Backscattering modeling of wheat using vector radiative transfer theory
Huang, Bo; Chen, Yan; He, Lei; Tong, Ling; Wang, Yong
A microwave backscattering model of winter wheat based on the vector radiative transfer theory has been established. The model focused on the distribution of wheat ears that are directly related to the yield. In addition, characteristics of the wheat growth have been adequately considered. Compared to the measured values, the model effectively simulated the microwave backscattering characteristics of winter wheat. Intercomparison of the winter wheat model and modified Michigan Microwave Canopy Scattering (MIMICS) model using experimental data shows that the winter wheat model had better cross-polarized simulation results than the modified MIMICS model did. This improvement was attributed to the special attention paid to the cross-polarization after the booting stage. After booting, wheat ear started to appear and grow in size. Wheat ear contributed greatly to cross-polarized backscatter. The inclusion of the ear as one of the model components was significant in modeling the observed cross-polarized backscattering.
Huang, Bo, & Chen, Yan, & He, Lei, & Tong, Ling, & Wang, Yong. (April 2015). Backscattering modeling of wheat using vector radiative transfer theory. Journal of Applied. Remote Sensing, (9:1), p.1-12. Retrieved from http://hdl.handle.net/10342/8851
Huang, Bo, and Chen, Yan, and He, Lei, and Tong, Ling, and Wang, Yong. "Backscattering modeling of wheat using vector radiative transfer theory". Journal of Applied. Remote Sensing. 9:1. (1-12.), April 2015. July 26, 2021. http://hdl.handle.net/10342/8851.
Huang, Bo and Chen, Yan and He, Lei and Tong, Ling and Wang, Yong, "Backscattering modeling of wheat using vector radiative transfer theory," Journal of Applied. Remote Sensing 9, no. 1 (April 2015), http://hdl.handle.net/10342/8851 (accessed July 26, 2021).
Huang, Bo, Chen, Yan, He, Lei, Tong, Ling, Wang, Yong. Backscattering modeling of wheat using vector radiative transfer theory. Journal of Applied. Remote Sensing. April 2015; 9(1) 1-12. http://hdl.handle.net/10342/8851. Accessed July 26, 2021.