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.