Abstract: Soil moisture products derived from microwave remote sensing data are commonly used in the studies of large-scale water resources or climate change. However, the spatial resolutions of these products are usually too coarse to be used in regional  or watershedscale studies. Therefore, it is necessary to spatially downscale the coarseresolution soil moisture products for use in regional or watershedscale studies. The UCLA method is one of the methods for spatially downscaling soil moisture products. In this method, the spatial indices (Ts/VI indices) calculated from land surface temperature and vegetation index are used as auxiliary variables for spatial downscaling. In this paper, we compared the performance of the UCLA method for spatially downscaling the coarseresolution AMSR-E soil moisture products, using three Ts/VI indices as auxiliary variables, i.e., the soil wetness index (SW), temperature vegetation dryness index (TVDI), and vegetation temperature condition index (VTCI). These auxiliary variables were calculated from the products of MODIS land surface temperature (MYD11A1) and MODIS vegetation index (MYD13A2). The downscaled results using the three Ts/VI indices were all reasonable. However, the downscaled results using TVDI and VTCI were better than using SW. Therefore, we concluded that TVDI and VTCI are more suitable than SW to be used as the auxiliary variable when applying the UCLA method for downscaling soil moisture products. Finally, we discussed the error sources of applying the UCLA method, such as measurement errors of coarse resolution soil products, calculation errors from spatial indices, and errors from the UCLA method itself, and we also discussed the potential improvements of future research.