Drought is a natural and hydrometeorological phenomenon that is caused by high temperatures and low precipitation conditions. Few studies in the literature used remote sensing and spatial-temporal images in assessing the drought problem in the United Arab Emirates. The main factor to be aware of drought occurrences in the UAE is climate change, which is expected to be more intense. This research aims to use GIS and remote sensing techniques for spatial drought assessment in the UAE using Ordinary Least Squares regression (OLS) analysis. In this research, Moderate Resolution Imaging Spectroradiometer Terra Daily was used with the high-resolution satellite-derived Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data to assess drought in the northern part of UAE from 2010 to 2020. This research focuses on several indicators, including soil moisture index (SMI), vegetation condition index (VCI), vegetation health index (VHI), precipitation condition index (PCI), and standardized precipitation index (SPI), to analyse and quantify drought. SPI of ground observation was used as a true drought condition on the model. OLS analysis was utilized to Evaluate the contribution of different factors (vegetation, surface temperature, and precipitation). The result indicates that the overall accuracy of OLS models for winter, summer, and winter summer together was 60%, 65%, and 72%, respectively. The significant indexes for the winter were VCI, PCI, and SPI. In November 2010, 2015, and 2020, PCI and SPI indicated that most of the United Arab Emirates experienced exceptional drought. The land Surface temperature (LST) index was found to be significant for Sharjah station in winter due to urbanization and the infrastructure of Sharjah station compared to other stations. During the summer, SMI was found to be more significant. A slight and moderate drought was found in the western area, which is almost covered by desert using SMI in the summer. VCI and VHI data in 2010 show that the worst drought condition was fou