In many applications, spatio-temporal sampling procedures allow for temporal adaptability, in the sense that the set of spatial sampling locations can be modified between different observation times. This chapter describes and illustrates some related approaches, mainly based on entropy criteria. The effect of variable transformations such as averages and/or maxima over given time periods or space regions, often considered in the formulation of critical indicators, is evaluated in terms of entropy-based information measures. The chapter considers the sampling network real-time adaptation based on the observations, and the quantification of the information loss derived from transformation of the data. The adaptive sampling design procedure is formulated, and illustrated with a simulated example. The effect of transformations on predictive information and, as a consequence, on the sampling designs is studied. The temperature data given by 37 stations located in the Upper Austria region are analyzed in relation to the above mentioned aspects. Controlled Vocabulary Terms Data transformation; sampling