WQEye (v1): A Python-based software for machine learning-based retrieval of water quality parameters from Sentinel-2 and Landsat-8/9 remote sensing data aided by Google Earth Engine

Despite substantial progress in Remote Sensing ( RS )-based estimation of Water Quality Parameters (WQPs) using Machine Learning (ML) models, there remains a lack of end-to-end software that automates the entire workflow. To address this gap, this paper introduces WQEye (v1), an open-source software offering a user-friendly interface comprising six modular components: (1) Data loader; (2) RS sampling module supporting Sentinel-2 (S2) and Landsat-8/9 (L89) through Google Earth Engine; (3) Matching module for finding coincident in-situ and satellite observations; (4) Preprocessing module; (5) ML module supporting Random Forest (RF), Artificial Neural Networks (ANNs), and recently proposed Kolmogorov-Arnold Networks (KANs), along with hyperparameter tuning, model training, validation, and interpretability analysis using Shapely Additive Explanations (SHAP); and (6) Export module for generating WQP maps for spatiotemporal analysis. The reliability of WQEye's workflow was validated through the estimation of different WQPs (turbidity, chlorophyll-a, dissolved oxygen, and fluorescent dissolved organic matter) using both S2 and L89 data across five different locations in the United States. KAN consistently demonstrated statistically superior performance compared to RF and ANN across all WQPs, achieving approximately 5% lower MAPE and 6% higher R 2 than ANN, the second-best model. Cross-regional assessment of the ML models further demonstrated the superior generalization capability of the KAN model compared to the other models across both the S2 and L89 datasets. Moreover, S2 imagery outperformed L89 data across all WQPs. The findings prove the reliability of WQEye's workflow for the estimation of WQPs using RS data, but the final accuracy of outputs depends on the detectability of the target WQP in RS imagery, as non-optically active parameters like dissolved oxygen yielded lower estimation accuracies compared to optically active ones. WQEye is freely available at https://github.com/ATDehkordi/WQEye to promote applied RS applications in environmental monitoring and water resource management. • WQEye is an open-source, modular software with a user-friendly interface. • It automates the full workflow from data preparation to machine learning modeling. • It supports Sentinel-2 and Landsat-8/9 remote sensing data. • WQEye provides ready-to-use implementations of Random Forests, Artificial Neural Networks, and Kolmogorov–Arnold Networks.