TY - GEN
T1 - BRDF modeling and analysis of the pseudo-invariant calibration site in northwest China
AU - Wenjie, Hu
AU - Yuqing, He
AU - Xiuqing, Hu
AU - Jibiao, Zhu
AU - Xingwei, He
N1 - Publisher Copyright:
© 2023 SPIE. All rights reserved.
PY - 2023
Y1 - 2023
N2 - As an important parameter characterizing the bi-directional reflectance of objects, the Bidirectional Reflectance Distribution Function (BRDF) is one of the key parameters in the on-orbit substitute calibration of satellite remote sensors based on stable ground targets. It is a crucial factor affecting the calibration accuracy. With the development of quantitative remote sensing, hyperspectral BRDF measurement for calibration and spectral analysis of land surface features has become increasingly important. Efficient and high-quality methods for collecting multi-angle reflectance data of land surfaces are currently a research focus. This study uses co-observation of two spectrometer to measure the BRDF characteristics of the pseudo-invariant calibration site in wild environment. Based on the selected results of the pseudo-invariant calibration site in Northwest China by previous research, BRDF for three different types of land surfaces were measured including desert, Gobi, and saline-alkali land. Based on the Ross-li kernel-driven model, a hyperspectral BRDF characteristic data set of the three different surface types is fitted, and the spatial distribution characteristics and hotspot effects of the BRDF directional reflection of the ground targets are analyzed. The results show that different surface types have different directional reflectance values and different hot spot effects. Therefore, a stable target radiation reference library with different radiation brightness levels can be constructed to provide a benchmark model for long-term consistent radiometric calibration of Chinese remote sensing satellites.
AB - As an important parameter characterizing the bi-directional reflectance of objects, the Bidirectional Reflectance Distribution Function (BRDF) is one of the key parameters in the on-orbit substitute calibration of satellite remote sensors based on stable ground targets. It is a crucial factor affecting the calibration accuracy. With the development of quantitative remote sensing, hyperspectral BRDF measurement for calibration and spectral analysis of land surface features has become increasingly important. Efficient and high-quality methods for collecting multi-angle reflectance data of land surfaces are currently a research focus. This study uses co-observation of two spectrometer to measure the BRDF characteristics of the pseudo-invariant calibration site in wild environment. Based on the selected results of the pseudo-invariant calibration site in Northwest China by previous research, BRDF for three different types of land surfaces were measured including desert, Gobi, and saline-alkali land. Based on the Ross-li kernel-driven model, a hyperspectral BRDF characteristic data set of the three different surface types is fitted, and the spatial distribution characteristics and hotspot effects of the BRDF directional reflection of the ground targets are analyzed. The results show that different surface types have different directional reflectance values and different hot spot effects. Therefore, a stable target radiation reference library with different radiation brightness levels can be constructed to provide a benchmark model for long-term consistent radiometric calibration of Chinese remote sensing satellites.
KW - Bidirectional Reflectance Distribution Function
KW - Hyperspectral model
KW - pseudo-invariant calibration site
KW - Unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85181532228&partnerID=8YFLogxK
U2 - 10.1117/12.3007854
DO - 10.1117/12.3007854
M3 - Conference contribution
AN - SCOPUS:85181532228
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - AOPC 2023
A2 - Feng, Yutao
A2 - Yang, Zongyin
A2 - Liu, Dong
PB - SPIE
T2 - 2023 Applied Optics and Photonics China: Optical Spectroscopy and Imaging; and Atmospheric and Environmental Optics, AOPC 2023
Y2 - 25 July 2023 through 27 July 2023
ER -