Estimation of vegetation aboveground biomass in the arid oasis can provide important evidence for evaluating the stability of the oasis ecosystem and estimating regional carbon storage. This research targeted the delta oasis of the Weigan-Kuqa Rivers and used ENVI 5.3 software to preprocess Landsat 8 Operational Land Imager (OLI) image data to survey vegetation aboveground biomass in the study area. We extraced vegetation indices and band factors reflecting aboveground biomass information, combined with measured data from sample plots and used conventional statistical models, multiple stepwise regression and partial least square regression methods to establish an optimal model of vegetation aboveground biomass, so as to reveal the spatial distribution characteristics of vegetation aboveground biomass in this oasis. It was found: 1) There was a extremely significant positive correlation between the 20 selected remote sensing factors and the measured aboveground biomass and the values of the correlation coefficients ranged from 0.5-0.7 (P<0.01). 2) The optimal estimation models for arbors and shrub aboveground biomass were multiple stepwise regression models. The partial least squares regression models were the best models for estimating the aboveground biomass of herbs and crops. The verification determination coefficients of the model were above 0.6, and the root-mean-square error and mean absolute error were both lower. 3)The vegetation aboveground biomass in the study area was typically within the range of 280-1450 g·m-2,with an area of about 6973.82 ha. Land with low lever aboveground biomass (<65 g·m-2) accounted for about 15.02% of the total land area in the survey area. The ranking of aboveground biomass from high to low for different vegetation categories was: Crops>arbors>shrubs>herbs. For the various vegetation types, the remote sensing estimation model based on the spectral characteristics of ground objects was able to accurately estimate vegetation aboveground biomass in the arid oasis, and carry out remote sensing quantitative inversion of spatial distribution characteristics of its vegetation.