In addition to thematic maps, remote sensing provides estimates of area in different thematic categories. Areal estimates are frequently used for resource inventories, management planning, and assessment analyses. Misclassification causes bias in these statistical areal estimates. For example, if a small percentage of a common cover type is misclassified as a rare cover type, then the area occupied by the rare type can be severely overestimated. Many categories are rare in detailed classification systems. I present an informal method to anticipate the approximate magnitude of this bias in statistical areal estimates, before a remote sensing study is conducted. If the anticipated magnitude is unacceptable, then statistical calibration methods should be used to produce unbiased areal estimates. I then discuss existing statistical methods that calibrate for misclassification bias with a sample of reference plots.