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The Global Ecosystem Dynamics Investigation (GEDI) Light Detection And Ranging (LiDAR) altimetry mission was recently launched to the International Space Station with a capability of providing billions of high-quality measurements of vertical structures globally. This study assesses the accuracy of the GEDI LiDAR altimetry estimation of lake water levels. The difference between GEDI’s elevation estimates to in-situ hydrological gauge water levels was determined for eight natural lakes in Switzerland. The elevation accuracy of GEDI was assessed as a function of each lake, acquisition date, and the laser used for acquisition (beam). The GEDI elevation estimates exhibit an overall good agreement with in-situ water levels with a mean elevation bias of 0.61 cm and a standard deviation (std) of 22.3 cm and could be lowered to 8.5 cm when accounting for instrumental and environmental factors. Over the eight studied lakes, the bias between GEDI elevations and in-situ data ranged from -13.8 cm to +9.8 cm with a standard deviation of the mean difference ranging from 14.5 to 31.6 cm. Results also show that the acquisition date affects the precision of the GEDI elevation estimates. GEDI data acquired in the mornings or late at night had lower bias in comparison to acquisitions during daytime or over weekends. Even though GEDI is equipped with three identical laser units, a systematic bias was found based on the laser units used in the acquisitions. Considering the eight studied lakes, the beams with the highest elevation differences compared to in-situ data were beams 1 and 6 (standard deviations of -10.2 and +18.1 cm, respectively). In contrast, the beams with the smallest mean elevation difference to in-situ data were beams 5 and 7 (-1.7 and -2.5 cm, respectively). The remaining beams (2, 3, 4, and 8) showed a mean difference between -7.4 and +4.4 cm. The standard deviation of the mean difference, however, was similar across all beams and ranged from 17.2 and 22.9 cm. This study highlights the importance of GEDI data for estimating water levels in lakes with good accuracy and has potentials in advancing our understanding of the hydrological significance of lakes especially in data scarce regions of the world.
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remote sensing
Article
Analysis of GEDI Elevation Data Accuracy for Inland
Waterbodies Altimetry
Ibrahim Fayad 1, *, Nicolas Baghdadi 1, Jean Stéphane Bailly 2,3 , Frédéric Frappart 4
and Mehrez Zribi 5
1CIRAD, CNRS, INRAE, TETIS, University of Montpellier, AgroParisTech, 34093 Montpellier CEDEX 5,
France; nicolas.baghdadi@teledetection.fr
2INRAE, IRD, Institut Agro, LISAH, Univ Montpellier, 34060 Montpellier CEDEX 1, France;
bailly@agroparistech.fr
3AgroParisTech, 75005 Paris, France
4
LEGOS, CNES, CNRS, IRD, UPS-14 Avenue Edouard Belin, Universit
é
de Toulouse, 31400 Toulouse, France;
frederic.frappart@legos.obs-mip.fr
5
CESBIO (CNRS/UPS/IRD/CNES/INRAE), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France;
mehrez.zribi@ird.fr
*Correspondence: ibrahim.fayad@inrae.fr
Received: 7 July 2020; Accepted: 18 August 2020; Published: 21 August 2020


Abstract:
The Global Ecosystem Dynamics Investigation (GEDI) Light Detection And Ranging
(LiDAR) altimetry mission was recently launched to the International Space Station with a capability
of providing billions of high-quality measurements of vertical structures globally. This study assesses
the accuracy of the GEDI LiDAR altimetry estimation of lake water levels. The dierence between
GEDI’s elevation estimates to in-situ hydrological gauge water levels was determined for eight
natural lakes in Switzerland. The elevation accuracy of GEDI was assessed as a function of each lake,
acquisition date, and the laser used for acquisition (beam). The GEDI elevation estimates exhibit an
overall good agreement with in-situ water levels with a mean elevation bias of 0.61 cm and a standard
deviation (std) of 22.3 cm and could be lowered to 8.5 cm when accounting for instrumental and
environmental factors. Over the eight studied lakes, the bias between GEDI elevations and in-situ
data ranged from
13.8 cm to +9.8 cm with a standard deviation of the mean dierence ranging from
14.5 to 31.6 cm. Results also show that the acquisition date aects the precision of the GEDI elevation
estimates. GEDI data acquired in the mornings or late at night had lower bias in comparison to
acquisitions during daytime or over weekends. Even though GEDI is equipped with three identical
laser units, a systematic bias was found based on the laser units used in the acquisitions. Considering
the eight studied lakes, the beams with the highest elevation dierences compared to in-situ data
were beams 1 and 6 (standard deviations of
10.2 and +18.1 cm, respectively). In contrast, the beams
with the smallest mean elevation dierence to in-situ data were beams 5 and 7 (
1.7 and
2.5 cm,
respectively). The remaining beams (2, 3, 4, and 8) showed a mean dierence between
7.4 and
+4.4 cm. The standard deviation of the mean dierence, however, was similar across all beams and
ranged from 17.2 and 22.9 cm. This study highlights the importance of GEDI data for estimating
water levels in lakes with good accuracy and has potentials in advancing our understanding of the
hydrological significance of lakes especially in data scarce regions of the world.
Keywords: lidar; GEDI; elevations; lakes; altimetry
1. Introduction
Freshwater resources are a renewable resource that is vital for the sustainability of all life forms
on Earth. Surface fresh waters, which are found in the form of snow and glaciers, rivers, lakes,
Remote Sens. 2020,12, 2714; doi:10.3390/rs12172714 www.mdpi.com/journal/remotesensing
Remote Sens. 2020,12, 2714 2 of 22
and reservoirs, are significantly vulnerable to the increasing climate change risks [
1
]. Projected changes
in surface freshwater are region dependent and range from reduced renewable surface water resources
to changes in flood magnitude and frequency [
2
,
3
]. These changes are expected to aect water resources
management and intensify the competition for water among agricultural, industrial, energy, and the
ecosystem sectors. Currently, 1 billion people depend on lakes for domestic consumption [
4
], and this
figure is estimated to reach 5.5 billion people in the next 20 years [
5
]. Globally, water is unequally
distributed, and water volumes are not constant due to unequal volumes of water-replenishment and
water-depletion. Freshwater is replenished through direct rainfall, whereas its consumption is mostly
the sum of evaporation, ground seepage, outlet flow and anthropogenic activities, such as irrigation.
Therefore, for proper management of freshwater from lakes, rivers, and reservoirs, the monitoring of
water volumes and water levels is essential. In general, water surfaces are monitored using in-situ
gauge observations. These gauges measure the temporal variations of water levels in lakes, reservoirs,
and rivers. However, in-situ monitoring of water levels is scarce and sometimes impractical in many
regions of the world due to several reasons: (1) the decline of gauging networks globally due to the
cost of installation and maintenance, and their sparsity in developing countries [
6
]; (2) the limited
accessibility and costly charges for acquiring in-situ water level data as they are considered sensitive
information [
7
]; and, finally, (3) the diculty to monitor water levels across any free water surface,
especially in areas where the channel network is not well defined, such as in the case of floodplains
and wetlands. In this context, the development of new techniques for the global monitoring of water
levels through satellite observations is required.
In the past decades, conventional radar altimeters, which were initially developed for the
monitoring of sea and ocean surface topography, have been successfully used for the monitoring
and evaluation of water surface height levels of lakes, rivers and wetlands [
8
15
]. Owing partly
to their ability in providing precise water surface elevations over large water bodies, all-weather
operability, and global data coverage, radar altimeters are increasingly being used for the monitoring
of in-land waterbodies (river, lakes, reservoirs) [16]. To date, there have been thirteen radar altimeter
missions (Geosat, ERS-1/2, Geosat Follow-on, Topex/Poseidon, Envisat, Jason 1/2/3, Cryosat-2, HY-2A,
Saral/Altika, and Sentinel-3 A and B). Radar altimeter missions are assured with continuity of
measurements for the next decade through Jason-Continuity of Service/Sentinel-6 (Jason-CS A in 2020
and Jason-CS B in 2026), Sentinel 3 C and D (planned, respectively, for 2021 and post-2021). Finally,
the Surface Water and Ocean Topography (SWOT) will be the first mission to provide elevation maps
after 2022 using low incidence Synthetic-Aperture Radar (SAR) interferometry techniques.
To measure surface elevations, a satellite radar altimeter first sends a radar pulse towards the
earth, and accurately measures the amount of time it takes the transmitted pulse to be received by the
satellite sensor in order to derive the altimetric range (distance between satellite and the reflecting
surface). Then, surface elevation is estimated by calculating the dierence between the elevation of
the satellite that is referenced to an ellipsoid and the altimetric range. However, due to technical
reasons, the satellite radar altimeter does not record all the power reflected by all the targets within
the instrument window, which can vary between a few hundred meters (Cryosat-2, Sentinel-3) to
several kilometers. Instead, satellite altimeters only track a small window within their footprint,
in which the size, depending on the satellite mission, can vary between several tens of meters to
1024 m (Envisat in the 20 MHz) [
15
]. Over land areas, surface elevations can vary greatly within
the altimeter footprint. Therefore, the surrounding areas of water bodies smaller than the satellite
footprint, often contaminates the returned signal. Thus, the accuracy on the estimation of water surface
elevations can rapidly decrease from several centimeters for large lakes to several decimeters for small
lakes [
9
,
17
,
18
]. Recently, with the emergence of new altimeter instruments, such as the altimeters used
in ESA’s Cryosat-2, and ESA’s Sentinel-3 missions, the monitoring of small water bodies should be less
problematic. Cryosat-2, as well as the recently launched Sentinel-3 satellite, are equipped with a new
Synthetic-aperture radar altimeter (SRAL) instrument, which uses an along-track beam formation in
order to generate a smaller footprint strips (~300 m along-track, and ~1 km across-track). These strips
Remote Sens. 2020,12, 2714 3 of 22
can later be superimposed and averaged to improve the elevation estimation accuracy [
19
]. However,
even with the new altimeters, the accuracy of the measurements could still be aected by the size
of the water body. For example, for Shu et al. [
20
], the lowest root mean square error (RMSE) on
the water elevation level using the Sentinel-3 altimeter varied between ~4 cm (bias =20.89 cm) and
~20 cm
(bias =0.26 cm)
. Huang et al. [
21
] used Sentinel-3A data to monitor water levels along the
Brahmaputra River (rived bed width varies between ~100 m to more than 1000 m). They reported
that the standard deviation of the dierence between the gauged station recorded water levels and
Sentinel-3A derived water levels ranged from 41 to 76 cm. Normandin et al. [
22
] compared 18 water
levels derived from Sentinel-3A with gauge records from 5 in-situ stations in the Inner Niger Delta.
Their results showed that with only taking into account the closest ones, the RMSE ranged from 16 cm
to 70 cm. Finally, Bogning et al. [
23
] found that the RMSE on the estimation of Ogoou
é
river (river bed
width varies from ~300 m to ~ 1000 m) water levels using Sentinel-3 data varied from 41 cm to 89 cm.
Satellite laser altimeters, similar to conventional radar altimeters, can also be used to measure,
and monitor inland water levels. Currently, only three satellite LiDAR missions have been launched.
The Geoscience Laser Altimeter System (GLAS) which was carried onboard the Ice, Cloud, and land
Elevation Satellite (ICESat-1), was the first operational laser altimeter, and operated between 2003
and 2009 [
24
]. ICESat-1 carried two laser altimeters operating at visible wavelengths (green and
near-infrared), and each near-infrared laser produced ~60 m footprint on the surface of the Earth at
~170 m along-track intervals, and firing 40 pulses per second (40 Hz). The waveform of each GLAS shot
is sampled to 544 or 1000 bins over land areas at a temporal resolution of 1 ns. The vertical resolution of
waveforms acquired over land is ~15 cm [
24
]. ICESat-1 was succeeded in 2018 by ICESat-2 that carried
the Advanced Topographic Laser Altimeter System (ATLAS). In contrast to GLAS, ATLAS is equipped
with a single 532 nm wavelength laser that emits six beams that are arranged into three pairs. Beam
pairs are separated by ~3 km across-track with a pair spacing of 90 m. The nominal footprint of ATLAS
is 17 m with a spacing interval of 0.7m along-track. The most recent spaceborne LiDAR system is
GEDI on board the International Space Station (ISS), which launched in December 2018, with on-orbit
checkout in April 2019. GEDI’s mission is to provide information about canopy structure, biomass,
and topography and is estimated to acquire 10 billion cloud free shots in its two years mission [
25
].
GEDI is comprised of three lasers emitting 1064 nm light, at a rate of 242 Hz. One of the lasers’ output
is split into two beams (half the power of the full laser), called coverage beams, while the other two
lasers remain at full power. At any given moment, four beams are incident on the ground, where
each beam is dithered across track to produce eight tracks of data. The 8 produced tracks, henceforth
referred to as beams, are separated by ~600 m across-track, with a footprint diameter of ~25 m and a
distance between footprint centers of 60 m along-track [25].
An advantage of laser altimeters for water level monitoring is their small footprint and high-density
sampling in comparison to radar altimeters, which makes them more suitable for small water bodies,
as the footprint acquired over water is less likely to carry terrain information. However, atmospheric
parameters, such as cloud height, cloud thickness, and cloud optical depth, could aect the viability of
the echoed LiDAR data [
26
]. For Abdallah et al. [
27
], the standard deviation between lake water levels
from ICESat-1 GLAS and in-situ water gauge levels was 11.6 cm (bias =
4.6 cm). In
Baghdadi et al.
[
28
],
the accuracy (RMSE) to estimate water levels over Lake Geneva was found to be around 5 cm for
footprints that are completely acquired over water, and decreased to about 15 cm for transitioning
footprints (footprints over both terrain and water). However, for very narrow rivers, ICESat-1 GLAS
was unsuccessful in determining river water levels, and water level estimated accuracy was around
114 cm [28].
The objective of this paper was to analyze, for the first time, the quality of GEDI data, with the
aim of retrieving water levels of several lakes in Switzerland using the first available GEDI data, that
were released in January 2020 for an acquisition period ranging from mid-April 2019 up to mid-June
2019. This paper is organized in five sections. A description of the studied lakes and datasets is given
Remote Sens. 2020,12, 2714 4 of 22
in Section 2. The results of the evaluation of GEDI elevations are given in Section 3, followed by a
discussion in Section 4. Finally, the main conclusions are presented in the last section.
2. Study Domain and Datasets
2.1. Studied Lakes
Switzerland has around 1500 lakes. The largest lakes are at the northern foot of the Jura (Lakes
Geneva, Neuch
â
tel and Biel), on the Plateau (Lakes Bodensee, Zurich, and Walensee), in the Lower
Alps and the Northern Alps (Lakes Thun, Lucerne, Sempach, Brienz, and Zug), and in the Southern
Alps (Lakes Lugano and Maggiore). In addition to these, there exists hundreds of small natural lakes
and reservoirs, which can be mainly found in the Alps. In this study, we selected eight lakes in
Switzerland (Figure 1). The surface areas of the studied lakes vary greatly between 14 km
2
, and 528 km
2
(Table 1). Three lakes (Geneva, Neuch
â
tel, and Lucerne) have surface areas greater than 100 km
2
,
and the five remaining lakes have surface areas ranging between 14 and 49 km
2
(Walensee, Zürich,
Obersee (Zürich), Sempach, and Thun). The average water level elevation for the majority of the
lakes is between 405 and 433 m, while Lake Geneva has an average water level elevation of 372.05 m,
and Lake Sempach and Lake Thun have an average water level elevations of 503.66 m and 557.67 m,
respectively. Table 1lists the associated information about the gauge stations, and the number of GEDI
acquisitions over each of the eight case study lakes. Lake boundaries have been provided by the Global
Lakes and Wetland Databases (GLWD) [29] and will be used to extract the GEDI footprints that were
acquired over each of the eight lakes.
Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 23
Figure 1. Location of the studied lakes in Switzerland (top left). (a): Geneva; (b): Neuchâtel; (c): Zürich;
(d): Obersee (Zürich); (e): Lucerne; (f): Walensee; (g): Sempach ; (h): Thun. The green transects
represent the GEDI tracks over each lake.
Table 1. Global Ecosystem Dynamics Investigation (GEDI) acquisition dates between April and June
2019 and available GEDI shot count over the studied lakes. The star (*) indicates that the average level
of water was available for 2018, not 2019.
Lake
GEDI Acquisition
Dates
dd/mm (Hours)
GEDI
Shots
Count
Average Water
Level in 2019
(Gauges)
Approximate
Size
(km2)
Geneva 20/04 (08:37); 04/05
(03:12); 28/05 (17:23) 7451 372.05 m 584
Neuchâtel 21/04 (12:37); 28/04
(09:54); 29/05 (21:25) 5089 429.30 m 429
Zürich
02/05 (08:08); 04/05
(03:12)
11/05 (00:30); 08/06
(13:10)
1711 405.91 m 49
Obersee
(Zürich) 02/05 (08:08) 967 405.91 m 21
Figure 1.
Location of the studied lakes in Switzerland (top left). (
a
)
:
Geneva; (
b
): Neuch
â
tel; (
c
): Zürich;
(
d
): Obersee (Zürich); (
e
): Lucerne; (
f
): Walensee; (
g
): Sempach; (
h
): Thun. The green transects
represent the GEDI tracks over each lake.
Remote Sens. 2020,12, 2714 5 of 22
Table 1.
Global Ecosystem Dynamics Investigation (GEDI) acquisition dates between April and June
2019 and available GEDI shot count over the studied lakes. The star (*) indicates that the average level
of water was available for 2018, not 2019.
Lake GEDI Acquisition Dates
dd/mm (Hours) GEDI Shots Count
Average Water
Level in 2019
(Gauges)
Approximate Size
(km2)
Geneva 20/04 (08:37); 04/05 (03:12);
28/05 (17:23) 7451 372.05 m 584
Neuchâtel 21/04 (12:37); 28/04 (09:54);
29/05 (21:25) 5089 429.30 m 429
Zürich 02/05 (08:08); 04/05 (03:12)
11/05 (00:30); 08/06 (13:10) 1711 405.91 m 49
Obersee (Zürich) 02/05 (08:08) 967 405.91 m 21
Lucerne 20/04 (08:37); 22/05 (23:59) 2523 433.51 m*113
Walensee 20/04 (08:37); 02/05 (08:08) 605 419.05 m 24
Sempach 04/05 (12:37); 22/05 (23:59)
08/06 (13:10) 592 503.66 m 14
Thun 20/04 (08:37); 21/04 (12:37) 2303 557.67 m 48
2.2. Datasets
2.2.1. In-Situ Water Levels from Gauge Stations
Water level records from in-situ gauge stations over lakes were obtained free of charge from the
Hydrology Department of the Federal Oce for the Environment (FOEN) (www.hydrodaten.admin.ch).
FOEN currently monitors the quantity and quality of surface water and groundwater through a network
of 260 gauging stations across Switzerland. In this study, the comparison between GEDI footprint
elevations and the in-situ lake water elevations was done by comparing the footprint elevation at GEDI
acquisition time and the reported in-situ daily-mean water level elevation corresponding to the GEDI
acquisition date.
2.2.2. GEDI Data Products
The Global Ecosystem Dynamics Investigation (GEDI) onboard the International Space Station
(ISS), which commenced operations in early 2019, uses three onboard lasers that produce eight parallel
tracks (beams) of observations. GEDI lasers illuminate a surface or footprint on the ground with a 25 m
diameter, at a frequency of 242 Hz, over which 2D or 3D structures are measured. The footprints are
separated by ~60 m (center to center) along the beam, and the beams are separated by ~600 m. As the
ISS is not maintained in a repeating orbit [
25
], the repeat cycle of GEDI acquisitions are not guaranteed.
However, GEDI has the ability to rotate the instrument up to six degrees, allowing the lasers to be
pointed as much as 40 km on either side of the ISS’s ground track [
25
]. Over our studied lakes, there are
on average two acquisition series per lake during the first two months of available GEDI data (Table 1),
which correspond to the time period between mid-April 2019 and mid-June 2019. GEDI measures
vertical structures using a 1064-nm laser pulse, and the echoed waveforms are digitized to a maximum
of 1246 bins with a vertical resolution of 1 ns (15 cm), corresponding to a maximum of 186.9 m of height
ranges, with a vertical accuracy over relatively flat, non-vegetated surfaces of ~3 cm [30].
In order to measure 3D structures, GEDI uses its onboard telescope that collects the light reflected
by the ground, vegetation, and even clouds. The collected light, which represents the amount of
laser energy reflected from surface objects within the footprint at dierent heights, is converted to
voltage, and recorded as a function of time in 1 ns intervals. Then, object heights are calculated by
multiplying the recorded time by the speed of light, which produces the full-waveform. The recorded
waveform can then be used to derive a variety of height metrics, such as vegetation canopy heights,
canopy vertical profiles, and relative height (RH, i.e., vertical distribution relative to the ground).
Remote Sens. 2020,12, 2714 6 of 22
GEDI is also capable of deriving topographic elevations in the same manner as conventional radar
altimeters, i.e., by calculating the range from the surface to the system, which is then converted to
the sea surface’s height above a reference ellipsoid. Over flat surfaces, such as water surfaces, or
bare-grounds, the recorded waveform has a Gaussian form (single peak or mode) similar in shape to
the transmitted pulse (Figure 2a). Waveforms recorded within footprints over complex geometries
(e.g., forests) will be multi-modal in shape, with each mode representing a reflection from a distinct
surface height (Figure 2b). Therefore, in order to precisely estimate water surface elevation, the location
of the mode in the waveform, representing the water surface, should be determined as accurately
as possible.
Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 23
(a) (b)
(c)
Figure 2. Typical GEDI waveforms over a lake (a) and a forest stand (b). An unusable waveform due
probably due to cloud conditions is shown in (c).
Before any metric can be determined in the received waveforms, the first waveform processing
step is, as described in the Algorithm Theoretical Basis Document (ATBD) [31,32], consists in the
smoothing of waveforms. Waveform smoothing allows minimizing the noise in the signal, and thus
permitting the determination of the useful part of the waveform within the corresponding footprint.
Waveform smoothing is performed by means of a Gaussian filter with various widths. As mentioned
in the ATBD, currently a width of 6.5 ns was used for the Gaussian filter (smooth width). After
smoothing, two locations in the waveform denoted as searchstart and searchend are determined (Figure
3). searchstart and searchend are, respectively, the first and last positions in the signal where the signal
intensity is above the following threshold:


=

+
.
(1)
where ‘mean’ is the mean noise level, σ’ is the standard deviation of noise of the smoothed waveform,
and ‘v’ is a variable, currently set at 4. After determining the locations of searchstart and searchend, the
region between them, denoted as the waveform extent, is extended by a predetermined number of
sample bins, currently set to 100 bins at both sides. Inside the waveform extent, the highest (toploc)
and lowest (botloc) detectable returns are determined (Figure 3). toploc and botloc, respectively,
represent the highest and lowest locations inside the waveform extent were two adjacent intensities
are above a threshold. The threshold equation used to determine toploc and botloc is the same as
Equation (1), with ‘v’ an integer fixed to 2, 3, 4, and 6. In the ATBD, the value of ‘v’ used to determine
toploc is named ‘Front_threshold’ and ‘Back_threshold for botloc. Currently, six configurations or
algorithms, representing different threshold and smoothing settings, were used to determine
waveform metrics with high precision in a variety of acquisition scenarios (Table 2). Finally, the
location of distinctive peaks or modes in the waveform, such as the ground peak, or top of canopy
peaks are determined using a second Gaussian filtering of the waveform section between toploc and
botloc, and then finding all the zero crossings of the first derivative of the filtered waveform (Figure
3). The width of the second Gaussian filter (Smoothwidth_zcross) is fixed to either 3.5 or 6.5 ns.
Finally, the position of the ground return within the waveform is determined using the position of
the last detected peak. Therefore, the geolocation (longitude, latitude, and elevation) of the ground
Figure 2.
Typical GEDI waveforms over a lake (
a
) and a forest stand (
b
). An unusable waveform due
probably due to cloud conditions is shown in (c).
Before any metric can be determined in the received waveforms, the first waveform processing
step is, as described in the Algorithm Theoretical Basis Document (ATBD) [
31
,
32
], consists in the
smoothing of waveforms. Waveform smoothing allows minimizing the noise in the signal, and thus
permitting the determination of the useful part of the waveform within the corresponding footprint.
Waveform smoothing is performed by means of a Gaussian filter with various widths. As mentioned in
the ATBD, currently a width of 6.5 ns was used for the Gaussian filter (smooth width). After smoothing,
two locations in the waveform denoted as searchstart and searchend are determined (Figure 3). searchstart
and searchend are, respectively, the first and last positions in the signal where the signal intensity is
above the following threshold:
threshold =mean +σ.v(1)
where ‘mean’ is the mean noise level, ‘
σ
’ is the standard deviation of noise of the smoothed waveform,
and ‘v’ is a variable, currently set at 4. After determining the locations of searchstart and searchend,
the region between them, denoted as the waveform extent, is extended by a predetermined number of
sample bins, currently set to 100 bins at both sides. Inside the waveform extent, the highest (toploc) and
lowest (botloc) detectable returns are determined (Figure 3). toploc and botloc, respectively, represent
Remote Sens. 2020,12, 2714 7 of 22
the highest and lowest locations inside the waveform extent were two adjacent intensities are above
a threshold. The threshold equation used to determine toploc and botloc is the same as Equation (1),
with ‘v’ an integer fixed to 2, 3, 4, and 6. In the ATBD, the value of ‘v’ used to determine toploc is
named ‘Front_threshold’ and ‘Back_threshold for botloc. Currently, six configurations or algorithms,
representing dierent threshold and smoothing settings, were used to determine waveform metrics
with high precision in a variety of acquisition scenarios (Table 2). Finally, the location of distinctive
peaks or modes in the waveform, such as the ground peak, or top of canopy peaks are determined
using a second Gaussian filtering of the waveform section between toploc and botloc, and then finding
all the zero crossings of the first derivative of the filtered waveform (Figure 3). The width of the second
Gaussian filter (Smoothwidth_zcross) is fixed to either 3.5 or 6.5 ns. Finally, the position of the ground
return within the waveform is determined using the position of the last detected peak. Therefore,
the geolocation (longitude, latitude, and elevation) of the ground return is interpolated using its oset to
the start of the received waveform. The six dierent algorithms used for the detection of the waveform
metrics, generally lead to six dierent elevations of the ground return. However, since waveforms
acquired over water are in general uni-modal waveforms, only two dierent sets of algorithms produce
dierent elevations. Sets 1 and 4 are similar, and sets 2, 3, 5, and 6 are similar. Therefore, in this
study, which is carried on waveforms acquired over water surfaces, only the elevations produced from
algorithms 1 and 2 were analyzed.
Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 23
return is interpolated using its offset to the start of the received waveform. The six different
algorithms used for the detection of the waveform metrics, generally lead to six different elevations
of the ground return. However, since waveforms acquired over water are in general uni-modal
waveforms, only two different sets of algorithms produce different elevations. Sets 1 and 4 are
similar, and sets 2, 3, 5, and 6 are similar. Therefore, in this study, which is carried on waveforms
acquired over water surfaces, only the elevations produced from algorithms 1 and 2 were analyzed.
Figure 3. Example of a GEDI waveform acquired over a lake and corresponding waveform metrics; 1
ns corresponds to 15 cm sampling distance in the waveform.
Table 2. The different parameters used in each of the six algorithms for the interpretation of the
received waveforms.
Algorithm Smooth Width Smoothwidth_Zcross Front_ Threshold Back_ Threshold
1 6.5 6.5 3 6
2 6.5 3.5 3 3
3 6.5 3.5 3 6
4 6.5 6.5 6 6
5 6.5 3.5 3 2
6 6.5 3.5 3 4
GEDI data used in this study are already processed and published by the Land Processes
Distributed Active Archive Center (LP DAAC). Currently, there are three data products (L1B, L2A,
and L2B) that are available for download. The L1B data product [30] contains detailed information
about the transmitted and received waveforms, the location and elevation of each waveform
footprint, and other ancillary information, such as mean and standard deviation of the noise, and
acquisition time. The L2A data product [31] contains data of elevation and height metrics of the
vertical structures within the waveform. These height metrics are issued from the processing of the
received waveforms from the L1B data product. Finally, the L2B data product [32] provides footprint-
level vegetation metrics, such as canopy cover, vertical profile metrics, Leaf Area Index (LAI), and
foliage height diversity (FHD). In this study, the received waveforms, their geolocation (longitude,
and latitude), as well as their acquisition times, were extracted from the L1B data product. In the L2A
data product, the derived metrics are also grouped by algorithm. Therefore, for each beam, the
metrics derived from each of the six algorithms, as well as the parameters used for each algorithm,
are available. Therefore, we extracted from L2A for each beam, and for each of algorithms 1 and 2,
the following variables: (1) the position within the waveform, as well as the elevation of toploc and
botloc, (2) the latitude and longitude, as well as the elevation of the lowest peak or mode, (3) the
amplitude of the smoothed waveform’s lowest detected mode (zcross_amp), (4) the width of
Gaussian fit of the received waveform (rx_gwidth), and (5) the number of detected modes
(num_detectedmodes). No metrics were extracted from the L2B data product as they were not
relevant to this study.
Figure 3.
Example of a GEDI waveform acquired over a lake and corresponding waveform metrics; 1
ns corresponds to 15 cm sampling distance in the waveform.
Table 2.
The dierent parameters used in each of the six algorithms for the interpretation of the
received waveforms.
Algorithm Smooth Width Smoothwidth_Zcross Front_ Threshold Back_ Threshold
1 6.5 6.5 3 6
2 6.5 3.5 3 3
3 6.5 3.5 3 6
4 6.5 6.5 6 6
5 6.5 3.5 3 2
6 6.5 3.5 3 4
GEDI data used in this study are already processed and published by the Land Processes
Distributed Active Archive Center (LP DAAC). Currently, there are three data products (L1B, L2A,
and L2B) that are available for download. The L1B data product [
30
] contains detailed information
about the transmitted and received waveforms, the location and elevation of each waveform footprint,
and other ancillary information, such as mean and standard deviation of the noise, and acquisition
time. The L2A data product [
31
] contains data of elevation and height metrics of the vertical structures
within the waveform. These height metrics are issued from the processing of the received waveforms
Remote Sens. 2020,12, 2714 8 of 22
from the L1B data product. Finally, the L2B data product [
32
] provides footprint-level vegetation
metrics, such as canopy cover, vertical profile metrics, Leaf Area Index (LAI), and foliage height
diversity (FHD). In this study, the received waveforms, their geolocation (longitude, and latitude),
as well as their acquisition times, were extracted from the L1B data product. In the L2A data product,
the derived metrics are also grouped by algorithm. Therefore, for each beam, the metrics derived from
each of the six algorithms, as well as the parameters used for each algorithm, are available. Therefore,
we extracted from L2A for each beam, and for each of algorithms 1 and 2, the following variables:
(1) the position within the waveform, as well as the elevation of toploc and botloc, (2) the latitude and
longitude, as well as the elevation of the lowest peak or mode, (3) the amplitude of the smoothed
waveform’s lowest detected mode (zcross_amp), (4) the width of Gaussian fit of the received waveform
(rx_gwidth), and (5) the number of detected modes (num_detectedmodes). No metrics were extracted
from the L2B data product as they were not relevant to this study.
2.2.3. Filtering of GEDI Waveforms
Not all GEDI acquisitions are viable, as atmospheric conditions and clouds can aect them
(Figure 2c). Therefore, two filters were applied to remove erroneous lower quality returns. The first
filter applied removes waveforms with reported elevations that are significantly higher than the
corresponding Shuttle Radar Topography Mission (SRTM) DEM elevation [
33
] (i.e., we removed all
waveforms were |GEDI elevation—SRTM| > 100 m). Since we are only interested with waveforms that
are acquired over water, we removed all waveforms having two or more peaks or modes. A multi-modal
waveform is a strong indication that the waveform was acquired over areas with complex geometry
(e.g., vegetation or considerable relief). Information regarding the number of detected modes for each
waveform were acquired from the L2A data product. Over the eight studied lakes, 21242 GEDI shots
were available for comparison with the lake gauge data (Figure 1, Table 1). From these shots, only 4637
(21.8%) provided exploitable waveforms.
GEDI data accessible through NASA’s LP DAAC contain a quality flag (quality_flag) for each
acquired waveform. A waveform with a quality flag set to ‘1
0
indicates that the waveform meets certain
criteria based on energy, sensitivity, amplitude, and real-time surface tracking quality, and thus can be
processed further. However, in this study, after the application of the SRTM DEM filter, waveforms
with either value of the quality_flag (0 or 1) showed similar characteristics (e.g., defined single peak,
high signal to noise ratio, etc.). Therefore, all waveforms were analyzed regardless of the value of the
quality_flag.
2.2.4. Transformation of GEDI Elevations
In order to conduct a consistent analysis between the elevations provided by GEDI and water
elevation from gauge stations, the heights from both datasets must refer to the same vertical datum. In
this study, the geolocated GEDI waveform elevations are relative to the WGS 84 ellipsoid, while gauge
stations are provided as orthometric heights with reference to the French height system (NGF-IGN69)
for lakes Geneva and Neuch
â
tel, and the Swiss height measurement reference system (LN02) for the
other lakes. The tide gauge at Marseille determines the ‘zero level’ for all elevations in France, while
the reference for all height measurements in Switzerland is the “Rep
è
re Pierre du Niton” in the harbor
of Geneva (stone). The elevation of this stone was evaluated in 1902 to be 373.6 m over sea level.
The conversion to orthometric heights of GEDI shots acquired over lakes Geneva and Neuch
â
tel
was made using the following equation:
HIGN69 =hwgs84 NIGN69 (2)
Here,
HIGN69
is the derived orthometric height of GEDI footprints from leveling with respect to
NGF-IGN69,
hwgs84
is the GEDI footprint elevation above the WGS 84 ellipsoid, and
NIGN69
are the
French gravimetric geoid heights (e.g., between 48.49 and 50.41 m for Lake Geneva). The value for
Remote Sens. 2020,12, 2714 9 of 22
NIGN69
was obtained by bilinear interpolation of a 1 km NGF-IGN69 Geoid Height Grid provided by
the French National Institute of Geographic and Forest Information (IGN) (geodesie.ign.fr).
For the remaining lakes, to convert between ellipsoidal elevations and orthometric heights with
respect to LN02, a two-step process is required. First, ellipsoidal elevations of GEDI footprints
were converted to orthometric elevations with respect to the new Swiss height system LHN95
(Landeshöhennetz 1995) using the following equation:
HLHN95 =hwgs84 NCHGEO2004 (3)
where
HLHN95
is the converted GEDI footprint elevation with respect to LHN05, and
NCHGEO2004
the Swiss gravimetric geoid heights. Then, GEDI footprint elevations, which are now orthometric
elevations with respect to LHN95, are converted to the Swiss height system (LN02) by means of three
grids. Three grids are required, as height conversion between LHN95 and LN02 cannot be modeled
by a single oset. This is due to their dierent way of gravity reduction, the treatment of vertical
movements, and the constraints introduced in LN02. Therefore, the conversion between orthometric
LHN95 heights and LN02 heights was made using the following equation [34]:
Hln02 =HLHN95 +Hnorm Hscale
gboug
gHLHN95 (4)
where
Hln02
are the GEDI footprint elevations with respect to the Swiss height system (LN02),
Hnorm
is a
1 km grid describing the dierence between LN02 and normal heights,
Hscale
is a 1 km grid scale factor
used to transform between normal heights and orthometric heights,
gboug
is a 1 km grid representing
the Bouguer anomalies, and g is the average normal gravity equal to 980,000 mGal. The Swiss geoid
grid (CHGeo2004), as well as the three grids used in the transformation between LHN95 and LN02
heights, were obtained from the Swiss Federal Oce of Topography (www.swisstopo.admin.ch).
3. Results
This section will begin with an analysis of two exemplary GEDI waveforms acquired on lakes
(Figure 2). Then, the remainder of the results section will analyze the quality of the GEDI elevations for
each lake, date, and finally for each beam.
Figure 2a shows a perfect example of a viable GEDI waveform over water surfaces (usable
waveform with a high signal-to-noise ratio). The waveform presents a single distinct peak corresponding
to the water surface, with very low noise level. In contrast, Figure 2c shows a GEDI waveform with
very high noise level and no distinctive peaks, which renders such waveforms useless. The example
waveform shown in Figure 2c could correspond to acquisitions in the presence of clouds over our
study area.
The comparison between GEDI elevations and in-situ elevations registered from the hydrological
gauge stations shows that the parameters used in algorithm a1 (Smoothwidth_zcross of 6.5 ns, Table 2)
provide more precise elevations in comparison to algorithm a2 (Smoothwidth_zcross of 3.5 ns, Table 2).
Using the entire database from all the lakes in this study (8 lakes and 4637 viable waveforms), GEDI
footprint elevations in comparison to in-situ gauge station elevations showed a mean elevation
dierence of 0.61 cm with a1 and 7.8 cm with a2. The standard deviation of the mean dierence
between GEDI footprint elevations and gauge station readings is 22.3 cm using a1 and 23.7 cm using
a2. The root mean square error (RMSE) on GEDI elevations is slightly higher using a2 with a value of
24.9 cm against 22.3 cm using a1.
3.1. Analysis of GEDI Waveforms for Each Lake
The precision of elevations estimated from GEDI waveforms was studied separately for each
lake using all GEDI beams from all acquisition dates. Table 3shows a mean dierence (MD) between
GEDI and in-situ elevations that varies between
13.8 cm (under-estimation by GEDI) and +9.8 cm
Remote Sens. 2020,12, 2714 10 of 22
(over-estimation by GEDI). The reported standard deviation from MD varied between 14.5 cm and
31.6 cm.
Table 3.
Summary statistics of elevations from GEDI acquisitions for each of the 8 studied lakes
(Mean MD, standard deviation std, and root mean square error RMSE of the dierence between GEDI
elevations and in-situ elevations) using data from all acquisition dates given in Table 1and from all
the beams.
ID Lake
GEDI—Hydrological Gauges
MD
(cm)
Std
(cm)
RMSE
(cm) GEDI Shots Count
1 Geneva +0.4 14.5 14.5 319
2 Neuchâtel +9.8 20.0 22.3 799
3 Zürich 4.4 18.7 19.2 1026
4 Obersee (Zürich) 13.8 18.1 22.8 266
5 Lucerne +0.9 20.6 20.6 691
6 Walensee +5.8 15.9 16.9 547
7 Sempach +2.4 22.3 22.4 217
8 Thun 1.8 31.6 31.7 772
Figure 4a shows an example of GEDI data for a transect with its 8 beams, acquired on May 29th
2019 at 9:25 p.m. over lake Neuch
â
tel in Switzerland (GEDI elevations for all lakes can be found in
Appendix A, Figure A1). This example shows what has also been observed over the other lakes, albeit
with dierent elevation precision depending on the acquisition date, or beam. Over the transect in
Figure 4a, the mean dierence (MD) between elevations from GEDI and those reported by the gauge
station varied between
6.4 (under-estimation by GEDI) for beam 1 and +45.2 cm (over-estimation by
GEDI) for beam 6 (Figure 4b). The standard deviation from MD varied between 5.5 cm for beam 7
and 17.5 cm for beam 4. Using elevations from all the beams acquired on May 29th 2019 over Lake
Neuch
â
tel, the calculated MD was in the order of +6.1 cm (over-estimation by GEDI) with a standard
deviation of 16.1 cm. Moreover, over some GEDI footprints, we observed on some beams, elevations
that deviated greatly from the mean of all GEDI elevations, with some of these elevations being 50 cm
further from the mean. Despite all verification, we were unsuccessful in explaining the reason for
such elevation dierences, even though these points were acquired in the middle of the lake, and their
corresponding waveforms showed very high signal to noise ratio, and resembled in form to other
waveform from other footprints.
3.2. Analysis of GEDI Waveforms by Date
Table 4shows the mean dierence and the standard deviation between elevations from GEDI and
in-situ gauge records, using data over all lakes, grouped by date. Results show that the mean dierence
(MD) between elevations from GEDI and in-situ gauges varied between
26.8 cm (under-estimation
by GEDI) and +15.2 cm (over-estimation by GEDI). The lowest bias corresponded to data acquired the
mornings of April 28, and May 02 and 04, or late at night on May 22. The highest recorded bias was
observed on acquisitions that were made around noon (e.g., April 21, May 28, and June 08), in the
early evening (May 29), GEDI acquisitions taken over the weekend (e.g., April 20 and 21, June 08), or
before a holiday (e.g., May 22). These strong biases could be due to several phenomenon. (1) Increased
perturbations of the water surface due to human activities taking place at these times. The reported
standard deviation from MD shows that it varies between 12.7 cm and 24.9, with a standard deviation
lower than 15 cm for morning acquisitions (e.g., April 28, May 02, and 04), with the exception of June
08, which corresponds to acquisitions taken around noon. (2) Currents generated by thermal eects or
winds [35,36].
Remote Sens. 2020,12, 2714 11 of 22
Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 23
Figure 4a shows an example of GEDI data for a transect with its 8 beams, acquired on May 29th
2019 at 9:25 p.m. over lake Neuchâtel in Switzerland (GEDI elevations for all lakes can be found in
Appendix A, Figure A1). This example shows what has also been observed over the other lakes, albeit
with different elevation precision depending on the acquisition date, or beam. Over the transect in
Figure 4a, the mean difference (MD) between elevations from GEDI and those reported by the gauge
station varied between −6.4 (under-estimation by GEDI) for beam 1 and +45.2 cm (over-estimation by
GEDI) for beam 6 (Figure 4b). The standard deviation from MD varied between 5.5 cm for beam 7
and 17.5 cm for beam 4. Using elevations from all the beams acquired on May 29th 2019 over Lake
Neuchâtel, the calculated MD was in the order of +6.1 cm (over-estimation by GEDI) with a standard
deviation of 16.1 cm. Moreover, over some GEDI footprints, we observed on some beams, elevations
that deviated greatly from the mean of all GEDI elevations, with some of these elevations being 50
cm further from the mean. Despite all verification, we were unsuccessful in explaining the reason for
such elevation differences, even though these points were acquired in the middle of the lake, and
their corresponding waveforms showed very high signal to noise ratio, and resembled in form to
other waveform from other footprints.
(a)
(b)
Figure 4. (a) GEDI shots across Lake Neuchâtel on May 29, 2019. at 21:25 p.m. (b) GEDI elevations of
all shots acquired over water plotted for each beam (1 to 8). The reference elevation on this date was
429.53 m (dashed horizontal line). Abscissa = GEDI shot number.
Figure 4.
(
a
) GEDI shots across Lake Neuch
â
tel on May 29, 2019. at 21:25 p.m. (
b
) GEDI elevations of
all shots acquired over water plotted for each beam (1 to 8). The reference elevation on this date was
429.53 m (dashed horizontal line). Abscissa =GEDI shot number.
Table 4.
Summary statistics (mean MD, standard deviation std, and root mean square error RMSE) of
the dierence between GEDI and in-situ elevations for all the studied lakes (cf. Table 1) aggregated by
date (except for May 11th due to the low number of acquisitions).
GEDI
Acquisition Date
dd/mm (hh:mm)
GEDI—Hydrological Gauges
MD
(cm)
Std
(cm)
RMSE
(cm)
GEDI
Shots
Count
MD by Lake ID (cm)
1 2 3 4 5 6 7 8
20/04 (08:37) +9.6 20.4 22.6 1005
10.7
- - - 3.6 20.8 - 13.3
21/04 (12:37) 26.8 22.7 35.1 368 - 8.1 - - - - - 38.9
28/04 (09:54) +6.5 12.7 14.3 51 - 6.5 - - - - - -
02/05 (08:08) 7.7 15.4 17.2 1358 - - 10.7 13.8 - 1.0 - -
04/05 (03:12) 2.3 12.9 13.1 303 1.1 - 10.3 - - - 10.7 -
22/05 (23:59) +0.4 24.9 24.9 469 - - - - 3.8 - 27.4 -
28/05 (17:23) 9.2 21.7 23.6 58 - - - - - 9.2 - -
29/05 (21:25) +14.4 19.2 24.0 603
9.1
14.4 - - - - - -
08/06 (13:10) +15.2 14.3 20.9 401 - - 17.3 - - - 12.2 -
Remote Sens. 2020,12, 2714 12 of 22
3.3. Analysis of GEDI Waveforms by GEDI Beam
Figure 5shows the summary of the statistics calculated from the dierence between GEDI and
hydrological gauge elevations for each date and each beam, using data from all lakes. These statistics
were first calculated for each GEDI beam and for each date. Only the statistics with at least 30 GEDI
shots for each date/beam pair are reported in this section. Results show that the bias (elevations
from GEDI—elevations from gauge stations) varied depending on the acquisition date, and the beam.
For certain beams, at certain dates, elevation from GEDI had the same order of magnitude as in-situ
elevations, while, for other dates, and even for the same beam, the bias was very significant (Figure 5a).
For example, on 20 April 2019, elevations from GEDI acquired over beam 1 showed a bias of
2.9 cm
which increased to +21.9 cm on 8 June 2019. Similarly, Figure 5b shows that the standard deviation
from the mean dierence between GEDI and gauge station elevations varied between 4.6 cm (beam 6,
8 June 2019) and 22 cm (beam 5, 21 April 2019).
Remote Sens. 2020, 12, x FOR PEER REVIEW 13 of 23
(a)
(b)
Figure 5. Mean (a) and standard deviation (b) of the difference between GEDI elevations and
hydrological elevations for each date and each beam using data from all lakes.
The statistics were then calculated for each GEDI beam, using all the dates. Results in Table 5
show that the difference between elevations from GEDI and in-situ gauge records differed according
to the laser they were acquired with. The mean difference between GEDI and in-situ elevations varied
between −10.2 cm (under-estimation by GEDI) and +18.1 cm (over-estimation by GEDI). The beams
with the highest difference were beams 1 (coverage beam) and beams 6 (full power beam) with a
mean difference of, respectively, −10.2 cm and +18.1 cm. In contrast, the beams that captured
elevations with the smallest diversion from in-situ elevations were beams 5 and 7 (both full power
beams), with a mean difference of −1.7 cm, and −2.5 cm, respectively, while the remaining beams (2,3,
4, and 8) show the mean observed difference varied between −7.4 cm and +4.4 cm. Finally, the
standard deviation from the mean difference between elevations from GEDI and elevations from in-
situ gauges were similar across beams, with a standard deviation that varied between 17.2 cm and
22.9 cm.
Figure 5.
Mean (
a
) and standard deviation (
b
) of the dierence between GEDI elevations and
hydrological elevations for each date and each beam using data from all lakes.
The statistics were then calculated for each GEDI beam, using all the dates. Results in Table 5
show that the dierence between elevations from GEDI and in-situ gauge records diered according to
the laser they were acquired with. The mean dierence between GEDI and in-situ elevations varied
between
10.2 cm (under-estimation by GEDI) and +18.1 cm (over-estimation by GEDI). The beams
with the highest dierence were beams 1 (coverage beam) and beams 6 (full power beam) with a mean
dierence of, respectively,
10.2 cm and +18.1 cm. In contrast, the beams that captured elevations
with the smallest diversion from in-situ elevations were beams 5 and 7 (both full power beams), with a
mean dierence of
1.7 cm, and
2.5 cm, respectively, while the remaining beams (2, 3, 4, and 8) show
Remote Sens. 2020,12, 2714 13 of 22
the mean observed dierence varied between
7.4 cm and +4.4 cm. Finally, the standard deviation
from the mean dierence between elevations from GEDI and elevations from in-situ gauges were
similar across beams, with a standard deviation that varied between 17.2 cm and 22.9 cm.
Table 5.
Summary statistics for each GEDI beam (Mean MD and standard deviation std) for the dierence
between GEDI and in-situ elevations using all acquired GEDI waveforms (from all acquisition dates
over all lakes).
GEDI Beam Number
GEDI—Hydrological Gauges
MD
(cm)
Std
(cm)
RMSE
(cm)
GEDI Shots
Count
110.2 21.8 24.1 490
2+5.5 22.9 23.6 439
36.6 18.1 19.3 401
47.4 19.4 20.8 417
51.7 24.8 24.9 641
6+18.1 22.8 29.1 538
72.5 20.0 20.2 724
8+4.4 17.2 17.8 987
Finally, we present an analysis of the distribution of the dierence between GEDI elevation
estimates for each beam in comparison to in-situ elevations. The dierence (D) between GEDI and
in-situ elevations has been grouped into five intervals: (
100,
25), [
25,
10), [
10, +10), [+10, +25),
and [+25, +100) cm. Figure 6shows that the lowest elevation dierences were obtained using beams 3,
4, and 8. Overall, GEDI elevations from beam 8 were the most accurate, followed by beams 3 and 4,
then beams 1, 2, and 7, and finally beams 5 and 6 showed large dierences between elevations from
GEDI and those from in-situ gauge stations. For beam 8, 57% of the shots had a dierence with in-situ
elevations between
10 and 10 cm, followed by a small percentage of shots with D between
100 and
10 cm and between 10 and 100 cm. For beams 3 and 4, the dierence between GEDI and in-situ
elevations was between
10 and +10 cm for ~50% of the shots, with a small percentage of shots with D
between +25 and +100 cm (less than 5%), and between
100 and
25 cm (~14%). The dierence in
elevations D for beams 1, 2, and 7 was between
25 and +25 cm for, respectively, 78, 75 and 81% of the
shots. Finally, for beam 6, 44% of the shots showed very high over-estimation of GEDI elevations (D
between +25 and +100 cm), while, for beam 5, the elevation dierences were distributed almost equally
among the five classes of D. Moreover, results showed that almost 43% of the shots with an elevation
dierence D in the range (
100,
25 cm] or in the range [+25, +100 cm) were obtained from beams 5
and 6 (19.7% from beam 5, and 22.1% from beam 6). In contrast, only 5.3% and 6.5% of shots in beams
3 and 4 showed an elevation dierence D in the range (100; 25 cm] or in the range [+25;+100 cm).
Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 23
Table 5. Summary statistics for each GEDI beam (Mean MD and standard deviation std) for the
difference between GEDI and in-situ elevations using all acquired GEDI waveforms (from all
acquisition dates over all lakes).
GEDI Beam Number
GEDI—Hydrological Gauges
MD
(cm)
Std
(cm)
RMSE
(cm)
GEDI Shots
Count
1 −10.2 21.8 24.1 490
2 +5.5 22.9 23.6 439
3 −6.6 18.1 19.3 401
4 −7.4 19.4 20.8 417
5 −1.7 24.8 24.9 641
6 +18.1 22.8 29.1 538
7 −2.5 20.0 20.2 724
8 +4.4 17.2 17.8 987
Finally, we present an analysis of the distribution of the difference between GEDI elevation
estimates for each beam in comparison to in-situ elevations. The difference (D) between GEDI and
in-situ elevations has been grouped into five intervals: (−100, −25), [−25, −10), [−10, +10), [+10, +25),
and [+25, +100) cm. Figure 6 shows that the lowest elevation differences were obtained using beams
3, 4, and 8. Overall, GEDI elevations from beam 8 were the most accurate, followed by beams 3 and
4, then beams 1, 2, and 7, and finally beams 5 and 6 showed large differences between elevations from
GEDI and those from in-situ gauge stations. For beam 8, 57% of the shots had a difference with in-
situ elevations between −10 and 10 cm, followed by a small percentage of shots with D between −100
and −10 cm and between 10 and 100 cm. For beams 3 and 4, the difference between GEDI and in-situ
elevations was between −10 and +10 cm for ~50% of the shots, with a small percentage of shots with
D between +25 and +100 cm (less than 5%), and between −100 and −25 cm (~14%). The difference in
elevations D for beams 1, 2, and 7 was between −25 and +25 cm for, respectively, 78, 75 and 81% of
the shots. Finally, for beam 6, 44% of the shots showed very high over-estimation of GEDI elevations
(D between +25 and +100 cm), while, for beam 5, the elevation differences were distributed almost
equally among the five classes of D. Moreover, results showed that almost 43% of the shots with an
elevation difference D in the range (−100, −25 cm] or in the range [+25, +100 cm) were obtained from
beams 5 and 6 (19.7% from beam 5, and 22.1% from beam 6). In contrast, only 5.3% and 6.5% of shots
in beams 3 and 4 showed an elevation difference D in the range (−100; −25 cm] or in the range
[+25;+100 cm).
Figure 6. Percentage of GEDI shots for each beam with the difference between GEDI and in-situ
elevations (D) grouped intro 5 intervals: (−100, −25) [−25, −10), [−10, +10), [10, +25), and [25, +100) cm.
Figure 6.
Percentage of GEDI shots for each beam with the dierence between GEDI and in-situ
elevations (D) grouped intro 5 intervals: (
100,
25) [
25,
10), [
10, +10), [10, +25), and [25, +100) cm.
Remote Sens. 2020,12, 2714 14 of 22
3.4. GEDI Waveform Metrics and Elevation Accuracy
In this section, we present the eect of some GEDI waveform metrics that could potentially be
aected by sensor saturation, and thus have an eect on the GEDI elevation estimations. The correlation
between the amplitude of the smoothed waveform at the lowest detected mode (zcross_amp from the
L2A product) and the precision on the elevation has been analyzed. This analysis was carried on only
two dates (20 April, and 02 May) which had the maximum number of GEDI acquisitions (~1000 shots
for each acquisition date). On April 20th (Figure 7a), the variable zcross_amp varied between 280 and
4000 amplitude counts (AC). For zcross_amp between 280 and 600 AC, the dierence D (dierence
between GEDI and in-situ elevations) increased with zcorss_amp from
50 to about +40 cm. For
zcross_amp between 600 and 3000 AC, the dierence D was stable with a value around +50 cm for
beam 5 and 0 cm for beam 3. For values of zcross_amp between 3000 and 4000 AC the dierence
between GEDI and in-situ elevations decreased from +40 cm to around
10 cm. A similar pattern was
observed for acquisitions taken on 2 May 2019 (Figure 7b), especially for zcross_amp between 3000
and 4000 AC. For zcross_amp between 600 and 3000 AC on May 2, the dierence D was stable and was
around 0 cm. However, it decreased to
60 cm for zcross_amp around ~4000 AC. For zcross_amp,
less than 600 AC, the dierence D remained stable but with strong fluctuations for low zcross_amp
values (zcross_amp less than 400 AC). The increased uncertainties for waveforms with higher values
of zcross_amp (higher than 3000 AC) are most probably due to the specular reflection of the water
that saturates the detector [
29
]. Moreover, a large portion of the waveforms with zcross_amp higher
than 3000 AC were also observed as having a wider peak which indicates some form of saturation.
Indeed, the analysis of the dierence D and the width of Gaussian (width of the return peak in the
case of unimodal waveforms) fit to the received waveforms (rx_gwidth, available in the L2A data
product) shows two clusters (Figure 8). The first cluster corresponds to rx_gwidth values between 4.5
and 9 ns, and the second cluster corresponds to rx_gwidth between 11 and 17 ns. Figure 8shows that
the waveforms from the second cluster have a slight under-estimation of elevations of around 10 cm.
Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 23
3.4. GEDI Waveform Metrics and Elevation Accuracy
In this section, we present the effect of some GEDI waveform metrics that could potentially be
affected by sensor saturation, and thus have an effect on the GEDI elevation estimations. The
correlation between the amplitude of the smoothed waveform at the lowest detected mode
(zcross_amp from the L2A product) and the precision on the elevation has been analyzed. This
analysis was carried on only two dates (20 April, and 02 May) which had the maximum number of
GEDI acquisitions (~1000 shots for each acquisition date). On April 20th (Figure 7a), the variable
zcross_amp varied between 280 and 4000 amplitude counts (AC). For zcross_amp between 280 and
600 AC, the difference D (difference between GEDI and in-situ elevations) increased with zcorss_amp
from −50 to about +40 cm. For zcross_amp between 600 and 3000 AC, the difference D was stable with
a value around +50 cm for beam 5 and 0 cm for beam 3. For values of zcross_amp between 3000 and
4000 AC the difference between GEDI and in-situ elevations decreased from +40 cm to around −10
cm. A similar pattern was observed for acquisitions taken on 2 May 2019 (Figure 7b), especially for
zcross_amp between 3000 and 4000 AC. For zcross_amp between 600 and 3000 AC on May 2, the
difference D was stable and was around 0 cm. However, it decreased to −60 cm for zcross_amp
around ~4000 AC. For zcross_amp, less than 600 AC, the difference D remained stable but with strong
fluctuations for low zcross_amp values (zcross_amp less than 400 AC). The increased uncertainties
for waveforms with higher values of zcross_amp (higher than 3000 AC) are most probably due to the
specular reflection of the water that saturates the detector [29]. Moreover, a large portion of the
waveforms with zcross_amp higher than 3000 AC were also observed as having a wider peak which
indicates some form of saturation. Indeed, the analysis of the difference D and the width of Gaussian
(width of the return peak in the case of unimodal waveforms) fit to the received waveforms
(rx_gwidth, available in the L2A data product) shows two clusters (Figure 8). The first cluster
corresponds to rx_gwidth values between 4.5 and 9 ns, and the second cluster corresponds to
rx_gwidth between 11 and 17 ns. Figure 8 shows that the waveforms from the second cluster have a
slight under-estimation of elevations of around 10 cm.
(a) (b)
Figure 7. Difference between GEDI and in-situ elevations as a function of zcross_amp using data from
all lakes. (a) 20 April 2020 GEDI acquisitions. (b) 02 May GEDI acquisitions.
Figure 8. Difference between GEDI and in-situ elevations as a function of rx_gwidth using data from
all lakes and all dates.
Figure 7.
Dierence between GEDI and in-situ elevations as a function of zcross_amp using data from
all lakes. (a) 20 April 2020 GEDI acquisitions. (b) 02 May GEDI acquisitions.
Remote Sens. 2020,12, 2714 15 of 22
Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 23
3.4. GEDI Waveform Metrics and Elevation Accuracy
In this section, we present the effect of some GEDI waveform metrics that could potentially be
affected by sensor saturation, and thus have an effect on the GEDI elevation estimations. The
correlation between the amplitude of the smoothed waveform at the lowest detected mode
(zcross_amp from the L2A product) and the precision on the elevation has been analyzed. This
analysis was carried on only two dates (20 April, and 02 May) which had the maximum number of
GEDI acquisitions (~1000 shots for each acquisition date). On April 20th (Figure 7a), the variable
zcross_amp varied between 280 and 4000 amplitude counts (AC). For zcross_amp between 280 and
600 AC, the difference D (difference between GEDI and in-situ elevations) increased with zcorss_amp
from −50 to about +40 cm. For zcross_amp between 600 and 3000 AC, the difference D was stable with
a value around +50 cm for beam 5 and 0 cm for beam 3. For values of zcross_amp between 3000 and
4000 AC the difference between GEDI and in-situ elevations decreased from +40 cm to around −10
cm. A similar pattern was observed for acquisitions taken on 2 May 2019 (Figure 7b), especially for
zcross_amp between 3000 and 4000 AC. For zcross_amp between 600 and 3000 AC on May 2, the
difference D was stable and was around 0 cm. However, it decreased to −60 cm for zcross_amp
around ~4000 AC. For zcross_amp, less than 600 AC, the difference D remained stable but with strong
fluctuations for low zcross_amp values (zcross_amp less than 400 AC). The increased uncertainties
for waveforms with higher values of zcross_amp (higher than 3000 AC) are most probably due to the
specular reflection of the water that saturates the detector [29]. Moreover, a large portion of the
waveforms with zcross_amp higher than 3000 AC were also observed as having a wider peak which
indicates some form of saturation. Indeed, the analysis of the difference D and the width of Gaussian
(width of the return peak in the case of unimodal waveforms) fit to the received waveforms
(rx_gwidth, available in the L2A data product) shows two clusters (Figure 8). The first cluster
corresponds to rx_gwidth values between 4.5 and 9 ns, and the second cluster corresponds to
rx_gwidth between 11 and 17 ns. Figure 8 shows that the waveforms from the second cluster have a
slight under-estimation of elevations of around 10 cm.
(a) (b)
Figure 7. Difference between GEDI and in-situ elevations as a function of zcross_amp using data from
all lakes. (a) 20 April 2020 GEDI acquisitions. (b) 02 May GEDI acquisitions.
Figure 8. Difference between GEDI and in-situ elevations as a function of rx_gwidth using data from
all lakes and all dates.
Figure 8.
Dierence between GEDI and in-situ elevations as a function of rx_gwidth using data from
all lakes and all dates.
3.5. Modelling GEDI Estimation Erorrs
In the previous sections, we showed that there were several instrumental and environmental
factors aecting the acquired GEDI waveforms, thus producing an important dierence between in-situ
and GEDI estimated elevations. Among these factors, the provided zcross_amp, rx_gwidth from
the L2a data product, and the derived GEDI viewing angle (
θ
) has been examined. zcross_amp and
rx_gwidth were chosen as they are an indicator of saturation as seen in the previous section, whereas
the viewing angle has been demonstrated in Urban et al. [
37
] to increase elevation errors for ICESat-1
GLAS when the viewing angle deviates from nadir due to precision attitude determination. In this
paper, the GEDI viewing angle (θ) has been estimated using the following equation:
θ=tan1 d
H!(5)
where dis the distance between an acquired GEDI shot and the position of GEDI projected at nadir
onto the WGS84 reference ellipsoid and His the elevation of GEDI over the referenced ellipsoid.
In addition to the previous factors, several additional environmental factors have also been
considered since in-situ water levels do not necessarily provide water elevation across the surface of
lakes as standing waves (seiches), and wind-generated waves are commonly present over lakes. Over
the studied lakes, no direct information about waves were available, therefore, they were substituted
by proxy variables. In essence, we chose the factors that influence the creation and the form of standing,
and wind-generated waves (e.g., wave heights and wave direction). These factors include wind speed,
wave direction, and lake depth. Wind speeds were acquired at each GEDI acquisition date using
meteorological data from the nearest weather stations. Wave direction and average lake depth at
each GEDI footprint were acquired from the LATLAS project (swisslakes.net) using GEDI footprint
coordinates for lake depth, and wind direction and GEDI footprint coordinates to determine the wave
direction. Nonetheless, these factors were only available for five of the eight studied lakes (Geneva,
Neuchâtel, Zürich, Obersee (Zürich), and Lucerne).
In Section 3.2, it was shown that GEDI acquisition dates and times could influence the accuracy
of GEDI elevation estimates. Therefore, two additional factors were considered for the modelling of
GEDI estimation errors. (1) The acquisition time of a GEDI shot (Time of Day, TOD) was converted to a
value between 1 and 3 representing, respectively, acquisitions taken in the morning (6 a.m. to 12 a.m.),
afternoon (12 a.m. to 6 p.m.), and evening (6 p.m. to 12 p.m.). (2) The acquisition date of a GEDI shot
was converted to a value between 1 and 7 representing the acquisition day (Day of Week, DOW).
Finally, GEDI estimation errors were modeled using the previously mentioned factors in a
Random Forest regressor (RF). Random Forests are an ensemble of machine learning algorithms used
for classification or regressing by fitting a number of decision trees on various sub-samples of the
dataset, and uses averaging to improve the predictive accuracy and control over-fitting [
38
]. Compared
to linear models, RF is advantageous for being able to model also nonlinear relationships (threshold
Remote Sens. 2020,12, 2714 16 of 22
eect) between the variable to explain and the explanatory variables. For this study, the number of
trees in the RF were set to 100 trees, with a tree depth equal set to the square root of the number of
available factors. In order to train and assess the model accuracy, we randomly split the database into
70% for training and 30% for validation (and accuracy estimation).
The random Forest regression results for the five lakes combined showed that we were able to
explain ~82% of the error (GEDI—in-situ elevations) variance and reduce the RMSE on the elevation
estimation from 20.2 to 8.4 cm (Figure 9).
Remote Sens. 2020, 12, x FOR PEER REVIEW 17 of 23
Figure 9. Comparison between the modeled elevation difference as a function of instrumental and
environmental factors and the obtained elevation difference between GEDI and in-situ elevations.
Moreover, the factors that contributed the most on the difference between GEDI and in-situ
elevations were determined. This process was conducted using the percentage increase in the mean
square error of the regressions (%IncMSE, estimated with out-of-bag cross validation) from the factor
importance test for the random forests model (average and standard deviations of 50 repetitions)
(Figure 10). The factor importance test shows that the most important factors for the modeling of
errors is related to the viewing angle of GEDI (47.6%), followed by zcross_amp (47.2%) wave
direction (45.5%), depth (43.5%), wind speed (40.2%), and rx_gwidth (30.2%). The least important
factors are the effect of the acquisition time (TOD 22.3%) and date (DOW 18.0%).
Figure 10. Variables’ order of importance in the error estimation random forest regression model with
the %IncMSE (higher values mean higher importance).
The modeling of GEDI errors for each lake separately did not show any differences specific to
the location, geography, or geometry of the lake. For the five lakes tested, the random forest regressor
was able to explain between 70.1 to 83.3% of the error (GEDI—in-situ elevations) variance, with an
accuracy on the GEDI elevations between 5.6 and 10 cm (Table 6).
Figure 9.
Comparison between the modeled elevation dierence as a function of instrumental and
environmental factors and the obtained elevation dierence between GEDI and in-situ elevations.
Moreover, the factors that contributed the most on the dierence between GEDI and in-situ
elevations were determined. This process was conducted using the percentage increase in the mean
square error of the regressions (%IncMSE, estimated with out-of-bag cross validation) from the factor
importance test for the random forests model (average and standard deviations of 50 repetitions)
(Figure 10). The factor importance test shows that the most important factors for the modeling of
errors is related to the viewing angle of GEDI (47.6%), followed by zcross_amp (47.2%) wave direction
(45.5%), depth (43.5%), wind speed (40.2%), and rx_gwidth (30.2%). The least important factors are the
eect of the acquisition time (TOD 22.3%) and date (DOW 18.0%).
Remote Sens. 2020, 12, x FOR PEER REVIEW 17 of 23
Figure 9. Comparison between the modeled elevation difference as a function of instrumental and
environmental factors and the obtained elevation difference between GEDI and in-situ elevations.
Moreover, the factors that contributed the most on the difference between GEDI and in-situ
elevations were determined. This process was conducted using the percentage increase in the mean
square error of the regressions (%IncMSE, estimated with out-of-bag cross validation) from the factor
importance test for the random forests model (average and standard deviations of 50 repetitions)
(Figure 10). The factor importance test shows that the most important factors for the modeling of
errors is related to the viewing angle of GEDI (47.6%), followed by zcross_amp (47.2%) wave
direction (45.5%), depth (43.5%), wind speed (40.2%), and rx_gwidth (30.2%). The least important
factors are the effect of the acquisition time (TOD 22.3%) and date (DOW 18.0%).
Figure 10. Variables’ order of importance in the error estimation random forest regression model with
the %IncMSE (higher values mean higher importance).
The modeling of GEDI errors for each lake separately did not show any differences specific to
the location, geography, or geometry of the lake. For the five lakes tested, the random forest regressor
was able to explain between 70.1 to 83.3% of the error (GEDI—in-situ elevations) variance, with an
accuracy on the GEDI elevations between 5.6 and 10 cm (Table 6).
Figure 10.
Variables’ order of importance in the error estimation random forest regression model with
the %IncMSE (higher values mean higher importance).
Remote Sens. 2020,12, 2714 17 of 22
The modeling of GEDI errors for each lake separately did not show any dierences specific to the
location, geography, or geometry of the lake. For the five lakes tested, the random forest regressor
was able to explain between 70.1 to 83.3% of the error (GEDI—in-situ elevations) variance, with an
accuracy on the GEDI elevations between 5.6 and 10 cm (Table 6).
Table 6.
Accuracy of elevations from GEDI acquisitions for each of the 5 lakes without and with
accounting for the sources of error (RMSE), using data from all acquisition dates given in Table 1and
from all the beams.
Lake Error Modelling
Coecient of
Determination (R2)
GEDI—Hydrological Gauges
RMSE Before
Correction (cm)
RMSE After
Correction (cm)
Geneva 75.8 14.5 5.6
Neuchâtel 70.1 22.3 10.0
Zürich 75.0 19.2 9.6
Obersee (Zürich) 76.7 22.8 7.6
Lucerne 83.3 20.6 9.0
4. Discussion
Using GEDI data extracted from the algorithms developed by the GEDI team, the accuracy of the
GEDI water surface elevation estimates seems to be high enough. Overall, the standard deviation from
the mean dierence between GEDI and in-situ elevations is ~22 cm with no apparent bias. Moreover,
given GEDI’s small footprint diameter (25 m), GEDI should provide better elevation estimates in
comparison to, for example, radar altimeters for narrower water surfaces, such as rivers. On the other
hand, while GEDI uses the same laser specs as those used for GLAS on board ICESat-1, the precision
obtained by GEDI is inferior to that obtained using ICESat-1. In fact, Baghdadi et al. [
29
] in their study
over Swiss lakes, observed an accuracy (RMSE) of elevation estimates in the order of ~5 cm using
ICESat-1 data.
GEDI’s smaller footprint means that GEDI waveforms within the footprints could easily be
aected by small disturbances coming, for instance, from water surface roughness, which leads to
uncertainties in the estimation of water surface elevations. For example, GEDI acquisitions with the
highest mean dierence to in-situ elevations, and highest standard deviation were acquisitions taken
during the weekend (e.g., April 21, June 08), or before a holiday (e.g., May 22). The uncertainties at
these acquisition dates could be explained in part by the increased human activities over the water
surface (e.g., ships) which pollutes the return waveform. Moreover, these uncertainties are also the
result of small currents generated by thermal eects [
35
] or winds [
36
], that disrupts the water surface.
Finally, over large lakes, water surface is not entirely flat due to the presence of wind-generated waves
and seiches. Therefore, GEDI, depending on the angle of incidence, can over- or under- estimate the
water surface level by providing elevations from the trough or the top of the waves. In our study of the
eects of GEDI’s viewing angle over each lake and each date, we observed that, generally, uncertainties
on the estimation of elevations increased with increasing viewing angle. Moreover, the acquisitions
with the large deviation to the mean elevation dierence between GEDI and in-situ elevations were
acquisitions with the largest viewing angle.
The time of GEDI acquisitions also introduces uncertainties on the elevation estimates. For
example, during sunlit GEDI acquisitions, photons from the sun reflecting at the water surface could
contaminate the returned echo and increase the noise. In this study, we found that GEDI elevation
estimates with the lowest standard deviation and bias to the in-situ elevations were acquisitions taken
during the morning or late at night when lake water surfaces are usually calm, cooler with low wind
speeds. For these acquisition times (April 28 and May 02, 04, and 22), the mean dierence between
GEDI and in-situ elevations was 6±15 cm.
Remote Sens. 2020,12, 2714 18 of 22
The analysis of the precision of elevations from GEDI according to the used laser in the acquisition
did not show any dierence between coverage and full power laser. Nonetheless, some beams showed
systematic dierences in comparison to others. In general, the most accurate elevations came from
beams 1–4 (coverage laser) and beams 7–8 (full power laser). Beams 5–6, which also correspond to
a full power laser, showed the least precise elevations across all dates. Moreover, there were some
dierences on the elevation estimation accuracies across the beams. For example, footprints acquired
from beam 8 had better precision than footprints from beam 7, albeit both beams are produced using
the same laser unit onboard GEDI. Similar observations have been noted for beams 3 and 4, which
were found to be more precise than beams 1 and 2. However, these uncertainties could be mostly
explained by the errors introduced from the viewing angle of GEDI, which diers from one beam to
another on a given acquisition date.
In general, the dierences between GEDI and in-situ elevations are due to both instrumental
and environmental factors. In our modeling of the errors, the two most contributing factors were
the viewing angles of GEDI, and the saturation on some of the acquired waveforms assuming this
occurs through the zcross_amp indicator. However, these factors could be corrected in unimodal
waveforms. Another form of uncertainties is related to the uneven water surface due to standing and
wind-generated waves. This was apparent by the high contribution of environmental factors, like the
wave direction, depth of the lake at each GEDI footprint, and the wind speed. Lake depth is a direct
indicator of the wave heights as waves near the shore (low depth) are higher than waves farther away,
while wind speed controls the height of the generated wave. In this study, the contribution of wind
speed on the errors appears to be lower than other factors. However, this is due to the low wind speeds
at the present GEDI acquisition (maximum encountered wind speed of 12 km/h), which suggests small
wind-generated waves. In addition to instrumental and environmental factors, the acquisition date
and time of GEDI can also directly aect its accuracy. The time of the acquisition during the day,
as well as the day of the week on which an acquisition took place, both can have eects on the echoed
waveforms as seen previously. The eect of these two factors is related to the noise from the sun on the
GEDI receiver, and the increased human activities over each lake during certain days.
Finally, the provided quality_flag from the L1B data product could help, in theory, to select GEDI
data with higher accuracy on elevations. Using data issued after the application of our filter (|SRTM
elevation—GEDI elevation| > 100 m), we observed only a slight decrease of the root mean square error
on GEDI elevation estimates when we considered only the waveforms with a quality_flag =1. For
algorithm a1, we observed a decrease in the RMSE of around 1.4 cm (from 22.3 cm using data with
both quality_flag =0 and 1 to 20.9 cm using only quality_flag =1). Similarly, for a2, the decrease in the
RMSE was around 0.8 cm (from 24.9 cm using data with both quality_flag =0 and 1 to 24.1 cm using
only quality_flag =1).
5. Conclusions
In this study, we analyzed GEDI data in order to determine its accuracy of elevation estimation
over lake surfaces using algorithms provided by the Land Processes Distributed Active Archive Center
(LP DAAC). The objective was to study the quality of the first two months of GEDI data (min-April
until mid-June 2019) acquired over several lakes in Switzerland. Overall, 4367 GEDI shots out of 21,242
available shots were exploitable and analyzed over the eight studied lakes.
This first analysis of GEDI data from the first two months of acquisitions showed a very low
mean elevation dierence between GEDI and in-situ gauge station elevations, in the order of 0.61
±
22.3 cm for one standard deviation. While GEDI’s reported vertical accuracy in this study was well
below the 50 cm vertical accuracy provided in Dubayah et al. [
31
], it still remains higher than what
was previously obtained using the ancient LiDAR satellite ICESat-1. In fact, the vertical accuracy of
GLAS onboard ICESat-1 was better than 10 cm, as demonstrated by Baghdadi et al. [29].
Remote Sens. 2020,12, 2714 19 of 22
The analysis of GEDI data by lake showed that the vertical precision varied from under-estimation
by GEDI of
13.8 cm for certain lakes, to over-estimations of +9.8 cm for others, with a varying standard
deviation between 14.5 and 31.6 cm.
The investigation of GEDI’s vertical accuracy by date showed a mean dierence between GEDI and
in-situ gauge station elevations varying between
26.8 and +15.2 cm. The lowest bias corresponded to
data acquired in the morning or late at night. The highest recorded bias was observed on acquisitions
that were made around noon, in the early evening, and over the weekend. Moreover, the dierence in
GEDI’s elevation accuracy according to the acquisition date is also aected, in part, by the instrument’s
viewing angle at acquisition time (larger viewing angle leads to lower accuracies). However, the full
eects of the viewing angle were not studied in its entirety due to the small number of available
acquisitions at the time of this writing.
The analysis of GEDI data by beam number showed that the dierence between GEDI and gauge
stations’ elevations varied depending on the acquisition date and the beam. Certain beams at certain
dates showed that elevations from GEDI were very similar to in-situ readings (fluctuations of few
cm). Summary statistics calculated for each GEDI beam using acquisitions from all dates showed that
the beams with the highest elevation dierences to in-situ readings were beams 1 and 6 (
10.2 and
+18.1 cm, respectively). In contrast, the beams with the smallest mean elevation dierence to in-situ
readings were beams 5 and 7 (
1.7 and
2.5 cm, respectively). The remaining beams (2, 3, 4, and 8)
showed a mean dierence between
7.4 and +4.4 cm. The standard deviation of the mean dierence,
however, was similar across all beams (between 17.2 and 22.9 cm).
The analysis of the metrics, such as the amplitude or width of the modes, did not allow further
investigation of GEDI elevation estimation accuracy, even though a certain dependence was found
between these metrics and the quality of GEDI data.
Nonetheless, accounting for instrumental and environmental factors increased the accuracy
(RMSE) of GEDI estimates to 8.4 cm for all lakes and from 5.6 to 10 cm (with no apparent bias) when
modeling the errors for each lake independently.
Following the first analysis done on the first GEDI data sets, we can conclude that GEDI has a
strong potential for precise estimation of water surfaces of any size. Moreover, a better estimate of
GEDI metrics by the LP DAAC can be expected in the near future, allowing for reprocessed data with
a better elevation precision.
Author Contributions:
Conceptualization, I.F. and N.B.; Data curation, I.F.; Formal analysis, I.F., N.B. and J.S.B.;
Investigation, I.F., N.B. and F.F.; Methodology, I.F., N.B.; Software, I.F.; Validation, I.F., N.B., J.S.B., F.F. and M.Z.;
Writing—original draft, I.F.; Writing—review & editing, I.F. and N.B. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research received funding from the French Space Study Center (CNES, TOSCA 2020 project),
the Research Infrastructure IR Data Terra, and the National Research Institute for Agriculture, Food and the
Environment (INRAE).
Acknowledgments:
The authors would like to thank the GEDI team and the NASA LPDAAC (Land Processes
Distributed Active Archive Center) for providing GEDI data. The authors would also like to thank the Swiss
Federal Oce for the Environment (FOEN) for providing the national gauge database.
Conflicts of Interest: The authors declare no conflict of interest.
Remote Sens. 2020,12, 2714 20 of 22
Appendix A
Remote Sens. 2020, 12, x FOR PEER REVIEW 20 of 23
lower accuracies). However, the full effects of the viewing angle were not studied in its entirety due
to the small number of available acquisitions at the time of this writing.
The analysis of GEDI data by beam number showed that the difference between GEDI and gauge
stations’ elevations varied depending on the acquisition date and the beam. Certain beams at certain
dates showed that elevations from GEDI were very similar to in-situ readings (fluctuations of few
cm). Summary statistics calculated for each GEDI beam using acquisitions from all dates showed that
the beams with the highest elevation differences to in-situ readings were beams 1 and 6 (−10.2 and
+18.1 cm, respectively). In contrast, the beams with the smallest mean elevation difference to in-situ
readings were beams 5 and 7 (−1.7 and −2.5 cm, respectively). The remaining beams (2, 3, 4, and 8)
showed a mean difference between −7.4 and +4.4 cm. The standard deviation of the mean difference,
however, was similar across all beams (between 17.2 and 22.9 cm).
The analysis of the metrics, such as the amplitude or width of the modes, did not allow further
investigation of GEDI elevation estimation accuracy, even though a certain dependence was found
between these metrics and the quality of GEDI data.
Nonetheless, accounting for instrumental and environmental factors increased the accuracy
(RMSE) of GEDI estimates to 8.4 cm for all lakes and from 5.6 to 10 cm (with no apparent bias) when
modeling the errors for each lake independently.
Following the first analysis done on the first GEDI data sets, we can conclude that GEDI has a
strong potential for precise estimation of water surfaces of any size. Moreover, a better estimate of
GEDI metrics by the LP DAAC can be expected in the near future, allowing for reprocessed data with
a better elevation precision.
Author Contributions: Conceptualization, I.F. and N.B.; Data curation, I.F.; Formal analysis, I.F., N.B. and J.S.B.;
Investigation, I.F., N.B. and F.F.; Methodology, I.F., N.B.; Software, I.F.; Validation, I.F., N.B., J.S.B., F.F. and M.Z.;
Writing—original draft, I.F.; Writing—review & editing, I.F. and N.B. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received funding from the French Space Study Center (CNES, TOSCA 2020 project), the
Research Infrastructure IR Data Terra, and the National Research Institute for Agriculture, Food and the
Environment (INRAE).
Acknowledgments: The authors would like to thank the GEDI team and the NASA LPDAAC (Land Processes
Distributed Active Archive Center) for providing GEDI data. The authors would also like to thank the Swiss
Federal Office for the Environment (FOEN) for providing the national gauge database.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
(a) (b)
Remote Sens. 2020, 12, x FOR PEER REVIEW 21 of 23
(c) (d)
(e) (f)
(g) (h)
Figure A1. Difference between GEDI and in-situ elevations of all shots acquired over water for each
lake on all dates, plotted for each beam (1 to 8). Abscissa = GEDI shot number. (a) Geneva, (b)
Neuchâtel, (c) Zürich, (d) Obersee (Zürich), (e) Lucerne, (f) Walensee, (g) Sempach, and (h) Thun.
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Sci. USA 2014, 111, 3245–3250, doi:10.1073/pnas.1222460110.
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Global flood risk under climate change. Nat. Clim. Chang. 2013, 3, 816–821, doi:10.1038/nclimate1911.
4. Jiménez Cisneros, B.E.; Oki, T.; Arnell, N.W.; Benito, G.; Cogley, J.G.; Döll, P.; Jiang, T.; Mwakalila, S.S.
Freshwater Resources. In Climate Change 2014 Impacts, Adaptation, and Vulnerability; Field, C.B., Barros, V.R.,
Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Eds.; Cambridge University Press: Cambridge, UK, 2014; pp.
229–270. ISBN 978-1-107-41537-9.
5. Calmant, S.; Seyler, F. Continental surface waters from satellite altimetry. Comptes Rendus Geosci. 2006, 338,
1113–1122, doi:10.1016/j.crte.2006.05.012.
6. Shiklomanov, A.I.; Lammers, R.B.; Vörösmarty, C.J. Widespread decline in hydrological monitoring
threatens Pan-Arctic research. Eos 2002, 83, 13, doi:10.1029/2002EO000007.
Figure A1.
Dierence between GEDI and in-situ elevations of all shots acquired over water for each lake
on all dates, plotted for each beam (1 to 8). Abscissa =GEDI shot number. (
a
) Geneva, (
b
) Neuch
â
tel,
(c) Zürich, (d) Obersee (Zürich), (e) Lucerne, (f) Walensee, (g) Sempach, and (h) Thun.
Remote Sens. 2020,12, 2714 21 of 22
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article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... In the last couple of decades, active remote sensing technologies such as radar or LiDAR based sensors have become an essential source of information for the monitoring of inland water body levels due to their validated high accuracies [1][2][3][4][5][6] and as a way to fill-in for the ever-decreasing water-level gauge stations that has been reported worldwide [7,8]. ...
... The values of the previously mentioned metrics (SNR, VA, A, and gwidth) were extracted using algorithm a1 as it showed the highest accuracy among the six possible algorithms in the study conducted by Fayad et al. [4] over several lakes in Switzerland including Lake Geneva. Next, an analysis of GEDI elevations using metric values extracted from the remaining algorithms (a2 to a6) was made more succinctly in comparison to a1. ...
... While the ubRMSE was relatively similar (66.2 cm vs. 68.9 cm) for both releases (V1 and V2), the ubRMSE from V1 was higher than what was previously obtained in Fayad et al. [4] (ubRMSE between ~14 and ~31 cm) and Frappart et al. [12] (ubRMSE between ~10 and ~30 cm) using smaller subsets of V1 over Lake Geneva. Therefore, an in-depth analysis of these differences as a function of different acquisitions parameters (amplitude (A) and width (gwidth) of the surface return mode, SNR, and VA) is required. ...
Article
Full-text available
Spaceborne LiDAR altimetry has been demonstrated to be an essential source of data for the estimation and monitoring of inland water level variations. In this study, water level estimates from the Global Ecosystem Dynamics Investigation (GEDI) were validated against in situ gauge station records over Lake Geneva for the period between April 2019 and September 2020. The performances of the first and second releases (V1 and V2, respectively) of the GEDI data products were compared, and the effects on the accuracy of the instrumental and environmental factors were analyzed in order to discern the most accurate GEDI acquisitions. The respective influences of five parameters were analyzed in this study: (1) the signal-over-noise ratio (SNR); (2) the width of the water surface peak within the waveform (gwidth); (3) the amplitude of the water surface peak within the waveform (A); (4) the viewing angle of GEDI (VA); and (5) the acquiring beam. Results indicated that all these factors, except the acquiring beam, had an effect on the accuracy of GEDI elevations. Nonetheless, using VA as a filtering criterion was demonstrated to be the best compromise between retained shot count and water level estimation accuracy. Indeed, by choosing the shots with a VA ≤ 3.5°, 74.6% of the shots (after an initial filter) were retained with accuracies similar to choosing A > 400 (46.2% retained shots), SNR > 15 dB (63.3% retained shots), or gwidth < 10 bins (46.5% of retained shots). Finally, the comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant, but higher, bias and fewer deviations to the in situ data than V1. Indeed, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r = 0.66) and 42.8 cm with V1 (r = 0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA ≤ 3.5°). Regarding the bias, a higher value was observed with V2, but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number and strong dependence on GEDI dates.
... Data were collected in March 2019. Its product L2A was validated by in situ data using 8 lakes in Switzerland, which found that the mean difference between the elevations and that of hydrological stations varied from -13.8 cm to + 9.8 cm with standard deviations ranging from 14.5 to 31.6 cm (Fayad et al. 2020). In addition, Xiang et al. (2021) compared ICESat-2, ICESat, and GEDI over the Great Lakes and lower Mississippi River using in situ data from 22 gauge stations. ...
... According to the parameter settings in the data processing algorithm theory document (Fayad et al. 2020), the width of the second Gaussian filter (Smoothwidth_ zcross) determines the position of the last detected peak (ground echo). Algorithms 1 and 4 were fixed to 6.5 ns, and the remaining Algorithms 2, 3, 5 and 6 were set to 3.5 ns. ...
... First, the above useful data were screened by a quality flag that equals 1, which means that the L1B waveforms met certain criteria based on energy, sensitivity, amplitude, and real-time surface tracking. Hence, they could be further processed (Fayad et al. 2020). Subsequently, the waveform number flag that equals 1 was selected to guarantee waveform returning from the lake surface. ...
Article
Full-text available
Monitoring lake water levels is important to fully understand the characteristics and mechanism of lake dynamic change, the impact of climate change and human activities on lakes, etc. This paper first individually evaluated the performance of the newly released Global Ecosystem Dynamics Investigation (GEDI) and the successor of the Ice, Cloud, and Land Elevation Satellite mission (ICESat-2) for inland lake level retrieval over four typical lakes (Chaohu Lake, Hongze Lake, Gaoyou Lake and Taihu Lake) using in situ gauge data, then the lake levels of the two missions were combined to derive long time-series lake water levels. A comparison of the mission results with in situ water levels validated the accuracy of the ICESat-2 with R varying from 0.957 to 0.995, MAE 0.03 m-0.10 m and RMSE 0.04 m-0.13 m; however, larger bias occurred in GEDI results with R spanning from 0.560 to 0.952, MAE 0.31 m-0.38 m and RMSE 0.35 m-0.46 m. Before the lake levels were combined, GEDI bias correction was carried out. The correlation coefficients and annual change rate differences between the combined and the in situ data were 0.964 and 0.06 m/yr, 0.852 and 0.05 m/yr, 0.888 and 0.05 m/yr, and 0.899 and 0.02 m/yr for Lake Chaohu, Hongze, Gaoyou and Taihu, respectively. Except for individual months and seasonal differences caused by GEDI estimations, the general trend of monthly, seasonal, and annual dynamics of inland lake water levels captured by combined GEDI and ICESat-2 missions were consistent with measurements from hydrological stations. These encouraging results demonstrate that combining the two missions has great potential for frequent and accurate lake level monitoring and could be a valuable resource for the study of hydrological and climatic change.
... Atmospheric factors mostly include the type and composition of clouds at the acquisition time [16,17]. Finally, water surface state factors can include air and water temperatures, atmospheric pressure, humidity, wind and gust speeds, wind-waves information (i.e., height, direction, and period), and swell-waves information (i.e., height, direction, and period) [18]. Therefore, our objectives in this study are to (1) assess the accuracy of GEDI water level estimates over the five Great Lakes; (2) propose a series of models that will estimate the errors on the acquired GEDI waveforms as function of the previously mentioned factors, in order to correct GEDI elevation estimates; (3) assess the influence of each group of factors on the uncertainty of the acquired GEDI elevations; (4) assess the generalizability of our approach. ...
... In contrast, with uncorrected GEDI estimates, the RMSE on the water level estimates ranged from 0.57 m (Lake Erie) to 0.68 m (Lake Ontario), with a bias that ranged from 0.43 m (Lake Erie) to 0.61 m (Lake Ontario). On the other hand, the uncertainties obtained with GEDI were consistent with other studies, such as the study of Xiang et al. [13] over the five Great Lakes, or the studies of Fayad et al. [14,18] and the study of Frappart et al. [6] over several lakes in France and Switzerland. Therefore, model-free GEDI elevation estimates are not recommended for the retrieval of water surface levels. ...
Article
Full-text available
The Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station has acquired more than 35 billion shots globally in the period between April 2019 and August 2021. The acquired shots could offer a significant database for the measure and monitoring of inland water levels over the Earth’s surface. Nonetheless, previous and current studies have shown that the provided GEDI elevation estimates are significantly less accurate than any available radar or LiDAR altimeter. Indeed, our analysis of GEDI’s altimetric capabilities to retrieve water levels over the five North American Great Lakes presented estimates with a bias that ranged between 0.26 and 0.35 m and a root mean squared error (RMSE) ranging between 0.54 and 0.68 m. Therefore, our objective in this study is to post-process the original GEDI water level estimates from an error model taking instrumental, atmospheric, and lakes surface state factors as proxies, which affect the physical shape of the waveforms, hence introducing uncertainties on the elevation estimates. The first tested model, namely a random forest regressor (RFICW) with the instrumental, atmospheric, and water surface state factors as inputs, was validated temporally (trained on a given year and validated on another) and spatially (trained on a given lake and validated on the remaining four). The results showed a significant decrease in elevation estimation errors both temporally and spatially. The temporally validated models showed an RMSE on the corrected elevation estimates of 0.18 m. Concerning the spatially validated model, the results varied based on the lake data used for training. Indeed, the most accurate spatially validated model showed an RMSE of 0.17 m, while the least accurate model showed an RMSE of 0.26 m. Finally, given that an elevation correction model using all the factors (instrumental, atmospheric, and water surface state factors) presents a best-case scenario, as water surface state factors are only available over a selected number of lakes globally, three additional models based on random forest were tested. The first, RFI, uses only instrumental factors as correction factors, RFIC uses both instrumental and atmospheric factors, while the third, RFIW, uses instrumental and water surface state factors. The temporal validation of these models showed that the model using instrumental factors, while less accurate than the remaining two models, was capable of correcting the original GEDI elevation estimates by a factor of two across the five lakes. On the other hand, the RFIC model was the most accurate between the three, with a slight degradation in comparison to the full model. Indeed, the RFIC model showed an RMSE on the estimation of water levels of 0.21 m.
... It began to collect data in March, 2019. Its product L2A was validated by in-situ data using 8 lakes in Switzerland, which found that the mean difference between the elevations and that of Hydrological stations varying from − 13.8 cm to + 9.8 cm with standard deviations ranging from 14.5 to 31.6 cm (Fayad et al., 2020). In addition, Xiang et al. (2021) compared ICESat-2, ICESat, and GEDI over the Great Lakes and lower Mississippi River using in-situ data from 22 gauge stations. ...
... Besides, the product contains a preliminary set of quality ags and metrics that can be used to lter shots with poor geolocation performance and waveforms of low signal quality (Roy et al., 2021). According to the parameter setting in data processing algorithm theory document (Fayad et al., 2020), only the results from algorithm 2 were adopted in our study. Table 1 Study lakes and the observations distribution of ICESat-2 and GEDI satellites In-situ data ...
Preprint
Full-text available
Monitoring lake water levels is important to fully understand the characteristics and mechanism of lakes’ dynamic change, the impact of climate change and human activities on lakes, etc. This paper first individually evaluated the performance of newly released Global Ecosystem Dynamics Investigation (GEDI) and the successor of Ice, Cloud, and Land Elevation Satellite mission (ICESat-2) for inland lake level retrieval over four typical lakes (Chaohu Lake, Hongze Lake, Gaoyou Lake and Taihu Lake) using in-situ gauge data, then lake levels of the two missions were combined to derive long time-series lake water levels. Compared with in-situ water levels, the validations revealed that very accurate results were obtained by ICESat-2 with R varing from 0.957 to 0.995, MAE 0.03 m-0.10 m and RMSE 0.04 m-0.13 m, however, larger bias occurred in GEDI results with R spanning from 0.560 to 0.952, MAE 0.31 m-0.38 m and RMSE 0.35 m-0.46 m. Before combination, the bias correction of GEDI was carried out. The annual change rate differences between the combined and the in-situ data were 0.06 m/yr, 0.05 m/yr, 0.05 m/yr and 0.02 m/yr for Lake Chaohu, Hongze, Gaoyou and Taihu, respectively. The monthly, seasonal and annual dynamics of inland lake water levels captured by combined GEDI and ICESat-2 missions agree well with measuremnts from hydrological stations. These encouraging results demonstrate the great potential for frequent and accurate lake level monitoring, which could be a valuable resource for the studies of hydrological and climatic change.
... Huang et al., 2019). Laser altimeters have a smaller footprint and sample at a higher density, which allows them to sample smaller water bodies; however, they are affected by atmospheric parameters (Fayad et al., 2020). The Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) laser altimeters (both launched in 2018) have a footprint size of 25 and ∼17.5 m, acquired every 60 m and ∼70 cm along track, with a ∼600 and ∼90 m separation between tracks, respectively (Dubayah et al., 2020;Madson & Sheng, 2021). ...
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Full-text available
Simple models continue to be important for continental‐scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND; Nobre et al., 2011, https://doi.org/10.1016/j.jhydrol.2011.03.051), Teng Vaze Dutta (TVD; Teng et al., 2013, http://hdl.handle.net/102.100.100/97033?index=1), and Floodwater Depth Estimation Tool (FwDET; Cohen, Brakenridge, et al., 2018, https://doi.org/10.1111/1752-1688.12609). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM‐H (Gallant et al., 2011, https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/72759), MERIT (Yamazaki et al., 2019, https://doi.org/10.1029/2019WR024873), or FABDEM (Hawker et al., 2022, https://doi.org/10.1088/1748-9326/ac4d4f) can affect different models differently; and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability.
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With massive geospatial coverage and adequate time series of sea surface height, spatio-temporal multi-mission satellite altimetry tidal modelling emerges as a profound potential solution for increasing accuracy and minimising variation across multiple offshore applications. Therefore, this article attempts to review the current implementation of satellite altimetry in the applicable area of studies relevant to conventional oil and gas applications toward sustainable energy applications. The implication of current spatio-temporal enhancement of tidal measurement by satellite altimetry at the coastal area and the offshore zone is discussed mainly to elaborate on current achievement as well as to gauge potential future optimisation for offshore applications in the energy industry. Spatio-temporal enhancement in conventional oil and gas field applications improves the integration of various offshore construction applications. The impact of this application is more significant as engineering construction adopts stringent and higher vertical data accuracy acceptance criteria. More comprehensive spatial information coverage of tidal regime, co-tidal range, the offshore co-tidal pattern should be more accessible by more intensive spatio-temporal enhancement attempts in various studies and implementations. This leads to higher reliability and integrity of offshore vertical references derivation.
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LiDAR is an excellent source of elevation data used in many surveys. The spaceborne handle system, Global Ecosystem Dynamics Investigation (GEDI), provides ground elevation information with high accuracy except for areas with steep slopes. GEDI data have a lot of noise from atmospheric conditions, and therefore filtering procedures are mandatory to select the best dataset. The dataset presents uncertainties of different magnitudes, with values reaching more than 100 m of difference between the reference data and the GEDI data. The challenge is to find a criterion to determine a threshold to filter accurate GEDI samples. This research aims to identify the threshold based on the difference values between the reference data and the GEDI data to select the maximum number of samples with low RMSE values. Therefore, we used the Kolmogorov–Smirnov (KS) non-parametric test to define the best threshold based on a normal distribution. Our results demonstrated a lower RMSE value with a high number of samples when compared with the quality flag parameter threshold, even using sensitivity parameter thresholds. This method is useful for achieving the best possible accuracy from GEDI data worldwide.
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A continuously operating GNSS station within a lake interior is uncommon, but advantageous for testing the GNSS Interferometric Reflectometry (GNSS-IR) technique. In this research, GNSS-IR is used to estimate ten years of lake surface heights for Lake Taupō in New Zealand. This is achieved using data collected from station TGHO, approximately 4 km from the lake’s shoreline. Its reliability is assessed by comparisons with shoreline gauges and satellite radar altimetry lake surface heights. Relative RMS differences between the daily averaged lake gauge and GNSS-IR lake surface heights range from ± 0.027 to ± 0.028 m. Relative RMS differences between the satellite radar altimetry lake surface heights and the GNSS-IR lake surface heights are ± 0.069 m and ± 0.124 m. The results show that the GNSS-IR technique at Lake Taupō can provide reliable lake surface height estimates in a terrestrial reference frame. A new ground-based absolute satellite radar altimetry calibration/validation approach based on GNSS-IR is proposed and discussed.
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Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.
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Radar altimetry provides unique information on water stages of inland hydro-systems. In this study, the performance of seven altimetry missions, among the most commonly used in land hydrology (i.e., European Remote-Sensing Satellite-2 (ERS-2), ENVIronment SATellite (ENVISAT), Satellite with Argos and ALtika (SARAL), Jason-1, Jason-2, Jason-3 and Sentinel-3A), are assessed using records from a dense in situ network composed of 19 gauge stations in the Inner Niger Delta (IND) from 1995 to 2017. Results show an overall very good agreement between altimetry-based and in situ water levels with correlation coefficient (R) greater than 0.8 in 80% of the cases and Root Mean Square Error (RMSE) lower than 0.4 m in 48% of cases. Better agreement is found for the recently launched missions such as SARAL, Jason-3 and Sentinel-3A than for former missions, indicating the advance of the use of the Ka-band for SARAL and of the Synthetic-aperture Radar (SAR) mode for Sentinel-3A. Cross-correlation analysis performed between water levels from the same altimetry mission leads to time-lags between the upstream and the downstream part of the Inner Niger Delta of around two months that can be related to the time residence of water in the drainage area.
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Lakes and reservoirs have been identified as sentinels of climate change. Tonle Sap is the largest lake in both the Mekong Basin and Southeast Asia and because of the importance of its ecosystem, it is has been described as the “heart of the lower Mekong”. Its seasonal cycle depends on the annual flood pulse governed by the flow of the Mekong River. This study provides an impact analysis of recent climatic events from El Niño 1997/1998 to El Niño 2015/2016 on surface storage variations in the Tonle Sap watershed determined by combining remotely sensed observations, multispectral images and radar altimetry from 1993 to 2017. The Lake's surface water volume variations are highly correlated with rainy season rainfall in the whole Mekong River Basin (R = 0.84) at interannual time-scale. Extreme droughts and floods can be observed when precipitation deficit and excess is recorded in both the Tonle Sap watershed and the Mekong River Basin during moderate to very strong El Niño/La Niña events (R = −0.70) enhanced by the Pacific Decadal Oscillation (R = −0.68). Indian and Western North Pacific Monsoons were identified as having almost equal influence. Below normal vegetation activity was observed during the first semester of 2016 due to the extreme drought in 2015.
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Radar altimetry is now commonly used for the monitoring of water levels in large river basins. In this study, an altimetry-based network of virtual stations was defined in the quasi ungauged Ogooué river basin, located in Gabon, Central Africa, using data from seven altimetry missions (Jason-2 and 3, ERS-2, ENVISAT, Cryosat-2, SARAL, Sentinel-3A) from 1995 to 2017. The performance of the five latter altimetry missions to retrieve water stages and discharges was assessed through comparisons against gauge station records. All missions exhibited a good agreement with gauge records, but the most recent missions showed an increase of data availability (only 6 virtual stations (VS) with ERS-2 compared to 16 VS for ENVISAT and SARAL) and accuracy (RMSE lower than 1.05, 0.48 and 0.33 and R² higher than 0.55, 0.83 and 0.91 for ERS-2, ENVISAT and SARAL respectively). The concept of VS is extended to the case of drifting orbits using the data from Cryosat-2 in several close locations. Good agreement was also found with the gauge station in Lambaréné (RMSE = 0.25 m and R2 = 0.96). Very good results were obtained using only one year and a half of Sentinel-3 data (RMSE < 0.41 m and R2 > 0.89). The combination of data from all the radar altimetry missions near Lamabréné resulted in a long-term (May 1995 to August 2017) and significantly improved water-level time series (R² = 0.96 and RMSE = 0.38 m). The increase in data sampling in the river basin leads to a better water level peak to peak characterization and hence to a more accurate annual discharge over the common observation period with only a 1.4 m3·s−1 difference (i.e., 0.03%) between the altimetry-based and the in situ mean annual discharge.
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Over the last few decades, satellite altimetry has proven to be valuable for monitoring lake levels. With the new generation of altimetry missions, CryoSat-2 and Sentinel-3, which operate in Synthetic Aperture Radar (SAR) and SAR Interferometric (SARIn) modes, the footprint size is reduced to approximately 300 m in the along-track direction. Here, the performance of these new modes is investigated in terms of uncertainty of the estimated water level from CryoSat-2 data and the agreement with in situ data. The data quality is compared to conventional low resolution mode (LRM) altimetry products from Envisat, and the performance as a function of the lake area is tested. Based on a sample of 145 lakes with areas ranging from a few to several thousand km², the CryoSat-2 results show an overall superior performance. For lakes with an area below 100 km², the uncertainty of the lake levels is only half of that of the Envisat results. Generally, the CryoSat-2 lake levels also show a better agreement with the in situ data. The lower uncertainty of the CryoSat-2 results entails a more detailed description of water level variations.
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Satellite radar altimetry has been widely used in the monitoring of water levels of lakes, rivers and wetlands in the past decades. The conventional pulse-limited radar altimeters have a relatively large ground footprint, which limits their capability to retrieve surface elevation information over small and medium-sized water bodies. A new generation of satellite radar altimeter system, a dual-frequency SAR radar altimeter (SRAL) onboard the Copernicus Sentinel-3 satellite, has produced densely sampled elevation measurements with a smaller footprint for the Earth's surfaces since June 2016, owing to the Delay-Doppler processing technique. Four standard SRAL SAR altimetry waveform retracking algorithms (known as retrackers) have been designed to retrieve elevation measurements for different types of surfaces: Ice-Sheet retracker for polar ice sheets, SAMOSA-3 retracker for open ocean and coastal zones, OCOG retracker for sea-ice margins, and Sea-Ice retracker for sea ice. In this research, we evaluated the performances of the Sentinel-3 SRAL SAR altimetry retrackers over lakes, particularly over seasonally ice-covered lakes in one hydrological cycle. For 15 lakes and reservoirs with different sizes and at varying latitudes in the northern hemisphere, we compared the lake water levels estimated by each of standard SRAL SAR retrackers against in-situ water level measurements for different seasons (a full hydrologic cycle) during 2016–2017. Our evaluation shows that Sea-Ice retracker was unable to provide continuous estimates of lake water levels, as a result of the high rate of missing data. Although the precision and relative accuracy of lake water level estimates from these three standard SRAL SAR retrackers are similar, the SAMOSA-3 retracker has the least bias in comparison with ground-based gauge measurements. When the lakes in the mid- and high-latitude regions were covered by ice in the winter season, these three standard SAR retrackers generated erroneous lake water level measurements, significantly lower than the true lake water levels recorded by in-situ gauge stations. The measurement errors of these three standard retrackers increase with the growth of the lake ice thickness. To address the negative effect of the seasonal ice cover, we developed a new bimodal correction algorithm. We demonstrate that our bimodal correction algorithm can retrieve the ice thickness and reliably estimate water levels for the ice-covered lakes in winter, hence enabling the generation of temporally consistent lake water level measurements throughout all seasons for lake hydrological analysis.
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for the first time ever, this study aims at applying Sentinel-3A to the Great Brahmaputra River (GBR) and validating water levels derived from this newly-launched altimetry satellite mission. The GBR is divided into three primary parts: (1) a large section of the Yarlung Zangbo River in China, also termed the Upper Brahmaputra River (UBR) in this study, featured by high elevation, complex terrain, narrow river widths (from less than 100 to 400 m), and limited in situ measurements; (2) the Middle Brahmaputra River (MBR) with widths varying from ∼400 m to ∼1 km; (3) the Lower Brahmaputra River (LBR), dominated by braided channels with wide river channels (up to several kilometers). For the altimetry data, both waveform retracking and hooking effect correction were applied to mitigate the influence caused by land contamination and to improve the accuracy of water level retrievals. Water levels were derived from 41 virtual stations (VSs) across the GBR and different retracking algorithms were compared with in situ data from two gauging stations in the UBR. Time series of altimetry-based water levels were categorized into three types based on the quality: no data (type 1), degraded (type 2), and good (type 3). Results showed that the VSs (type 1) only existed in the mountainous regions, accounting for ∼ a half of the total in the UBR. Validation with the gauged water levels showed that the TIC algorithm performed best among all of the retrackers applied, followed by the Ice-1 algorithm. The standard deviation of the difference between the gauged and TIC-derived water levels ranged from 0.41 to 0.76 m among four different VSs (type 3). In addition, the quality of VSs in the LBR was best, followed by the MBR. Our study has demonstrated the capability of Sentinel-3A in monitoring water levels in the GBR, thereby paving the way for future applications such as discharge estimation and hydrologic/hydrodynamic applications.
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Surface water bodies (lakes, reservoirs, and rivers) are key components of the water cycle and are important water resources. Water level and storage vary greatly under the impacts of climate change and human activities. Due to sparse in-situ monitoring networks, a comprehensive national-scale monitoring dataset of surface water bodies in China is not available. Over the last two decades, satellite altimetry has been used successfully for inland water monitoring. Here, we use CryoSat-2 radar altimetry to monitor water level variations of large lakes, reservoirs and rivers across China and demonstrate its potential to complement available in-situ monitoring datasets for the country. In this study, over 1000 lakes and reservoirs, and 6 large rivers are investigated. The results show that surface water varied greatly over the past 6years, e.g. in the Tibetan Plateau, the Junggar Basin, the Northeast China Plain, and the central Yangtze River basin. Estimated changes in volume indicate that surface water variation contributes significantly to terrestrial storage variation, especially in the Qaidam Basin and the Tibetan Plateau. CryoSat-2 is capable of measuring regional-scale river level at high spatial resolution and competitive accuracy as demonstrated by comparison with available in-situ gauging data. The results are encouraging with RMSE values ranging from 0.24 to 0.35m for the Heilongjiang-Amur River, 0.22 to 0.6m for the Yellow River and 0.22 to 0.5m for the Songhua River. Comparatively, accuracy is much lower over the Yangtze and Pearl Rivers (RMSE ~2.6m and ~3.3m), probably due to intensive inland waterway navigation. CryoSat-2 shows great potential for monitoring surface water at national scale in China.
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For at least 20 years, nadir altimetry satellite missions have been successfully used to first monitor the surface elevation of oceans and, shortly after, of large rivers and lakes. For the last 5-10 years, few studies have demonstrated the possibility to also observe smaller water bodies than previously thought feasible (river smaller than 500 m wide and lake below 10 km²). The present study aims at quantifying the nadir altimetry performance over a medium river (200 m or lower wide) with a pluvio-nival regime in a temperate climate (the Garonne River, France). Three altimetry missions have been considered: ENVISAT (from 2002 to 2010), Jason-2 (from 2008 to 2014) and SARAL (from 2013 to 2014).