ABSTRACT Estimation of above-ground carbon stocks on planted forests is absolutely essential for understanding to what extent the plant species contribute to the global carbon cycle and towards minimizing climate change. Therefore, the main objective of this study was to estimate above-ground carbon stock of Acacia decurrens plantation in the Fetam watershed, Banja woreda, Amhara region, Ethiopia based on field measurement, remote sensing and GIS techniques. To address the objective, both primary and secondary data types were collected. Twenty sampling plots were selected which has an area of 100m 2 each plot. A non-destructive method was used to measure the diameter at breast height (DBH) and Height. We analyzed the above-ground biomass data for 1008 trees with a diameter ≥ 5 cm and a conventional height of 1.3m from Acacia decurrens plantation. An allometric equation method was used to estimate the biomass and carbon stock of this plantation. Five vegetation indices (VIs) were extracted (NDVI, EVI, SR, NDI45, and GNDVI) from the Sentinel-2 image. The relationship between the amount of the above-ground carbon stock (AGCS) and VIs from Sentinel-2 image was established by the linear regression model. The results of this study showed that the average biomass was about 120.4±37.1 ton ha -1 with the average carbon stock of about 56.6±17.41 ton ha Normalized difference index (NDI45) had shown a strong correlation with AGCS (r = 0.88; R 0.78) as compared to other VIs. Stepwise linear regression was fitted for establishing the model between field AGCS and VIs (R 2 -1 = 0.78, p < 0.05). The total estimated carbon stock of Acacia decurrens was approximately 874,913.178 tons. The study concluded that a reliable estimate of AGCS can be made through a combination of high-resolution satellite imagery and a few samples from the ground. This study could provide useful information on the carbon stock capacity of the Acacia decurrens plantation forest in this study area. So that continuous Acacia decurrens plantation forest inventory should be conducted. Key words: Carbon Stock, Regression analysis, Remote Sensing, Sentinel-2, Vegetation Indices . We found 2 =