Detecting vegetation changes in arid and semi-arid regions, particularly identifying areas affected by vegetation drying, remains a major challenge in remote sensing. This study aimed to identify and analyze areas affected by vegetation drying in the rangelands of Chaharmahal-va-Bakhtiari Province, Iran, using remote sensing data from 2013 and 2023. Image differencing was performed for several indices, including the Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Tasseled Cap Transformation (TCT), and Simple Ratio (SR). Thresholds were then applied to the difference values of each index to identify dried patches. To validate the results, field surveys and sampling were conducted to assess the agreement between the identified patches and actual field conditions. The results showed that the SR index performed best in identifying dried patches, achieving an overall accuracy of 75% and a Kappa coefficient of 0.75, indicating substantial agreement with the field data. The VHI index ranked second, with an overall accuracy of 60% and a Kappa coefficient of 0.40. The TCT index achieved an overall accuracy of 52% and a Kappa coefficient of 0.32, whereas the NDVI index showed the lowest performance, with an overall accuracy of 40% and a Kappa coefficient of 0.28. These findings suggest that SR was more effective than the other evaluated indices in detecting vegetation drying in the study area. The SR index could therefore serve as a useful tool for monitoring vegetation drying and supporting early warning systems and the sustainable management of rangeland resources.