Soil moisture plays a crucial role in vegetation growth. However, the long-term infuence of soil moisture on vegetation growth was not sufciently understood in many regions, especially in developing countries, due to the lack of ground measurements. Remote sensing data provide a promising way to overcome this limitation. In this study, a long-term spatiotemporal variation in remote sensing surface soil moisture (SSM) and vegetation indices (VIs) and their relationship during 1988–2015 were analyzed over Iran. Trend-free pre-whitening Mann–Kendall test was applied for the detection of the trends in soil moisture and VIs time series. Also, the validity of the “dry gets drier, wet gets wetter” (DGDWGW) paradigm was examined throughout the country. Finally, the consistency between SSM and vegetation indices trends was investigated using homogeneity Chi-squared test. The results showed that the monthly average of the SSM over 45% of Iran is lower than 0.15 m3/m3. Seasonal average of SSM is 0.17 and 0.12 m3/m3 in spring and winter, respectively. On average, monthly SSM trends are downward in 70% of Iran, which 30% of those is statistically signifcant. Over the past 28 years, about 50% and 35% of Iran got drier with rates of 0.24 × 10−2 (spring) and 0.78 × 10−3 (summer) m3/m3 per year. According to DGDWGW paradigm examining, 14% of Iran follows the DGDWGW paradigm. Summer and spring normalized diference vegetation index (NDVI) and enhanced vegetation index values are less than 0.1 in about 45% and 65% of the areas, respectively. The NDVI values are decreased in 40% of Iran in the last 28 years, of which half of those are statistically signifcant. SSM trends were consistent with vegetation indices using a homogeneity test. About 45% of SSM trends agree in sign with NDVI values. Life zone in the Southeast of Iran is arid desert scrubs, and in this area with sparse vegetation cover, mixing the spectral of soil and vegetation causes a serious problem in vegetation indices repr
