The atmospheric conditions will lead to the distortion of the ground radiance or reflectance recorded by satellite sensors which inevitably hampers the successful regional scale aboveground carbon density quantification which is critical to the understanding of forest contribution to the regional carbon cycles. Hence, the appropriate algorithms of atmospheric correction are necessary. The objective of this paper was to assess the utility of radiation correction algorithms for estimating aboveground forest carbon storage with the multi-temporal Landsat remote images(TM/OLI) and quantify the carbon storage in a forest plantation, and five atmospheric correction methods, two absolute modeling methods (6S, FLAASH), two absolute image-based methods (IACM, QUAC), and one relative method (PIF) were compared. Forest carbon storage was estimated using the viable biomass empirical statistical models. Parameters for the regression equation were determined by analyzing the relationship between the data of the selected vegetation indices derived from Landsat images and the field-measured data (height and diameter) using data from different tree stands in study area. In order to evaluate the accuracy of the carbon storage of Pinus massoniana forest derived from multi-temporal remote sensing images, we acquired the forest subcompartment survey data in 1997, 2002 and 2006 and conducted several field surveys in 2010 and 2013. Consequently, we found that the surface reflectance of Pinus massoniana forest decreased evidently after atmospheric correction in visible band, but the surface reflectance in nearinfrared band and shortwave-infrared band, as well as NDVIs had a significant increase. And different atmospheric correction models had significant different effects on the estimation of the carbon storage of Pinus massoniana forest. By studying the correlation between the field-measured data, the IACM-corrected and 6S-corrected MNDVI data were most suitable for estimating the carbon storage of Pinus massoniana forest in the study area, with an exponential regression model appeared to have the highest degree of agreement and the lowest relative error with the measured data. In addition, we also found that the relative error of NDVIs of Pinus massoniana forest in multi-temporal remote sensing images decreasd 85.16% after PIF correction, and the estimation accuracy of the forest carbon storage was improved simultaneously. The results suggested that more attentions should be paid to choose the appropriate atmospheric correction when remote sensing images were applied to quantitative analyzing and information collecting in field. And relative radiometric correction of remote sensing images is quite an important preprocessing technique, which can significantly improve the precision of the estimated results, especially in multi-temporal remote sensing monitoring. This study also confirmed that the combination of IACM and PIF could preferably reduce the atmosphere effect, which would be suitable for multi-temporal forest carbon storage estimation and other related quantitative remote sensing researches.