Assessing Spatiotemporal Variations of Sentinel-1 InSAR Coherence at Different Time Scales over the Atacama Desert (Chile) between 2015 and 2018

Abstract

This study investigates synthetic aperture radar (SAR) time series of the Sentinel-1 mission acquired over the Atacama Desert, Chile, between March 2015 and December 2018. The contribution analyzes temporal and spatial variations of Sentinel-1 interferometric SAR (InSAR) coherence and exemplarily illustrates factors that are responsible for observed signal differences. The analyses are based on long temporal baselines (365–1090 days) and temporally dense time series constructed with short temporal baselines (12–24 days). Results are compared to multispectral data of Sentinel-2, morphometric features of the digital elevation model (DEM) TanDEM-X WorldDEM™, and to a detailed governmental geographic information system (GIS) dataset of the local hydrography. Sentinel-1 datasets are suited for generating extensive, nearly seamless InSAR coherence mosaics covering the entire Atacama Desert (>450 × 1100 km) at a spatial resolution of 20 × 20 meter per pixel. Temporal baselines over several years lead only to very minor decorrelation, indicating a very high signal stability of C-Band in this region, especially in the hyperarid uplands between the Coastal Cordillera and the Central Depression. Signal decorrelation was associated with certain types of surface cover (e.g., water or aeolian deposits) or with actual surface dynamics (e.g., anthropogenic disturbance (mining) or fluvial activity and overland flow). Strong rainfall events and fluvial activity in the periods 2015 to 2016 and 2017 to 2018 caused spatial patterns with significant signal decorrelation; observed linear coherence anomalies matched the reference channel network and indicated actual episodic and sporadic discharge events. In the period 2015–2016, area-wide loss of coherence appeared as strip-like patterns of more than 80 km length that matched the prevailing wind direction. These anomalies, and others observed in that period and in the period 2017–2018, were interpreted to be caused by overland flow of high magnitude, as their spatial location matched well with documented heavy rainfall events that showed cumulative precipitation amounts of more than 20 mm.

Reconstructed Aeolian Surface Erosion in Southern Mongolia by Multi-Temporal InSAR Phase Coherence Analyses

“…With the installment of contemporary InSAR assets such as Terra SAR X, Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 1/2, and Sentinel-1, which have better resolution, revisiting times, and resolving power on the baseline conditions, nowadays some research has begun to establish methods of monitoring sand dune dynamics and eolian migrations as shown in Gaber et al (2018), Gómez et al (2018), and Havivi et al (2018). A detailed study of phase coherence behaviors in arid deserts which may involve eolian erosions was reported by Ullmann et al (2019) who showed that surface characteristics such as slope and wetness are not crucial for the loss of phase coherence, the so-called decorrelation. Thus, the prime factor affecting decorrelation is the change of topography induced by eolian migrations.…”

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

“…The IW2 datasets were processed using the Sentinel applications platform (SNAP) software (version 7.0.2), IDL 8.7.2, and ENVI 5.5. A processing chain developed in preliminary work [7,41,42] was used to processes the terrain-corrected gamma nought VV (GVV) and VH (GVH) intensities (following the approach of [43], which is approximated as "Terrain-Flattening" function in SNAP). The TanDEM-X DEM (Section 2.2.3) with a pixel spacing of 12 by 12 m was used for the calibration, the terrain flattening, and the range-doppler terrain correction.…”

“…Therefore, we consider an exponential model as appropriate, which is in accordance to results from other studies (e.g., [21]). Z value (ZVAL): The ZVAL relates the RMSH and CORL by the ratio shown in Equation (7). The intention of this parameter is to account for vertical and horizontal variations in a single index [30].…”

“…This data allows for a wide range of applications suited to support physio-geographical research. Amongst others relevant applications, such as the detection of ground movement via interferometric SAR [6] or change detection (e.g., [7]), SAR data is also suited to estimate the surface roughness, as it is sensitive to horizontal and vertical variations of the surface elevation at scales <1 m [8]. This ability enlarges the spectrum of suited remote sensing methods for the characterization of land surface properties, e.g., to date, digital elevation models (DEM), derived from spaceborne sensors, are frequently used to characterize the macro-and meso-topography (e.g., [9]), while multi-spectral optical data allow for characterizing spectral soil and substrate properties (e.g., [10]).…”

Use of TanDEM-X and Sentinel Products to Derive Gully Activity Maps in Kunene Region (Namibia) Based on Automatic Iterative Random Forest Approach

“…Given that gullies are dynamic features, behavioral indicators based on temporal patterns [i.e. Normalized Difference Vegetation Index (NDVI), radar coherence [58], and backscatter intensity time series] are included as gully descriptors. This is important in light of the fact that trends of historical data differ between adjacent areas [57], [59], [60].…”