半经验的核驱动双向反射分布函数（BRDF）模型已广泛应用于定量遥感的各个领域之中，但是，目前版本的核驱动模型依然存在着一些问题，从而影响了核驱动模型在某些研究领域的进一步推广和应用，具体问题如下： （1）当地物的几何光学散射占主导时（即fvol≈0），现有核驱动模型依然略微低估了热点方向的反射率，从而影响了基于热点反射率进行估算的一些植被结构参数（如聚集指数）的估算精度。针对这一问题，我们提出了一种改进几何光学核热点效应的方法，基于地球反射的极化和方向传感器（POLDER）数据库的验证结果表明：改进后的模型相对于现有模型，对热点反射率的拟合精度有了进一步的提高。 （2）在对物理BRDF模型的热点效应进行评估时，由于缺乏足够的热点观测（地面数据）或合适的评估方法（机载或星载数据），因此难以直接对模型的热点效应进行评估。针对这一问题，我们使用改进热点效应后的模型（RTLSR_C），基于POLDER数据库评估了PROSAIL模型的热点效应，结果表明：RTLSR_C模型的热点参数C1和C2可以作为基准参数，来反映所处理的多角度数据的热点高度和宽度特征，展现出了评估各种物理BRDF模型热点效应的潜力，从而有助于进一步提高基于热点特征反演的各种植被结构参数的估算精度。 （3）在研究星载聚集指数（CI）产品的季节变化特征时，部分CI产品的精度可能会受到降雪等事件的影响，从而影响了星载CI产品的整体质量，针对这一问题，我们基于中分辨率成像光谱仪（MODIS）积雪产品，研究了雪对星载CI产品季节变化和反演精度的影响，并对CI产品生产算法进行了改进，结果表明：积雪会降低主算法估算的CI产品的精度，而备用算法可以校正该影响，得到更高精度的CI，基于改进后的算法生产的CI产品在积雪覆盖区域有着更高的精度，从而更适合作为输入参数，应用于相关生态和气象模型中。

The semi-empirical, kernel-driven, linear Bidirectional Reflectance Distribution Function (BRDF) model has been widely used in many fields of quantitative remote sensing. However, the current version of the kernel-driven model still has some issues that influence the further usage of kernel-driven model. (1) In some situations in which the weight of the volumetric scacttering kernel (fvol) is no longer significant (e.g., fvol=0), the current kernel-driven model still slightly underestimates the hotspot effect. In this paper, we devise a method to enhance the overlap function inherent in the KLSR using a physical hotspot function. A validation experiment based on the POLDER BRDF database shows that this method further improves the fits between the models and observations in the vicinity of hotspot directions. (2) To date, the hotspot effect of the PROSAIL model has rarely been assessed by multi-angular measurements with sufficient hotspot observations due to the lack of accurate hotspot measurements (for field measurements) or appropriate methods (for airborne and spaceborne measurements). In this paper, we design a framework to utilize the improved kernel-driven model to assess the hotspot effect of the PROSAIL model. The results indicate that C1 and C2 in the improved kernel-driven model can be used as benchmarked parameters to qualify the amplitude and width of the hotspot effect for the simulated multi-angular data of physical BRDF models, and thus present the potential for the assessment of the hotspot effect of various BRDF models. (3) Recent studies reported that ephemeral snow during winter decreases the accuracy of MODIS BRDF retrievals during leaf-off seasons, which leads to lower CI quality. In this study, we investigate the influence of snow on the magnitude and seasonal variation of the CI retrieved from the MODIS BRDF products based on field-measured CI and statistics from the global MODIS CI product. Results indicate that the backup algorithm can effectively correct abnormally large CI values and obtain more reasonable CI retrievals than the main algorithm without any constraints in snow-covered areas. The improved global MODIS CI product, in which the backup algorithm is used to process the snow-covered pixels, has improved accuracy for CI retrievals in snow-covered areas and thus is probably more suitable as the input parameter for ecological and meteorological models.