Pristine peatlands being excellent storage for terrestrial Carbon (C) play a crucial role in regulating climate and water and provide several important ecosystem services. However, peatlands have been heavily altered (e.g., by draining the water table), increasing greenhouse gas (GHG) emissions. Restoring peatlands requires a comprehensive characterization, including knowledge of peat depth (PD; m). Traditionally, this requires the physical insertion of a push probe, which is time-consuming and labor-intensive. It has been shown that non-invasive proximal sensing techniques such as electromagnetic induction and ground penetrating radar can add value to PD data. In this research, we want to assess the potential of proximally sensed gamma-ray (γ-ray) spectrometry (i.e., potassium [K], thorium [Th], uranium [U], and the count rate [CR]) and terrain attributes data (i.e., elevation, slope, SAGAWI, and MRVBF) to map PD either alone or in combination across a small (10 ha) peatland area in ØBakker, Denmark. Here, the PD varies from 0.1 m in the south to 7.3 m in the north. We use various prediction models including ordinary kriging (OK) of PD, linear regression (LR), multiple LR (MLR), LR kriging (LRK), MLR kriging (MLRK) and empirical Bayesian kriging regression (EBKR). We also determine the minimum calibration sample size required by decreasing sample size in decrements (i.e., n = 100, 90, 80,…, 30). We compare these approaches using prediction agreement (Lin’s concordance correlation coefficient; LCCC) and accuracy (root mean square error; RMSE). The results show that OK using maximum calibration size (n = 108) had near perfect agreement (0.97) and accuracy (0.59 m), compared to LR (ln CR; 0.65 and 0.78 m, respectively) and MLR (ln K, Th, CR and elevation; 0.85 and 0.63 m). Improvements are achieved by adding residuals; LRK (0.95 and 0.71 m) and MLRK (0.96 and 0.51 m). The best results were obtained using EBKR (0.97 and 0.63 m) given all predictions were positive and no significant change in agreement and standard errors with the decrement of calibration sample size (e.g., n = 30). The results have implications towards C stocks assessment and improved land use planning to control GHG emissions and slow down global warming.