Physical BRDF models are usually very complex and difficult to invert. We usually need to employ a priori knowledge in this or that way

fix some parameter values and invert some others. Usually most of us agree that non-sensitive parameters should be fixed

but there has not been any consensus on how to define the sensitivity of a parameter in inversion. Li and Strahler

Li and Wang also suggested that only those the most sensitive and most uncertain parameters should be inverted by using a subset of observations

but they failed to spell out how to determine such “most sensitive and most uncertain” parameters and how to find such a subset of observations. This lacking of consensus and quantitative rules makes inversion of physical BRDF models a case-by-case “trick” or an “art but science”.We tried to develop a general framework for BRDF model inversion. It is based on accumulation of knowledge and an inversion strategy which we called Multi-stage

Sample-direction Dependent

Target-decisions （MSDT）.Our MSDT inversion strategy is based on an Uncertainty and Sensitivity Matrix （USM） of parameters at given directions/bands of observations. Its definition is somehow analogous to the partial derivative matrix used in Newton methods for minimization

but there are three significant differences: 1) the uncertainty of the initial guess is taken into account; 2) It is less dependent on the initial guess; 3) all elements have the same unit and therefore quantitatively comparable. An example of USM from Li-Strahler GOMS model and ASAS sampling will be presented

and it is obvious from the matrix what parameter should be inverted first

and what subset of observations should be used. In order to expressed the USM clearly for more complex sampling patterns

contours may be used .To demonstrate the MSDT strategy

we used Changping flight data to invert all the 7 parameters in Li-Strahler GOMS model using all the samples at the same time

then invert step by step depending on the USM-based MSDT

the effect is encouraging. 