To address the limitations of traditional remote sensing methods, which often inadequately consider environmental parameters and result in limited accuracy for Chlorophyll-a (Chl-a) concentration inversion in inland waters, in this study, we developed a high-precision multisource inversion method tailored for reservoirs in Hebei Province. Sentinel-2 multispectral sensor data were integrated with three key environmental parameters—water temperature (Temp), pH, and electrical conductivity (EC)—to construct enhanced multisource features. Various machine learning models were systematically evaluated and optimized to improve both model robustness and predictive accuracy. Results demonstrate that incorporating environmental parameters significantly strengthens feature representational capacity, with correlation coefficients between multisource combinations and Chl-a reaching 0.830. The optimal linear regression model based on the Temp × pH × Mean_Rbands feature achieved a testing-set coefficient of determination (R2) of 0.847. Across all multisource features, the mean testing-set R2 was 0.700 ± 0.087, compared with 0.339 ± 0.164 for spectral-only methods. The spatiotemporal analysis of the 2025 growing season revealed a distinct seasonal rhythm—increasing in spring and summer, and differentiating in autumn. Spatially, Lincheng Reservoir recorded the highest annual average concentration (0.0103 mg/L), with near-shore areas identified as sensitive high-concentration zones. This multisource fusion strategy provides robust methodological support for high-precision water quality monitoring and dynamic eutrophication assessment in inland reservoirs.