Use of Sentinel-3 OLCI Images and Machine Learning to Assess the Ecological Quality of Italian Coastal Waters

Abstract

Understanding and monitoring the ecological quality of coastal waters is crucial for preserving marine ecosystems. Eutrophication is one of the major problems affecting the ecological state of coastal marine waters. For this reason, the control of the trophic conditions of aquatic ecosystems is needed for the evaluation of their ecological quality. This study leverages space-based Sentinel-3 Ocean and Land Color Instrument imagery (OLCI) to assess the ecological quality of Mediterranean coastal waters using the Trophic Index (TRIX) key indicator. In particular, we explore the feasibility of coupling remote sensing and machine learning techniques to estimate the TRIX levels in the Ligurian, Tyrrhenian, and Ionian coastal regions of Italy. Our research reveals distinct geographical patterns in TRIX values across the study area, with some regions exhibiting eutrophic conditions near estuaries and others showing oligotrophic characteristics. We employ the Random Forest Regression algorithm, optimizing calibration parameters to predict TRIX levels. Feature importance analysis highlights the significance of latitude, longitude, and specific spectral bands in TRIX prediction. A final statistical assessment validates our model’s performance, demonstrating a moderate level of error (MAE of 0.51) and explanatory power (R2 of 0.37). These results highlight the potential of Sentinel-3 OLCI imagery in assessing ecological quality, contributing to our understanding of coastal water ecology. They also underscore the importance of merging remote sensing and machine learning in environmental monitoring and management. Future research should refine methodologies and expand datasets to enhance TRIX monitoring capabilities from space.

Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis

“…Another example of the contribution in the control and monitoring of water conditions to preserve marine ecosystems that will allow fishing for maritime food is established in the study carried out on the coasts of the Mediterranean, where space images, remote sensing, satellites, and machine learning were used. With this, the results yielded a method to evaluate ecological quality and environmental management [16]. In [17], the use of remote sensors is shared, which allows data to be obtained about the quality of the water in the coastal lagoon of the Mar Menor in the Southeast of Spain, where water parameters are measured for the purpose of making strategic decisions to provide preventive assistance in the event of ecological disasters that condition the environmental balance that compromises the continued provision of ecosystem services such as aquaculture, fishing, and some industrial activities in the area.…”