Characteristics of reflection spectrum, multi-spectral images and temperature of lettuce canopy were gained to judge the lettuce’s water stress condition which could lead to a precise, rapid & stable test of lettuce moisture and enlarged the models’ universality. By the extraction of lettuce’s multi-sensor characteristics in 4 different levels, quantitative analysis model of spectrum including 4 characteristic wavelengths, characteristic model of multi-spectral image and CWSI were established. These multi-sensor characteristics were fused by using the BP artificial neural network. Based on the fused multi-sensor characteristics, the lettuce moisture evaluation model was established. The results showed that the correlation coefficient of multi-spectral images model, spectral characteristics model and information fusion model were in turn increased, the correlation coefficients were respectively 0.8042, 0.8547 and 0.9337. It was feasible to diagnose lettuce water content by using multi-sensor information fusion of reflectance spectroscopy, multi-spectral images and canopy temperature. The correct rate and robustness of the discriminating model from multi-sensor information fusion were better than those of the model from the single-sensor information.