Hyperspectral imaging technology can non-destructively detect physicochemical information of plants with different dimensions. Existing researches often focus on analyzing the average spectrum of hyperspectral images, ignoring the information of their spatial dimensions. In this study, the model plant Arabidopsis thaliana was used as the research object to explore the influence of spatial resolution difference caused by different scanning speeds of hyperspectral imaging on the measurement of plant canopy moisture content, and to provide optimization for rapid online detection of plant canopy moisture content by hyperspectral imaging program. An open-line hyperspectral image of the Arabidopsis canopy was extracted using an indoor online hyperspectral imaging system at 20, 30 and 40 mm·s^-1, and the average of the Arabidopsis thaliana canopy reflectance spectrum was extracted. Secondly, the quantitative analysis model of canopy water content and the average reflectance spectrum of Arabidopsis thaliana was established by Partial Least Squares Regression (PLSR). The determination coefficient (R²), root mean square error (root), mean squared error (RMSE) and relative variance deviation (RPD) were used to evaluate the model. The PLSR model based on pre-processing spectra such as the original spectrum, Multiplicative Scatter Correction (MSC) algorithm and Savitsky-Golay smoothing algorithm is compared. The best spectral pre-processing method is selected for subsequent data processing. Finally, the successive projections algorithm (SPA) is used to analyze and compare the prediction accuracy based on the optimal feature wavelength and the full wavelength, and to determine the influence of the hyperspectral image scanning speed on the canopy water content prediction of Arabidopsis thaliana. The results show that when the scanning speed was increased from 20 to 30 mm·s^-1, the full-band PLSR model based on MSC pretreatment predicted that the coefficient of canopy moisture content in Arabidopsis was reduced by 0.88%, less than 1%. When the scanning speed was increased from 20 to 40 mm·s^-1, the coefficient of determination of canopy water content in Arabidopsis was reduced by 2.3%. It shows that while the scanning speed is properly increased, the high water content prediction accuracy of the plant canopy can be ensured. Changing the hyperspectral scanning speed can more effectively utilize the spatial information of the hyperspectral image space. After the scanning speed is appropriately increased, the spatial dimension information of the hyperspectral image changes, improving the image collection efficiency of the actual production application and reducing the data processing time.