As any physically-based BRDF models were established on some assumptions

there always exist some differences between the simulated data and the measured data.When using the model to invert the ground parameters

the accuracy will be decreased if we use all measured data without distinguishing them.A merit function is usually used as the fitness of the modeled value and that of measured.The least-squares（LS） criterion

traditionally selected as the merit function

lacks the robustness when there are some stochastic errors in the measured data

though it can deal with the normal distribution errors.The least median of squares（LMS） method has the potential to find the abnormal data which belong to the stochastic errors.So we can improve the accuracy of the inversion through kicking away the abnormal data relative to the model with LMS.Using LMS and LS as the merit function separately

in this paper we take the multi-stage inversion of the SAIL model as an example to inverse the ground parameter.It has demonstrated that

toward the measured data which have some errors or can’t be simulated by the model

this approach is robust to estimate the parameters.