TY - GEN
T1 - Assessing Performance of the Kernel-Driven BRDF Models in Retrieving Snow Albedo Based on the bic-PT Model
AU - DIng, Anxin
AU - Jiao, Ziti
AU - Dong, Yadong
AU - Zhang, Xiaoning
AU - Cui, Lei
AU - Yin, Siyang
AU - Chang, Yaxuan
AU - Guo, Jing
AU - Xie, Rui
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Recently, Jiao et al. developed a snow kernel in the kernel-driven bidirectional reflectance distribution function (BRDF) model framework to better characterize the anisotropic reflectance of pure snow surface. In this study, we assess performances of this snow kernel in the kernel-driven model framework and original kernel-driven model (hereinafter named the RTS and RTR models) in retrieving snow albedo based on the bicontinuous photon tracking (bic-PT) model. Our results show that: (1) The spectral albedo retrieved by these two models has a high consistency with the bic-PT model. However, the results of the spectral albedo for RTR model has a slight underestimation, especially at SZA=0° in the visible bands, and the RTS model performs well compared with simulated data. (2) The albedo retrieved by these two models agrees reasonably well with the simulated data (R2=~0.9). Yet, the result of the RTR model underestimates 0.50% and 0.52% compared simulated albedo in the red and near-infrared bands, respectively, and the RTS model has a negligible bias for all bands. This assessment provide a priori knowledge of these two models performances, and thus, suggests the RTS model can be applied to future researches of estimating snow albedo.
AB - Recently, Jiao et al. developed a snow kernel in the kernel-driven bidirectional reflectance distribution function (BRDF) model framework to better characterize the anisotropic reflectance of pure snow surface. In this study, we assess performances of this snow kernel in the kernel-driven model framework and original kernel-driven model (hereinafter named the RTS and RTR models) in retrieving snow albedo based on the bicontinuous photon tracking (bic-PT) model. Our results show that: (1) The spectral albedo retrieved by these two models has a high consistency with the bic-PT model. However, the results of the spectral albedo for RTR model has a slight underestimation, especially at SZA=0° in the visible bands, and the RTS model performs well compared with simulated data. (2) The albedo retrieved by these two models agrees reasonably well with the simulated data (R2=~0.9). Yet, the result of the RTR model underestimates 0.50% and 0.52% compared simulated albedo in the red and near-infrared bands, respectively, and the RTS model has a negligible bias for all bands. This assessment provide a priori knowledge of these two models performances, and thus, suggests the RTS model can be applied to future researches of estimating snow albedo.
KW - bic-PT model
KW - kernel-driven models
KW - model assessment
KW - snow albedo
KW - snow kernel
UR - http://www.scopus.com/inward/record.url?scp=85077673649&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8899099
DO - 10.1109/IGARSS.2019.8899099
M3 - Conference contribution
AN - SCOPUS:85077673649
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4052
EP - 4055
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -