scholarly journals Comparative Analysis of GEDI’s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies

2022 ◽  
Vol 14 (2) ◽  
pp. 340
Author(s):  
Ibrahim Fayad ◽  
Nicolas Baghdadi ◽  
Frédéric Frappart
Keyword(s):  
Water Level ◽  
Water Surface ◽  
Strong Dependence ◽  
Ecosystem Dynamics ◽  
Estimation Accuracy ◽  
In Situ Data ◽  
Gauge Station ◽  
Water Level Variations ◽  
Lower Variability

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.

scholarly journals Accuracy of Copernicus Altimeter Water Level Data in Italian Rivers Accounting for Narrow River Sections

2021 ◽  
Vol 13 (21) ◽  
pp. 4456
Author(s):  
Cristina Deidda ◽  
Carlo De Michele ◽  
Ali Nadir Arslan ◽  
Silvano Pecora ◽  
Nicolas Taburet
Keyword(s):  
Water Level ◽  
Satellite Data ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Drought Risk ◽  
Satellite Measurements ◽  
Joint Measurement ◽  
Initial Water ◽  
In Situ Data

Information about water level is essential for hydrological monitoring and flood/ drought risk assessment. In a large part of Italian river network, in situ instruments for measuring water level are rare or lacking. Here we consider the satellite measurements of water level retrieved by Copernicus altimetric missions (Sentinel 3A, Sentinel 3B, Jason 2/3), and compare these with in situ data, from 19 gauging stations in Italy with a river section in the range of [50, 555] m. The results highlight the potentiality of altimetric satellite measurements for water level retrieval in a case study of Italian rivers. By comparing synchronous satellite and in situ water level difference (i.e., difference between two successive measurements in time of satellite data compared to the difference of two successive measurements in time of in situ data), we found a median value of Pearson correlation of 0.79 and 0.37 m of RMSE. Then, from water level differences, we extracted the satellite water level values with two different procedures: (1) assuming as the initial water level of the satellite measurements the first joint measurement (satellite–in situ data) and (2) calibrating the initial water level, minimizing the mean absolute error metric. The results show the feasibility of using satellite data for water level retrieval in an operative and automatic perspective.

scholarly journals AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 327-334 ◽  
Author(s):  
Paulo Henrique Costa ◽  
Eric Oliveira Pereira ◽  
Philippe Maillard
Keyword(s):  
Water Level ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Small Lakes ◽  
Water Surface Area ◽  
Volume Fluctuations ◽  
Calm Water ◽  
Gauging Station

Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of &lt;i&gt;in situ&lt;/i&gt; gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often “contaminated” by the surrounding land. To try to solve this, we created a “lake” retracker that rejects waveforms that cannot be attributed to “calm water” and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby &lt;i&gt;in situ&lt;/i&gt; gauging station. Although the “lake” retracker worked well with calm water, the presence of waves and other factors was such that the standard “ice1” retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.

Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

2020 ◽  
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Sensing Data

&lt;p&gt;Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.&lt;/p&gt;&lt;p&gt;In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM&amp;#8217;s &amp;#8220;Database of Hydrological Time series of Inland Waters&amp;#8221; (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.&lt;/p&gt;&lt;p&gt;Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.&lt;/p&gt;

Discharge of the river Rhine from multi-sensor data from empirical and physical methods

2021 ◽  
Author(s):  
Luciana Fenoglio-Marc ◽  
Elena Zahkavova ◽  
Matthias Gärtner ◽  
Bahtiyor Zohidov ◽  
Salvatore Dinardo ◽  
...  
Keyword(s):  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Water Discharge ◽  
River Rhine ◽  
Rating Curve ◽  
Physically Based ◽  
Curve Method ◽  
Semi Empirical

&lt;p&gt;River discharge is a key variable to quantify the water cycle and its flux.&amp;#160; This study focuses on the river Rhine, of width between 200 and 500 meters.&amp;#160;River discharge is evaluated in this paper from the Sentinel-3 altimeter water level using various approches, which are the empirical rating curve method, the semi-empirical Bjerklie method and the physically-based method based on hydraulic equations.&lt;/p&gt;&lt;p&gt;The Sentinel-3 GPOD ESA products from the SAMOSA+ retracker perform better than the standard Copernicus products that use the OCOG and ocean retrackers. Root-mean-square errors (RMSEs) between altimetry and in-situ stations are between 0.10 m and 0.30 m at 10 of the 17 virtual tide gauge locations.&amp;#160;The empirical rating curve method applied to the altimetric water level and in-situ discharge provides estimates of the water discharge with accuracy of 3-7% (expressed as RMSE normalized with the mean of the discharge).&lt;/p&gt;&lt;p&gt;The performance of the semi-empirical Bjerklie method and of the physically-based Manning algorithm to estimate the river discharge is assessed from water surface slope, elevation and top width data for different part of the river and flow conditions. Firstly, daily synthetic water surface slopes and elevations are generated from selected in-situ gauges and mean top river widths. Secondly the input to the discharge algorithm comes from the 1D-hydrodynamic model Sobek. Various choises for reach lengths and for number of observed time-series are considered. Different time sampling are used to study the effect of the repeat cycle of nadir altimeter and SWOT missions. The effect of the priori information on the accuracy of the flow water discharge is investigated.&lt;/p&gt;

scholarly journals Analysis of water level variations in Brazilian basins using GRACE

2012 ◽  
Vol 2 (2) ◽  
pp. 76-87 ◽  
Author(s):  
A. Matos ◽  
D. Blitzkow ◽  
F. Almeida ◽  
S. Costa ◽  
I. Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Water Level ◽  
Height Anomaly ◽  
Coefficient Of Determination ◽  
North East ◽  
Water Level Variations ◽  
Gravity Recovery ◽  
Water Height ◽  
Pampas Region

Analysis of water level variations in Brazilian basins using GRACEA comparison between daily in-situ water level time series measured at ground-based hydrometric stations (HS - 1,899 stations located in twelve Brazilian basins) of the Agência Nacional de Águas (ANA) with vertically-integrated water height anomaly deduced from the Gravity Recovery and Climate Experiment (GRACE) geoid is carried out in Brazil. The equivalent water height (EWH) of 10-day intervals of GRACE models were computed by GRGS/CNES. It is a 6-year analysis (July-2002 to May-2008). The coefficient of determination is computed between the ANA water level and GRACE EWH. Values higher than 0.6 were detected in the following basins: Amazon, north of Paraguay, Tocantins-Araguaia, Western North-East Atlantic and north of the Parnaíba. In the Uruguay (Pampas region) and the west of São Francisco basins, the coefficient of determination is around 0.5 and 0.6. These results were adjusted with a linear transfer function and two second degree polynomials (flood and ebb period) between GRACE EWH and ANA water level. The behavior of these two polynomials is related to the phase difference of the two time series and yielded four different types of responses. This paper shows seven ANA stations that represent these responses and relates them with their hydro-geological domain.

scholarly journals AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 327-334
Author(s):  
Paulo Henrique Costa ◽  
Eric Oliveira Pereira ◽  
Philippe Maillard
Keyword(s):  
Water Level ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Small Lakes ◽  
Water Surface Area ◽  
Volume Fluctuations ◽  
Calm Water ◽  
Gauging Station

Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of <i>in situ</i> gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often “contaminated” by the surrounding land. To try to solve this, we created a “lake” retracker that rejects waveforms that cannot be attributed to “calm water” and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby <i>in situ</i> gauging station. Although the “lake” retracker worked well with calm water, the presence of waves and other factors was such that the standard “ice1” retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.

scholarly journals Analysis of GEDI Elevation Data Accuracy for Inland Waterbodies Altimetry

2020 ◽  
Vol 12 (17) ◽  
pp. 2714
Author(s):  
Ibrahim Fayad ◽  
Nicolas Baghdadi ◽  
Jean Stéphane Bailly ◽  
Frédéric Frappart ◽  
Mehrez Zribi
Keyword(s):  
Standard Deviation ◽  
Space Station ◽  
Mean Difference ◽  
Water Levels ◽  
Ecosystem Dynamics ◽  
Systematic Bias ◽  
In Situ Data ◽  
Natural Lakes ◽  
The Mean

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.

STORM ICE OIL WIND WAVE WATCH SYSTEM (SIOWS): WEB GIS APPLICATION FOR MONITORING THE ARCTIC

2017 ◽  
Author(s):  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Sergey Azarov ◽  
Sergey Azarov ◽  
Ekaterina Balashova ◽  
...  
Keyword(s):  
Satellite Data ◽  
Wind Wave ◽  
Arctic Region ◽  
Web Gis ◽  
The Arctic ◽  
Flexible Tool ◽  
In Situ Data ◽  
Current State ◽  
Watch System

Working with satellite data, has long been an issue for users which has often prevented from a wider use of these data because of Volume, Access, Format and Data Combination. The purpose of the Storm Ice Oil Wind Wave Watch System (SIOWS) developed at Satellite Oceanography Laboratory (SOLab) is to solve the main issues encountered with satellite data and to provide users with a fast and flexible tool to select and extract data within massive archives that match exactly its needs or interest improving the efficiency of the monitoring system of geophysical conditions in the Arctic. SIOWS - is a Web GIS, designed to display various satellite, model and in situ data, it uses developed at SOLab storing, processing and visualization technologies for operational and archived data. It allows synergistic analysis of both historical data and monitoring of the current state and dynamics of the "ocean-atmosphere-cryosphere" system in the Arctic region, as well as Arctic system forecasting based on thermodynamic models with satellite data assimilation.

scholarly journals Critical Dynamics of Defects and Mechanisms of Damage-Failure Transitions in Fatigue

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2554
Author(s):  
Oleg Naimark ◽  
Vladimir Oborin ◽  
Mikhail Bannikov ◽  
Dmitry Ledon
Keyword(s):  
Fatigue Damage ◽  
Kinetic Equations ◽  
Experimental Methodology ◽  
Preliminary Deformation ◽  
Scaling Properties ◽  
In Situ Data ◽  
Number Of Cycles ◽  
Fish Eye ◽  
Very High

An experimental methodology was developed for estimating a very high cycle fatigue (VHCF) life of the aluminum alloy AMG-6 subjected to preliminary deformation. The analysis of fatigue damage staging is based on the measurement of elastic modulus decrement according to “in situ” data of nonlinear dynamics of free-end specimen vibrations at the VHCF test. The correlation of fatigue damage staging and fracture surface morphology was studied to establish the scaling properties and kinetic equations for damage localization, “fish-eye” nucleation, and transition to the Paris crack kinetics. These equations, based on empirical parameters related to the structure of the material, allows us to estimate the number of cycles for the nucleation and advance of fatigue crack.

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