Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations

Abstract

Spaceborne remote sensing can track ecosystems changes thanks to continuous and systematic coverage at short revisit intervals. Active remote sensing from synthetic aperture radar (SAR) sensors allows day and night imaging as they are not affected by cloud cover and solar illumination and can capture unique information about its targets. However, SAR observations are affected by the coupled effect of viewing geometry and terrain topography. The study aims to assess the impact of global digital elevation models (DEMs) on the normalization of Sentinel-1 backscattered intensity and interferometric coherence. For each DEM, we analyzed the difference between orbit tracks, the difference with results obtained with a high-resolution local DEM, and the impact on land cover classification. Tests were carried out at two sites located in mountainous regions in Romania and Spain using the SRTM (Shuttle Radar Topography Mission, 30 m), AW3D (ALOS (Advanced Land Observation Satellite) World 3D, 30 m), TanDEM-X (12.5, 30, 90 m), and Spain national ALS (aerial laser scanning) based DEM (5 m resolution). The TanDEM-X DEM was the global DEM most suitable for topographic normalization, since it provided the smallest differences between orbital tracks, up to 3.5 dB smaller than with other DEMs for peak landform, and 1.4–1.9 dB for pit and valley landforms.

Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring

“…In our study, our choice was driven by the use of the CNES generic processing chain which can be used worldwide and the availability of the 30 m SRTM DEM model. In the framework of surface type classification, [40] have investigated the influence of the DEM employed for terrain normalization of backscatter and coherence data. The authors show that high-resolution TanDEM-X DEM (20 or 30 m resolution) was the global DEM providing the largest reduction of terrain induced variability.…”

Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping

“…However, misclassifications were still observed and may be related to the inclusion of 2017 data (with the related noise problem) in the training sample. Other possible sources of error were the under-correction of slopes facing the sensor [23,49,54] and the elongation of the path traversed within the forest canopy on backslopes [42]. Such errors may be alleviated if topographic information is included (orientation, slope, incidence angle, etc.)…”

Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

“…As for the limitations of this study, the most important is related to the design of NFIs, which are not optimized for calibrating and validating remote sensing products [49], the large differences between the amount of data collected by the two SAR sensors, which limited a like-for-like comparison, and the available DEM (SRTM DEM) used for terrain normalization as a more precise DEM (e.g., Lidar based or Tandem-X DEM) allows for improved scattering area estimation reducing the effect of topography on the backscatter and thus improving the retrieval of the target biophysical characteristic [34,51]. Funding: This research was funded by the Romanian National Authority for Scientific Research and Innovation and the European Regional Development Fund through the project "Prototyping an Earth-Observation based monitoring and forecasting system for the Romanian forests" (EO-ROFORMON, grant P_37_651/105058).…”