Abstract.Vertical change is often measured in the cryosphere via digital elevation model (DEM) differencing to assess glacier and icesheet mass balances. This requires the signal of change to outweigh the noise associated with the datasets. On the ice-free earth, land-level change is much smaller in magnitude and thus requires more accurate DEMs for differencing and identification of change. Previously, this has required high-resolution data at small scales. For the first time we measure land-level changes at the 5 scale of entire mountain belts in the south-central Andes using the SRTM-C (collected in 2000) and the TanDEM-X (collected from 2010-2015), both spaceborne radar DEMs. Long-standing errors in the SRTM-C are corrected using the TanDEM-X as a control surface and applying cosine-fit co-registration to remove~1/10 pixel (~3 m) shifts, Fast Fourier Transform and filtering to remove SRTM-C short-and long-wavelength stripes, and blocked shifting to remove remaining complex biases.The datasets are then differenced and outlier pixels are identified as potential signal for the case of gravel-bed channels and 10 hillslopes. We are able to identify signals of incision and aggradation (with magnitudes down to~3 m in best case) in two > 100 km river reaches, with increased geomorphic activity downstream of knickpoints. Anthropogenic gravel excavation and piling is prominently measured, with magnitudes exceeding ±5 m (up to > 10 m for large piles). These values correspond to conservative rates of 0.2 to > 0.5 m/yr for vertical changes in gravel-bed rivers. For hillslopes, since we require stricter cutoffs for noise, we are only able to identify one major landslide with a deposit volume of 16±0.15×10 change can be garnered from TanDEM-X auxiliary layers, however, these are more difficult to quantify. The methods presented can be extended to any region of the world with SRTM-C and TanDEM-X coverage where vertical land-level changes are of interest, with the caveat that remaining vertical uncertainties in primarily the SRTM-C limit detection in steep and complex topography.