Soil salinization is one of the most important reasons for soil degradation. Rapid and accurate monitoring of soil salinity has positive effects on sustainable agricultural development and ecological environment protection. This study proposed a new method of surface soil salinity estimation in coastal areas based on digital photographs to obtain soil salinity information quickly and conveniently under complicated weather conditions. 52 bare surface soil samples and photographs were collected under sunny and cloudy weather in the coastal area of Yancheng, Jiangsu province. Parameters such as soil electrical conductivity (EC), pH value and soil water content were measured in the lab. Using RStudio software for photo processing, firstly, three color components were extracted from RGB color space, then five color spaces (HIS, CIEXYZ, CIELAB, CIELUV, and CIELCH) were obtained from color space conversion. Three parameters were extracted from each color space. Hence there were 16 parameters from 6 color spaces for CIELAB, CIELUV, and CIELCH having the same parameter L. The correlation analysis of soil EC and color parameters indicated that the color purity and brightness were significantly correlated with soil EC, while color hue was insignificantly correlated with soil EC. Random forest and leave one out cross validation methods were used to establish soil EC estimation model with randomly 70% dataset, and the rest 30% dataset was used for validating. Repeated 100 times to get the optimal model, and finally, the accuracy of the best model reached $R_{val}^{2}$=0.75, RMSE_val=3.52, RPD_val=2.02. By analyzing the importance of color parameters, we found that color purity and color brightness contributed most to the model, and color hue contributed relatively little. To sum up, the color parameters obtained from digital images provided a new approach for soil salinity estimation effectively. Combined with the unmanned aerial vehicle, this study proposed a new perspective for quantitative assessment of surface parameters, which would provide technical support and effective means for the precise management of precision agriculture and coastal ecological environment in future.