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This study demonstrates an application-oriented approach to estimate area-wide surface roughness from Sentinel-1 time series in the semi-arid environment of the Orog Nuur Basin (southern Mongolia) to support recent geomorphological mapping efforts. The relation of selected mono- and multi-temporal SAR features and roughness is investigated by using an empirical multi-model approach and selected 1D and 2D surface roughness indices. These indices were obtained from 48 high-resolution ground-based photogrammetric digital elevation models, which were acquired during a single field campaign. The analysis is backed by a time series analysis, comparing Sentinel-1 features to temporal-corresponding observations and reanalysis datasets on soil moisture conditions, land surface temperature, occurrence of precipitation events, and presence and development of vegetation. Results show that Sentinel-1 features are hardly sensitive to the changing surface conditions over none to sparsely vegetated land, indicating very dry conditions throughout the year. Consequently, surface roughness is the dominating factor altering SAR intensity. The best correlation is found for the combined surface roughness index Z-Value (ratio between the root mean square height and the correlation length) and the mean summer VH intensity with an r2 coefficient of 0.83 and an Root-Mean-Square Error of 0.032.

Surface Roughness Estimation in the Orog Nuur Basin (Southern Mongolia) Using Sentinel-1 SAR Time Series and Ground-Based Photogrammetry