TY - JOUR
T1 - A field UAV system (FUAS) with dual spectrometers for BRDF measurement
AU - Hu, Xiuqing
AU - He, Xingwei
AU - Sheng, Yuechao
AU - Xu, Na
AU - Chen, Lin
AU - Hu, Wenjie
AU - Tao, Bingcheng
AU - Zhang, Lu
AU - Wang, Ling
AU - He, Yuqing
AU - Sun, Zhongqiu
N1 - Publisher Copyright:
© 2025 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2025
Y1 - 2025
N2 - This paper describes a Field UAV System (FUAS) with dual spectrometers for Bidirectional Reflectance Distribution Function (BRDF) measurement, which include the unmanned aerial vehicle (UAV) spectrometer measurement system, the ground-based spectrometer measurement system, the sun photometer and the all-sky imager. UAV was used to obtain the field multi-angular spectra in the hemispherical space. While the ground-based spectrometer was carried out to measure the solar diffuser, which continuously records the changes of the field diffuse light and illumination. Through a series of data processing such as data screening, geometric calibration, spectral correction, diffuse light correction, and model fitting, the bidirectional reflectance factor (BRF) in the field is calculated by combining the measured radiance data of the target and that of a solar diffuser with the same solar geometry. The UAV-measured multi-angular reflectance data of the Gould field were fitted based on the RossThick-LiSparse Reciprocal (RTLSR) model utilizing Bayesian inversion, and exhibits high simulating accuracy in characterizing BRDF features (RRMSE ~ < 5%, uncertainty < 1%). The BRDF calculated by the retrieved model parameter was compared with that derived from MODIS BRDF products, and the relative deviation between them can be maintained at about 5% at seven channels of MODIS, indicating that the constructed model based on BRDF data from FUAS can describe the directional reflectance characteristics of the calibration sites well. The FUAS exhibits significant potential for future applications in precise field calibration and environmental monitoring.
AB - This paper describes a Field UAV System (FUAS) with dual spectrometers for Bidirectional Reflectance Distribution Function (BRDF) measurement, which include the unmanned aerial vehicle (UAV) spectrometer measurement system, the ground-based spectrometer measurement system, the sun photometer and the all-sky imager. UAV was used to obtain the field multi-angular spectra in the hemispherical space. While the ground-based spectrometer was carried out to measure the solar diffuser, which continuously records the changes of the field diffuse light and illumination. Through a series of data processing such as data screening, geometric calibration, spectral correction, diffuse light correction, and model fitting, the bidirectional reflectance factor (BRF) in the field is calculated by combining the measured radiance data of the target and that of a solar diffuser with the same solar geometry. The UAV-measured multi-angular reflectance data of the Gould field were fitted based on the RossThick-LiSparse Reciprocal (RTLSR) model utilizing Bayesian inversion, and exhibits high simulating accuracy in characterizing BRDF features (RRMSE ~ < 5%, uncertainty < 1%). The BRDF calculated by the retrieved model parameter was compared with that derived from MODIS BRDF products, and the relative deviation between them can be maintained at about 5% at seven channels of MODIS, indicating that the constructed model based on BRDF data from FUAS can describe the directional reflectance characteristics of the calibration sites well. The FUAS exhibits significant potential for future applications in precise field calibration and environmental monitoring.
KW - bayesian inversion
KW - bidirectional reflectance distribution function
KW - Field UAV system
KW - MODIS
KW - RTLSR model
UR - https://www.scopus.com/pages/publications/105020722735
U2 - 10.1080/01431161.2025.2562603
DO - 10.1080/01431161.2025.2562603
M3 - Article
AN - SCOPUS:105020722735
SN - 0143-1161
VL - 46
SP - 8051
EP - 8079
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
IS - 21
ER -