A Forward Kernel Function for Fitting in situ Measured Snow Bidirectional Reflectance Factor
QU Ying1, 2, LIU Qiang3, 4，LIU Su-hong2, 4
1. School of Geographical Sciences, Northeast Normal University, Changchun 130024, China 2. School of Geography, Beijing Normal University, Beijing 100875, China 3. College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China 4. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
Abstract：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 proposed 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 empirical 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.
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