Soil erosion is one of the most significant environmental threats worldwide. Many prediction models have been developed to estimate soil erosion and associated sediment yield. By incorporating important factors affecting erosion and due to low data requirements, semi-quantitative models are exceptionally suited for estimation. This study applies the MPSIAC semi-quantitative model along with geographical information system and remote sensing techniques to estimate sediment yield in the Golabad watershed, a semi-arid region in central Iran. Nine data layers of the model were generated from Landsat ETM+ imagery, adapted regional maps and field surveys. Geographic information system was applied to integrate the layers together and generate the sediment yield map. The results showed a range of sediment yield from 263.3 to 496.9 t km-2 yr–1 with an average of 356.4 t km-2 yr–1. However, it seems that descriptions of the model are sometimes broad for making reliable scoring. Nevertheless, this model is generally less data demanding and provides an efficient way to estimate sediment yield in ungauged basins. It was found that hills are the most sensitive land types to sediment yield in the region. It was suggested the advantages of integrating GIS and remote sensing techniques when estimating sediment yield, subsequently achieving better soil conservation practices.