AI-Aquatica-RS: A Modular Python Framework for Reproducible Fusion of Remote-Sensing-Derived Spectral Indices and In Situ Water-Quality Observations

Remote-sensing-derived spectral indices and in situ measurements provide complementary information for aquatic monitoring, but their practical integration is complicated by asynchronous observations, heterogeneous tables, missing acquisitions, and non-reproducible preprocessing. This study presents AI-Aquatica-RS, a modular Python framework for spectral-index calculation, station-aware nearest-neighbor temporal alignment, feature-set construction, regression benchmarking, command-line execution, and structured result export. The software was evaluated using a fully synthetic controlled benchmark; no real satellite scenes or field-monitoring measurements were used. The benchmark comprised 1080 daily in situ-like observations from six stations and 181 unique remote-sensing-like acquisitions generated as water-like surface-reflectance proxies. A ±3-day alignment tolerance produced a shared complete-case cohort of 954 records. To ensure a fair comparison, the in situ-only, spectral-index-only, and fused configurations were evaluated on exactly the same 667 training and 287 validation records. The fused configuration achieved the best performance using ridge regression (RMSE = 3.481 NTU, MAE = 2.768 NTU, R2 = 0.729), compared with RMSE values of 5.086 NTU for the in situ-only configuration, and 5.538 NTU for the spectral-index-only configuration. The benchmark demonstrates reproducible execution and recovery of complementary information under controlled conditions; it does not constitute environmental validation. AI-Aquatica-RS provides an extensible software layer for future studies using real satellite products, monitoring networks, sensor-specific preprocessing, and spatially blocked validation.