Often, little importance is given to the problem of how uncertainty propaga
tes in models driven by remotely sensed data, and what the effects of uncer
tainty might be on the output of these models. In this paper, a general pro
cedure to support a characterisation of uncertainty in the generation of re
mote sensing (RS) products is proposed. The procedure can be used with mode
ls characterised by any degree of complexity and driven by any kind of data
. It provides two useful tools to analyse models: uncertainty analysis (UA)
, which allows the assessment of the uncertainty associated with model outp
ut, and sensitivity analysis (SA), which is useful to determine how much ea
ch source of uncertainty contributes to model output uncertainty. Uncertain
ty modelling, i.e. finding suitable tools to represent uncertainty, is a ke
y step in performing UA and SA. A general error model for quantitative rast
er data is described. Different applications of UA and SA are proposed, and
, in the last part of the paper, an example of UA and SA on a model for bur
ned area detection is discussed. (C) 2001 Elsevier Science Inc. All rights
reserved.