A Parametric Model for Estimating Relations Between Unprecisely Located Field Measurements and Remotely Sensed Data

Mislocations in the acquisition of field measurements may affect estimates of functional relations between field variables and their remotely sensed radiometric responses. Under such field errors least squares (LS) estimators perform poorly because they present significant systematic biases. New statistics have been developed to achieve better estimates of parameters of the functional model when such errors occur. An example uses thematic mapper data and field measurements from a forest inventory. Bias and consistency of new statistics are analyzed with extensive simulations, showing their higher reliability compared with LS statistics. Finally, the effects of nonspatially and spatially related field errors are compared.