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.

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 Use of Global Reanalysis Data in the Study of the Aridity Index in the Magdalena-Cauca Macro-Basin, Colombia

10.29007/92l9 ◽  
2018 ◽  
Author(s):  
Carolina Vega-Viviescas ◽  
David A. Zamora ◽  
Erasmo A. Rodríguez
Keyword(s):  
Southern Oscillation ◽  
Pearson Correlation ◽  
Regional Scale ◽  
Correlation Coefficients ◽  
Aridity Index ◽  
Remote Sensing Data ◽  
Reanalysis Data ◽  
In Situ Data ◽  
Global Reanalysis

The Magdalena-Cauca macro-basin (MCMB) in Colombia, by its tropical location, annually experiences the effects of movement of the Intertropical Convergence Zone, and it is highly affected by interannual macro-climatic phenomena, such as El Niño– Southern Oscillation (ENSO). With the aim of increasing the use of global reanalysis and remote sensing data for supporting water management decisions at the watershed scale and within the framework of the eartH2Observe research project, the aridity index (AI) was calculated with three different data sources. Precipitation products and AI results were compared with their corresponding in-situ national official data. The comparison shows high correlations between the AI derived from observed data and AI obtained from the reanalysis, with Pearson correlation coefficients above 0.8 for two of the products investigated. This shows the importance of using global reanalysis data in water availability studies on a regional scale for the MCMB and the potential of this information in others macrobasins in Colombia including the Orinoquia and Amazon regions, where in-situ data is scarce.

scholarly journals Surface waters properties in the Laptev and the East-Siberian Seas in summer 2018 from in situ and satellite data

2019 ◽  
Author(s):  
Anastasiia Tarasenko ◽  
Alexandre Supply ◽  
Nikita Kusse-Tiuz ◽  
Vladimir Ivanov ◽  
Mikhail Makhotin ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Masses ◽  
Sea Surface ◽  
Ekman Transport ◽  
Water Displacement ◽  
Wind Speeds ◽  
In Situ Data ◽  
Ocean Salinity

Abstract. Variability of surface water masses of the Laptev and the East-Siberian seas in August–September 2018 is studied using in situ and satellite data. In situ data was collected during ARKTIKA-2018 expedition and then completed with satellite estimates of sea surface temperature (SST) and salinity (SSS), sea surface height, satellite-derived wind speeds and sea ice concentrations. Derivation of SSS is still challenging in high latitude regions, and the quality of Soil Moisture and Ocean Salinity (SMOS) SSS retrieval was improved by applying a threshold on SSS weekly error. The validity of SST and SSS products is demonstrated using ARKTIKA-2018 continuous thermosalinograph measurements and CTD casts. The surface gradients and mixing of river and sea waters in the free of ice and ice covered areas is described with a special attention to the marginal ice zone. The Ekman transport was calculated to better understand the pathway of surface water displacement. T-S diagram using surface satellite estimates shows a possibility to investigate the surface water masses transformation in detail.

scholarly journals Estimating daily surface NO<sub>2</sub> concentrations from satellite data - A case study over Hong Kong using land use regression models

2016 ◽  
Author(s):  
Jasdeep S Anand ◽  
Paul S Monks
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Satellite Data ◽  
Monitoring Network ◽  
Fine Scale ◽  
Land Use Regression ◽  
Vertical Column ◽  
Mixed Effect ◽  
In Situ Data

Abstract. Land Use Regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during 2005-2015. In-situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in-situ data shows that the mixed effect LUR model using OMI data has a high predictive power (adj. R2 = 0.84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0.11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

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 Freeboard, snow depth and sea-ice roughness in East Antarctica from in situ and multiple satellite data

2011 ◽  
Vol 52 (57) ◽  
pp. 242-248 ◽  
Author(s):  
Thorsten Markus ◽  
Robert Massom ◽  
Anthony Worby ◽  
Victoria Lytle ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Correlation Coefficient ◽  
Satellite Data ◽  
Snow Depth ◽  
Large Scale ◽  
Mean Difference ◽  
Laser Altimeter ◽  
In Situ Data ◽  
Ice Roughness

AbstractIn October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km × 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50–500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. the results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

scholarly journals Inside the hydro-physics processes at the plunge point location: an analysis by satellite and in situ data

2011 ◽  
Vol 8 (1) ◽  
pp. 1193-1223
Author(s):  
A. T. Assireu ◽  
E. Alcântara ◽  
E. M. L. M. Novo ◽  
F. Roland ◽  
F. S. Pacheco ◽  
...  
Keyword(s):  
Transition Zone ◽  
Landsat Tm ◽  
Density Currents ◽  
Wet Season ◽  
Satellite Measurements ◽  
Reservoir Water ◽  
In Situ Data ◽  
River Reservoir

Abstract. The plunge point locates the main point of mixing between river and the epilimnion reservoir water. The plunge point monitoring is essential to understand how it will be the behavior of density currents and its implications for reservoir. The applicability of satellite imagery products from different sensors (Landsat TM band 6 thermal signatures and visible channel) for characterization of the river-reservoir transition zone is presented in this study. We demonstrate the feasibility of the Landsat TM band imagery to discern the subsurface river plumes and the plunge point. The spatial variability of the plunge point evident in the hydrologic data illustrates the advantages of synoptic satellite measurements over in situ point measurements alone to detect the river-reservoir transition zone. It is indicated that the river flowing as underflow contributes to the thermal stability of the water column during wet season (summer-autumn). During the dry season, when the river-reservoir water temperature differences vanish and the river circulation is characterized by interflow-overflow, the river water inserts into the reservoir upper layers, affecting water quality. The results indicate good agreement between hydrologic and satellite data and that the jointly use of thermal and visible channel, operational monitoring of plunge point is feasible. The deduced information about the density current from this product could potentially be assimilated for numerical modeling and hence be of significant interest for environmental and climatological research.

scholarly journals Trophic status assessment of small turbid lakes comparing remote sensing and in situ data: case study at lower Danube floodplain

2020 ◽  
Vol 32 ◽  
pp. 53-63
Author(s):  
Stefan Kazakov ◽  
Valko Biserkov ◽  
Luchezar Pehlivanov ◽  
Stoyan Nedkov
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Suspended Matter ◽  
Trophic Status ◽  
Remote Sensing Data ◽  
Water Quality Monitoring ◽  
Floodplain Lakes ◽  
In Situ Data

The aim of the study was to compare in situ and remote sensing data, in order to assess the applicability of satellite images in water quality monitoring of floodplain lakes. Two indicators of trophic status were compared: chlorophyll a and total suspended matter. Two lakes on Lower Danube floodplain were selected: Srebarna and Malak Preslavets. Data were obtained in July and August 2018. Sentinel 2 MSI L1c images were analyzed in SeNtinel Application Platform (SNAP), (v. 6.0). According to in situ data, Srebarna Lake indicated status of eutrophication, while Malak Preslavets experienced hypertrophic conditions. Satellite data indicated eutrophic conditions for both lakes. Comparing the results from in situ and satellite data, chlorophyll a showed higher correlation (r = 0.66) and comparable results. On the other hand, significantly overestimation of suspended matter according to satellite data were found, as well weaker correlation (r = 0.57) between both methods. Remote sensing i.e. Sentinel products are emerging as a powerful tool in environmental observation. Although weather conditions could have significant impact on environmental dynamic especially in floodplain lakes, combining and comparing of different methods could improve the preciseness of the methodology as well as assessment reliability.

scholarly journals Small scale spatial variability of bare-ice albedo at Jamtalferner, Austria

2020 ◽  
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Full Range ◽  
Temporal Variations ◽  
Small Scale ◽  
Landsat 8 ◽  
Dry Ice ◽  
In Situ Data ◽  
Depth Analysis ◽  
The Difference

Abstract. As Alpine glaciers recede, they are quickly becoming snow free in summer and, accordingly, spatial and temporal variations in ice albedo increasingly affect the melt regime. To accurately model future developments, such as deglaciation patterns, it is important to understand the processes governing broadband and spectral albedo at a local scale. However, little in situ data of ice albedo exits. As a contribution to this knowledge gap, we present spectral reflectance data from 325 to 1075 nm collected along several profile lines in the ablation zone of Jamtalferner, Austria. Measurements were timed to closely coincide with a Sentinel 2 and Landsat 8 overpass and are compared to the respective ground reflectance products. The brightest spectra have a maximum reflectance of up to 0.7 and consist of clean, dry ice. In contrast, reflectance does not exceed 0.2 at dark spectra where liquid water and/or fine grained debris are present. Spectra can roughly be grouped into dry ice, wet ice, and dirt/rocks, although transitions between types are fluid. Neither satellite captures the full range of in situ reflectance values. The difference between ground and satellite data is not uniform across satellite bands, between Landsat and Sentinel, and to some extent between ice surface types (underestimation of reflectance for bright surfaces, overestimation for dark surfaces). We wish to highlight the need for further, systematic measurements of in situ spectral albedo, its variability in time and space, and in- depth analysis of time-synchronous satellite data.

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