Due to the complexity of natural systems and the phenomenon of climate change, species distribution models are used to understand the impact of climate change on potential niche of species. For this purpose, many studies used WorldClim database derived from the interpolation of temperature and precipitation records from 1950-2000. Due to lack of meteorological stations and their uneven distribution, extreme fluctuations of precipitation and temperature in arid and semi-arid regions, climatic variables produced by interpolation methods may not produce reliable results. High uncertainties of Kriging-based methods and the temporal asymmetry between current studies and the WorldClim database may result in inappropriate predictions. Satellite remote sensing as an effective tool reduces field measurement gaps and partially removes existing problems. Therefore, in the present study, the potential of remote sensing in producing bioclimatic variables, determining the factors affecting potential distribution, preferential tendency of Artemisia sieberi Besser and Artemisia aucheri Boiss. to environmental factors, as well as modelling distribution of these two species of Iran-Touranian rangelands in Isfahan province in comparison with bioclimatic variables produced by geostatistical method during the period 2001-2017 has been investigated. The digital elevation model (DEM), land surface products (MOD11A1 and MOD11A2) and vegetation indices (MOD13A3) extracted from MODIS sensor and also downscaled and calibrated PERSIANN-CDR were employed to produce remote sensing-based bioclimatic variables. Seven modelling algorithms that involve a broad range of analytical approaches were used to predict species distribution within the framework of consensus method. The optimistic (RCP 2.6) and pessimistic (RCP 8.5) emission scenarios of CCSM4, MPI-ESM-LR and NorESM1-ME models for the years 2050 and 2070 were evaluated to investigate the trend in spatiotemporal variations of distribution under climate change. Destruction and reduction of vegetation distribution as a result of climate change in Iran intensify drought and desertification. Optical/thermal remote sensing-based dryness indices at different spatial and temporal resolution are one of the available methods for monitoring drought and soil moisture changes. In this context, the present study utilized the MODIS-based TVDI, iTVDI and VDI indices to scrutinize the effect of dryness on soil moisture in three vegetation communities: Artemisia . (sagebrush), Astragalus . (astragale) and grassland during a wet (2004) and dry year (2008). The MOD13A2, MOD11A2, and MOD09A1 products related to active growing period in wet and dry year were used to extract dryness and vegetation indices. The findings of this study showed that considering the evaluation criteria of statistical models, remote sensing with acceptable accuracy has the efficiency to produce bioclimatic variables and as a result improve species distribution prediction. The results of climate change impact prediction also showed that in CCSM4 and MPI-ESM-LR models, the maximum and minimum displacement of Ar. sieberi will be occur under RCP8.5 and RCP2.6 scenarios in 2050, respectively. Climate change only for the NorESM1-ME model under the RCP2.6 scenario in 2050 will have an increasing effect on suitable habitat for Ar. sieberi , but in other cases a decreasing effect will observed. The habitat of Ar. aucheri in the NorESM1-ME model under both scenarios at both time periods will shift less than the other two models. The maximum displacement of this species will occur under the MPI-ESM-LR model, which is approximately twice the displacement under the CCSM4 model. Evaluation of dryness indices using the downscaled PERSIANN-CCS and field-measured soil moisture indicated that iTVDI was strongly affected by recent precipitation events and could be an effective index for monitoring soil moisture status in semi-arid regions of Iran, but in insignificant relationship between the VDI and precipitation or soil moisture indicates an inconsistency of this index in detecting water stress. Among vegetation indices, NDVI showed stronger linear relationship with precipitation compared to EVI and NDII7, especially in grasslands. In general, the results indicated that a combined analysis of vegetation indices and effective dryness indices would be a useful approach to drought assessment and management over arid and semi-arid rangelands.