DocumentCode
143838
Title
To derive BRDF archetypes from POLDER-3 BRDF database
Author
Ziti Jiao ; Yadong Dong ; Hu Zhang ; Xiaowen Li
Author_Institution
Sch. of Geogr., Beijing Normal Univ., Beijing, China
fYear
2014
fDate
13-18 July 2014
Firstpage
3586
Lastpage
3589
Abstract
In this study, based on kernel-driven linear BRDF model, a new spectral vegetation index named anisotropic flat index (AFX) and a hotspot kernel function are described. Anisotropic Flat Index (AFX), which is created by normalization of net scattering magnitude with the isotropic scattering, can summarize the variability of basic dome-bowl anisotropic reflectance pattern of the terrestrial surface. The hotspot kernel function is modified with the exponential approximation to generate a so-called RossThickChen kernel (KRTC). Using the POLDER-3 multi-angular observations, a classification scheme for BRDF typology is created and a BRDF archetype data is established. The results show that the AFX effectively summarizes BRDF archetypes that provide additional information on vegetation structures and other anisotropic reflectance characteristics of the land surface. The RTCLSR model can significantly capture the hotspot signatures, the BRDF archetypes derived in this way provides a significantly different hotspot signatures from those derived from the MODIS BRDF product.
Keywords
radiometry; vegetation; AFX; BRDF archetype data; BRDF typology classification scheme; KRTC; MODIS BRDF product; POLDER-3 BRDF database; POLDER-3 multiangular observation; RTCLSR model; RossThickChen kernel; anisotropic flat index; basic dome-bowl anisotropic reflectance pattern variability; exponential approximation; hotspot kernel function; hotspot signature; isotropic scattering; kernel-driven linear BRDF model; land surface anisotropic reflectance characteristic; net scattering magnitude normalization; spectral vegetation index; terrestrial surface; vegetation structure; Biological system modeling; Databases; Kernel; Land surface; Scattering; Shape; Vegetation mapping; Albedo; BRDF archetype; bayes;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location
Quebec City, QC
Type
conf
DOI
10.1109/IGARSS.2014.6947258
Filename
6947258
Link To Document