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.

Full text

remote sensing
Article
Investigating the Impact of Digital Elevation Models on
Sentinel-1 Backscatter and Coherence Observations
Ignacio Borlaf-Mena 1, 2, * , Maurizio Santoro 3, Ludovic Villard 4, Ovidiu Badea 1,5 and
Mihai Andrei Tanase 1,2
1Romanian National Institute for Research and Development in Forestry, INCDS “Marin Drăcea”,
Department of Forest Monitoring, Bulevardul Eroilor 128, 077190 Voluntari, Romania; obadea@icas.ro (O.B.);
mihai.tanase@uah.es (M.A.T.)
2Department of Geology, Universidad de Alcalá, Geography and Environment, Calle Colegios 2,
28801 Alcaláde Henares, Spain
3Gamma Remote Sensing, Worbstrasse 225, 3073 Gümligen, Switzerland; santoro@gamma-rs.ch
4Centre d’Etudes Spatiales de la Biosphère, 31400 Toulouse, France; ludovic.villard@cesbio.cnes.fr
5
Faculty of Silviculture and Forest Engineering, Department of Forest Engineering, “Transilvania” University,
Forest Management Planning and Terrestrial Measurements, Ludwig van Beethoven Str. 1,
500123 Bra¸sov, Romania
*Correspondence: ignacio.borlaf@edu.uah.es
Received: 27 July 2020; Accepted: 10 September 2020; Published: 16 September 2020


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.
Keywords:
synthetic aperture radar (SAR); radiometric terrain normalization; digital elevation model
(DEM); coherence; backscatter; Sentinel-1; LiDAR; land cover classification
1. Introduction
Synthetic aperture radar (SAR) is an active imaging system with several advantages over optic
sensors, such as Landsat OLI (Operational Land Imager) or Sentinel-2 MSI (Multi-Spectral Imager).
SARs are independent of solar illumination and use wavelengths that can penetrate cloud cover
and have unique interactions with ground targets. Furthermore, the capability to transmit and
receive signals enables the use of both phase and polarization information, to monitor, among others,
landslides, avalanches, snowmelt, and forests [
1
]. Terrain orientation affects the intensity of the
backscattered signal based on lambert cosine law. The signal is further affected by the terrain scattering
area. The SAR technique uses the return time to convert a table of recorded echoes into an image
Remote Sens. 2020,12, 3016; doi:10.3390/rs12183016 www.mdpi.com/journal/remotesensing

Remote Sens. 2020,12, 3016 2 of 23
(focusing). These times are shortened in sensor-facing steep slopes, causing echoes to overlap and
slopes to appear shortened in the focused image (a pixel represents more area). Both effects can
be compensated for by using the acquisition geometry parameters and a digital elevation model
(DEM). We refer to this compensation as topographic normalization. The backscattered intensity is
normalized by accounting for the scattering area and the local incidence angle [
2
,
3
]. The coherence
normalization is based on removing the topographic phase component from the interferogram before
estimation [
4
]. Normalization results are heavily dependent on the DEM characteristics and quality [
2
,
3
]
as they can be generated using different data sources, including remote sensing (optic, SAR, airborne
laser scanning—ALS), and processing techniques (ALS point cloud, stereography, interferometry,
and radargrammetry)
ALS uses the delay between emission and reception of light pulses to determine the 3D position of
objects. When the pulses return, the instrument registers an intensity profile, which can be completely
(full-waveform) or partially recorded (discrete return, i.e., the position of the leading edge before the
peak) [
5
]. Airborne discrete return systems have become the source of national elevation datasets for
many countries [
6
,
7
]. Spaceborne full-waveform Light Detection and Ranging (space LiDAR, such as
the ICESat (Ice, Cloud, and land Elevation Satellite)) data have also been employed in the context of
topographic mapping as a primary source for calibration or validation of global elevation datasets
derived from other sensors [
8
,
9
]. However, global topographic mapping from space based on LiDAR
is difficult, due to the sparse sampling, and the sensitivity to cloud cover.
Stereoscopic techniques are based on differences in the line of sight to objects (parallax) for
common points (tie-points) in an overlapping set of images. Results are dependent on tie-point
quantity, image contrast, noise, and features (such as shadows and homogeneous surfaces), which
may pose problems [
10
–
12
]. These techniques have been applied over aerial [
6
,
11
] and satellite optical
images [
9
,
13
], as well as SAR imagery (i.e., radargrammetry) [
10
,
14
]. Stereoscopic processing of optical
images was used to generate the ASTER global DEM (ASTER GDEM) and the ALOS (Advanced Land
Observation Satellite) World 3D Digital Surface Model (ALOS AW3D DSM). The ASTER GDEM was
created using imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) onboard the Terra satellite. ASTER stereo pairs were formed from two near-infrared images
(nadir, backward) with 15m resolution. The AW3D DEM was based on data from the Panchromatic
Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the ALOS. PRISM stereo acquisitions
were formed with three panchromatic images (forward, nadir, backward) with 2.5 m resolution. In
both cases, the most challenging task was masking clouds, snow, ice, or water on every acquisition [
9
]
to avoid the introduction of outliers. Afterwards, images from each individual acquisition were
matched, and elevation was calculated. All height estimates from individual acquisitions were stacked
to ensure continuity and reduce noise [
9
,
13
]. The AW3D was corrected for biases using preexisting
data, such as ICESat shots and the preexisting Shuttle Radar Topography Mission (SRTM) DEM (see
next paragraphs) [9].
Even though the processing chains for ASTER GDEM and AW3D were similar, their accuracies
are different. Studies comparing global DEMs based on ground control points (GCP) report that
the ASTER GDEM has larger uncertainties and is affected by striping, hummock-like artifacts, and
outliers [
12
,
15
–
17
]. These artifacts may stem from the tie point generation, the choice of band (NIR), its
relatively low spatial resolution [
16
,
17
], or unremoved cloud patterns [
15
]. AW3D performed better,
although hillslope and step-like artifacts (scene mismatch) have been found [12,16–19].
SAR interferometry uses two co-registered SAR images acquired from close orbits. The
interferogram (i.e., the phase difference between the two SAR images) relates to the 3D position
of each target on the ground. Thereof, an interferogram reproduces the topographic information,
which appears in the form of fringes as phase is measured between 0 and 2
π
. To obtain absolute phase
values, from which elevation can be estimated, the interferogram is unwrapped. Unwrapping may be
hindered in areas of steep topography or areas affected by the lack of coherence between images in

Remote Sens. 2020,12, 3016 3 of 23
consequence of changes between acquisitions (wind-induced motion, precipitation, etc.) [
1
,
14
]. SAR
interferometry was used to generate two global DEMs, the SRTM DEM, and the TanDEM-X DEM.
The SRTM acquired data over 80% of the Earth’s land surface (60
◦
N–56
◦
S) on an 11-day orbital
flight in February 2000. SRTM operated two antennas physically separated in space by 60 m at C-band,
as well as X-band. At C-band, a gap-free coverage was obtained with single-pass interferograms. The
interferometric height was reconstructed (unwrapping) and re-gridded into map coordinates with
variable-resolution smoothing. Data takes were combined using coincident tie points [
20
]. The main
artifacts of the SRTM dataset were related to striping from uncompensated movements of the mast,
voids in correspondence of steep slopes or for low coherence areas, or coarser than nominal spatial
detail from the re-gridding step [
17
,
20
–
22
]. The global TanDEM-X DEM was generated using SAR data
acquired during 2010–2015 by the TanDEM-X and TerraSAR-X satellites flying in formation. Individual
scenes were focused, multi-looked (sample averaging) to 10–12 m pixel spacing and unwrapped.
The DEM was generated in an iterative process with the first global coverage using data acquisition
parameters (baseline) adequate for moderate terrain. The second global coverage was shifted half a
swath, and its unwrapping was aided by the data from the first coverage. Over some areas, further
coverages were acquired from a different viewing geometry to avoid errors caused by topographic
distortions or volumetric scattering (e.g., forest, desert) [
23
,
24
]. Areas with height ambiguities were
infilled using radargrammetric processing of the scenes. TanDEM-X DEM data are distributed with
12.5 m (0.4 arcsec, original), 30 m (1 arcsec), and 90 m resolution (3 arcsec). The latter was generated by
the unweighted average of the overlapping 12.5 m pixels [17].
All DEMs are affected by contributions from elements covering the terrain, such as cities or
vegetation, thus reporting elevations higher than those recorded for the ground control points [
15
,
18
].
For this reason, elevation refers to surface elevation rather than terrain elevation. For the specific
case of vegetation, the main reason for this is the different penetration of each wavelength. On the
one hand, the nanometric-scale wavelengths employed for generating photogrammetric DEMs have
limited penetration, and thus the tie points and the generated surface tend to reflect canopy surface
height. On the other hand, the centimetric wavelengths employed by SAR sensors are able to penetrate
further, albeit the scattering center height depends on the frequency employed and the vegetation
structure [
20
,
25
–
27
]. Furthermore, the quality of InSAR-based DEM also depends on the spatial and
temporal baselines, or unaccounted sensor movements.
Despite the rather large range of DEMs available, their effect on terrain normalization is poorly
understood. Hoekman [
28
] suggested that the SRTM 90 m spacing may not be adequate for the
radiometric normalization of the SAR backscatter in complex terrains. Recently Truckenbrodt [
29
]
compared and tested the SRTM (30 and 90 m), AW3D (30 m), and TanDEM-X (90 m) DEMs in the
context of radiometric terrain normalization of Sentinel-1 data. They analyzed the deviation of each
DEM from the pixel-wise median of all DEMs and performed a regression analysis between terrain
flattened
γ0
and the local incidence. The deviation analysis showed that the SRTM DEMs have the
smallest difference from the median values, but the 30 m SRTM version had high deviation artifacts at
one test site. The same errors were found for SRTM and AW3D, as the latter has been infilled with
data from the former due persistent cloud cover. Both AW3D and TanDEM-X DEM contained outliers
or noise over water areas. The 90 m TanDEM-X DEM was found to contain several large artifacts
in mountainous areas. Regression analysis showed that all the analyzed DEMs largely removed the
terrain influence, with complete removal (i.e., slope of 0) being observed in some experiments using
higher resolution DEMs (SRTM 1 arcsec, AW3D) [29].
The objective of this study was to investigate the impact of global DEMs on the normalization
of SAR backscatter and coherence observations by Sentinel-1 at two sites characterized by complex
topography. To build on previous literature [
29
], we analyzed the performance of 12.5 (resampled to
20 m, see Section 3), 30, and 90 m pixel Tandem-X DEMs, along with AW3D (30 m), SRTM (
30 m
).
A very high-resolution (5 m) ALS DEM was used to benchmark results. The impact of terrain
normalization was assessed by investigating the inter-orbit variability of the observations by land

Remote Sens. 2020,12, 3016 4 of 23
cover and landforms. Then, we evaluated a land cover classification scheme based on the observations
normalized for topography.
2. Study Area and Satellite Data
The study area consisted of an N-S transect over the Romanian Carpathians (11,700 km
2
) and
the National Park of Sierra Nevada in Spain (2360 km
2
) (Figure 1). We could not add additional sites,
due to TanDEM-X scientific proposal area limitations [
30
]. The sites were selected to account for the
different vegetation types and structures encountered in the temperate and Mediterranean climates.
Due to the more humid climatic conditions, the vegetation in the Carpathians is characterized by
denser, taller, and more diverse forest types (broadleaf, needleleaf, mixed) when compared to the
sparser and shorter forests dominated by pine species encountered in the Sierra Nevada.
Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 24
2. Study Area and Satellite Data
The study area consisted of an N-S transect over the Romanian Carpathians (11,700 km
2
) and the
National Park of Sierra Nevada in Spain (2360 km
2
) (Figure 1). We could not add additional sites, due
to TanDEM-X scientific proposal area limitations [30]. The sites were selected to account for the
different vegetation types and structures encountered in the temperate and Mediterranean climates.
Due to the more humid climatic conditions, the vegetation in the Carpathians is characterized by
denser, taller, and more diverse forest types (broadleaf, needleleaf, mixed) when compared to the
sparser and shorter forests dominated by pine species encountered in the Sierra Nevada.
Figure 1. Extent of the study areas (a, Romania, Ro; c, Spain, Sp) and digital elevation models (DEMs)
used for synthetic aperture radar (SAR) data processing. The yellow box indicates the location of the
area covered by the Sentinel-1 dataset used for the analysis. The red box indicates the extent of the
subset shown in the right-hand side panels (b, Romania; d, Spain).
Histograms of natural land covers for each site have been plotted to show their frequencies
relative to the slopes they occupy (Figure 2). Needleleaf and mixed forests occupy steep slopes,
whereas grasslands and broadleaf forests occupy moderate slopes at the Romanian site. At the
Spanish site, bare and needleleaf forests occupy moderate slopes, although for the former, a
significant fraction of the pixels occupy steep or very steep slopes.
For each site, we assembled the SRTM 1-arcsecond, i.e., 30 m, DEM [31] from the United States
Geological Service (USGS) Earth Explorer [32], the AW3D 30 m DEM from the Japan Aerospace
Exploration Agency (JAXA) Earth Observation Research Center (EORC) [33] and the TanDEM-X
DEM (©DLR 2019) with a pixel spacing of 12.5 m (original resolution), 30 m and 90 m (resampled)
from the German Aerospace Agency [34]. In addition, for the Spanish site, we used an ALS-based
DEM to benchmark the results obtained from three global DEMs. The ALS DEM was available
Figure 1.
Extent of the study areas (
a
, Romania, Ro;
c
, Spain, Sp) and digital elevation models (DEMs)
used for synthetic aperture radar (SAR) data processing. The yellow box indicates the location of the
area covered by the Sentinel-1 dataset used for the analysis. The red box indicates the extent of the
subset shown in the right-hand side panels (b, Romania; d, Spain).
Histograms of natural land covers for each site have been plotted to show their frequencies relative
to the slopes they occupy (Figure 2). Needleleaf and mixed forests occupy steep slopes, whereas
grasslands and broadleaf forests occupy moderate slopes at the Romanian site. At the Spanish site,
bare and needleleaf forests occupy moderate slopes, although for the former, a significant fraction of
the pixels occupy steep or very steep slopes.

Remote Sens. 2020,12, 3016 5 of 23
Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 24
through the Spanish national plan of orthophotography (PNOA) from the National Center of
Geographic Information of Spain (CNIG) [35,36]. The ALS DEM was created from ALS point clouds
with a density of 0.5 returns/m
2
. The ALS scan over our study area was performed in 2014 with the
LEICA ALS60 sensor. The points were translated from ellipsoidal to ortho-metric heights, assigned
color (RGB and near-infrared) from PNOA orthophotographs, and classified automatically using
TerraScan [37,38]. Classification eliminates returns considered noise and filters point to avoid
oversampling due to flight strip overlap. Ground points classification is based on slope, rugosity, and
return count. Vegetation and Buildings are classified based on height, separating both based on NDVI
values. The DEM is generated by calculating the mean value of all ground returns within a 5m pixel
[39]. The reported accuracies of the DEM products are presented in Table 1.
Figure 2. Distribution of the slopes for grassland, bare soils, and forest land. The latter has been
separated according to leaf type.
Table 1. Reported accuracies of the DEM used in this study.
DEM
Product
Pixel
Spacing
Accuracy Relative Vertical
Accuracy Coverage Reference
Horizontal Vertical
SRTM DEM ~30 m ≤12.6 m ≤9 m ≤9.8 m Nearly global
(60° N–56° S) [40]
AW3D DSM ~30 m - <7 m >3 m (slope ≤ 20%)
>5m (slope > 20%) Global [41]
TanDEM-X
DEM ~12.5 m <10 m <10 m 2 m (slope ≤ 20%)
4 m (slope > 20%) Global [23]
PNOA
LiDAR DEM ~5 m ≤0.5m ≤0.5 m - Spain [42]
The SAR dataset consisted of a time series of Sentinel-1 dual-polarized (VV and VH) images
acquired in the Interferometric Wide Swath (IWS) mode. The images were obtained in Single Look
Complex format (SLC) with a pixel spacing of 14.1 m in azimuth and 2.3 m in range. All SAR images
were resampled to a pixel size matching the different DEMs used (see Section 3.2). For the Romanian
site, 21 images acquired between 2016/12/30 and 2017/02/06 from three relative orbits (7, 29, 131) were
used. For the Spanish site, 18 images acquired between 2018/08/21 and 2018/10/08 from relative orbits
1 and 81 were used.
3. Methods
The following analyses and processes where carried away using the software GDAL/OGR [43],
GAMMA software [44], GRASS (Geographic Resources Analysis Support System) [45], Python [46],
Rasterio [47], Pandas [48], Geopandas [49], Numpy [50], Scipy [51], and Matplotlib [52].
3.1. DEM Assembly
The global DEMs were provided in equiangular geographic coordinates. The SRTM and the
AW3D DEMs height reference had to be shifted from geoidal to ellipsoidal heights without
resampling. The TanDEM-X (TDX) DEMs were provided as height above the ellipsoid. The Tandem-
X DEM at 30 m (TDX30) was used as provided. The Tandem-X 12.5 m DEM was resampled (bilinear
interpolation) to 20 m pixel spacing (TDX20) to reduce pixel size difference with respect to the multi-
looked Sentinel-1 image. The 90 m resolution Tandem-X DEM (TDX90) was resampled to 30 m
Figure 2.
Distribution of the slopes for grassland, bare soils, and forest land. The latter has been
separated according to leaf type.
For each site, we assembled the SRTM 1-arcsecond, i.e., 30 m, DEM [
31
] from the United States
Geological Service (USGS) Earth Explorer [
32
], the AW3D 30 m DEM from the Japan Aerospace
Exploration Agency (JAXA) Earth Observation Research Center (EORC) [
33
] and the TanDEM-X DEM
(
©
DLR 2019) with a pixel spacing of 12.5 m (original resolution), 30 m and 90 m (resampled) from
the German Aerospace Agency [
34
]. In addition, for the Spanish site, we used an ALS-based DEM
to benchmark the results obtained from three global DEMs. The ALS DEM was available through
the Spanish national plan of orthophotography (PNOA) from the National Center of Geographic
Information of Spain (CNIG) [
35
,
36
]. The ALS DEM was created from ALS point clouds with a density
of 0.5 returns/m
2
. The ALS scan over our study area was performed in 2014 with the LEICA ALS60
sensor. The points were translated from ellipsoidal to ortho-metric heights, assigned color (RGB and
near-infrared) from PNOA orthophotographs, and classified automatically using TerraScan [
37
,
38
].
Classification eliminates returns considered noise and filters point to avoid oversampling due to flight
strip overlap. Ground points classification is based on slope, rugosity, and return count. Vegetation
and Buildings are classified based on height, separating both based on NDVI values. The DEM is
generated by calculating the mean value of all ground returns within a 5m pixel [
39
]. The reported
accuracies of the DEM products are presented in Table 1.
Table 1. Reported accuracies of the DEM used in this study.
DEM
Product
Pixel
Spacing
Accuracy Relative Vertical
Accuracy Coverage Reference
Horizontal Vertical
SRTM DEM ~30 m ≤12.6 m ≤9 m ≤9.8 m Nearly global
(60◦N–56◦S) [40]
AW3D DSM ~30 m - <7 m >3 m (slope ≤20%)
>5m (slope >20%) Global [41]
TanDEM-X
DEM ~12.5 m <10 m <10 m 2 m (slope ≤20%)
4 m (slope >20%) Global [23]
PNOA
LiDAR DEM ~5 m ≤0.5m ≤0.5 m - Spain [42]
The SAR dataset consisted of a time series of Sentinel-1 dual-polarized (VV and VH) images
acquired in the Interferometric Wide Swath (IWS) mode. The images were obtained in Single Look
Complex format (SLC) with a pixel spacing of 14.1 m in azimuth and 2.3 m in range. All SAR images
were resampled to a pixel size matching the different DEMs used (see Section 3.2). For the Romanian
site, 21 images acquired between 30 December 2016 and 6 February 2017 from three relative orbits (7,
29, 131) were used. For the Spanish site, 18 images acquired between 21 August 2018 and 8 October
2018 from relative orbits 1 and 81 were used.
3. Methods
The following analyses and processes where carried away using the software GDAL/OGR [
43
],
GAMMA software [
44
], GRASS (Geographic Resources Analysis Support System) [
45
], Python [
46
],
Rasterio [47], Pandas [48], Geopandas [49], Numpy [50], Scipy [51], and Matplotlib [52].

Remote Sens. 2020,12, 3016 6 of 23
3.1. DEM Assembly
The global DEMs were provided in equiangular geographic coordinates. The SRTM and the
AW3D DEMs height reference had to be shifted from geoidal to ellipsoidal heights without resampling.
The TanDEM-X (TDX) DEMs were provided as height above the ellipsoid. The Tandem-X DEM at 30 m
(TDX30) was used as provided. The Tandem-X 12.5 m DEM was resampled (bilinear interpolation) to
20 m pixel spacing (TDX20) to reduce pixel size difference with respect to the multi-looked Sentinel-1
image. The 90 m resolution Tandem-X DEM (TDX90) was resampled to 30 m (bilinear interpolation).
The ALS DEM, originally projected to ETRS89 UTM zone 30N coordinate system, was translated to a
height above the ellipsoid and resampled to a 20 m pixel size (bilinear interpolation). As the ALS DEM
has not been re-projected, all products geocoded with it (i.e., geocoded Sentinel-1 backscatter) share
the same projection.
3.2. SAR Data Preparation
For each SAR image, the SLC sub-swathes were mosaicked, and the resulting image was
multi-looked by a factor of 7 in range and 2 in azimuth. The objective was to reduce noise and obtain
the SAR backscattered intensity at a pixel spacing close to the target 20 m used for the analysis. For a
given orbit, the first acquired image was used as a master. All remaining SAR images from the same
orbit were co-registered to the master image using an iterative process based on intensity matching
and spectral diversity aided by each DEM [
53
]. For each orbit, the master image was used to generate
a lookup table (LUT) relating map and range doppler coordinates. The LUT was used to orthorectify
the master and the co-registered images (interferograms and SAR backscatter) from the same orbit.
Interferograms were generated for each consecutive image pair (a-b, b-c, c-d, etc.), and the
DEM-estimated topographic phase was subtracted from each. The interferometric coherence was
estimated in a two-step adaptive approach [
54
,
55
]. The first estimate of coherence was obtained with a
3-by-3 window. To reduce the estimation bias due to the small window size [
56
], the coherence was
then recomputed using a window size inversely proportional to the initial estimate of the coherence.
As a trade-offbetween preserving spatial resolution and reducing the bias, the largest window size
was set to 9-by-9 pixels. In addition, when the estimation window included scatterers with a coherence
level different than the coherence of the target in the center of the window, the estimator masked out
such features to preserve the true coherence of the latter target.
The backscatter coefficient was calibrated to terrain flattened
γ0
, considering the scattering area
on the ellipsoid and on DEM surfaces (
Af lat
and
Aslope
) [
3
] and the incidence angle on the ellipsoid
and on DEM surfaces (
θre f
and
θloc
), as reported in equation 1 [
57
]. The parameter
n
can be employed
to account for volume effects [
57
]. The parameter was set to 1, an adequate value for most land
cover types, as dealing with volumetric effects was not the objective of this study. The backscatter
intensity and coherence images were orthorectified using the LUT and an inverse distance resampling.
LUT coordinates located more than two pixels (range) apart to its counterpart on the SAR image
were masked as no data. To reduce speckle, the multi-temporal backscatter images were averaged,
by polarization, in time. Seven images were averaged for each orbit for the Romanian site, while
nine images were averaged for each orbit for the Spanish site. Similarly, the six and eight coherence
images were averaged in time for each orbit and polarization for the Romanian and the Spanish
site, respectively.
γ0=σ0Af lat
Aslope cos θre f
cos θloc !n
(1)
3.3. Auxiliary Datasets
In support of the analysis, a land cover dataset was created for each site, based on the agreement
between the ESA CCI land cover map (2015) [
58
], the DLR’s global urban footprint (GUF) (2016) [
59
–
61
],
the ALOS PALSAR forest map (ALOS FNF) (2017) [
62
], and either Corine land cover map 2012

Remote Sens. 2020,12, 3016 7 of 23
(CLC) [
63
] (Romanian Site) or Spanish information system on soil occupation (SIOSE, 2014) [
64
]
(Spanish site, more detailed and recent) (Table 2). ESA CCI land cover maps are generated at 300 m
resolution using optical imagery time series from AVHRR, MERIS, SPOT-VGT, and POBA-V imagery,
as well as GlobCover unsupervised classification chain and machine learning [
58
]. GUF is generated
from TanDEM-X imagery at 12 m resolution based on amplitude and texture [
59
–
61
]. ALOS forest map
is generated based on local thresholding of annual composites of ALOS PALSAR 1/2 amplitude images
(25 m pixel side) [
62
]. Both CLC and SIOSE are based on photointerpretation of satellite and aerial
imagery, with minimum polygon surface of 25 Ha [63] and 1 Ha [65], respectively.
Table 2.
Composition of the analyzed land covers based on preexisting datasets. When a higher level
of Corine Land Cover or SIOSE has been employed (CLC (Corine land cover map) Lvl.1), the rest have
been filled as “x”. CCI LC forest types are further disaggregated by the fractional cover and were
therefore aggregated. GUF, global urban footprint; ALOS FNF, Advanced Land Observation Satellite
forest map; SIOSE, Spanish information system on soil occupation; CODIIGE, Board of directors of the
geographic information infrastructure of Spain.
CLC 2012
[63]
SIOSE 2014 [64]
(CODIIGE)
CCI LC 2015
[66]
GUF 2016
[59–61]
ALOS FNF
2017 [62]
Urban 1xx: Artificial
surfaces
1xx: Artificial
surfaces 190: Urban areas Urban -
Crops 21x: Arable
land
210: Crops
(herbaceous)
10, 20: Cropland,
11: Herbaceous cover
Other Other
Pasture 23x: Pastures 320: Pastures 11: Herbaceous
130: Grassland Other Other
Grassland 321: Grassland 320: Pastures 11: Herbaceous
130: Grassland Other Other
Bare 33x: Open
spaces 354: Bare 200: Bare areas Other Other
Broadleaf
forest
311: Forest
(broadleaf)
311: Forest
(broadleaf)
50–62: Tree cover,
broadleaved Other Forest
Needleleaf
forest
312: Forest
(needleleaf)
312: Forest
(needleleaf)
70–82: Tree cover,
needle leaved Other Forest
Mixed
forest
313: Forest
(mixed)
313: Forest
(mixed)
90: Tree cover,
mixed leaf type Other Forest
Water 5xx: Water
bodies
5xx: Water
bodies 210: Water Other Other
The ALOS FNF disagreed with the remaining data sets as part of the cities were classified as forest,
and part of the water was classified as “other” (not water, nor forest). For cities, no ALOS FNF condition
was applied, whereas the rest of the non-forest classes on other datasets were considered compatible
with non-forest classes from ALOS FNF (non-forest, water). The polygons with the agreement were
dissolved by the land cover to eliminate internal borders, and a negative buffer of 40 m was applied to
avoid edge effects.
The analyses were undertaken in this study are also related to landforms (Figure 3), i.e., features
of the terrain surface with a distinct and identifiable shape [
67
]. Landforms were labeled using the
GRASS GIS add-on “r.geomorphon” [
68
], with a search window of 25 pixels and a “flatness” threshold
of 5 degrees applied to the highest spatial resolution DEMs available for each site, i.e., the TanDEM-X
DEM at 30 m for the Romanian site and the PNOA DEM aggregated to 30 m for the Spanish site.

Remote Sens. 2020,12, 3016 8 of 23
Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 24
deviation (MAD, Equation (4)), and Offset (Equation (5)) for the products obtained from the global
DEMs.
(a) (b)
Figure 3. Landform classification. The general shape of the landforms (a), modified from GRASS
documentation, based on [68]) and landforms over the shaded relief for a subset of the Spanish study
area (b).
𝑅𝑀𝑆𝐷=1
𝑃𝑣
−𝑟

 (2)
𝑅𝑀𝑆𝐷𝑟𝑒𝑙=𝑅𝑀𝑆𝐷
𝑟̅ (3)
𝑀𝐴𝐷=1
𝑃𝑣
−𝑟

 (4)
𝑂𝑓𝑓𝑠𝑒𝑡=𝑣̅−𝑟̅ (5)
where 𝑃 is the total number of pixels, 𝑝 is a specific pixel and 𝑣

and 𝑟

are the variable values (i.e.,
backscatter) obtained for said pixel using a global DEM (𝑣) and the reference DEM (𝑟), whereas 𝑣̅ and 𝑟̅ are
the mean value for said variables.
3.5. Land cover classification
A Linear support vector machine (LinearSVM) classifier was selected for its robustness and short
execution times. We employed the Scikit-Learn implementation [69] with default options with a
regularization parameter of 1, primal problem optimization, 0.001 tolerance for stopping criteria, and
10.000 iterations maximum. The classifier was trained per orbit/DEM pair using 96,000 samples, using
VV- and VH-polarized backscatter and co-pol coherence as features.
For each orbit and land cover class, 70% of the valid sample (foreshortened and shadowed pixels
were masked during SAR processing) was used to calculate the median and the median absolute
deviation (MAD) of each SAR variable. Median and MAD values were then employed to calculate
the z-score for each predictor (VV- and VH-polarized backscatter and coherence) by land cover class.
Only samples with an absolute z-score below three were retained. Depending on the land cover class,
the number of pixels retained varied from several millions (forest and low vegetation) down to tens
of thousands (urban and water). For each class, 12,000 pixels (the number of pixels available for the
less extended class, i.e., water) were randomly selected and used for training (n). As low vegetation
and forest classes were further split into three sub-classes each (crops, pastures, grasslands, and
Figure 3.
Landform classification. The general shape of the landforms (
a
), modified from GRASS
documentation, based on [68]) and landforms over the shaded relief for a subset of the Spanish study
area (b).
3.4. Inter-Orbital Data Analysis
The topographic normalization (radiometric terrain normalization, topographic phase removal)
and distortion masking (e.g., foreshortening, layover, shadows) of each SAR-derived variable
(backscatter coefficients and coherence) were assessed using the inter-orbit range (IOR). IOR was
calculated pixelwise for each SAR variable by subtracting the maximum and the minimum values
available from all orbits. For example, for a pixel with data available from orbits a, b, and c, the IOR
would be
max(a,b,c)–min(a,b,c)
. The inter-orbit range was plotted by land cover class (boxplots).
The analysis was repeated by landforms for the needleleaf forest, the only common forest type
between both sites, and classes appearing near the mountain tops (grassland, bare). Because the only
difference in image processing is the DEM employed, low IOR values reflect improved topographic
effects removal.
In addition, the scattering area estimates, backscatter coefficient, and the interferometric coherence
obtained using the ALS DEM (the most detailed) were employed as a reference to assess the performance
of the global DEMs at the Spanish site. Said assessment was based on the root mean square deviation
(RMSD, Equation (2)), relative RMSD (RMSDrel, Equation (3)), mean absolute deviation (MAD,
Equation (4)), and Offset (Equation (5)) for the products obtained from the global DEMs.
RMSD =v
u
u
t1
P
P
X
p=1vp−rp2(2)
RMSDrel =RMSD
r(3)
MAD =1
P
P
X
p=1vp−rp(4)
O f f set =v−r(5)

Remote Sens. 2020,12, 3016 9 of 23
where
P
is the total number of pixels,
p
is a specific pixel and
vn
and
rn
are the variable values (i.e.,
backscatter) obtained for said pixel using a global DEM (
v
) and the reference DEM (
r
), whereas
v
and
r
are the mean value for said variables.
3.5. Land Cover Classification
A Linear support vector machine (LinearSVM) classifier was selected for its robustness and short
execution times. We employed the Scikit-Learn implementation [
69
] with default options with a
regularization parameter of 1, primal problem optimization, 0.001 tolerance for stopping criteria, and
10,000 iterations maximum. The classifier was trained per orbit/DEM pair using 96,000 samples, using
VV- and VH-polarized backscatter and co-pol coherence as features.
For each orbit and land cover class, 70% of the valid sample (foreshortened and shadowed pixels
were masked during SAR processing) was used to calculate the median and the median absolute
deviation (MAD) of each SAR variable. Median and MAD values were then employed to calculate the
z-score for each predictor (VV- and VH-polarized backscatter and coherence) by land cover class. Only
samples with an absolute z-score below three were retained. Depending on the land cover class, the
number of pixels retained varied from several millions (forest and low vegetation) down to tens of
thousands (urban and water). For each class, 12,000 pixels (the number of pixels available for the less
extended class, i.e., water) were randomly selected and used for training (n). As low vegetation and
forest classes were further split into three sub-classes each (crops, pastures, grasslands, and broadleaf,
needleleaf, and mixed forests), the total numbers of training samples selected were thrice as much (3n)
as for urban and water land cover classes.
The validation sample, formed by the remaining pixels (30%) of each class, was used to compute
the confusion matrix and associated error metrics (i.e., user and producer accuracies, Cohen’s Kappa,
as described below). Error metrics for valleys were calculated after resampling (nearest neighbor) the
landform layer to match the spatial resolution of the DEMs.
The confusion matrix C (Equation (6)) represents the occurrences of the predicted (rows) against
the actual land cover class (columns) (
r·r
dimensions, where
r
is the number of classes). Diagonal
cells (
cii
) count pixels with the same class in the classification and the reference dataset (True Positive,
TP). Cells over the diagonal count pixels of class
i
that have received other class (False Negative, FN),
whereas cells under the diagonal count pixels that have been classified as
i
, when they have other class
in the reference dataset (False Positive, FP). Accuracies for a specific class
i
are the count of correctly
classified pixels for the class (
cii
) divided by the number of pixels classified as
i
(count across columns,
ci+
), in the case of user accuracy (
UAi
, also called ‘precision’ in machine learning literature, Formula
(7)), or by the number actual
i
pixels (count across columns,
c+i
), in the case of producer accuracy (
PAi
,
also called ‘recall’ in machine learning literature, Formula (8)).
C=
c1,1 c1,jc1,r
ci,1 ci,jci,r
cr,1 cr,jcr,r
(6)
UAi=precisioni=
r
X
i=1
cii
ci+
=TP
TP +FP (7)
PAi=recalli=
r
X
i=1
cii
c+i
=TP
TP +FN (8)
Cohen’s Kappa [
70
] is a measure of agreement between the predicted cover and the one appearing
on the reference dataset.
ˆ
K=NPr
i=1cii −Pr
i=1(ci+·c+i)
N2−Pr
i=1(ci+·c+i)(9)

Remote Sens. 2020,12, 3016 10 of 23
where
r
is the number of rows,
cii
is the number of pixels where there is agreement between the
classification and the reference dataset (cells on the diagonal, with row
i
and column
i
),
ci+
and
c+i
are
the totals for row
i
(count of pixels classified as
i
) and column
i
(count of reference pixels with class
i
),
and finally,
N
is the total number of observations [
71
]. A Kappa value of 1 represents the complete
agreement between both, 0 represents a classifier performance similar to random guessing, and values
under 0 indicate results worse than random guessing.
4. Results
In Sections 4.1 and 4.2, we show results of the IOR analysis by land cover and land form,
respectively, Section 4.3 contains the ALS reference-based analysis, and Section 4.4 describes the
results of the classification comparison. Through Sections 4.1–4.3, coherence showed very small
differences based on the DEM employed, under 0.01 for the IOR based analyses, and under 0.02 for
ALS reference-based analysis. For this reason, these tables have been omitted, as they carried little to
no information.
4.1. Inter-Orbital Range by Land Cover
The inter-orbit range (IOR, Table 3) was analyzed as an indicator of the residual terrain influence
on the normalized SAR metrics (higher IOR, higher influence). The mean IOR for urban cover varied
very little between DEMs, except for TDX90. Crops and broadleaf forests presented little difference (up
to 0.3 dB) depending on the DEM employed, but increased for mixed forests (0.5 dB, Carpathians), and
classes appearing near mountain peaks, such as grassland (0.6 dB) and bare soil (1 dB). Needleleaf
forests had similarly high differences at the Romanian site (0.7 dB), whereas they were lower at the
Spanish site (0.4 dB).
Table 3.
Backscatter Inter-orbit range (IOR) by polarization and land cover class at each study site
(GL, grassland; BLF, NLF, and MLF, are broadleaf, needleleaf, and mixed forest). Cell color shows the
gradient between the lowest (green) and the highest value (yellow). “M.D.” column represents the
maximum difference between global DEMs for each specific land cover.
Romania Spain
AW SR TDX TDX TDX M. AW SR TDX TDX TDX ALS M.
3D TM 20 30 90 D. 3D TM 20 30 90 D.
Urban 5.4 5.4 5.3 5.4 6.9 1.6
Crops 2.3 2.3 2.2 2.2 2.3 0.1 1.3 1.4 1.3 1.3 1.5 1.2 0.2
GL 3.4 3.4 2.9 3.1 3.5 0.6
Bare 3.1 3.5 2.9 3.1 3.9 2.7 1.0
BLF 2.0 2.0 1.7 1.8 2.0 0.3
NLF 2.5 2.5 1.8 1.9 2.5 0.7 1.2 1.5 1.1 1.2 1.5 1.0 0.4
MLF 2.4 2.4 1.9 2.0 2.4 0.5
TDX20/30 was the global DEM with the lowest IOR, whereas the highest IOR values were observed
for the TDX90 DEM, followed by SRTM DEM. Results with AW3D over natural covers (bare, grasslands,
forest) varied depending on the site: At the Romanian site, its IOR was close to SRTM or TDX90 values;
whereas at the Spanish site, it was closer to the values obtained with TDX20/30 DEMs. ALS-based
PNOA DEM had the lowest IOR, with a large improvement over results with SRTM or TDX90 (up to
1.2 dB for bare areas, up to 0.5 for needleleaf forest), and a slight improvement over TDX20 results
(0.2 dB for bare areas, and 0.1 dB for needleleaf forest). IOR values for PNOA and TDX20/30 DEMs
showed less spread (Figure 4), concentrating around lower values, whereas the spread was larger for
SRTM and TDX90. The main differences between sites were the lower spread of IOR at the Spanish
site, and the behavior of AW3D IOR, which was closer to the TDX20/30 values at the Spanish site, but
closer to SRTM/TDX90 values at the Romanian site.

Remote Sens. 2020,12, 3016 11 of 23
Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 24
TDX20/30 was the global DEM with the lowest IOR, whereas the highest IOR values were
observed for the TDX90 DEM, followed by SRTM DEM. Results with AW3D over natural covers (bare,
grasslands, forest) varied depending on the site: At the Romanian site, its IOR was close to SRTM or
TDX90 values; whereas at the Spanish site, it was closer to the values obtained with TDX20/30 DEMs.
ALS-based PNOA DEM had the lowest IOR, with a large improvement over results with SRTM or
TDX90 (up to 1.2 dB for bare areas, up to 0.5 for needleleaf forest), and a slight improvement over
TDX20 results (0.2 dB for bare areas, and 0.1 dB for needleleaf forest). IOR values for PNOA and
TDX20/30 DEMs showed less spread (Figure 4), concentrating around lower values, whereas the
spread was larger for SRTM and TDX90. The main differences between sites were the lower spread
of IOR at the Spanish site, and the behavior of AW3D IOR, which was closer to the TDX20/30 values
at the Spanish site, but closer to SRTM/TDX90 values at the Romanian site.
Table 3. Backscatter Inter-orbit range (IOR) by polarization and land cover class at each study site
(GL, grassland; BLF, NLF, and MLF, are broadleaf, needleleaf, and mixed forest). Cell color shows the
gradient between the lowest (green) and the highest value (yellow). “M.D.” column represents the
maximum difference between global DEMs for each specific land cover.
Romania Spain
AW SR TDX TDX TDX M.
AW SR TDX TDX TDX ALS M.
3D TM 20 30 90 D. 3D TM 20 30 90 D.
Urban 5.4 5.4 5.3 5.4 6.9 1.6
Crops 2.3 2.3 2.2 2.2 2.3 0.1
1.3 1.4 1.3 1.3 1.5 1.2 0.2
GL 3.4 3.4 2.9 3.1 3.5 0.6
Bare
3.1 3.5 2.9 3.1 3.9 2.7 1.0
BLF 2.0 2.0 1.7 1.8 2.0 0.3
NLF 2.5 2.5 1.8 1.9 2.5 0.7
1.2 1.5 1.1 1.2 1.5 1.0 0.4
MLF 2.4 2.4 1.9 2.0 2.4 0.5
Figure 4. Boxplot representing the VV backscatter IOR for grassland, bare areas, and needleleaf forests
at both sites: Mean and median values (green triangle, orange line) and inter quantile-ranges
(whiskers) for 5–95%.
4.2. Inter-Orbital Ranges by Landform
Analyzing IOR values by land cover may have dampened potential differences as all pixels,
regardless of landform, were averaged by class. A more detailed analysis was conducted by
Figure 4.
Boxplot representing the VV backscatter IOR for grassland, bare areas, and needleleaf forests
at both sites: Mean and median values (green triangle, orange line) and inter quantile-ranges (whiskers)
for 5–95%.
4.2. Inter-Orbital Ranges by Landform
Analyzing IOR values by land cover may have dampened potential differences as all pixels,
regardless of landform, were averaged by class. A more detailed analysis was conducted by
disaggregating mean IOR values by landforms for the grassland, bare land, and needleleaf forests
(Table 4). These classes were selected as they often occur on steep slopes (Figure 4). Only landforms
with more than 1000 pixels have been analyzed to limit spurious results, due to small sample size.
Table 4.
IOR values disaggregated by landform for classes on mountain tops (grasslands, Romania;
bare, Spain). Cell color shows the gradient between the lowest (green) and the highest value (yellow)
for each row, which represents the IOR value for a specific landform when a certain was DEM employed.
“M.D.” column represents the maximum difference between global DEMs for each specific landform.
Romania (Grasslands) Spain (Bare)
AW SR TDX TDX TDX M. AW SR TDX TDX TDX ALS M.
3D TM 20 30 90 D. 3D TM 20 30 90 D.
peak 3.1 3.0 2.5 2.6 3.5 1.0 2.9 3.3 2.6 2.7 6.1 2.2 3.5
ridge 3.0 2.9 2.5 2.7 3.0 0.5 2.7 3.1 2.5 2.7 3.6 2.3 1.1
spur 3.2 3.2 2.7 2.9 3.1 0.5 3.1 3.4 2.9 3.0 3.9 2.7 1.0
slope 3.6 3.6 3.0 3.3 3.6 0.6 3.3 3.7 3.1 3.3 3.8 2.9 0.7
hollow 4.2 4.3 3.6 3.9 4.6 1.0 3.2 3.8 3.1 3.2 3.9 2.8 0.8
valley 4.5 4.7 4.1 4.4 5.5 1.4 3.0 3.4 2.9 3.1 4.7 2.6 1.8
Both grasslands and bare areas presented large differences between DEMs. In the case of
grasslands, the landforms valley, hollow, and peak showed the largest differences between DEMs
(1.0–1.4 dB). In the case of bare areas, peak and valley showed the largest differences (3.5 dB and 1.8 dB,
respectively), followed by ridge and spur (1.1 and 1.0 dB). For needleleaf forest, the largest differences
between DEMs were observed for concave landforms (Table 5): Hollow (0.8–0.9 dB at either site),
valleys (up to 0.8 dB at the Spanish site, and up to 1.4 dB at the Romanian site) and pits (up to 1.9 dB at
the Romanian site).

Remote Sens. 2020,12, 3016 12 of 23
Table 5.
IOR values disaggregated by landform for needleleaf forests. Cell color shows the gradient
between the lowest (green) and the highest value (yellow) for each row, which represents the IOR value
for a specific landform when a certain was DEM employed. “M.D.” column represents the maximum
difference between global DEMs for each specific landform.
Romania Spain
AW SR TDX TDX TDX M. AW SR TDX TDX TDX ALS M.
3D TM 20 30 90 D. 3D TM 20 30 90 D.
peak 2.3 2.2 2.0 2.1 2.3 0.3
ridge 2.2 2.2 1.9 2.0 2.2 0.4 1.2 1.3 1.1 1.1 1.3 1.0 0.2
spur 2.2 2.2 1.7 1.8 2.2 0.5 1.2 1.3 1.1 1.1 1.4 1.0 0.3
slope 2.3 2.3 1.7 1.8 2.4 0.7 1.2 1.5 1.1 1.2 1.5 1.0 0.4
hollow 2.8 2.8 1.9 2.0 2.9 0.9 1.4 1.9 1.3 1.4 2.1 1.1 0.8
valley 3.7 3.5 2.4 2.5 3.8 1.4 1.6 2.1 1.5 1.6 2.3 1.2 0.8
pit 4.5 3.9 2.6 2.7 4.0 1.9
Using the TDX20/30 generally yielded the lowest IOR among global DEMs, whereas using TDX90
yielded the highest. The absolute minimum was always observed when using the ALS DEM (0.1–0.4
dB lower when compared to TDX20/30). IOR values obtained with the ALS DEM for bare areas were
up to 3.9 dB lower when compared to the TDX90 DEM and up to 1.1 dB lower when compared with
SRTM. For needleleaf forest, IOR values for valleys using ALS DEM were up to 1.1 dB lower than those
obtained with TDX90.
4.3. Differences with and ALS-Derived DEM
The PNOA ALS-derived DEM provided the lowest IOR in all previous analyses at the Spanish site
and was used as a reference for quantitative analysis of the global DEMs (Table 6). For the scattering
area, the highest deviation (RMSD) was observed for the TDX90, followed by the SRTM DEMs. The
relative RMSD obtained with these DEMs was at least 10% higher when compared to the remaining
DEMs (TDX20, TDX30, and AW3D). The lowest RMSD were observed for AW3D (orbit 1) and TDX20
(orbit81), followed by TDX30. The MAD for the scattering area was higher when using the SRTM or
the TDX90 DEMs when compared to AW3D, TDX30, and TDX20. For both orbits, the SRTM-derived
scattering area was the least biased when compared to the ALS DEM (
−
4 m
2
for orbit 1 and 0.4 m
2
for
orbit 81), followed by TDX20 (−11 m2for orbit 1 and −12 m2for orbit 81).
Table 6.
Quality assessment for needleleaf forests using PNOA as a reference. Cell color shows the
gradient between the lowest (green) and the highest value (yellow).
O001 O081
Statistic AW SR TDX TDX TDX AW SR TDX TDX TDX
3D TM 20 30 90 3D TM 20 30 90
Sc. area (m2)
Abs. RMSD 97 188 102 109 213 115 214 113 123 245
Rel. RMSD 11% 21% 11% 12% 24% 12% 22% 11% 12% 25%
MAD 38 81 32 36 87 47 96 38 43 104
Offset 18 −4−11 14 –21 25 0.4 −12 20 −18
VV (dB)
Abs. RMSD 0.54 0.96 0.63 0.54 2.89 0.56 0.96 0.64 0.55 3.35
Rel. RMSD 5% 10% 6% 5% 29% 6% 10% 7% 6% 34%
MAD 0.38 0.64 0.41 0.36 0.74 0.38 0.63 0.41 0.35 0.75
Offset −0.19 −0.27 −0.27 −0.17 −0.35 −0.24 −0.30 −0.35 −0.23 −0.41

Remote Sens. 2020,12, 3016 13 of 23
For both orbits (1 and 81), the use of AW3D, TDX30, and TDX20 DEMs resulted in backscatter
coefficient (VV) values closest to those obtained using the ALS DEM, with relative RMSD under 8%.
For both orbits, the smallest offset with respect to the ALS DEM was obtained using TDX30, followed
by AW3D. In all cases, backscattering coefficient was underestimated when compared with ALS
DEM results.
4.4. Land Cover Classification
The DEM used for radiometric terrain normalization and topographic phase removal showed little
effect on the overall quality of the classification, regardless of the Sentinel-1 relative orbit (Table A1). The
Cohen’s Kappa was between 0.94 and 0.96. Analyzing the confusion matrices and the associated error
metrics (i.e., user accuracy, UE, producer accuracy, PE) showed that classes with a larger spatial extent
(low vegetation or forest) had very high accuracies (>95%). However, most pixels misclassified as low
vegetation (>85%) were, in fact, forest pixels according to the reference data. Water had a reasonable
accuracy (>80%). Depending on the DEM, the “source class” of pixels misclassified water pixels varied.
When using TDX20 for normalization, 21–27% of misclassified water pixels were recorded as forest
pixels on the validation dataset, 20–33% with TDX30, 33–50% with SRTM or AW3D, and 36–58% with
TDX90. The urban class had relatively small omission errors (10–15%), but the commission error was
high (around 40% for orbit 7, around 60% for orbit 29, and around 50% for obit 31), mostly due to
the misclassification of forests (52–78% of the pixels misclassified as urban were forest pixels in the
validation dataset).
Classification results were also analyzed by landform (Table A2), and in particular, for valleys.
Valleys were selected as they showed the largest differences between DEMs in previous tests, and
a reasonable number of samples were available (10 times more when compared to the number of
samples available for pit landform). When only valley pixels were considered, Cohen Kappa was
low (0.57–0.70). Using the TDX20 DEM allows for a marginal increase of the Cohen Kappa values
over the value obtained using the rest of the DEMs (0.05 for orbits 7, 0.03 for orbit 131, and 0.02 for
orbit 29). For valleys, user accuracy for low vegetation class dropped. In this context, the use of the
TDX20 DEM reduced commission errors up to 11% for low vegetation and up to 21.9% for water when
compared to the remaining DEMs (Figure 5). Using the TDX20 DEM also reduced the number of
forest pixels misclassified as water, representing a smaller percentage of the pixels misclassified as
such (17–27% less, depending on the orbit). Commission errors for urban cover increased for valley
landform, especially when terrain normalization is performed with any of the TDX DEMs.

Remote Sens. 2020,12, 3016 14 of 23
Remote Sens. 2020, 12, x FOR PEER REVIEW 13 of 24
Color composite: R, coherence; G, VH channel; B, VV channel
Land cover: Urban Low vegetation Forest Water
Figure 5. A small subset of the data around Leaota Peak. The first row shows the impact of terrain
normalization on the imagery (a,b). The dotted box is the area shown for classification maps. The
second row represents classified maps (b,c). White pixels indicate no data.
The backscatter coefficient (VV) for forests located on valleys was examined to better understand
the results obtained with the TDX DEMs (Figure 6). The boxplot showed that products normalized
using lower resolution DEMs (AW3D, SRTM, TDX90) had an increased frequency of low values on
the valley when compared to TDX20/30.
Figure 5.
A small subset of the data around Leaota Peak. The first row shows the impact of terrain
normalization on the imagery (
a
,
b
). The dotted box is the area shown for classification maps. The
second row represents classified maps (c,d). White pixels indicate no data.
The backscatter coefficient (VV) for forests located on valleys was examined to better understand
the results obtained with the TDX DEMs (Figure 6). The boxplot showed that products normalized
using lower resolution DEMs (AW3D, SRTM, TDX90) had an increased frequency of low values on the
valley when compared to TDX20/30.

Remote Sens. 2020,12, 3016 15 of 23
Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 24
Figure 6. Boxplot representing the VV backscatter coefficient for forests located on valleys by Sentinel-
1 relative orbit: Mean value (triangle) median value (orange) and inter quantile-ranges (whiskers) for
5–95%.
5. Discussion
The influence of the DEM employed for terrain normalization of backscatter and coherence data
variability was analyzed in three ways: (a) Comparing several orbital tracks (inter-orbit range, IOR);
(b) using the results obtained with an ALS-derived DEM as a reference; and (c) assessing land cover
classification results after a specific DEM is employed for normalization. Coherence varied very little
with the DEM employed, whereas the effect was larger on the backscatter coefficient.
Terrain normalization was better served by high-resolution DEMs (i.e., TDX20, ALS DEM,
AW3D at Spanish site), in agreement with prior research [17,29] (SRTM-1arcsec and AW3D
outperformed SRTM-3arcsec and TanDEM-X 90m). Higher resolution DEMs have reduced vertical
uncertainties (under 5 m over sloping terrain for TDX12.5 and AW3D) when compared to the SRTM
DEM (Table 1), which may have contributed to reducing IOR values. Some DEMs (TDX20, TDX90,
ALS) needed resampling prior, which may have impacted their performance. TDX20 and ALS DEMs,
were down-sampled, which may have reduced the DEM detail with an associated increase of IOR
values. However, the IOR values obtained after resampling was still smaller than those observed for
the lower resolution DEMs, underlining the importance of the vertical uncertainty of the original
DEM. TDX90 was resampled to a finer resolution. However, as resampling is a destructive operation,
the only expected impact was the smooth interpolation of the original data to a denser grid, which
does not provide additional information over the original DEM. The advantage provided by high-
resolution DEMs was dependent on the specific land cover, and the landform it occupies. For instance,
IOR for urban and crops showed little difference, as they occupy near-flat areas. Land cover classes
occupying steeper slopes (i.e., forests, grasslands) received the largest benefits of using a more
detailed DEM (minimum IOR). At the Romanian site, broadleaf forests showed smaller differences
than mixed and needleleaf forest, as the latter grew on steeper slopes. Variability for needleleaf forests
was smaller at the Spanish site, as it occupied milder slopes (Figure 3). Results were disaggregated
by landform, as the “typical” slope of each land cover might obscure landform related effects. Peak,
hollow, valley, and pit landforms showed the largest differences between the analyzed DEMs. Such
differences can be attributed to the sensitivity of SAR to remote sensing artifacts, such as shadowing
and foreshortening appearing with increasing slope. This affects DEM accuracy in sloped terrain, as
shown in Table 1, and propagates into any analysis based on pixel neighborhood, such as slope,
orientation [12], or terrain normalization. On these landforms, TDX20/30 clearly outperformed the
rest of the DEMs, as it provided an improved characterization of smaller terrain forms supporting
results reported by Grohmann et al. [17].
Figure 6.
Boxplot representing the VV backscatter coefficient for forests located on valleys by Sentinel-1
relative orbit: Mean value (triangle) median value (orange) and inter quantile-ranges (whiskers) for
5–95%.
5. Discussion
The influence of the DEM employed for terrain normalization of backscatter and coherence data
variability was analyzed in three ways: (a) Comparing several orbital tracks (inter-orbit range, IOR);
(b) using the results obtained with an ALS-derived DEM as a reference; and (c) assessing land cover
classification results after a specific DEM is employed for normalization. Coherence varied very little
with the DEM employed, whereas the effect was larger on the backscatter coefficient.
Terrain normalization was better served by high-resolution DEMs (i.e., TDX20, ALS DEM, AW3D
at Spanish site), in agreement with prior research [
17
,
29
] (SRTM-1arcsec and AW3D outperformed
SRTM-3arcsec and TanDEM-X 90m). Higher resolution DEMs have reduced vertical uncertainties
(under 5 m over sloping terrain for TDX12.5 and AW3D) when compared to the SRTM DEM (Table 1),
which may have contributed to reducing IOR values. Some DEMs (TDX20, TDX90, ALS) needed
resampling prior, which may have impacted their performance. TDX20 and ALS DEMs, were
down-sampled, which may have reduced the DEM detail with an associated increase of IOR values.
However, the IOR values obtained after resampling was still smaller than those observed for the lower
resolution DEMs, underlining the importance of the vertical uncertainty of the original DEM. TDX90
was resampled to a finer resolution. However, as resampling is a destructive operation, the only
expected impact was the smooth interpolation of the original data to a denser grid, which does not
provide additional information over the original DEM. The advantage provided by high-resolution
DEMs was dependent on the specific land cover, and the landform it occupies. For instance, IOR for
urban and crops showed little difference, as they occupy near-flat areas. Land cover classes occupying
steeper slopes (i.e., forests, grasslands) received the largest benefits of using a more detailed DEM
(minimum IOR). At the Romanian site, broadleaf forests showed smaller differences than mixed and
needleleaf forest, as the latter grew on steeper slopes. Variability for needleleaf forests was smaller at
the Spanish site, as it occupied milder slopes (Figure 3). Results were disaggregated by landform, as
the “typical” slope of each land cover might obscure landform related effects. Peak, hollow, valley, and
pit landforms showed the largest differences between the analyzed DEMs. Such differences can be
attributed to the sensitivity of SAR to remote sensing artifacts, such as shadowing and foreshortening
appearing with increasing slope. This affects DEM accuracy in sloped terrain, as shown in Table 1,
and propagates into any analysis based on pixel neighborhood, such as slope, orientation [
12
], or
terrain normalization. On these landforms, TDX20/30 clearly outperformed the rest of the DEMs,
as it provided an improved characterization of smaller terrain forms supporting results reported by
Grohmann et al. [17].

Remote Sens. 2020,12, 3016 16 of 23
The use of an ALS-derived DEM resulted in the smallest IOR, pointing it as a suitable candidate to
benchmark global DEMs at the Spanish site. Taking the ALS DEM as a reference, the lowest deviation
and bias of the SAR metrics were observed for the AW3D and TDX20/30 DEMs. The results obtained
by AW3D were explained by the combined effect of the resolution employed for its generation (5 m)
and the low cover and height of Mediterranean forests. These factors may have eased the detection of
vegetation-free pixels, “pushing” the reported data nearer to the true terrain surface once resampled
to 30 m, as described by References [
17
,
18
]. TDX20/30 provided similar results thanks to the high
spatial resolution of the X-band sensor employed for its creation. In addition, shorter wavelengths (X-
as opposed to C-band) can capture finer spatial details [
1
,
14
,
72
]. In some cases, better results were
observed for the TDX30 DEM when compared to the TDX20 DEM, pointing to a trade-offbetween the
spatial detail and the effect of the DEM noise, with some improvement for slightly coarser resolutions
(30 m instead of 20), but a high dispersion when resolution becomes too coarse (TDX 90). TDX90 and
SRTM DEMs showed similar dispersion when compared to the ALS PNOA DEM reference values,
possibly due to the variable-resolution smoothing [
20
] employed during the reprojection of SRTM to
map coordinates, which may have decreased its detail, as shown in References [17,21,22].
DEM performance also varied across sites, with a larger inter-track variability being observed for
needleleaf forests at the Romanian site. Such differences were explained by the steeper slopes this land
cover class occupied in the Carpathians, as well as its characteristics (height and structure), which may
have complicated DEM generation, due to volume decorrelation, as reported by References [
20
,
24
]:
The Carpathians are covered by dense temperate forests, whereas sierra Nevada is populated by
Mediterranean forests with lower tree height and canopy density. Among all DEMs, AW3D IOR had a
distinct behavior. While at the Romanian site, the AW3D results were close to those observed to the
SRTM DEM, at the Spanish, the results were closer to those observed when using the TDX20/30 DEMs.
The quality mask layer showed that, at the Romanian site, the AW3D DEM has a large strip where
missing data (due persistent cloud cover) have been infilled with SRTM data, a problem mentioned by
Truckenbrodt [
29
]. For this reason, it is not possible to draw conclusions on the influence of canopy
characteristics on the performance of AW3D.
The characteristics of each DEM propagated into the land cover classification results. Overall
classification accuracy was similar regardless of the DEM, with reasonable accuracies for all classes
except urban. The low accuracy of urban surfaces was attributed to (i) the prevalence of steep slopes,
which difficult terrain normalization (not fully accounted scattering area) [
29
] and may introduce DEM
artifacts (mischaracterizing terrain surface) [
12
,
14
,
72
], and (ii) the prevalence of discontinuous urban
fabric at the Romanian site, which may cause confusion with ornamental and fruit tree cover present
around residential areas, and genuine forested lands.
When analyzing classification results for specific landforms (i.e., valleys), the AW3D, SRTM
and TDX90 showed larger commission errors (CE), due to the misclassification of the forest as low
vegetation or water. Such errors can be explained by the mischaracterization of the thin crevices of
the drainage network on the DEMs [
12
]. This is propagated to the lookup table and impacts both, the
distortion masking process and the terrain normalization. Distortion masking is affected because the
distance between the pixels in the range is altered. Therefore, such pixels are not marked as distorted
or shadowed. Terrain normalization is affected as it uses the LUT, orientation, and slope layers to
estimate the scattering area. In turn, the scattering area is overestimated, under-compensating the
radiometric effects, keeping a pseudo-shadow with lowered values (as opposed to true shadow, caused
by occlusion, which cannot be compensated). Therefore, forests are misclassified due to the apparently
low backscatter.
Using the TDX20 reduced commission error for low vegetation (11%) and water (12–22%) on
valleys, as well as the percentage of forest pixels among all pixels misclassified as water (decrease of
17–27%). This was explained by reduced pseudo-shadows when using the TDX20/30 DEMs. However,
the improvement came at the cost of a 3–6% increase of commission error for urban cover located on
valleys, which was caused by a slight increase in backscatter values over forests (overcompensation),

Remote Sens. 2020,12, 3016 17 of 23
and the large size difference between the validation sample for urban and forest (<10
3
vs. 7.5
·
10
4
at valleys), as misclassification of a small subset of the latter would be much larger when compared
with the sample size for the former. Even with this trade-off, classification results using TDX20 were
marginally better (0.01–0.05 higher Cohen’s Kappa). Furthermore, increased CE for urban cover on
valleys did not affect the overall CE for the urban class, which was reduced by 2–3% when using the
TDX20 DEM.
6. Conclusions
SAR observations are heavily affected by sensor-terrain geometry, which can be corrected using
a DEM. Choosing a DEM for SAR data terrain normalization is not a trivial choice, as it affects
backscattering coefficient variability, and mapping products generated downstream. High-resolution
TanDEM-X DEM (20 or 30 m resolution) was the global DEM providing the largest reduction of terrain
induced variability, followed by AW3D in sparse vegetation areas. Natural land covers (i.e., forest, bare
areas, grasslands) occupying steeper slopes and complex landforms (i.e., peaks, pits, valleys) received
the largest benefits. These benefits were felt on classification, where more forest pixels were classified
correctly due to a better compensation of low values (valley pseudo-shadow). An ALS-based DEM
was able to provide slightly better results (i.e., marginally reduced IOR) when compared to AW3D
and TDX20/30 DEMs. However, AW3D and TDX20/30 DEMs seem suitable candidates to replace
ALS-based local DEMs. However, AW3D should be checked for data infilling from older datasets (i.e.,
SRTM) as over such areas, its performance may be degraded.
This study showed the effect of several global DEMs on terrain normalization, highlighting their
advantages and shortcomings when normalizing Sentinel-1 imagery. Further research should expand
this analysis by including the recent NASADEM dataset (from re-processed SRTM) [
73
], using a
reference ALS-based DEM for temperate forests, and studying the DEM-dependent normalization
effects on SAR imagery acquired at different wavelengths. Finally, the effect of terrain normalization
could be tested on downstream quantitative products, such as biomass estimates or canopy cover.
Author Contributions:
Conceptualization and Methodology, I.B.-M. and M.A.T.; software, validation, formal
analysis, investigation, data curation, visualization, I.B.-M.; resources, O.B., M.A.T.; writing—original draft
preparation, I.B.-M.; writing—review and editing, I.B.-M., M.A.T., M.S. and L.V.; supervision, project administration
and funding acquisition, O.B. and M.A.T. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research was funded by the Romanian National Authority for Scientific Research and Innovation
and the European Regional Development Fund under the project “Prototyping an Earth-Observation based
monitoring and forecasting system for the Romanian forests” (EO-ROFORMON, project ID P_37_651/105058).
Acknowledgments:
TanDEM-X DEM data were provided by the German Aerospace Center (DLR), under the
proposal DEM_FOREST2614. We also would like to thank the publishers of the open datasets employed in this
study: DLR for making available the TanDEM-X Global Urban Footprint dataset, European Environment Agency
for providing the Corine Land Cover Dataset, European Space Agency for providing Sentinel-1 data and CCI
land cover map, National Geographic Institute of Spain for providing PNOA LiDAR DEM and SIOSE land cover
dataset, JAXA, for providing both AW3D DEM and ALOS forest/non-forest map, and USGS, for providing SRTM.
Finally, we would like to thank INCDS Marin Dracea and University of Alcal
á
for hosting the research activities.
Conflicts of Interest: The authors declare no conflict of interest.

Remote Sens. 2020,12, 3016 18 of 23
Appendix A
Table A1.
Confusion matrices by DEM and orbit (Reference >columns; Classified >Rows) at overall level. (Ur, urban; LV, low vegetation; Fo, forest; Wa, water. UA,
user accuracy, PA, producer accuracy).
O007 O029 O131
Ur LV Fo Wa UA Ur LV Fo Wa UA Ur LV Fo Wa UA
AW3D
Ur 19147 6602 7310 43 57.84 18698 6465 19317 24 42.01 18749 5044 12423 44 51.71
LV 2206 1548500 19344 271 98.61 2063 1544334 20493 321 98.54 2366 1545097 24852 358 98.25
Fo 250 10075 811265 380 98.70 759 13244 787900 395 98.21 357 9861 739556 389 98.59
Wa 0 506 506 5054 83.32 0 611 355 5007 83.83 0 688 468 4956 81.09
PA 88.63 98.90 96.76 87.93 86.89 98.70 95.15 87.12 87.32 99.00 95.14 86.24
SRTM
Ur 18868 6245 7204 40 58.31 18439 6055 19377 24 42.01 18725 5048 12952 48 50.92
LV 2278 1540604 19090 262 98.62 2108 1537069 20272 318 98.54 2283 1538477 23950 340 98.30
Fo 265 9900 806632 379 98.71 774 12931 782981 386 98.23 285 9564 735257 371 98.63
Wa 0 546 549 4944 81.87 0 566 283 4895 85.22 0 618 358 4861 83.28
PA 88.12 98.93 96.78 87.89 86.48 98.74 95.15 87.05 87.94 99.02 95.18 86.49
TDX20
Ur 29990 9446 11850 73 58.39 28857 8597 30849 36 42.23 29327 7184 18985 73 52.78
LV 3168 2411865 22525 363 98.93 3440 2410056 31421 475 98.55 3426 2407209 39924 475 98.21
Fo 364 16915 1270072 619 98.61 1088 17561 1188573 552 98.41 439 12460 1060469 540 98.75
Wa 1 1112 419 7772 83.53 0 960 251 7535 86.15 0 1063 311 7085 83.76
PA 89.46 98.87 97.33 88.05 86.44 98.89 95.00 87.64 88.36 99.15 94.71 86.69
TDX30
Ur 19156 6430 8530 42 56.08 18677 6280 21653 24 40.05 18793 4847 13386 46 50.69
LV 2034 1540695 16591 239 98.79 2010 1538410 20705 301 98.53 2205 1539752 27089 303 98.11
Fo 221 9505 808033 390 98.76 642 11234 773058 350 98.44 276 8255 712706 356 98.77
Wa 0 665 321 4954 83.40 0 718 220 4867 83.84 0 680 171 4678 84.61
PA 89.47 98.93 96.95 88.07 87.57 98.83 94.78 87.82 88.34 99.11 94.60 86.90
TDX90
Ur 19027 6599 7449 46 57.45 18439 5990 18774 26 42.65 18727 5161 13647 53 49.82
LV 2179 1542343 18571 245 98.66 2138 1538972 19112 309 98.62 2294 1540926 23537 344 98.33
Fo 240 9780 808189 396 98.73 794 13421 789226 382 98.18 309 9608 743312 370 98.63
Wa 9 666 921 4937 75.57 0 567 343 4905 84.35 0 641 357 4854 82.95
PA 88.68 98.91 96.77 87.78 86.28 98.72 95.38 87.25 87.80 99.01 95.19 86.35

Remote Sens. 2020,12, 3016 19 of 23
Table A2.
Confusion matrices by DEM and orbit (Reference >columns; Classified >Rows) for valley. (Ur, urban; LV, low vegetation; Fo, forest; Wa, water. UA, user
accuracy, PA, producer accuracy).
O007 O029 O131
Ur LV Fo Wa UA Ur LV Fo Wa UA Ur LV Fo Wa UA
AW3D
Ur 304 188 827 2 23.01 305 225 2478 1 10.14 298 175 1292 2 16.86
LV 25 5447 4117 26 56.65 20 5232 4292 30 54.65 27 5214 3984 33 56.32
Fo 2 537 76000 19 99.27 1 697 73596 18 99.04 3 606 70952 20 99.12
Wa 0 32 176 514 71.19 0 27 81 512 82.58 0 27 54 506 86.20
PA 91.84 87.80 93.69 91.62 93.56 84.65 91.48 91.27 90.85 86.58 93.01 90.20
SRTM
Ur 319 239 816 1 23.20 318 193 1974 2 12.79 318 177 949 1 22.01
LV 29 5195 3821 18 57.32 30 5137 4540 28 52.77 29 5102 4404 28 53.35
Fo 3 598 72313 17 99.15 0 689 70070 14 99.01 1 583 68204 15 99.13
Wa 0 29 128 429 73.21 0 27 84 421 79.14 0 29 58 420 82.84
PA 90.88 85.71 93.82 92.26 91.38 84.97 91.39 90.54 91.38 86.61 92.65 90.52
TDX20
Ur 495 410 1795 1 18.33 492 371 4221 2 9.67 485 277 1923 1 18.06
LV 35 8150 3874 29 67.42 35 8080 5511 47 59.09 33 7983 5496 44 58.89
Fo 3 994
113029
34 99.10 1 1022
104714
21 99.01 4 844 95033 26 99.09
Wa 0 38 108 724 83.22 0 31 36 673 90.95 0 27 48 596 88.82
PA 92.87 84.97 95.14 91.88 93.18 85.02 91.47 90.58 92.91 87.43 92.72 89.36
TDX30
Ur 329 274 1267 1 17.58 323 258 2910 1 9.25 324 205 1377 1 16.99
LV 19 5160 2846 15 64.18 25 5091 3604 24 58.22 19 5068 3732 23 57.32
Fo 3 594 72894 21 99.16 0 651 68921 12 99.05 1 553 64973 18 99.13
Wa 0 33 71 428 80.45 0 29 51 411 83.71 0 29 33 378 85.91
PA 93.73 85.13 94.57 92.04 92.82 84.44 91.30 91.74 94.19 86.56 92.67 90.00
TDX90
Ur 305 258 1046 0 18.96 304 243 2341 0 10.53 309 187 1256 0 17.64
LV 24 5299 3304 21 61.27 22 5183 3809 39 57.25 21 5216 3669 39 58.31
Fo 3 606 70640 19 99.12 2 725 68702 14 98.93 2 610 67360 12 99.08
Wa 0 26 259 451 61.28 0 28 116 437 75.22 0 25 106 439 77.02
PA 91.87 85.62 93.88 91.85 92.68 83.88 91.64 89.18 93.07 86.39 93.05 89.59

Remote Sens. 2020,12, 3016 20 of 23
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