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01.09.2016 | Original Article | Ausgabe 18/2016

Environmental Earth Sciences 18/2016

Study on a bidirectional reflectance distribution function inversion model based on HJ-1 CCD imagery

Zeitschrift:
Environmental Earth Sciences > Ausgabe 18/2016
Autoren:
Qingyan Meng, Yunxiao Sun, Xiaojuan Xue, Xingfa Gu, Rumiana Vatseva, Jia-hui Zhang, Tamas Jancso

Abstract

The surface bidirectional reflectance distribution function (BRDF) is an important factor in depicting the bidirectional reflectance characteristics of the land surface. In this study, BRDF is first inversed using a semiempirical, kernel-driven Algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface (Ambrals) model based on 4-day charge-coupled device (CCD) data from the HJ-1B satellite under clear sky conditions. Then, according to application needs, the inversion results of different angles are unified to the same observation angle to realize the radiometric normalization of BRDF at different viewing and incident directions. Finally, the inversed BRDF is compared with the measured BRDF in the principle plane and perpendicular plane of the Sun, respectively. The results show that: (1) The inversed BRDF based on the kernel-driven model is in good agreement with the measured BRDF. (2) The vegetation bidirectional reflectance in a backward scattering direction is higher than that in a forward scattering direction in the principle plane of the Sun. There is also a “hot spot” in the backward scattering direction. Additionally, the forward bidirectional reflectance is symmetrical relative to the backward one in the perpendicular plane of the Sun. (3) The geometric optical effect is more apparent in the visible bands of HJ-1B CCD, while the volume scattering effect is more significant in the near-infrared band. The Ambrals model and the procedures used in this study are effective and adapt to the characteristics of HJ-1B/CCD images. Therefore, our findings could advance the applications of the HJ-1 satellite and the development of quantitative remote sensing.

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