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 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 DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry

2015 ◽  
Vol 19 (10) ◽  
pp. 4345-4364 ◽  
Author(s):  
C. Schwatke ◽  
D. Dettmering ◽  
W. Bosch ◽  
F. Seitz
Keyword(s):  
Time Series ◽  
Water Level ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Altimeter Data ◽  
Inland Waters ◽  
New Approach ◽  
Inland Water ◽  
Rivers And Lakes

Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, for some years, this technology has also been used to retrieve water levels from reservoirs, wetlands and in general any inland water body, although the radar altimetry technique has been especially applied to rivers and lakes. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series of rivers and lakes available through the web service "Database for Hydrological Time Series over Inland Waters" (DAHITI). The new method is based on an extended outlier rejection and a Kalman filter approach incorporating cross-calibrated multi-mission altimeter data from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, including their uncertainties. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparisons with in situ gauge data and results from external inland altimeter databases. The new approach yields rms differences with respect to in situ data between 4 and 36 cm for lakes and 8 and 114 cm for rivers. For most study cases, more accurate height information than from other available altimeter databases can be achieved.

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;

scholarly journals Kalman filter approach for estimating water level time series over inland water using multi-mission satellite altimetry

2015 ◽  
Vol 12 (5) ◽  
pp. 4813-4855 ◽  
Author(s):  
C. Schwatke ◽  
D. Dettmering ◽  
W. Bosch ◽  
F. Seitz
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Water Level ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Altimeter Data ◽  
New Approach ◽  
Inland Water ◽  
Filter Approach

Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, since some years, this technology is also used for observing inland water levels of lakes and rivers. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series available through the web service "Database for Hydrological Time Series over Inland Water" (DAHITI). The method is based on a Kalman filter approach incorporating multi-mission altimeter observations and their uncertainties. As input data, cross-calibrated altimeter data from Envisat, ERS-2, Jason-1, Jason-2, Topex/Poseidon, and SARAL/AltiKa are used. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparison with in-situ gauge data and results from external inland altimeter databases. The new approach yields RMS differences with respect to in-situ data between 4 and 38 cm for lakes and 12 and 139 cm for rivers, respectively. For most study cases, more accurate height information than from available other altimeter data bases can be achieved.

scholarly journals The Effect of Climate Changes on The Fluctuation of The Water Level of Al- Razzaza Lake, Iraq

2021 ◽  
pp. 4464-4474
Author(s):  
Shahad A. Al-Qaraghuli ◽  
Azhar A. Hassan ◽  
Rafa A. Albaldawi ◽  
Omnia K. Abd
Keyword(s):  
Water Level ◽  
Surface Area ◽  
Water Surface ◽  
Climate Changes ◽  
Spectral Band ◽  
Water Levels ◽  
Sharp Change ◽  
Significant Information ◽  
Water Index ◽  
Water Surface Area

      One of the serious environmental challenges that Iraq faces is climate changes and impacts of changing weather patterns and extreme global weather events. This paper addresses changes in the temporal and spatial characteristics of water levels of Razzaza Lake and response to climatic changes using archived series of Multispectral satellite images Landsat. TM, ETM+ and OLI images acquired on 1990, 2000 and of 2016. In order to extract, mapping the water surface area of the Razzaza Lake, Multispectral spectral band rationing the Normalized Difference Water Index (NDWI) technique was adopted, and the climatic elements data for the period (1990-2016) were analyzed which provide significant information of surface water. The results show that Razzaza Lake has a particularly sharp change rate in the water level and there are significant   fluctuations on lake level and water surface area over the time.

Denoising of river surface photogrammetric DEMs using deep learning

2021 ◽  
Author(s):  
Radosław Szostak ◽  
Przemysław Wachniew ◽  
Mirosław Zimnoch ◽  
Paweł Ćwiąkała ◽  
Edyta Puniach ◽  
...  
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Water Level ◽  
Water Surface ◽  
Research University ◽  
Static Characteristic ◽  
Water Levels ◽  
Training Dataset ◽  
Observation Data ◽  
Characteristic Points

&lt;p&gt;Unmanned Aerial Vehicles (UAVs) can be an excellent tool for environmental measurements due to their ability to reach inaccessible places and fast data acquisition over large areas. In particular drones may have a potential application in hydrology, as they can be used to create photogrammetric digital elevation models (DEM) of the terrain allowing to obtain high resolution spatial distribution of water level in the river to be fed into hydrological models. Nevertheless, photogrammetric algorithms generate distortions on the DEM at the water bodies. This is due to light penetration below the water surface and the lack of static characteristic points on water surface that can be distinguished by the photogrammetric algorithm. The correction of these disturbances could be achieved by applying deep learning methods. For this purpose, it is necessary to build a training dataset containing DEMs before and after water surfaces denoising. A method has been developed to prepare such a dataset. It is divided into several stages. In the first step a photogrammetric surveys and geodetic water level measurements are performed. The second one includes generation of DEMs and orthomosaics using photogrammetric software. Finally in the last one the interpolation of the measured water levels is done to obtain a plane of the water surface and apply it to the DEMs to correct the distortion. The resulting dataset was used to train deep learning model based on convolutional neural networks. The proposed method has been validated on observation data representing part of Kocinka river catchment located in the central Poland.&lt;/p&gt;&lt;p&gt;This research has been partly supported by the Ministry of Science and Higher Education Project &amp;#8220;Initiative for Excellence &amp;#8211; Research University&amp;#8221; and Ministry of Science and Higher Education subsidy, project no. 16.16.220.842-B02 / 16.16.150.545.&lt;/p&gt;

scholarly journals Validation of Sentinel-3A Based Lake Level over US and Canada

2020 ◽  
Vol 12 (17) ◽  
pp. 2835
Author(s):  
Karina Nielsen ◽  
Ole Baltazar Andersen ◽  
Heidi Ranndal
Keyword(s):  
Water Level ◽  
Satellite Altimetry ◽  
Lake Level ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
The United States ◽  
Water Levels ◽  
Winter Period ◽  
Large Lakes ◽  
In Situ Data

Satellite altimetry is an important contributor for measuring the water level of continental water bodies. The technique has been applied for almost three decades. In this period the data quality has increased and the applications have evolved from the study of a few large lakes and rivers, to near global applications at various scales. Products from current satellite altimetry missions should be validated to continuously improve the measurements. Sentinel-3A has been operating since 2016 and is the first mission operating in synthetic aperture radar (SAR) mode globally. Here we evaluate its performance in capturing lake level variations based on a physical and an empirical retracker provided in the official level 2 product. The validation is performed for more than 100 lakes in the United States and Canada where the altimetry based water levels are compared with in situ data. As validation measures we consider the root mean squared error, the Pearson correlation, and the percentage of outliers. For the US sites the median of the RMSE value is 25 cm and 19 cm and the median of the Pearson correlations are 0.86 and 0.93 for the physical and empirical retracker, respectively. The percentage of outliers (median) is 11% for both retrackers. The validations measures are slightly poorer for the Canadian sites; the median RMSE is approximately 5 cm larger, the Pearson correlation 0.1 lower, and the percentage of outliers 5% larger. The poorer performance for the Canadian sites is mainly related to the presence of lake ice in the winter period where the surface elevations are not able to map the surface correctly. The validation measures improve considerably when evaluated for summer data only. For both areas we show that the reconstruction of the water level variations based on the empirical retracker is significantly better compared to that of the physical retracker in terms of the RMSE and the Pearson correlation.

scholarly journals Can GNSS-R Detect Abrupt Water Level Changes?

2020 ◽  
Vol 12 (21) ◽  
pp. 3614
Author(s):  
Sajad Tabibi ◽  
Olivier Francis
Keyword(s):  
Water Level ◽  
Vertical Velocity ◽  
Energy Demand ◽  
Water Surface ◽  
Spatial Scales ◽  
Satellite System ◽  
Water Levels ◽  
Level Gauge ◽  
Surface Reflection ◽  
Microwave Radar

Global navigation satellite system reflectometry (GNSS-R) uses signals of opportunity in a bi-static configuration of L-band microwave radar to retrieve environmental variables such as water level. The line-of-sight signal and its coherent surface reflection signal are not separate observables in geodetic GNSS-R. The temporally constructive and destructive oscillations in the recorded signal-to-noise ratio (SNR) observations can be used to retrieve water-surface levels at intermediate spatial scales that are proportional to the height of the GNSS antenna above the water surface. In this contribution, SNR observations are used to retrieve water levels at the Vianden Pumped Storage Plant (VPSP) in Luxembourg, where the water-surface level abruptly changes up to 17 m every 4-8 h to generate a peak current when the energy demand increases. The GNSS-R water level retrievals are corrected for the vertical velocity and acceleration of the water surface. The vertical velocity and acceleration corrections are important corrections that mitigate systematic errors in the estimated water level, especially for VPSP with such large water-surface changes. The root mean square error (RMSE) between the 10-min multi-GNSS water level time series and water level gauge records is 7.0 cm for a one-year period, with a 0.999 correlation coefficient. Our results demonstrate that GNSS-R can be used as a new complementary approach to study hurricanes or storm surges that cause abnormal rises of water levels.

scholarly journals Satellite altimetry over small reservoirs in the Brazilian semiarid region

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
Alfredo Ribeiro Neto ◽  
Sajedeh Behnia ◽  
Mohammad J. Tourian ◽  
Fábio Araújo da Costa ◽  
Nico Sneeuw
Keyword(s):  
Time Series ◽  
Water Level ◽  
Satellite Altimetry ◽  
Water Storage ◽  
Semiarid Region ◽  
Northeast Brazil ◽  
Drought Period ◽  
Small Reservoirs ◽  
Brazilian Semiarid

ABSTRACT Northeast Brazil is one of the most populated semiarid regions in the world. The region is highly dependent on reservoirs for human water supply, irrigation, industry, and livestock. The objective of this study was to validate water level time series from the satellites Envisat, SARAL, Sentinel-3A/-3B, Jason-2/-3 in small reservoirs in Northeast Brazil. In total, we evaluated the water level time series of 20 reservoirs. The Sentinel-3B outperforms the other altimeters with a maximum RMSE of 0.21 m. In seven reservoirs with updated depth-area-volume curves, the altimetric water level was used to calculate the corresponding volume. The obtained volume was then compared to the volume given by the same curve by using in situ stage. Our investigations showed that, in the case of small reservoirs, the precision of water level time series derived from satellite altimetry is mainly governed by the seasonal variability of the water storage especially at the end of the 2012-2017 drought period.

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