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Modelling hydro-meteorological variables over land and atmosphere comprise of ground sampling at selected locations and predicting over the other locations. Remote sensing data can be effectively used to improve predictions by prudently choosing sampling locations of variables co-dependent on the prediction variable. This paper presents a framework for estimating the representative area of a ground sample and thereby determining the number of samples required for prediction with a given level of uncertainty and spatial resolution. Application of the proposed framework for soil moisture as the prediction variable is presented using Google Earth Engine and Scikit-learn libraries implemented in Python 3 programming language.