Monitoring Coastal Lagoon Water Quality through Remote Sensing: The Mar Menor as a Case Study

Abstract

The Mar Menor is a hypersaline coastal lagoon located in the southeast of Spain. This fragile ecosystem is suffering several human pressures, such as nutrient and sediment inputs from agriculture and other activities and decreases in salinity. Therefore, the development of an operational system to monitor its evolution is crucial to know the cause-effect relationships and preserve the natural system. The evolution and variability of the turbidity and chlorophyll-a levels in the Mar Menor water body were studied here through the joint use of remote sensing techniques and in situ data. The research was undertaken using Operational Land Imager (OLI) images on Landsat 8 and two SPOT images, because cloudy weather prevented the use of OLI images alone. This provided the information needed to perform a time series analysis of the lagoon. We also analyzed the processes that occur in the salt lagoon, characterizing the different spatio-temporal patterns of biophysical parameters. Special attention was given to the role of turbidity and chlorophyll-a levels in the Mar Menor ecosystem with regard to the programs of integral management of this natural space that receives maximum environmental protection. The objective of the work has been fulfilled by answering the questions of the managers: when did the water quality in the Mar Menor begin to change? What is happening in the lagoon? Is remote sensing useful for monitoring the water quality in the Mar Menor? The answers to these questions have allowed the generation of a methodology and monitoring system to track the water quality in the Mar Menor in real-time and space. The tracking system using satellite images is open to the incorporation of images provided by new multispectral sensors.

Sentinel-2 Satellites Provide Near-Real Time Evaluation of Catastrophic Floods in the West Mediterranean

“…Moreover, the straightforward nature of the strategy and its independence from ancillary training data makes it applicable and accessible for a wide variety of stakeholders, managers, and end users, especially in data-poor or remote areas. The Mar Menor is suffering several human pressures, such as sediment and nutrient inputs from agriculture and other activities, so implementation of operational strategies to monitor its evolution is critical to protect its fragile natural ecosystem [50]. In this regard, the open data policy and long-term mission commitment of Sentinel-2 opens future promising time series evaluation over years and even decades that can be an important tool to provide crucial missing information at the local or regional scale in a rapid and non-intrusive manner.…”

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

“…According to superiority of the ML algorithms, this study has been designed to fulfill the following objectives: (1) To select the specific variables that most related to the Chl-a production using wrapper feature selection algorithms in the Mar Menor lagoon; (2) to develop a predictive model to estimate the Chl-a concentration based on multilayer neural network (MLNN) and support vector regression (SVR) models; (3) to validate the performance of predictive models using different evaluation metrics and identify the best method in estimation of the Chl-a concentration for the Mar Menor lagoon. Several studies have been carried out on the eutrophication process and the water quality parameters of the Mar Menor lagoon [17,[34][35][36]. However, to the best of our knowledge, there is no previous research using machine learning models to predict water quality parameters in this lagoon, specifically Chl-a concentration.…”

Autonomous Systems for the Environmental Characterization of Lagoons

“…Remote sensing techniques based on satellite imagery have been used to overcome some of these limitations (e.g. [7][8][9]). Satellite imagery enables the monitoring of large extents.…”