Evaluation of Linear Kernel-Driven BRDF Models over Snow-Free Rugged Terrain
Evaluation of Linear Kernel-Driven BRDF Models over Snow-Free Rugged Terrain
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DOI：
10.3390/rs15030786
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Wenzhe Zhu;D. You;Jianguang Wen;Yong Tang;Baochang Gong;Yuan Han
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文献类型：
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作者：
Wenzhe Zhu;D. You;Jianguang Wen;Yong Tang;Baochang Gong;Yuan Han
半经验核驱动模型由于其简单的形式和物理上有意义的近似而被广泛用于表征各向异性反射。近年来，一些核驱动模型与地形效应相结合，以改善崎岖地形上双向反射率的拟合。然而，在随后的遥感应用之前，需要对各种模型的性能进行广泛的评估。本文利用模拟brf和观测brf，对无雪崎岖地形上3种典型的核驱动brf模型RTLSR、TCKD和KDST-adjusted TCKD （KDST-TCKD）进行了研究。与模拟数据相比，RTLSR的拟合误差（NIR/Red RMSE）从0.0358/0.0342逐渐增大到0.0471/0.0516，平均斜率（α）从9.13°增大到33.40°。然而，TCKD和KDST-TCKD模型的拟合精度总体较好，TCKD模型的拟合误差从0.0366/0.0337逐渐减小到0.0252/0.0292，KDST-TCDK模型的NIR/Red RMSE的最佳拟合从0.0192/0.0269减小到0.0169/0.0180。与沙盒数据（α为8.4°~ 30.36°）相比，RTLSR模型的NIR/Red RMSE范围为0.0147/0.0085 ~ 0.0346/0.0165，TCKD模型的NIR/Red RMSE范围为0.0144/0.0086 ~ 0.0298/0.0154，KDST-TCKD模型的RMSE范围为0.0137/0.0082 ~ 0.0234/0.0149。使用MODIS数据，与RTLSR模型相比，TCKD和KDST-TCKD模型在崎岖地形中表现出更显著的改进。在相对平坦的地形（α 30°）上，两者的RMSE差异在0.003以内，TCKD模式的RMSE比RTLSR降低了0.01左右；KDST-TCKD近似为0.02，在稀树草原甚至可以达到0.0334。因此，TCKD和KDST-TCKD模型在崎岖地形中，特别是在平均坡度较大的情况下，总体上优于RTLSR模型。其中，KDST-TCKD模型由于考虑了地形效应、地向生长和组分谱，表现最好。
Semi-empirical kernel-driven models have been widely used to characterize anisotropic reflectance due to their simple form and physically meaningful approximation. Recently, several kernel-driven models have been coupled with topographic effects to improve the fitting of bidirectional reflectance over rugged terrains. However, extensive evaluations of the various models’ performances are required before their subsequent application in remote sensing. Three typical kernel-driven BRDF models over snow-free rugged terrains such as the RTLSR, TCKD, and the KDST-adjusted TCKD (KDST-TCKD) were investigated in this paper using simulated and observed BRFs. Against simulated data, the fitting error (NIR/Red RMSE) of the RTLSR gradually increases from 0.0358/0.0342 to 0.0471/0.0516 with mean slopes (α) increases from 9.13° to 33.40°. However, the TCKD and KDST-TCKD models perform an overall better fitting accuracy: the fitting errors of TCKD gradually decreased from 0.0366/0.0337 to 0.0252/0.0292, and the best fit from the KDST-TCDK model with NIR/Red RMSE decreased from 0.0192/0.0269 to 0.0169/0.0180. When compared to the sandbox data (α from 8.4° to 30.36°), the NIR/Red RMSE of the RTLSR model ranges from 0.0147/0.0085 to 0.0346/0.0165, for the TCKD model from 0.0144/0.0086 to 0.0298/0.0154, and for the KDST-TCKD model from 0.0137/0.0082 to 0.0234/0.0149. Using MODIS data, the TCKD and KDST-TCKD models show more significant improvements compared to the RTLSR model in rugged terrains. Their RMSE differences are within 0.003 over a relatively flat terrain (α 30°), the RMSE of the TCKD model has a decrease of around 0.01 compared to that of the RTLSR; for KDST-TCKD, it is approximately 0.02, and can even reach 0.0334 in the savannas. Therefore, the TCKD and KDST-TCKD models have an overall better performance than the RTLSR model in rugged terrains, especially in the case of large mean slopes. Among them, the KDST-TCKD model performs the best due to its consideration of topographic effects, geotropic growth, and component spectra.