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This paper investigates the retrievals of the canopy closure and leaf area index (LAI) of the Moso bamboo forest from the Landsat Thematic Mapper data using a constrained linear spectral unmixing method. A new approach for endmember collection based on the real scenario simulation of the Moso bamboo forest is developed. Four fraction images (i.e., sunlit canopy, shaded canopy, sunlit background, and shaded background) are calculated and used to develop the canopy closure and LAI. The results show that the predicted crown closure, which was inverted from the sunlit and shaded canopies, has a good agreement with the observed crown closure (R2 = 0.725). The accuracy assessment indicates that the root mean square error (rmse) and the relative root mean square error (rmse_r) are 10% and 13.37% for the predicted crown closure, respectively. The LAI has the highest correlation coefficient with the shaded background, and it can be fitted by an exponential model (R2 = 0.497). The linear relationship between the predicted and observed LAI values is significant at a level of 99% (P <; 0.01 and R2 = 0.459), and the LAI can be predicted by the exponential model.