Abstract: Drought and waterlogging are two main abiotic stresses for plants, with serious impacts on plant physiological activities. In this study, the vertical and canopy leaf images of young sandalwood were obtained by SLR camera, with leaf segmentation algorithm being used to extract leaves and color features. We examined the color change of sandalwood leaves and water content inversion in different angles under two stress conditions. The results showed that leaf brightness decreased while the green component increased in the early stage (the first six days) of drought stress. After that, the brightness began to increase and green component began to decrease. Under water stress, the brightness of leaves decreased and yellow component increased in the whole stress cycle. The changes of control group was similar to that of the drought group, but the inflection point appeared later. Under the range of 50% to 70% for water content of leaves, the value of R, G, B channel of color images would decrease with the increases of water content. When the water content of leaves was less than 40%, the R channel value was larger than the G channel value. When using the extreme learning machine to retrieve the water content index, the corrected color components improved the fitness and the prediction accuracy. The vertical image was more suitable for retrieving water content of leaves, with the error of determination coefficient and average absolute percentage being 0.8352 and 2.3%, respectively. The canopy images were more accurate in expressing the equivalent water thickness of blades, with the above indices of 0.7924 and 9.3%, respectively.