Assessing soil moisture is an essential measure of aridity and has become a significant field of study for monitoring dry conditions, particularly following recent years' climate change developments. This article aims to evaluate three drought indices, the Perpendicular Drought Index (PDI), Soil Moisture Monitoring Index (SMMI), and Modified Soil Moisture Monitoring Index (MSMMI), within the Gozlu agricultural enterprise located in Konya Province, Turkey, which is characterized by a semi-arid ecosystem. It also tests the correlation between long-term precipitation data and the selected drought indices while exploring the relationship between the most correlated drought index with long-term precipitation spatial data on the one hand and the spatial distribution of soil texture on the other. This study was conducted in two stages. In the first stage, we used Sentinel-2 data to derive drought indices and the ERA5 dataset to calculate the long-term mean total monthly precipitation (LT-MTMP). We then performed a Pearson correlation coefficient analysis between these two data sets. In the second stage, we collected 100 composite samples from a 0-30 cm depth in the study area to determine the soil texture using the hydrometer method based on the USDA classification system. We then created an interpolated map of soil texture classes. Subsequently, the soil texture map was combined with the drought index map, which showed the highest correlation with LT-MTMP by geospatial techniques to explain the relationship between the spatial distribution of soil texture and the drought index. The research findings indicate that the PDI index has the highest negative correlation coefficient (r = -0.69, P < 0.01) with LT-MTMP, meaning that a decrease in the drought index corresponds to an increase in precipitation spatially. On the other hand, the SMMI and MSMMI indices possess correlation coefficients of (r = -66, P < 0.01) and (r = -24, P < 0.05), respectively. Laboratory analysis revealed four distinct soil textures within the studied area: Sandy Loam (SL), Loam (L), Sandy Clay Loam (SCL) and Clay Loam (CL). This approach provides a deeper understanding of soil data behavior over several years when linked drought indices with long-term climate factors. Additionally, geo-statistical combination techniques enabled the interpretation of the relationship between the spatial distribution of soil moisture and soil texture, thus facilitating land management and providing a useful tool for drought management plans.