An Improved Kernel-Driven BRDF Model Coupled with Topography: KDCT

被引：0
|
作者：
Hao, Dalei [1 ,2 ]
Wen, Jianguang [1 ]
Xiao, Qing [1 ]
Wu, Shengbiao [1 ,2 ]
Cheng, Juan [1 ,2 ]
机构：
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
来源：
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年
关键词：
BRDF; RTLSR; reflectance; kernel-driven model; rugged terrain; topography; LAND-SURFACE ALBEDO;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Rugged terrain complicates the BRDF modeling mainly by the modulation of sun-target-sensor geometry and shadowing effects. An improved kernel-driven BRDF model coupled with topography (KDCT) is put forward by combining the RTLSR model used in the algorithm for MODIS bidirectional reflectance anisotropies of land surface (AMBRALS) and the anisotropic reflectance model for rugged terrain (dESM). The improved model was compared with the original RTLSR model by using the simulated data based on the radiosity approach and the MODIS reflectance data. The validation results revealed that the improved KDCT model outperforms the RTLSR model without topographic consideration and can significantly improve the ability of the kernel-driven model to process the multi-angular reflectance measurements over rugged terrain.
引用
收藏
页码：3959 / 3962
页数：4
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