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In the context of climate change, extreme meteorological events such as severe storms produced by an isolated high-level atmospheric depression (known in Spanish as “Depresión Aislada en Niveles Altos”—DANA) are becoming increasingly frequent in the Mediterranean region, posing significant risks to ecosystems and human infrastructure. This study evaluates the impact of a DANA event in October 2024 on the water quality of Albufera Lake (Spain), a crucial Mediterranean wetland. A comprehensive evaluation was conducted by combining field data on physicochemical and biological parameters with satellite observations (Sentinel-2 and Landsat-8) to assess alterations before and after the event. Variables such as conductivity, nitrate, and total solids exhibited significant reductions immediately following the DANA, with conductivity decreasing by 82% compared to pre-event levels. These alterations signify a substantial renewal of the lake system driven by heavy rainfall and subsequent water releases. However, the lake demonstrated signs of recovery toward pre-event conditions over the following month. These results are consistent with previous findings, underscoring the system’s resilience and the necessity of periodic water releases to maintain ecological balance. The use of remote sensing tools effectively captured these dynamics, offering valuable insights for the long-term monitoring of water quality. This study highlights the urgent need for proactive management strategies to mitigate the effects of increasingly intense meteorological disturbances.

Seagrasses play a pivotal role in maintaining marine ecosystems, supporting biodiversity, and preventing sediment loss during storms. Their capacity for photosynthesis and growth is linked to light availability in the continental shelf waters. Satellite platforms such as Landsat (USGS) and Sentinel (ESA) provide accessible imagery for the monitoring of these submerged plants. This study employed the PRISMA methodology to conduct a systematic review of the literature, with the objective of identifying articles focused on these seagrasses and their detection via satellite imagery. The identified methodologies included the use of vegetation and water indices, which were validated through empirical observations, as well as supervised classification algorithms, such as Random Forest, Maximum Likelihood, and Support Vector Machine. These approaches were applied to Mediterranean and other coastal regions, revealing changes in seagrass cover due to anchor damage in tourist areas and trawling scars that resemble plough marks. Such tools are vital for informing management actions, such as the implementation of restrictions on anchoring and bottom trawling, in order to protect these vulnerable ecosystems. By enabling targeted interventions, this approach facilitates the preservation of seagrass meadows, which are also critical for carbon sequestration and the sustainability of marine habitats.