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Remote sensing is one of most important methods to estimate forest canopy closure at large. There are mainly three kinds of remote sensing algorithms for canopy closure retrieval: statistical models, physical models and mixed models. Most practices used statistical models, which are lacking physical explanation and limited in local areas. The physical models are with clearer understanding on mechanism, which can be used in large areas. However, due to the higher complexity, physical models are less applied. The Stochastic Radiative Transfer (SRT) model is applicable in simulating forests with horizontally distributed heterogeneity, which may stand for different canopy closure. Exploring the inversion method using the SRT model would help to improve the efficiency and precision of canopy closure inversion. In this paper, based on the SRT model, an inversion method has been proposed on canopy closure retrieval of Yunnan Pine forests. The fundamental is the quantitative relationship between the canopy closure and the probability of finding foliage elements in SRT Model. To match the Yunnan Pine crown shape, an equivalent model was used to correct the cylinder shape assumption. Then, a look-up-table was constructed to inverse the canopy closure to reflectances from GF-1 and Landsat-8 satellite images. The probability of finding foliage elements and leaf area index are determined in the case of a minimum difference between simulated reflectances and satellite observations, in order to calculate the canopy closure based on the stochastic Beer–Lambert–Bouguer law. The 30 plots of field data were used to assess the inversion accuracy. A statistical inversion method based on NDVI is conducted for comparison. Results show that the inversion can accurately map canopy closure of Yunnan pine forests in the study area（R2=0.8345，RMSE=0.0688）. Reflectance of the bands used for retrieval performs sensitivity to canopy closure. And using composite image from GF-1 and Landsat-8 is feasible. The equivalent shape correction model is reasonable which reduces the RMSE by 0.0466, and the algorithm is flexible in different crown cases. This study can provide supports on both forward models and inversion methods for large scale forest canopy closure retrieval. The research could be extended to any tree species by changing the model parameter input, and any crown type by crown shape equivalent correction.