Spectral Mixture Analysis（SMA） is one of the main topics in quantitative remote sensing research. It is able to provide land cover information at sub-pixel levels for practical applications. With the emergence of improved algorithms, SMA has made significant progress in many aspects, including spectral mixture models, endmember determination, endmember fraction inversion, and accuracy assessment. This study focused on these four key components in SMA and reviewed the available models and algorithms developed in last two decades. Moreover, the deficiencies of existing studies were analyzed. These deficiencies include the absences of widely accepted model selection criteria for linear and nonlinear spectral mixture analysis models and the unstable inversion of existing spectral mixture analysis caused by the high spectral correlation between endmembers. Finally, the study summarized the directions for future research, which include quantitatively evaluating the amplitude and spectral shape of multiple scattering among endmembers, identifying the factors that contribute to the nonlinear component in mixture observed signals by using radiative transfer models and laboratory measurement experiments,improving the robustness of linear spectral mixture analysis models, and suppressing high sensitivity to noise error signals resulting from collinearity with some insights from available statistical regression models for collinearity issues.

线性混合像元分解;非线性混合模型;端元光谱变异;共线性;软分类精度