基于前向散射核函数拟合冰雪反射光谱各向异性

对地表反射光谱的各向异性进行建模和拟合，是遥感对地观测的重要研究内容。传统的线性核驱动模型拟合方法使用的核函数是基于植被覆盖地表辐射传输模型导出的，因此难以准确地描述冰雪覆盖地表前向散射强的特征。提出一种在线性核驱动模型中增加前向散射核函数的拟合方法，并采用地面多角度观测架测量的冰雪反射光谱对该方法的有效性进行了验证。验证结果表明该方法能够较好地拟合冰雪反射光谱的各向异性(R2=0.997 5, RMSE=0.022 6)，准确地反映冰雪前向散射较强的特征。通过对提出的方法与经验函数法、传统线性核驱动模型拟合方进行比较，可以发现线性核驱动模型方法明显优于经验函数拟合法，其中增加前向散射核函数能够显著提高对冰雪覆盖地表二向反射因子的拟合精度，并在各波段都有稳定的拟合效果。

A Forward Kernel Function for Fitting in situ Measured Snow Bidirectional Reflectance Factor

Modelling and fitting the reflectance anisotropy of land surfaces is one of the most important issues in remote sensing studies.In the traditional linear kernel-driven model,the most widely used kernel functions are derived from radiative transfer model of vegetation canopy.Therefore,it is not validate to represent the forward scattering effect of snow/ice surfaces.We pro-posed a method by adding a forward kernel function to the traditional linear kernel-driven model,and validate it with in situ measured bidirectional reflectance factor (BRF)data.The validation results show that this method is efficient for fitting the BRF of snow/ice surfaces (R2=0.997 5,RMSE=0.022 6).We also compared it with empirical functions and the traditional linear kernel-driven model.The results show that:(1)The fitting results of linear kernel-driven model are better than those of empiri-cal functions;(2)The fitting results can be significantly improved by adding the forward kernel function;(3)The fitting results of the improved linear kernel-driven model are stable at different wavelengths.