The global climate change has led to an increasing concern about the dynamics of the carbon storages of the forest ecosystem. In the past a few decades, remote sensing technology has been frequently applied in measuring forests' carbon storage on various scales. Nevertheless, little work has been done in the estimation of the carbon storage of the masson's pine, which is a widespread pine species in central and southern China. Therefore, this paper aims to develop a model based on remote sensing technology to estimate the carbon storage of Pinus massoniana forest using the case of the Hetian Basin in County Changting, Fujian province, southeastern China. We have carried out field measurements with 50 sampling sites in November 2010, in order to acquire basic data of Pinus massoniana forest in the study area. Each sampling site has a size of 20×20m to match the pixel size of remote sensing imagery. The filed-acquired data were correlated with the corresponding vegetation spectral information derived from a near-synchronized Advanced Land Observing Satellite (ALOS) image. To examine whether the image needs to be radiometrically corrected before it can be used for the task, the ALOS image was radiometrically corrected with the ICM and IACM models respectively. The difference between the two models lies in the latter corrects not only solar illumination and terrain effects but also atmospheric effects. Five vegetation indices were then derived from the ICM- and IACM-corrected images, as well as the original DN-based image. This is to determine which index would be most suitable for estimating the carbon storage of Pinus massoniana forest in the area. The five indices used are the Normalized Difference Vegetation Index (NDVI), the Difference Vegetation Index (DVI), the Perpendicular Vegetation Index (PVI), the Soil Adjusted Vegetation Index (SAVI), and the Soil Adjusted Ratio Vegetation Index (SARVI). By studying the agreement between the field-measured data and the data of the five selected vegetation indices derived from the ALOS image using regression analysis, the IACM-corrected NDVI data with an exponential regression model appeared to have the highest degree of agreement with the filed data and thus was utilized to calculate the carbon storage of Pinus massoniana forest in the Hetian Basin area. Accuracy assessment revealed that the model-estimated data were strongly correlated with field-measured data, suggested by a R² of 0.979, a root mean square error of 3.01t/hm², and a relative error of -1.95%. The estimated data show a slight underestimate by 2% when compared with the measured data. This suggests that the remote-sensing based model can be effectively used for estimating the carbon storage of the Pinus massoniana forest in the study area. Nevertheless, an atmosphere correction for the remote sensing image should be carried out before it can be put in use, because this study has confirmed that the IACM-corrected data, which has been radiometrically corrected for atmosphere effects, can significantly improve the precision of the estimated results. Based on the retrieved estimate model, the carbon storage of Pinus massoniana forest in the Hetian Basin in 2010 was revealed, which was 114.58×10⁴t in total, with a density of 34.92t/hm².