Date: Fri, 4 Dec 1998 11:03:10 -0500 (EST)
From: Pierre Goovaerts 

1. discretize the range of variation of your data using a given number of thresholds, say 5: the first threshold would be 0% (which is close to the median of your sample distribution) and 4 other thresholds corresponding to the 0.6, 0.7, 0.8 and 0.9 quantiles of your distribution.
2. for each threshold, code each observation into an indicator value which is zero if the measured percentage is larger than the threshold and one otherwise.
3. Compute and model the 5 corresponding indicator semivariograms, that is the semivariograms of indicator values.
4. Use indicator kriging to interpolate the probabilities to be no greater than each of the 5 thresholds at the nodes of your interpolation grid.
5. At each location, you can now model the conditional cumulative distribution function which provides you with the probability that the unknown percentage value is no greater than any given threshold. You could use the mean of that distribution as your estimate and the variance as a measure of uncertainty.

[Here is] a review paper that I recently wrote for soil scientists in which I present (or try to present!) in layman terms a broad overview of main applications of geostat.

Goovaerts, P. 1998.
  Geostatistical tools for characterizing the spatial variability of
  microbiological and physico-chemical soil properties.
  Biology and Fertility of Soils, 27(4): 315-334.