Freshwater ecosystems are increasingly affected by eutrophication, sediment loading, and other anthropogenic pressures, creating a growing need for monitoring frameworks that are spatially extensive, temporally consistent, and methodologically robust. Although in situ sampling remains essential, its limited spatial coverage and operational constraints have accelerated the use of satellite remote sensing combined with artificial intelligence (AI) and machine learning (ML) for water quality assessment. This review critically examines recent studies published between 2020 and March 2026 on the estimation of physicochemical water quality parameters in lakes and rivers using remote sensing, with particular attention to the methodological structure of image processing workflows rather than performance metrics alone. The synthesis shows that predictive performance is strongly conditioned by three interrelated stages: atmospheric correction (AC), spectral feature construction, and validation design. Across the reviewed studies, substantial variation is observed in atmospheric correction processors, spectral engineering strategies, and model architectures, leading to differences in the spectral inputs and analytical conditions used for model development. Validation approaches remain highly heterogeneous and often rely on internal data splits without geographically independent testing, which weakens claims of model generalizability. In addition, few studies explicitly distinguish algorithmic, matchup, and preprocessing uncertainties, revealing a persistent gap in uncertainty reporting. Overall, the review suggests that improvements attributed to newer ML models may partly reflect upstream preprocessing choices rather than algorithmic superiority alone. Future research should prioritize transparent reporting of atmospheric correction pipelines, structured uncertainty decomposition, standardized validation protocols, and cross-site transferability assessments. By synthesizing these methodological patterns, this review provides a consolidated methodological synthesis that supports improved reproducibility, comparability, and operational reliability of remote-sensing-based freshwater quality monitoring.
Zúñiga-Grajeda et al. (2026) studied this question.