# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 5 Conclusions
Our multi-temporal analysis reveals that bi-static TanDEM-X synthetic aperture radar imagery is highly suitable to study glacier volume changes over large regions, in a semi-operational way and in frequently cloudy regions such as Fuego-Patagonia. Even shorter time periods of 2-3 years can be used for balancing if image pairs with favorable acquisition conditions are available. Uncertainties and limitations (in particular for short temporal baselines) arise from the unknown radar penetration in frozen fern and snow. It is, therefore, recommended that regular acquisitions be carried out over fast changing regions such as glaciers and that the timing of acquisitions is coordinated according to previously available data or reference data sets, preferentially to the peak of summer melting conditions. More specifically dedicated research on radar signal penetration under different surface conditions and fern/snow structures are required in order to further reduce these uncertainties. The following are available online at www.mdpi.com/2072-4292/10/2/188/s1, Figure S1: Distribution of elevation changes on off-glacier areas for the different mosaics. The panels show the absolute changes for the timesteps of (a) summer 2015/16 (b) summer 2012 (c) autumn 2014 and (d) winter 2015/16, Figure S2: Every Scene as shown in Figure S1 is horizontally co-registered following Nuth and Kaab [24]. (a) Curve fit of scene 3 (summer 2012) for the first 6 iterative steps to horizontally align the respective TDX to the reference SRTM dataset. Colored lines visualize the fitted function parameters (formula at plots bottom): beige vertical is angular offset, blue horizontal: amplitude, green horizontal: oscillation center. (b) Histogram showing the improvement of dh offset on non-glaciated ground during the coregistration steps, Figure S3: Scene on Tyndall glacier showing dataset before (left) and after filtering (right). The filter cuts out disproportionately high (low) values in relation to its surrounding, Figure S4: Off-ice elevation changes for different slope bins. Grey bars indicate the area covered while the blue diamonds refer to the median elevation change off ice. Median absolute deviations of elevation changes are given as blue bars. Area weighted mean absolute deviations: (a) 2.85 m (b) 2.80 m (c) 3.14 m (d) 3.22 m, Figure S5: (a) Cyan showing coverage of SRTM and TDX data for dh/dt differencing and data gaps to extrapolate for in the inventory. (b) hypsometric area distribution; colors similar to a, diamonds and bars draw mean dh/dt and respective so for each altitudinal bin, Table S1: List of Landsat scenes used for semiautomatic outline mapping, Table S2: Mean glacier elevation change 2000-2015/16 for various SPI glaciers. ELA, AAR and type are taken from DeAngelis [1], as is Area except for 1: Upsala was summed up with tributaries Cono and Bertachi (not listed).Unnamed glaciers in [1] where named with corresponding RGI-ID through spatial join with our RGI-based inventory. Footnote 1: [[https://www.dmpi.com/2015/16/01/0](https://www.dmpi.com/2015/16/01/0)]([https://www.dmpi.com/2015/16/01/0](https://www.dmpi.com/2015/16/01/0))  
Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.