The Bayesian maximum entropy (BME) approach has emerged in recent years as a new spatio-temporal geostatistics methods. By capitalizing on various sources of information and data, BME introduces an epistemological framework which produces predictive maps that are more accurate and in many cases computationally more efficient than those derived with traditional techniques. It is a general approach that does not need to make assumptions regarding linear valuation, spatial homogeneity or normal distribution. BME can integrate a priori knowledge and soft data without losing any useful information they contain and improve accuracy of the analysis. This paper first introduces the basic theory of BME and stages of BME estimation, and then briefly describes its development and application in soil and environmental sciences. Finally the application of this method is also summarized and prospected. After years of development and practice, the BME method has been proved to be a mature outstanding approach, which has a broad prospect of application in evaluation of resources and environment.