European Space Agency's Satellite Research Articles

Integrating Sentinel 2A data for selecting the best methods in observation of biological pigments in coastal water

Coastal waters serve as critical ecosystems, supporting diverse marine life and offering invaluable resources to human communities. Understanding the distribution and dynamics of biological pigments, such as chlorophyll and phytoplankton, is essential for assessing the health and productivity of these ecosystems. Remote sensing technology has revolutionized our ability to monitor these pigments, providing a synoptic view of coastal waters. Sentinel-2A, a European Space Agency satellite, offers high-resolution multispectral data, making it a valuable resource for coastal studies. This paper presents a comprehensive analysis of Sentinel-2A data integration techniques for the observation of biological pigments in coastal waters along the coasts of Mediterranean Sea (Abu-Qir Area) from seven different sampling sites for the presence of different chlorophyll forms. Water samples were collected and physico-chemical characteristics like dissolved oxygen, pH, conductivity and chlorophyll were measured. In chlorophyll estimation, different forms of were calculated viz. chl a, b, c and carotenoids using different methodologies. Images from the Sentinel Multispectral Imager (MSI) sensor were taken on the same sample date. The phyto-planktonic forms, carotenoids, and their ratios in the sampling sites were also determined analytically. With the integration of satellite images (Sentinel 2A), results indicated types of chlorophyll, carotenoids, and ratios' distributions in the Abu-Qir Area of the Mediterranean Sea, Egypt. Our results demonstrate that the kind of species, illumination levels, and nutrient concentrations may all affect how much chlorophyll is absorbed by phytoplankton. Additionally, the band ratios of the different pigment types were found to be connected, such as b3/b8, or the same band might interact with other bands like b5, b4, b6, and b7, or b8a. Additionally, it was noticed that the green band and near infrared bands were useful for detecting pigments integrated with sampling datasets.

Astrometry in crowded fields towards the Galactic bulge

Context. The astrometry towards the Galactic bulge is hampered by high stellar crowding and patchy extinction. This effect is particularly severe for optical surveys such as the European Space Agency satellite Gala. Aims. In this study, we assess the consistency of proper motion measurements between optical (Gaia DR3) and near-infrared (VIRAC2) catalogues in comparison with proper motions measured with the Hubble Space Telescope (HST) observations in several crowded fields towards the Galactic bulge and in Galactic globular clusters. Methods. Assuming that the proper motion measurements are well characterised, the uncertainty-normalised proper motion differences between pairs of catalogues are expected to follow a normal distribution. A deviation from a normal distribution defines the inflation factor r. By multiplying the proper motion uncertainties with the appropriate inflation factor values, the Gaia (VIRAC2) proper motion measurements are brought into a 1σ agreement with the HST proper motions. Results. The inflation factor (r) depends on stellar surface density. For the brightest stars in our sample (G < 18), the dependence on G-band magnitude is strong, corresponding to the most precise Gaia DR3 proper motions. We used the number of observed Gaia DR3 sources as a proxy for the stellar surface density. Assuming that the HST proper motion measurements are well determined and free from systematic errors, we find that Gaia DR3 proper motion uncertainties are better characterised, having r < 1.5 in fields with a stellar number density with fewer than 200 Gaia DR3 sources per arcmin2, and are underestimated by up to a factor of 4 in fields with stellar densities higher than 300 sources per arcmin2. For the most crowded fields in VIRAC2, the proper motion uncertainties are underestimated by a factor of 1.1 up to 1.5, with a dependence on J-band magnitude. In all fields, the brighter sources have the higher r value. At the faint end (G > 19), the inflation factor is close to 1, meaning that the proper motions already fully agree with the HST measurements within 1σ. Conclusions. In the crowded fields common to both catalogues, VIRAC2 proper motions agree with HST proper motions and do not need an inflation factor for their uncertainties. Because of the depth and completeness of VIRAC2 in these fields, it is an ideal complement to Gaia DR3 for proper motion studies towards the Galactic bulge.

Sidestepping the inversion of the weak-lensing covariance matrix with Approximate Bayesian Computation

Weak gravitational lensing is one of the few direct methods to map the dark-matter distribution on large scales in the Universe, and to estimate cosmological parameters. We study a Bayesian inference problem where the data covariance C, estimated from a number ns of numerical simulations, is singular. In a cosmological context of large-scale structure observations, the creation of a large number of such N-body simulations is often prohibitively expensive. Inference based on a likelihood function often includes a precision matrix, Ψ=C−1. The covariance matrix corresponding to a p-dimensional data vector is singular for p≥ns, in which case the precision matrix is unavailable. We propose the likelihood-free inference method Approximate Bayesian Computation (ABC) as a solution that circumvents the inversion of the singular covariance matrix. We present examples of increasing degree of complexity, culminating in a realistic cosmological scenario of the determination of the weak-gravitational lensing power spectrum for the upcoming European Space Agency satellite Euclid. While we found the ABC parameter estimate variances to be mildly larger compared to likelihood-based approaches, which are restricted to settings with p<ns, we obtain unbiased parameter estimates with ABC even in extreme cases where p/ns≫1. The code has been made publicly available11https://github.com/emilleishida/CorrMatrix_ABC. to ensure the reproducibility of the results.

Highlighting the role of earth observation Sentinel5P TROPOMI in monitoring volcanic eruptions: a report on Hunga Tonga, a Submarine Volcano

ABSTRACT The present study utilized earth observation Sentinel5P TROPOMI (TROPOspheric Monitoring Instrument) data for monitoring submarine volcanic activities. The world has seen a sequence of abrupt volcanic eruptions within one-month duration i.e., on 20th December 2021, 13th January 2022 and 15th January 2022. This caused the emissions of natural gaseous pollutants such as Sulphur Dioxide (SO2), Hydrogen Sulphide (H2S) and ash particles. Atmospheric SO2, AAI (Absorbing Aerosol Index) and Cloud fraction data are retrieved from the TROPOMI onboard the Copernicus Sentinel5P European Space Agency satellite. Results confirmed the increased concentration of SO2 column density after 20th December, and it was observed to be 0.002 mol/m2, the highest concentration during the period of study. Generally, SO2 concentration stays within 0.001 mol/m2 or less. Concentration was seen increasing after the first eruption and reached maximum after 15th January 2022 eruption to a level up to 0.002 mol/m2. Similar outcomes were observed for AAI for the chosen site. AAI values increased to 25,000 fold (AAI values of 1.25) during the first eruption and increased to 29,000 fold on 15th January 2022 after eruptions (AAI values of 1.45) as compared AAI values of 0.005 or negative AAI values before eruptions. A cloud fraction of value 1 is observed during this time, along with the top most cloud height at 14,233.16 m (14.23 kms) above the crater of the volcano (maximum estimation limit by S5P). These values are higher as compared to isolated islands, as there are no other sources of emissions at surrounding environment. The outcomes support the role of Earth Observation datasets for disaster monitoring and management plans for mitigation.

Gaia DR3 in 6D: the search for fast hypervelocity stars and constraints on the galactic centre environment

ABSTRACT The third data release (DR3) of the European Space Agency satellite Gaia provides coordinates, parallaxes, proper motions, and radial velocities for a sample of ∼34 million stars. We use the combined 6D phase space information to search for hypervelocity stars (HVSs), unbound stars accelerated by dynamical processes happening in the Galactic Centre. By looking at the kinematics of Gaia DR3 stars in Galactocentric coordinates and by integrating their orbits in the Galactic potential, we do not identify any HVS candidates with a velocity higher than 700 km s−1 and robustly observed kinematics. Assuming a scenario wherein the interaction between a stellar binary and the massive black hole Sgr A* is responsible for HVS ejections from the Galactic Centre, we derive degenerate limits on the ejection rate of HVSs and the slope of the initial mass function of the primary star among binaries in the Galactic Centre. Our results indicate that the HVS ejection rate is ≲8 × 10−5 yr−1 assuming a Salpeter mass function, and this upper limit becomes progressively smaller for an increasingly top-heavy mass distribution. A fiducial HVS ejection rate of 10−4 yr−1 prefers a mass function slope ≲−2.35, disfavouring previously claimed top-heavy initial mass functions among stars in the Galactic Centre.

Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Assessing Urban Landslide Dynamics through Multi-Temporal InSAR Techniques and Slope Numerical Modeling

Landslides threaten more than before the urbanized areas and are a worldwide growing problem for the already affected communities and the local authorities committed to landslide risk management and mitigation. For this reason, it is essential to analyze landslide dynamics and environmental conditioning factors. Various techniques and instruments exist for landslide investigation and monitoring. Out of these, Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) techniques have been widely used in the last decades. Their capabilities are enhanced by the availability of the active Sentinel-1 mission, whose 6-day revisiting time enables near real-time monitoring of landslides. Interferometric results, coupled with ground measurements or other approaches such as numerical modeling, significantly improve the knowledge of the investigated surface processes. In this work, we processed the C-band SAR images of the available European Space Agency (ESA) satellite missions, using MT-InSAR methods to identify the surface deformations related to landslides affecting the Iași Municipality (Eastern Romania). The results (i.e., velocity maps) point out the most active landslides with velocities of up to 20 mm/year measured along the satellite Line of Sight (LOS). Following, we focused on the most problematic landslide that affects the Țicău neighborhood and is well-known for its significant implications that it had. To better understand its behavior and the sensitivity of the displacements to the environmental factors (i.e., rainfall), we carried out 2D numerical modeling using a finite difference code. The simulated displacement field is consistent with the InSAR displacements and reveals the most active sectors of the landslide and insights about its mechanism.

Fusion of Sentinel-1 and Sentinel-2 data in mapping the impervious surfaces at city scale.

Urbanization creates new development in open spaces and agricultural fields, synonymous with increasing impervious surfaces. Such surfaces restrain the natural infiltration of water, and directly affect the non-point source pollution. Thus, consequential events like flooding and surface water degradation require spatial and quantitative information on impervious surfaces. Remote sensing technologies are widely used in impervious surface mapping of various geographical locations for environmental monitoring. In this study, the datasets from recently launched European Space Agency satellites (Sentinel-1 and Sentinel-2) and random forest classifier are used. The impervious surface growth of the study area, Lahore city, in 2015 and 2021, and growth trends are assessed. Results are validated with classification accuracy and comparison with similar datasets. The objective is to develop a reliable impervious surface mapping method with land cover quantification technique from multisource datasets. With a chi-square value of greater than 3.84 obtained from the McNemar test, the performance of fused data was superior to that of optical alone data in the classification. Over a 5-year period, Lahore grew at an annual rate of 2.14% comparable to the findings of Copernicus Land Services and the Atlas of Urban Expansion with an underestimation of 1% and 8.75%, respectively. Improvements in overall accuracy (2.7%) and kappa coefficient (5%) were seen in classified maps from fused datasets. Fusion of Sentinel datasets provide a reliable means of impervious surface mapping at city scale as an indicator of environmental quality which is valuable for the sustainable management of the city.

Validation of the High-Altitude Wind and Rain Airborne Profiler during the Tampa Bay Rain Experiment

This paper deals with the validation of rain rate and wind speed measurements from the High-Altitude Wind and Rain Airborne Profiler (HIWRAP), which occurred in September 2013 when the NASA Global Hawk unmanned aerial vehicle passed over an ocean rain squall line in the Gulf of Mexico near the North Florida coast. The three-dimensional atmospheric rain distribution and the associated ocean surface wind vector field were simultaneously measured by two independent remote sensing and two in situ systems, namely the ground-based National Weather Service Next-Generation Weather Radar (NEXRAD); the European Space Agency satellite Advanced Scatterometer (ASCAT), and two instrumented weather buoys. These independent measurements provided the necessary data to calibrate the HIWRAP radar using the measured ocean radar backscatter and to validate the HIWRAP rain and wind vector retrievals against NEXRAD, ASCAT and ocean buoys observations. In addition, this paper presents data processing procedures for the HIWRAP instrument, including the development of a geometric model to collocate time-morphed rain rates from the NEXRAD radar with HIWRAP atmospheric rain profiles. Results of the rain rate intercomparison are presented, and they demonstrate excellent agreement with the NEXRAD time-interpolated rain volume scans. In our analysis, we find that HIWRAP produces wind and rain rates that are consistent with the supporting ground and satellite estimates, thereby providing validation of the geolocation, the calibration, and the geophysical retrieval algorithms for the HIWRAP instrument.

Monitoring Turbidity in San Francisco Estuary and Sacramento-San Joaquin Delta Using Satellite Remote Sensing.

This study utilizes satellite data to investigate water quality conditions in the San Francisco Estuary and its upstream delta, the Sacramento–San Joaquin River Delta. To do this, this study derives turbidity from the European Space Agency satellite Sentinel‐2 acquired from September 2015 to June 2019 and conducts a rigorous validation with in situ measurements of turbidity from optical sensors at continuous monitoring stations. This validation includes 965 matchup comparisons between satellite and in situ sensor data across 22 stations, yielding R 2 = 0.63 and 0.75 for Nephelometric Turbidity Unit and Formazin Nephelometric Unit (FNU) stations, respectively. This study then applies remote sensing to evaluate patterns in turbidity during the Suisun Marsh Salinity Control Gates Action (“Gates action”), a pilot study designed to increase habitat access and quality for the endangered Delta Smelt. The basic strategy was to direct more freshwater into Suisun Marsh, creating more low salinity habitat that would then have higher (and more suitable) turbidity than upstream river channels. For all seven acquisitions considered from June 29 to September 27, 2018, turbidity conditions in Bays and Sloughs subregions were consistently higher (and more suitable) (26–47 FNU) than what was observed in the upstream River region (13–25 FNU). This overall pattern was observed when comparing images acquired during similar tidal stages and heights.

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

Gaia EDR3 in 6D: searching for unbound stars in the galaxy

ABSTRACT The early third data release (EDR3) of the European Space Agency satellite Gaia provides coordinates, parallaxes, and proper motions for ∼1.47 billion sources in our Milky Way, based on 34 months of observations. The combination of Gaia DR2 radial velocities with the more precise and accurate astrometry provided by Gaia EDR3 makes the best data set available to search for the fastest nearby stars in our Galaxy. We compute the velocity distribution of ∼7 million stars with precise parallaxes, to investigate the high-velocity tail of the velocity distribution of stars in the Milky Way. We release a catalogue with distances, total velocities, and corresponding uncertainties for all the stars considered in our analysis1. By applying quality cuts on the Gaia astrometry and radial velocities, we identify a clean subset of 94 stars with a probability $P_\mathrm{ub}\gt 50 {{\ \rm per\ cent}}$ to be unbound from our Galaxy. In total, 17 of these have $P_\mathrm{ub}\gt 80{{\ \rm per\ cent}}$ and are our best candidates. We propagate these stars in the Galactic potential to characterize their orbits. We find that 11 stars are consistent with being ejected from the Galactic disc, and are possible hyper-runaway star candidates. The other six stars are not consistent with coming from a known star-forming region. We investigate the effect of adopting a parallax zero-point correction, which strongly impacts our results: when applying this correction, we identify only 12 stars with $P_\mathrm{ub}\gt 50{{\ \rm per\ cent}}$, 3 of these having $P_\mathrm{ub}\gt 80{{\ \rm per\ cent}}$. Spectroscopic follow-ups with ground-based telescopes are needed to confirm the candidates identified in this work.

Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China

Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. In this study, multi-temporal Sentinel-2 data acquired in the growing season in 2019 were applied to the random forest algorithm to generate the crop classification map at 10 m spatial resolution for the Shiyang River Basin. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for higher crop classification accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy &gt; 0.9) could be generated as early as late July. This study gave an inspiration in selecting targeted spectral bands and period of images for acquiring more accurate and timelier crop map. The proposed method could be transferred to other arid areas with similar agriculture structure and crop phenology.

Wind direction retrieval from Sentinel-1 SAR images using ResNet

This paper introduces a novel approach to estimate the wind direction over the sea from Synthetic Aperture Radar (SAR) images without any external information. The method employs deep residual network (ResNet), a variant of Convolutional Neural Network, to obtain high resolution (2 km by 2 km) aliased wind direction fields. Forty-seven SAR images of the European Space Agency satellites Sentinel-1 have been processed with ResNet, previously trained with other fifteen images. The areas of interest are the Mediterranean Sea and the Persian Gulf, two regional seas where the SAR images often present complex patterns associated to the wind field spatial structure reporting traces of the interaction with coastal orography, hence valuable test sites to evaluate the performance of the methodology here proposed. Statistical analysis was carried out comparing the SAR-derived wind directions with those from ECMWF atmospheric model, ASCAT scatterometer and in-situ gauges. It reports biases β of -1.1°, 2.4° and -4.6° respectively, and centered root mean square difference cRMSd<21°, consistent with the benchmark obtained comparing scatterometer with ECMWF wind directions over the areas imaged by SAR (β 2.1°, cRMSd=19°). These results are relevant because they include the coastal data not accounted in the benchmark. Analysis of selected cases showed that SAR-derived wind fields reproduce meteorological situations characterized by strong divergence. Notably, our ResNet is able to estimate the wind direction even in the absence of wind streaks on the SAR images and in presence of convective turbulence structures, atmospheric lee waves, and ships. Furthermore, the model is also able to derive the wind field over small areas, as the example of Venice lagoon has shown. Detailed analysis of selected cases raised the issue of the lack of data with true spatial resolution of ≈2 km and within half hour from the satellite pass time necessary for exhaustive comparisons.

The Effects of COVID-19 Lockdown on Air Quality in Macedonia

BACKGROUND: Faced with the novel coronavirus outbreak (2019-nCoV), various urgent and coordinated actions have been taken worldwide to reduce spread of the disease. Slowing down economic activities, transportation, restrictions of the human public gatherings, and interaction resulted with a tremendous decline in air pollutant concentration especially in nitrogen dioxide, registered by National Aeronautics and Space Administration and European Space Agency satellites.&#x0D; AIM: The aim of the study was to assess the impact of COVID-19 lockdown conditions on the air quality in selected cities in Macedonia.&#x0D; METHODS: Daily mean concentration of the particulate matter (PM10 and PM2.5), nitrogen dioxide, ozone, and carbon monoxide measured in the national air quality monitoring network, was analyzed separately comparing following periods: past week of February 2020 to the end of May 2020 with the same period in 2017–2019. Depending on the data distribution, parametric independent-samples t-test or nonparametric Mann–Whitney U-test was run to determine if there were differences in the pollutants concentration during the COVID-19 and non-COVID-19 period.&#x0D; RESULTS: Implementation of strict restrictions of the movement along with reduced economic activities and vehicular transport, led to notable decrement of air pollutant concentrations. We have found an evident decrease in the concentration levels of all pollutants measured during COVID-19 period in 2020, compared to those from 2017 to 2019 with exceptions for PM2.5 in Kumanovo and carbon monoxide in Skopje (7% and 3% higher concentration). The most notable decrement was for NO2, with a concentration 5–31% lower during COVID-19 period.&#x0D; CONCLUSIONS: Although beneficial to human health, there is a need to assess economic implications of the lockdown that could have a negative impact on the health as well.

The impact of COVID 19 on air pollution levels and other environmental indicators - A case study of Egypt

The outbreak of coronavirus disease (COVID-19) not only affected health and economics, but also its effect extended to include other aspects, such as the environment. Using Egypt as a case study, this paper presents the impact of COVID-19 pandemic on air pollution levels by studying nitrogen dioxide (NO2), ozone (O3), particulate matter represented in absorbing aerosol index (AAI), carbon monoxide (CO), and greenhouse gas (GHG) emissions. The paper also highlights the impact of COVID-19 pandemic on other environmental indicators including environmental noise, medical and municipal solid wastes. The paper presents the Egyptian COVID-19 story from its different angles including the development of confirmed COVID-19 cases, containment measures from the government, the impact on the country's economy and the national energy consumption so as to effectively evaluate the effect on both the air pollution levels and the other studied environmental indicators. For the other environmental indicators, a strong link was observed between COVID-19 lockdown and the reduction in environmental noise, beaches, surface and groundwater pollution. For environmental noise, this has been confirmed by officially governmental announcements which reported that the level of environmental noise in Egypt was reduced by about 75% during the lockdown period. On the other hand, there are some negative effects, including an increase in medical solid waste (from 70 to 300 ton/day), municipal solid waste, as well as a less efficient solid waste recycling process. For air pollution levels, the data were obtained from National Aeronautics and Space Administration (NASA) and European Space Agency satellite data sets. The data for the lockdown period in 2020 have been extracted and compared to the corresponding months in the selected baseline period (2015–2019) to identify the effect that the lockdown period had on the air pollution levels in Egypt with focus on Cairo and Alexandria governorates. It was found that the AAI decreased by about 30%, the NO2 decreased by 15 and 33% over Cairo and Alexandria governorates, respectively, and that the CO decreased by about 5% over both governorates. In addition, the GHG emissions in Egypt were reduced by at least 4% during the pandemic. In contrast, ozone levels increased by about 2% over Cairo and Alexandria governorates. It can be concluded that the implemented containment measures during COVID-19 pandemic had resulted in both positive and negative environmental impacts. The positive environmental impacts are not sustainable and deterioration on them is expected to occur after the lockdown as it was before the pandemic. Therefore, stricter laws must be enacted to protect the environment in Egypt.

Evaluation of long-term Northern Hemisphere snow water equivalent products

Abstract. Nine gridded Northern Hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS-2; the European Centre for Medium-Range Weather Forecasts (ECMWF) interim land surface reanalysis – ERA-Interim/Land and ERA5; the NASA Modern-Era Retrospective Analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2); the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) stand-alone passive microwave retrievals (NASA AMSR-E SWE versions 1.0 and 2.0) which do not utilize surface snow observations. Evaluation included validation against independent snow course measurements from Russia, Finland, and Canada and product intercomparison through the calculation of spatial and temporal correlations in SWE anomalies. The stand-alone passive microwave SWE products (AMSR-E v1.0 and v2.0 SWE) exhibit low spatial and temporal correlations to other products and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides performance comparable to the reanalysis-based products; RMSE over Finland and Russia for all but the AMSR-E products is ∼50 mm or less, with the exception of ERA-Interim/Land over Russia. Using a seven-dataset ensemble that excluded the stand-alone passive microwave products reduced the RMSE by 10 mm (20 %) and increased the correlation from 0.67 to 0.78 compared to any individual product. The overall performance of the best multiproduct combinations is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.

Detection of Seasonal Inundations by Satellite Data at Shkoder Urban Area, North Albania for Sustainable Management

The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkodër area, which occurred in the two last years (2017–2018). For instance, in March 2018 the Shkodër district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkodër lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkodër city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives.

Identifying Milky Way Open Clusters With Extreme Kinematics using PRIM

The recent (April 25, 2018) second data release (DR2) of the European Space Agency satellite Gaia provides a dramatic increase in the extent and precision of stellar attributes for an unprecedented...

SIMBIO-SYS/STC stereo camera calibration: Geometrical distortion.

The STereo imaging Channel (STC) is the first push-frame stereo camera on board an European Space Agency (ESA) satellite, i.e., the ESA-Japan Aerospace eXploration Agency mission BepiColombo. It was launched in October 2018, and it will reach its target, Mercury, in 2025. The STC main aim is to provide the global three-dimensional reconstruction of the Mercury surface. STC, the stereo channel of spectrometer and imagers for Mercury Planetary Orbiter BepiColombo-Integrated Observatory System, is based on an original optical design that incorporates the advantages of a compact unique detector instrument and the convenience of a double direction acquisition system. In fact, STC operates in a push-frame imaging mode and its two optical sub-channels will converge the incoming light on a single focal plane assembly allowing to minimize mass and volume. The focal plane of the instrument is housing six different filters: two panchromatic filters in the range 600-800 nm and four broadband filters with central wavelengths in the range 420-920 nm. In this paper, the geometrical calibration of the instrument, including the optical setups used, will be described. The methods used to derive the focal lengths, the boresights, and the reference systems of the different filter models are presented, and the related distortion results are discussed. The STC off-axis configuration forced to develop a distortion map model based on the RFM (rational function model). In contrast to other existing models, which allow linear estimates, the RFM is not referred to specific lens geometry, but it is sufficiently general to model a variety of distortion types, as it will be demonstrated in this particular case.