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Photosynthesis plays a vital role in crop growth, dry mater accumulation and yield formation. How to monitor it quickly and widely is still a problem so far. Taking the unmanned aerial vehicle (UAV) as the remote sensing platform, and a multispectral camera with six bands was mounted. To explore the feasibility of retrieving crop canopy photosynthetic parameters by using remote sensing technology, the cotton in budding period were studied. The camera was used to capture the image of cotton canopy at different times in one day (09:00, 11:00, 13:00, 15:00 and 17:00)，of which the reflectance information was extracted. The parameters of cotton photosynthetic (net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular carbon dioxide concentration (Ci) and transpiration rate (Tr)) were measured at the moment when the UAV was landed. Through the correlation analysis of the four photosynthetic parameters and the six-band reflectance, the retrieving model of different photosynthetic parameters at different times was established by univariate linear regression, principal component regression (PCR), ridge regression (RR) and partial leastsquares regression (PLSR), respectively. The results showed that the best retrieving models of net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs) and intercellular carbon dioxide concentration (Ci) were the univariate linear model based on the reflectance of the blue light band at 13:00, the univariate linear model based on the reflectance of the red light band at 15:00, the ridge regression model at 15:00 and the univariate linear model based on the red light band at 15:00,，respectively. The decision coefficients（R2） of the models were more than 0.5, and the relative errors（RE） were less than 9%. The research result can provide a certain reference for monitoring the photosynthesis of crops in a large scale.