The Atlantic Forest is one of the most threatened biodiversity hotspots and environmental impacts has made its landscape fragmented and heterogeneous. The heterogeneity of the fragments is a challenge for the characterization and quantification of forest resources, such as the stock of biomass and carbon. Methodologies based on remote sensing have been used, to improve these estimates without compromising execution costs. The objective was to estimate, with high spatial resolution passive remote sensing, the aboveground carbon stock in fragments of different successional stages of the Atlantic Forest. Forests were classified into initial, intermediate, and advanced successional stages. In each stratum, 10 plots (20x50 m) were established, and the carbon stock was calculated by adjusted Schumacher and Hall model. The reflectances of the blue, green, red, and near-infrared bands and vegetation indices (VIs) were obtained in the dry and rainy seasons, from MSI/Sentinel-2 images, with a resolution of 10 m. Artificial Neural Networks (ANN), with different combinations of variables, were trained and validated with simulated reflectance values. Carbon was estimated by ANN with the best performance in training and validation. The average carbon stock in the initial, intermediate, and advanced strata was 24.99, 35.79 and 82.28 Mg ha⁻¹, respectively, with a general average of 47.68 Mg ha⁻¹. The carbon estimates were better with the ANN trained with the reflectances of the rainy season. The addition of VIs did not improve ANN performance. The simulated spectral data were consistent and adequate to validate the selected ANN. The total carbon stock, modeled was 41,962.15 Mg, ranging from 6.68 to 108.29 Mg ha⁻¹, with an average of 48.70 Mg ha⁻¹. The carbon stock in the advanced stratum is more than three times that observed in the initial stratum, and they were efficiently estimated using high-resolution multispectral data, obtained in the rainy season, as inputs.