Abstract

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.

Highlights

  • The characterization of land surfaces properties is a key component in the study of landscape development and geomorphological mapping

  • Top soils in arid environments are usually characterized by very dry conditions and the influence of the moisture on the Synthetic Aperture Radar (SAR) signal is of minor relevance, which in turn means that the SAR signal is for the most part a function of the local incidence angle and the micro-topography, the surface roughness, respectively

  • The temporal variations over the plots locations, expressed by differencing the intensity values of consecutive acquisitions (∆VH and ∆VV), show, for the most part, small alterations < +/− 2 dB and a median of around 0 dB; a clear increase of VH and, especially, of VV intensities is observable in summer 2018, matching the period of increased normalized difference vegetation index (NDVI) values

Read more

Summary

Introduction

The characterization of land surfaces properties is a key component in the study of landscape development and geomorphological mapping. Amongst others relevant applications, such as the detection of ground movement via interferometric SAR [6] or change detection (e.g., [7]), SAR data is suited to estimate the surface roughness, as it is sensitive to horizontal and vertical variations of the surface elevation at scales

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.