This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency's (ESA) Sen2Cor algorithm, the platform processes ESA's Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data . Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R 2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R 2 = 0.83) and a RMSE of 0.32 m 2 /m 2 (12% of mean value).

Abstract:Remote sensing allowed monitoring the reservoir water level by estimating its surface extension. Surface extension has been estimated using different approaches, employing both optical (Landsat 5 TM, Landsat 7 ETM+ SLC-Off, Landsat 8 OLI-TIRS and ASTER images) and Synthetic Aperture Radar (SAR) images (Cosmo SkyMed and TerraSAR-X). Images were characterized by different acquisition modes, geometric and spectral resolutions, allowing the evaluation of alternative and/or complementary techniques. For each kind of image, two techniques have been tested: The first based on an unsupervised classification and suitable to automate the process, the second based on visual matching with contour lines with the aim of fully exploiting the dataset. Their performances were evaluated by comparison with water levels measured in situ (r 2 = 0.97 using the unsupervised classification, r 2 = 0.95 using visual matching) demonstrating that both techniques are suitable to quantify reservoir surface extension. However~90% of available images were analyzed using the visual matching method, and just 37 images out of 58 using the other method. The evaluation of the water level from the water surface, using both techniques, could be easily extended to un-gauged reservoirs to manage the variations of the levels during normal operation. In addition, during the period of investigation, the use of Global Navigation Satellite System (GNSS) allowed the estimation of dam displacements. The advantage of using as reference a GNSS permanent station positioned relatively far from the dam, allowed the exclusion of any interaction with the site deformations. By comparing results from both techniques, relationships between the orthogonal displacement component via GNSS, estimated water levels via remote sensing and in situ measurements were investigated. During periods of changing water level (April 2011-September 2011 and October 2011-March 2012, the moving average of displacement time series (middle section on the dam crest) shows a range of variability of ±2 mm. The dam deformation versus reservoir water level behavior differs during the reservoir emptying and filling periods indicating a hysteresis-kind loop.Keywords: dam displacements; water level; water surface; hysteresis; optical remote sensing; SAR; GNSS State of Art and IntroductionReal-time monitoring and protection of strategic structures such as dams are necessary since these have social, economic, and environmental importance. The evaluation of the coherence between expected displacements and water levels over time could reveal whether the structure may suffer damages, a signifier that eventually could indicate a compromised safety expectation. In this way it would be possible to ensure proper functionality of a dam and its durability over time, rectifying any potential structural deficiency. GNSS monitoring systems, used in combination with geotechnical, hydraulic and structural systems could allow the monitoring of real-time dam displacements, with high accuracy, even remotely...

Worldwide, the determination of the coordinates from a Global Navigation Satellite System (GNSS) survey (in Network Real Time Kinematic, Precise Point Positioning, or static mode) has been analysed in several scientific and technical applications. Many of those have been carried out to compare Precise Point Positioning (PPP), Network Real Time Kinematic (NRTK), and static modes’ solutions, usually, using the latter as the true or the most plausible solution. This approach is not always possible as the static mode solution depends on several parameters (baseline length, acquisition time, ionospheric, and tropospheric models, etc.) that must be considered to evaluate the accuracy of the method. This work aims to show the comparison among the GNSS survey methods mentioned above, using some benchmark points. The tests were carried out by comparing the survey methods in pairs to check their solutions congruence. The NRTK and the static solutions refer to a local GNSS CORS network’s analysis. The NRTK positioning has been obtained with different methods (VRS, FKP, NEA) and the PPP solution has been calculated with two different software (RTKLIB and CSRS-PPP). A statistical approach has been performed to check if the distribution frequencies of the coordinate’s residual belong to the normal distribution, for all pairs analysed. The results show that the hypothesis of a normal distribution is confirmed in most of the pairs and, specifically, the Static vs. NRTK pair seems to achieve the best congruence, while involving the PPP approach, pairs obtained with CSRS software achieve better congruence than those involving RTKLIB software.

In this work, the performance of the multi-GNSS (Global Navigation Satellite System) Precise Point Positioning (PPP) technique, in static mode, is analyzed. Specifically, GPS (Global Positioning System), GLONASS, and Galileo systems are considered, and quantifying the Galileo contribution is one of the main objectives. The open source software RTKLib is adopted to process the data, with precise satellite orbits and clocks from CNES (Centre National d’Etudes Spatiales) and CLS (Collecte Localisation Satellites) analysis centers for International GNSS Service (IGS). The Iono-free model is used to correct ionospheric errors, the GOT-4.7 model is used to correct tidal effects, and Differential Code Biases (DCB) are taken from the Deutsche Forschungsanstalt für Luftund Raumfahrt (DLR) center. Two different tropospheric models are tested: Saastamoinen and Estimate ZTD (Zenith Troposhperic Delay). For the proposed study, a dataset of 31 days from a permanent GNSS station, placed in Palermo (Italy), and a dataset of 10 days from a static geodetic receiver, placed nearby the station, have been collected and processed by the most used open source software in the geomatic community. The considered GNSS configurations are seven: GPS only, GLONASS only, Galileo only, GPS+GLONASS, GPS+Galileo, GLONASS+Galileo, and GPS+GLONASS+Galileo. The results show significant performance improvement of the GNSS combinations with respect to single GNSS cases.

Abstract

Many factors can influence the displacements of a dam, including water level variability and environmental temperatures, in addition to the dam composition. In this work, optical-based classification, thermal diachronic analysis, and a quasi-PS (Persistent Scatter) Interferometric SAR technique have been applied to determine both forcing factors and resulting displacements of the crest of the Castello dam (South Italy) over a one-year time period. The dataset includes Sentinel-1A images acquired in Interferometric Wide swath mode using the Terrain Observation with Progressive Scans SAR (TOPSAR); Landsat 8 Thermal Infrared Sensor (TIRS) thermal images, and Global Navigation Satellite System (GNSS) for interpreting the motion of the top of the dam retrieved via interferometry. Results suggest that it is possible to monitor both dam water level and temperature periodic forcing factors and resulting displacements via a synergistic use of different satellite images.

Nowadays, technical and scientific researches are focused on the use of Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) networks due to their global impact on the satellite positioning. This study aims to describe the main steps developed by the University of Palermo for the realization of the GNSS CORS network distributed in the western part of Sicily (Italy). Specifically, it focuses on data availability, preliminary studies and analyses involving the GNSS CORS network, the geodetic framework used, the coordinates and displacements time series retrieved over time and the statistical analysis with the Cumulative Distribution Function (CDF). The analyses allowed to verify the network operating service and the quality of the recorded data during the first period of testing procedure (2008-2012).

The matching between reservoirs’ water edge and digital elevation model’s (DEM) contour lines allowed determining the water level at the acquisition date of satellite images. A preliminary study was conducted on the Castello dam (Magazzolo Lake), between Alessandria della Rocca and Bivona (Agrigento, south-Italy). The accuracy assessment of the technique was than evaluated from the comparison between classified and reference objects using similarity metrics about the shape, theme, edge and position, through the plugin STEP of open source software GIS. Moreover, an independent GIS technique was implemented to evaluate the water level, based on a distances’ array between existing contour lines and nodes extracted from vectorised classification images. Results have shown the potentiality of the techniques when applied on an ideal case; advantages and disadvantages when the images are characterized by clear sky, and limits when images are acquired during not ideal atmospheric conditions