A comprehensive analysis of retrieving optically inactive indicators from multi-level remote sensing product(s) in...

The present research was carried out to retrieve dissolved oxygen (DOX) using the Copernicus Marine Services products from the Irish transitional and coastal waters. To achieve the research goal, the study developed and validated 2101 machine learning (ML)/artificial intelligence (AI) (supervised learning, stacking-ensembles, equations, and voting-based ensembles) and statistical models using multi-level (Level-3 and Level-4) Sentinel-3 OLCI (S3-OLCI) and Multi-sensor (MS) remote sensing (RS) datasets in conjunction with in-situ and modelled DOX datasets. While supervised models (e.g., K-nearest neighbours, Gradient boosting, and Extra decision trees) excelled in the training phase (EPA: MSE ≤ 0.03 with CI ± 0.02; Modelled: MSE ≈ 0 with CI ± 0) but showed limited generalizability on independent validation datasets (2022-2023), indicating poor model accuracy and sensitivity (EPA-2022: R² = -0.03 - 0.16; EPA-2023: R² = -0.09 - 0.1; Modelled-2022: R² = 0.37 - 0.53; Modelled-2023: R² = -1.39 - -0.26). In terms of product, S3-OLCI outperformed MS data with low uncertainty, whereas spatio-temporal analysis showed the highest DOX in inshore/semi-enclosed bays and the lowest offshore. Overall, the results underscore that model performance is determined by methodological characteristics rather than model quantity. Despite the validation challenges, the results highlight key difficulties in retrieving optically inactive water quality (WQ) indicators like DOX using RS and ML/AI approaches. The findings of the research could be effective for supporting the mapping of baseline oxygen conditions, the application of ML/AI techniques to retrieve WQ indicators from RS products and their further technological advancement, such as managing the anthropogenic water cycle (i.e., human-altered hydrological and nutrient dynamics).