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Rapid invasion of Spartina alterniflora into Chinese coastal wetlands has attracted much attention. Many field and remote sensing studies have examined the spatio-temporal dynamics of S. alterniflora invasion; however, spatially explicit quantitative analyses of S. alterniflora invasion and its underlying mechanisms at both patch and landscape scales are seldom reported. To fill this knowledge gap, we integrated multi-temporal unmanned aerial vehicle (UAV) imagery, light detection and ranging (LiDAR)-derived elevation data, and tidal and meteorological time series to explore the growth potential (lateral expansion rates and canopy greenness) of S. alterniflora over the intertidal zone in a subtropical coastal wetland (Zhangjiang estuarine wetland, Fujian, China). Our analyses of patch expansion indicated that isolated S. alterniflora patches in this wetland experienced high lateral expansion over the past several years (averaged at 4.28 m/year in patch diameter during 2014–2017), and lateral expansion rates ( y , m/year) showed a statistically significant declining trend with increasing inundation ( x , h/day; 3 ≤ x ≤ 18 ): y = − 0.17 x + 5.91 , R 2 = 0.78 . Our analyses of canopy greenness showed that the seasonality of the growth potential of S. alterniflora was driven by temperature (Pearson correlation coefficient r = 0.76 ) and precipitation ( r = 0.68 ), with the growth potential peaking in early/middle summer with high temperature and adequate precipitation. Together, we concluded that the growth potential of S. alterniflora was co-regulated by tidal and meteorological regimes, in which spatial heterogeneity is controlled by tidal inundation while temporal variation is controlled by both temperature and precipitation. To the best of our knowledge, this is the first spatially explicit quantitative study to examine the influences of tidal and meteorological regimes on both spatial heterogeneity (over the intertidal zone) and temporal variation (intra- and inter-annual) of S. alterniflora at both patch and landscape scales. These findings could serve critical empirical evidence to help answer how coastal salt marshes respond to climate change and assess the vulnerability and resilience of coastal salt marshes to rising sea level. Our UAV-based methodology could be applied to many types of plant community distributions.