and laboratory analysis. This research focuses on the development of an accurate model to estimate and monitor low water turbidity using Sentinel-2 images and field measurements in the Sidi Moussa lagoon of the Moroccan Atlantic coast. Also, the effect of various factors was mentioned and compared qualitatively to the water turbidity variations, including spatiotemporal variation, wind, depth, geomorphology, and vegetation. Among all the regressions analyzed, the Red band was the most successful remote sensing information for water turbidity with a good validation accuracy of R2 = 0.87. ANOVA analysis showed that measured turbidity values vary differently from upstream to downstream of the lagoon. The analysis of the seasonal effect on the water turbidity using one-year multitemporal Sentinel-2 images showed that in the dry season, the mean turbidity is lower than in the rainy season. The water turbidity map derived using the developed algorithm can be used to support wetland management, the conservation of natural resources, and the selection of aquaculture areas. These results highlight the potential contribution of Sentinel-2 to map and monitor the low water turbidity.