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In this letter, near-surface and root-zone soil moisture (RZSM), land surface temperature (LST), leaf area index, and vegetation water content were simulated at different spatial scales for three land cover types in North Central Florida under dynamic vegetation conditions. Insights into expected retrieval errors in soil moisture (SM) due to assumptions of static landscape were obtained from differences in the estimates using the static and dynamic land covers. Maximum differences of about 0.04 m3/m3 in near-surface SM and RZSM, and 5.1 K in LST were observed between estimates obtained over the vegetated and bare-soil regions during dry-soil conditions. During wet conditions, the maximum differences in near-surface SM and RZSM increased to about 0.05 m3/m3, while those in LST decreased to 3.6 K. The RZSM simulations generated at the two resolutions of 200 m and 10 km were used to implement an upscaling algorithm based on averaging, to illustrate the use of the synthetic data set for upscaling studies. This letter highlights the importance of simulating land surface states at multiple scales for heterogeneous landscapes under dynamic vegetation conditions and for developing accurate SM retrieval and scaling algorithms.