Evaluation of several recently developed sampling strategies within the coarse pixel scale for validation of coarse-resolution satellite albedo products

Abstract

Due to the spatial heterogeneity and the spatial scale mismatch between in situ and satellite-based measurements, optimal ground sampling should be made to increase the representativeness of in situ observations. Therefore, many ground sampling strategies have been proposed, but their performance within the coarse pixel has not been evaluated. Hence, this study evaluated four typical methods regarding their ability to obtain pixel scale ground ‘truth’. Random combination (RC) performs best, with the always fewest samples to satisfy representativeness errors (REs) of 3% in the case of a small number of samples. When the goal of sampling is to obtain in situ measurements with REs close to 0 at the expense of increasing the number of samples, cumulative representativeness sampling (CRS) is more effective than RC in less heterogeneous areas. Geo-statistical model-based sampling (GSS) does not work well because the number of samples within the coarse pixel scale cannot support a robust semi-variogram model. Stratified sampling (SS) is highly dependent on spatial heterogeneity and does not work well in the case of small sample sizes. This study gives important guidance for ground sample deployment within the coarse pixel for validation of coarse-resolution satellite albedo products over a heterogeneous surface.