Many environmental applications require high fidelity sampling of temporally and spatially distributed phenomena. We propose a MultiScale Sampling approach for efficiently sampling such phenomena. This approach introduces a hierarchy of sensors according to the sampling fidelity, spatial coverage, and mobility characteristics. We report the development of a two-tier MultiScale System where information from a low fidelity, high spatial (global) sensor actuates a mobile robotic node, carrying a high fidelity, low spatial coverage (spot measurement) sensor, to perform guided sampling in regions of high phenomenon variability. As a case study of the proposed MultiScale paradigm, we investigated the spatiotemporal distribution of light intensity in a forest under story. Performance of the MultiScale approach is verified in simulation and on a physical system. We also compare the two most recently used sampling approaches, Adaptive Sampling and MultiScale Sampling, empirically in simulation for both static and spatiotemporally varying phenomenon. Results indicate that MultiScale Sampling is suitable for the sampling of high spatiotemporal frequency varying fields and significantly outperforms Adaptive Sampling.