基于星载 POLDER 冰雪数据评价三个 BRDF 模型

Objective The snow/ice scatters sun radiation in a strong anisotropic fashion, especially in shortwave region, which in turn causes a significant difference in the study of the global energy balance and water cycles. Up to present, remote sensing community has developed a series of reflectance models for various applications in snow surface. Comprehensive comparison and evaluation of these models are essentially helpful in choosing an algorithm to produce satellite multi-angle remote sensing product. In this paper, we use the Polarization and Directionality of Earth Reflectances (POLDER) multi-angle snow data to compare and evaluate the performance of three models to characterize the snow scattering. Three models including the kernel-driven linear RossThick-LiSparseReciprocal (RTLSR) model as the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo operational algorithm, the Asymptotic Radiative Theory model (ART) and the lately-developed RTLSR-Snow (RTLSRS) model have been well used in some studies. Method First, the POLDER data are divided into pure snow data and impure snow data by the homogeneity index provided by the POLDER database, and then we use three BRDF models to fit (1) a single pure snow BRDF dataset; (2) the entire archive of the pure snow BRDF data; (3) a single impure snow BRDF dataset; and (4) the entire achieve of the impure snow BRDF data, respectively. We analyze the result with the R²、RMSE and bias. As the volumetric scattering kernel and geometric optical kernel contribute little to pure snow reflectances, we further simplify the RTLSRS model by keeping only isotropic scattering and snow scattering kernel in the kernel-driven model framework (i.e., Isotropic and Snow-kernel Model, ISM). The performance of the ISM model has further been evaluated using the POLDER pure snow data. Result The results are as follows: (1) The RTLSRS is the most accurate model among all models being considered. For a single pure snow BRDF dataset, the RTLSRS model has a RMSE value that is 45.45% and 81.45% lower than that of ART and RTLSR model, respectively. For a single impure snow BRDF dataset, the BRDF curve of RTLSRS model is generally similar with RTLSR model’s, but the RMSE is 67.5% lower than RTLSR. The RMSE of the ART model is the largest in this case, arriving at 0.136. (2) The accuracy of the RTLSRS model in simulating the pure snow data (R²=0.969, RMSE=0.012) is higher than that of the impure snow data (R²=0.935, RMSE=0.018). (3) The simplified ISM model can characterize the pure snow BRDF data well. The R² and RMSE can reach 0.949 and 0.034 for the entire POLDER pure snow data, even better than the ART model. Conclusion RTLSRS has the highest accuracy in fitting various POLDER BRDF snow data. Although the ISM has somewhat low accuracy relative to its original RTLSRS model, it shows a higher accuracy than the ART model in fitting the POLDER pure snow data. Our results also present that the index of the “homogeneity” provided by the entire archive of the POLDER snow database cannot necessarily meet the requirement to identify the pure snow pixels of POLDER snow data. Therefore, it is necessary to develop a new method to further refine the POLDER snow data and provide more details that can improve the understanding for potential users in relation to snow optical scattering.