[Objective]To obtain an accurate estimation of forest above-ground biomass ( AGB) ,the polynomial model integrating the terrain factors was presented based on the relationship of Synthetic Aperture Radar ( SAR ) response for forest AGB and terrain using the airborne P-band full Polarimetric SAR ( PolSAR) data acquired by CASMSAR.[Method]Firstly,the slope map and the true forest AGB map over the study area were obtained as reference data using LiDAR data, and the forest AGB map was trained by the field AGB data. The systematical sampling was carried out based on the reference data to analyze the relationships between the backscattering intensity and the forest AGB and to analyze the changes of these relationships when the slope varied. Secondly,the local incidence angle was calculated from the LiDAR DEM and the orbit parameters of the airborne P-band SAR platform,and the polynomial model was built integrating the features of intensity,local incidence angle and look angle. Some of the sample plots were used to train the model parameters,and the others were performed as the validation samples. In order to avoid the contingency caused by sample size,more experiments were implemented with different sample size from 20m × 20 m to 100 m × 100 m.[Result]In the case of the plots with the size of 90 m × 90 m,for the estimation model with the slope parameter ( called as the second set of features) and for that without the slope parameter ( called as the first set of features ) ,the following quantitative technical targets were achieved. With the slope from 0°to 5°,the determination coefficients(R2) were 0. 634 and 0. 634 respectively,the root mean squared error ( RMSE ) were 12. 07 t·hm -2 and 12. 08 t·hm -2 respectively,the overall accuracies were 78. 91% and 78. 89% respectively. With the slope from 5° to 10°,the R2 were 0. 524 and 0. 523 respectively,the RMSE were 13. 52 t·hm -2 and 13. 97 t·hm -2 respectively,the overall accuracies were 80. 57% and 80. 52% respectively. With the slope above 10°,the R2 were 0. 628 and 0. 519 respectively,the RMSE were 13. 16 t·hm -2 and 15. 70 t·hm -2 respectively,the overall accuracies were 81. 05% and 78. 55% respectively. In addition,with the plot size increasing,the precisions of both methods were all improved. Especially,the accuracy of the estimation model with the slope parameter was higher than that without the slope parameter.[Conclusion]It was shown that the terrain had little effects on the intensity of the SAR data when the slope less than 10°,while it had a significant effect when the slope increases to more than 10°. The refined model involving local incidence angle could improve the accuracy, demonstrating the effectiveness and stability of the refined model. In addition,the accuracy would increase and tend to be stable with the scale enlarging regardless of the adopted model considered the effect of terrain or not,which revealed that the plot scale for evaluating the estimation model needed to be valued. The size of the sample plots should be considered for a reliable evaluation.%【目的】利用国产合成孔径雷达(SAR)系统(CASMSAR)获取的机载 P －波段全极化 SAR(PolSAR)数据，分析SAR对森林地上生物量( AGB)的响应与地形的关系，建立融合地形因子的高精度多项式模型，以提高森林 AGB的估测精度。【方法】首先以基于机载激光雷达( LiDAR)数据得到的研究区坡度分布图与结合实测样地 AGB数据得到的森林AGB分布图作为参考数据进行系统抽样，分析森林AGB与P－波段PolSAR后向散射强度的关系以及不同坡度下二者的相关性变化；然后利用LiDAR得到的高精度数字高程模型( DEM)结合机载 P －波段的轨道数据计算当地入射角，进而建立以后向散射强度、当地入射角以及雷达视角为输入特征的多项式统计模型，同时将以上系统抽样得到的样本一部分作为模型训练样本，一部分作为精度检验样本。为避免样本尺度引起的偶然性，检验了20 m ×20 m至100 m ×100 m不同样地尺度下的估测精度。【结果】以90 m ×90 m样本为例，当坡度为0°～5°时，引入当地入射角(第2组特征)的估测精度与未引入当地入射角(第1组特征)的估测精度分别为：决定系数( R2)为0.634和0.634，均方根误差(RMSE)为12.07和12.08 t·hm －2，总精度(Acc．)为78.91%和78.89%；当坡度为5°～10°时，第2组特征与第1组特征的估测精度分别为：R2为0.524和0.523，RMSE 为13.52和13.97 t·hm －2，Acc．为80.57%和80.52%；当坡度大于10°时，第2组特征与第1组特征的估测精度分别为：R2为0.628和0.519，RMSE 为13.16和15.70 t·hm －2，Acc．为81.05%和78.55%。随着样地尺度增大，2组特征的估测精度均增大，且第2组特征的估测精度大于第1组。【结论】当坡度小于10°时，地形对森林的后向散射强度几乎无影响；当坡度大于10°时，地形的影响显著，在不同尺度下，引入当地入射角的估测模型均可以有效提高估测精度，充分说明模型的有效性和稳定性。此外，随着尺度增大，无论采用的模型是否考虑了地形影响，其估测精度都逐渐提高并趋于稳定，揭示出对复杂地形下森林AGB估测模型效果的评价必须考虑尺度的影响，且参考样地要足够大，否则难以得到客观的结论。
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