Hyperspectral data can capture the spectral changes caused by different concentrations of chemical oxygen demand (COD) in inland water bodies, and it is important to study the relationship between spectrum and COD concentrations for COD estimation. Support Vector Regression (SVR) model has the advantages of being suitable for small samples and good generalization ability, but it is difficult to select a parameter and prone to fall into the local extremum. In order to solve this problem, this study introduced Simulated Annealing-Particle Swarm Optimization (SA-PSO) into the parameter optimization process of SVR and proposed an improved SVR (SA-PSO-SVR) method to estimate the inland waters COD. This paper takes the Weihe River Basin as the research area, obtained the COD concentrations and spectral curves through field measurement. The sensitive band was determined by analyzing the response of spectral reflectance to COD at first in this paper, and the Simulated Annealing-Particle Swarm Optimization (SA-PSO) was introduced into the parameter optimization process of Support Vector Regression (SVR) to established an inversion model between the cod concentration and the sensitivity factor. The Orbita Hyper Spectral (OHS) hyperspectral data was used to verify the accuracy, and the distribution of COD concentration was obtained at last. Through spectral analysis, it can be seen that the measured above-surface spectra in this area demonstrated typical spectral signatures of second-class water, and the shape of the spectrum curve shows obvious double-peak characteristics. When the concentration increases, the reflection peak tends to move to the short wavelength direction and the reflection valley to the long wavelength direction. The Pearson’s correlation coefficient was used to analyze COD concentration and the spectral, the result showed that the best inversion factors are four band combinations of 518 nm/940.4 nm, 623.6 nm/636.8 nm, 729.2 nm/890.9 nm and 752.3 nm/857.9 nm. The model established by the SA-PSO-SVR method is accurate compared with models established by SVR, Back Propagation neural network, and linear regression method. The Mean-Relative-Error (MRE) and Root-Mean-Square-Error (RMSE) of the COD estimation model established by the SA-PSO-SVR method are 1.62% and 2.99 mg·L^-1 (R²=0.86), respectively. The optimal model established by the measured water surface spectra was applied to the hyperspectral satellite image. The RMSE and MRE are 4.47 mg·L^-1 and 11.87% respectively. The obtained COD inversion results of the Weihe-Xiashan reservoir area show: the overall concentration of COD is between 17 and 42 mg·L^-1, and COD concentration in the Hanxinba, the northeast region of XiaShan Reservoir, the confluence of the Qu River into the Wei River are higher than other waters. The experimental results show that SA-PSO-SVR is a feasible approach for the COD inversion of hyperspectral data, providing a reference for water resources management in the Weihe River Basin.