Landscape carbon (C) flux estimates help assess the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO2) emissions. Advances in remote sensing have led to coarse‐scale estimates of gross primary productivity (GPP; e.g., MODIS 17), yet efforts to develop spatial respiration products are lacking. Here we demonstrate a method to predict growing season soil respiration at a regional scale in a mixed subalpine ecosystem. We related field measurements (n = 396) of growing season soil respiration mostly from subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors using a Random Forest model (30‐m pixel size). We found that Landsat Enhanced Vegetation Index, growing season aridity index, temperature, precipitation, elevation, and slope aspect explained spatiotemporal variability in soil respiration. Our model had a psuedo‐r2 of 0.45 and root‐mean‐square error of roughly one quarter of the mean value of respiration. Predicted growing season soil respiration across the region was remarkably consistent across 2004, 2005, and 2006 (150‐day sums of 542.8, 544.3, and 536.5 g C/m2, respectively). Yet we observed substantial variability in spatial patterns of soil respiration predictions that varied among years, suggesting that our method is sensitive to changes in respiration drivers. Mean predicted growing season soil respiration was 73% of MODIS GPP, while predicted soil respiration was generally within 20% of nocturnal net ecosystem exchange from nearby eddy covariance towers. Thus, geospatial and remotely sensed data sets can be used to estimate soil respiration at landscape scales.