The vast territories that have been radioactively contaminated during
the 1986 Chernobyl accident provide a substantial data set of radioactive
monitoring data, which can be used for the verification and testing
of the different spatial estimation (prediction) methods involved
in risk assessment studies. Using the Chernobyl data set for such
a purpose is motivated by its heterogeneous spatial structure (the
data are characterized by large-scale correlations, short-scale variability,
spotty features, etc.). The present work is concerned with the application
of the Bayesian Maximum Entropy (BME) method to estimate the extent
and the magnitude of the radioactive soil contamination by 137Cs
due to the Chernobyl fallout. The powerful BME method allows rigorous
incorporation of a wide variety of knowledge bases into the spatial
estimation procedure leading to informative contamination maps. Exact
measurements (?hard? data) are combined with secondary information
on local uncertainties (treated as ?soft? data) to generate science-based
uncertainty assessment of soil contamination estimates at unsampled
locations. BME describes uncertainty in terms of the posterior probability
distributions generated across space, whereas no assumption about
the underlying distribution is made and non-linear estimators are
automatically incorporated. Traditional estimation variances based
on the assumption of an underlying Gaussian distribution (analogous,
e.g., to the kriging variance) can be derived as a special case of
the BME uncertainty analysis. The BME estimates obtained using hard
and soft data are compared with the BME estimates obtained using
only hard data. The comparison involves both the accuracy of the
estimation maps using the exact data and the assessment of the associated
uncertainty using repeated measurements. Furthermore, a comparison
of the spatial estimation accuracy obtained by the two methods was
carried out using a validation data set of hard data. Finally, a
separate uncertainty analysis was conducted that evaluated the ability
of the posterior probabilities to reproduce the distribution of the
raw repeated measurements available in certain populated sites. The
analysis provides an illustration of the improvement in mapping accuracy
obtained by adding soft data to the existing hard data and, in general,
demonstrates that the BME method performs well both in terms of estimation
accuracy as well as in terms estimation error assessment, which are
both useful features for the Chernobyl fallout study.