Salinity is a major environmental challenge in the coastal areas of Oman, which moves rapidly inward the arable lands affecting the high salinity tolerant date palm plantation. This rapid spread of salinity has risen the necessity for accurate, cost- effective and timely monitoring method to investigate the salinity stressed vegetation including date palm trees. Remote sensing technology provides accurate results over large scale of coverage in timely manner for different applications including salinity detection and mapping. The main objective of this study was to use the remote sensing techniques for the assessment of the salinity stress on date palms in Oman by examining the effectiveness of RGB camera for evaluating salinity stressed date palm leaves in controlled laboratory conditions and using Unmanned Aerial Vehicle (UAV) with a color camera to assess salinity effects on vegetation cover, especially on date palm trees in open field conditions. Soil and water samples were collected from selected sites and analysed for ground-truth verification. Two remote sensing platforms including lab-based and UAV color imaging were investigated in this study. Images were analysed in ENVI and MATLAB software. The obtained results verified that date palm leaves can be classified according to salinity stress using color image analysis with about 80% accuracy. The UAV image analysis demonstrated that Green Leaf Index (GLI) of aerial images is a good indicator of vegetation salinity status with R2 = 0.9128. In addition, the obtained percentages of area covered with vegetation and date palm trees were strongly and negatively correlated with salinity levels.The implemented method of image analysis has a potential to detect early the salinity stressed farms without the need for lab chemical analysis and plant destruction. This technique implements the color imaging which is customer available. It can be a strong foundation for the development of UAV color imaging on salinity stressed date palm monitoring systems, providing useful information for the real-time decision making on soil/water salinity management.