Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Owing to its flexibility, spectral mixture analysis (SMA) based on the linear spectral mixing model (LSMM) is a useful tool for subpixel vegetation cover estimation. Multiple scattering and endmember spectral variability are the two reasons that produce errors in the LSMM-retrieved cover fraction estimates. Although the anisotropic reflectance properties are ll documented, their effect has barely been investigated in the studies of subpixel vegetation cover estimation. This letter developed a series of controlled   in situ   experiments (using a checkerboard mixture design) to evaluate the anisotropic reflectance effect (ARE) on fractional vegetation cover estimation using SMA based on the LSMM. The results illustrate that ARE has a large impact on SMA for vegetation cover estimation, and the developed approach allowing for ARE produces more accurate estimates, as the value of root-mean-square error drops more than 50%. This letter may open a new perspective for using SMA to estimate vegetation cover by emphasizing the importance of integrating ARE and characterizing anisotropic reflectance properties of an endmember class as another source of intraclass variability that is likely to be ignored.
['Xin Tong', 'Tingxi Liu', 'Vijay P. Singh', 'Limin Duan', 'Di Long']
I am writing a research paper on a idea Development of In Situ Experiments for Evaluation of Anisotropic Reflectance Effect on Spectral Mixture Analysis for Vegetation Cover
[{"from": "human", "value": "I am writing a research paper on a idea Development of In Situ Experiments for Evaluation of Anisotropic Reflectance Effect on Spectral Mixture Analysis for Vegetation Cover"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Owing to its flexibility, spectral mixture analysis (SMA) based on the linear spectral mixing model (LSMM) is a useful tool for subpixel vegetation cover estimation. Multiple scattering and endmember spectral variability are the two reasons that produce errors in the LSMM-retrieved cover fraction estimates. Although the anisotropic reflectance properties are ll documented, their effect has barely been investigated in the studies of subpixel vegetation cover estimation. This letter developed a series of controlled   in situ   experiments (using a checkerboard mixture design) to evaluate the anisotropic reflectance effect (ARE) on fractional vegetation cover estimation using SMA based on the LSMM. The results illustrate that ARE has a large impact on SMA for vegetation cover estimation, and the developed approach allowing for ARE produces more accurate estimates, as the value of root-mean-square error drops more than 50%. This letter may open a new perspective for using SMA to estimate vegetation cover by emphasizing the importance of integrating ARE and characterizing anisotropic reflectance properties of an endmember class as another source of intraclass variability that is likely to be ignored."}]