Artificial Intelligence and Remote Sensing for Freshwater Quality Monitoring: A Systematic Review of Methods, Applications, Model Performance, and Research Gaps

ABSTRACT

Artificial intelligence (AI) integrated with remote sensing offers a scalable approach to freshwater‐quality monitoring, potentially extending the spatial and temporal coverage of conventional field measurements. However, evidence on model performance, validation, and transferability remains heterogeneous. This systematic review synthesized evidence on AI and remote sensing for freshwater‐quality monitoring, focusing on remote‐sensing platforms, target parameters, AI/machine‐learning (ML) methods, predictive performance, validation, and generalizability. Web of Science Core Collection, Scopus, PubMed/MEDLINE, ScienceDirect, and IEEE Xplore were searched for studies published from March 2000 to April 30, 2026. Study selection followed PRISMA 2020 guidance. Eligible studies used remote‐sensing data with AI/ML or deep‐learning methods to assess freshwater quality. Data on sensors, target variables, algorithms, performance metrics, validation, uncertainty, explainability, and reproducibility were extracted and synthesized descriptively. Thirty‐one primary studies (S01–S31) were included and consistently cross‐referenced throughout the manuscript and supplementary tables. Sentinel‐2/MSI (16/31; 51.6%) and Landsat (10/31; 32.3%) were the most frequently used platforms. Chlorophyll‐a (15/31; 48.4%), turbidity (11/31; 35.5%), and total suspended solids (8/31; 25.8%) were the most studied parameters. Random Forest (17/31; 54.8%), support‐vector methods (11/31; 35.5%), and artificial neural networks (10/31; 32.3%) were commonly applied. Internal validation predominated, while independent temporal, spatial, cross‐site, cross‐region, cross‐sensor, and external validation was uncommon. Uncertainty, explainability, and data/code availability were inconsistently reported. Overall, evidence supports strong within‐study predictive performance more than environmental generalizability. Future research should prioritize independent multi‐site and multi‐season validation, uncertainty quantification, explainable AI, standardized reporting, and reproducible workflows.