In order to investigate the feasibility of reflectance spectroscopy for the detection of chlorpyrifos pesticides in water, indoor and outdoor spectral data of chlorpyrifos samples in two different concentrations were obtained using a hyperspectral acquisition system composed of ASD’s FieldSpecPro Spectrometer. The partial least squares (PLS) and principal component analysis (PCA) algorithms were used to establish quantitative models for spectral data of chlorpyrifos samples. The results showed that the predictable ability of the model is significantly reliable. Correlation analysis (CA) was used to calculate the correlation coefficient to select the characteristic wavelength of the spectrum of chlorpyrifos samples. The characteristic wavelengths of indoor and outdoor experimental spectra with concentration ranges of 5～75 mg·L-1 were 388, 1 080, 1 276 and 356, 1 322, 1 693 nm, respectively. And the characteristic wavelengths were 367, 1 070, 1 276, 1 708, and 383, 1 081, 1 250, 1 663 nm in the range of 0.1～100 mg·L-1 experiments. The PLS algorithm was used to establish a quantitative model of the sample characteristic wavelength spectral data. Compared with the full-band model, the calibration set determination coefficient (R2C) of the PLS characteristic wavelength model with concentration range of 5～75 mg·L-1 was increased to 0.987 5 and 0.999 2 in the indoor and outdoor experiment, respectively. And the prediction set determination coefficient (R2P) was increased to 0.989 4 and 0.994 4, respectively. The root mean square error of the calibration set (RMSEC) was reduced to 2.841 and 0.714, respectively. The root mean square error of the prediction set (RMSEP) was reduced to 1.715 and 1.244, respectively. The R2C of the characteristic wavelength PLS model with concentration range of 0.1～100 mg·L-1 in the indoor and outdoor experiment was increased to 0.998 3 and 0.998 8, respectively. The R2P was increased to 0.998 4 and 0.999 0, respectively, and the RMSEC of the correction set was reduced to 1.383 and 1.186, respectively, and the RMSEP of the prediction set was reduced to 1.510 and 1.229, respectively. The ratio of standard deviation of the validation set to standard error of prediction (RPD) were increased, especially for experiments with a concentration range of 0.1～100 mg·L-1. The RPD value increased to 21.7 significantly, indicating that the quantitative model based on the characteristic wavelength has higher accuracy of prediction ability. However, comparative experiments with different concentration ranges show that the relative error of the low-concentration chlorpyrifos solution prediction by the ASD spectrograph is large and there is an objective detection limit. In order to ensure that the characteristic wavelengths of chlorpyrifos pesticides under different experimental conditions are analyzed and the universality and robustness of the model are enhanced, four bands are selected according to the characteristic wavelengths, that is, 351～393, 1 065～1 086, 1 245～1 281 and 1 658～1 713 nm used as characteristic bands. The characteristic band model has a total of 38 wavelength variables. Compared with the 432 wavelength variables of the full-band model, the model variable was reduced by 91.2%. The R2C of indoor and outdoor experimental PLS models with concentration range of 5～75 mg·L-1 were 0.993 7 and 0.987 8, and R2P were 0.979 8 and 0.998 2, and RMSEC were 1.69 and 2.516, and RMSEP were 1.987 and 0.659, respectively. The R2C values of the experimental PLS model with concentration range of 0.1～100 mg·L-1 were 0.988 2 and 0.980 7 for the indoor and outdoor experiments, and the R2P were 0.939 1 and 0.993 6, and the RMSEC were 3.345 and 3.942, and the RMSEP were 8.996 and 2.663, respectively. All of the model RPD values were more than 2.5 and met the quantitative analysis conditions. Therefore, the hyperspectral system of the paper for the rapid detection of chlorpyrifos pesticides in indoor and outdoor environments has a certain feasibility. The results of this study have practical application value for the rapid detection of non-point source pollutants such as organic phosphorus pesticides, which can provide a theoretical basis for the development of an instrument for the rapid detection of organophosphorus pesticides in farmland water.