It is usually assumed that the prior distributions of parameters and error are Gaussian distribution in remote sensing inversion.This assumption seems to be impractical in many cases.Prior distribution of parameters and error are very important in remote sensing inversion since many remote sensing inversion strategies take advantage of prior knowledge.We present a bootstrap method for estimating the prior distributions of parameters and error in this paper.This method relaxes the distribution assumption of parameters and error

and obtains those approximately exact distributions by means of prior data.Moreover

we classify prior data since they are collected from different classes

and implement statistical test for classified prior data.Results show that proper classification of prior data is reasonable.Finally

we take RossThick-LiTransit linear kernel-driven model as an example

and make a comparison of our method with usual Tikhonov regularizing inversion and Bayes inversion under normal hypothesis with NOAA-AVHRR observations.The result shows that classifying prior data and using the prior distribution obtained by bootstrap method can significantly decrease uncertainty of parameters.