Earth observation data play a vital role for efficient modeling of invasive species. Particularly, optical Sentinel-2 (S2) data with its capability of providing high spatial, spectral and temporal resolutions creates ample opportunities. However, few studies so far evaluated the combined use of S2 derived variables and environmental variables for modeling the distribution of invasive species. This study aims to compare the performance of models using S2 derived variables with environmental variables and their integration for modeling invasive Prosopis juliflora in the lower Awash River basin of Ethiopia. A total of 680 field data were used to train and validate the Random Forest (RF) approach. Model performances were evaluated using True Skill Statistics (TSS), kappa index, correlation, area under the curve (ROC), sensitivity and specificity. Our results demonstrated that modeling using S2 vegetation indices and S2 spectral bands showed higher performance compared to topoclimatic based variables with TSS of 0.91, 0.89, and 0.74, respectively. The ROC also confirmed the higher accuracy of S2 vegetation indices, S2 spectral bands and combined models compared to a topo-climatic based modeling. Interestingly, models using the integration of S2 derived variables with topo-climatic variables showed even better performance than the individual models. Our study highlighted that S2 derived variables and their integration with topo-climatic variables are highly recommended for efficient monitoring of invasive species distribution.