Abstract: Chlorophyll-a concentration is a crucial parameter characterizing water ecological environment quality. To address the issues of traditional Back Propagation (BP) neural networks, which are highly sensitive to initial values and tendency to local optima in chlorophyll-a inversion, this study proposes an SSA-BP inversion model optimized by using the Sparrow Search Algorithm (SSA). A novel inversion model was constructed by integrating remote sensing data from the DJI RTK300 UAV equipped with an AFX-10hyperspectral camera and synchronous ground sampling data from the Xiaojiang backwater area. The results demonstrate that: (1) The application of Savitzky-Golay (SG) smoothing significantly improved spectral data quality, increasing the determination coefficient (R²) of the SSA-BP model to 0.98; (2) Compared with traditional BP neural networks, the SSA-BP model showed comprehensive improvement in inversion accuracy, with the Quma water area exhibiting a 59.14% reduction in Mean Absolute Error (MAE), 60.78% decrease in Root Mean Square Error of Prediction (RMSEP), and 57.32% increase in Relative Percent Difference (RPD); (3) The SSA-BP model overcame the performance degradation of traditional BP models in low-concentration regions (where R² decreased from 0.94 to 0.76), maintaining stable high precision across different chlorophyll-a concentration gradients, with the highest R² reaching 0.98. This research confirms that the SSA-BP model significantly enhances the accuracy and adaptability of UAV hyperspectral remote sensing in chlorophyll-a inversion, providing a reliable technical approach for ecological environment monitoring in inland water bodies.