Space Agency Sentinel-1 Research Articles

3U CubeSat-Based Hyperspectral Remote Sensing by Offner Imaging Hyperspectrometer with Radially-Fastened Primary Elements.

This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera's hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat's ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency's Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat's suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications.

Four unreported emperor penguin colonies discovered by satellite

Predictions of the future emperor penguins population, linked to anthropogenic climate change, are stark. Current models suggest that if CO2 emissions continue to rise at present rates, almost all colonies will be quasi-extinct by the end of the century (Jenouvrier et al.2021). The monitoring of populations is crucial to tracking these changes and, if possible, implementing conservation measures. Recent work using satellite imagery to discover, track and monitor emperor penguin populations has proved to be a key technology in understanding the locations, numbers and trends of the species (Barbraud & Weimerskirch 2001, Trathan et al.2020, Jenouvrier et al.2021). It also enables the discovery of unrecorded breeding sites (Fretwell et al.2009), although there are inherent difficulties in determining what constitutes a new or undiscovered breeding colony (see Supplemental Material S1). In 2019, eight previously unreported emperor penguin breeding sites were found using the European Space Agency's Sentinel-2 satellite, a medium-resolution satellite with a spatial resolution of 10 m per pixel (Fretwell & Trathan 2021), bringing the number of known extant breeding locations to 61. Here, I report on the discovery of a further four breeding sites using Sentinel-2 and Maxar WorldView-2 imagery.

Using Open-Source Sentinel-1A Images for Flood Susceptibility Mapping in North Bihar, India

Natural calamities such as floods are a severe menace, causing huge socio-environmental threats. Remote sensing technologies have proven to be a boon in precisely mapping the spatiotemporal effects and spread of floods, allowing remedial measures to be implemented on time. This paper aims to demarcate the extent of flooded areas in the study area by the application of remote sensing techniques that involve combining temporal images acquired during the flood (crisis images) with images acquired before the flood (archive images). In disaster mapping applications, the image that is acquired before the event takes place is referred to as the archive image, while the image that is acquired at the time of the event is referred to as the crisis image. The research objectives were achieved through the analysis of freely available Sentinel-1A data to delineate the extent of flooded areas in the European Space Agency's-Sentinel Applications Platform (ESA-SNAP) environment. Multi-looking, radiometric calibration, and range doppler terrain correction (geometric) were applied to the temporal images for better visualization and distinction and for projecting the pixels onto the proper map system. Later, the archive and crisis images were overlaid to form a Red Green Blue (RGB) composite that showed the extent and spread of floods in north Bihar, where each color represented areas of different significance. Further, the flood map was overlaid onto the Google Earth optical layer for better visualization and comparison. The work demonstrated the applicability and use of remote sensing and GIS technology to quickly gain insight into the spatial and temporal distribution of floods in a given region and could be used as a precursor for efficient flood management and relief measures.

Exceptional landscape-wide cyanobacteria bloom in Okavango Delta, Botswana in 2020 coincided with a mass elephant die-off event

In 2020, nearly 400 elephants died within the Okavango Delta region in Botswana, creating the worst-ever elephant mass die-off event in history. This catastrophic event was widely blamed on toxic cyanobacterial blooms after lab results showed the presence of toxin-forming cyanobacteria in inland waters of the Delta. However, it did not explain why we saw this mass die-off of elephants in 2020 and not in previous years. We conducted a landscape-wide time-series analysis using freely available European Space Agency's Sentinel-2 and NASA's Landsat-8 satellite data. We used existing bio-optical models, Normalized Difference Chlorophyll Index and Green Line Height, as proxies for chlorophyll-a and phycocyanin (cyanobacteria) concentrations. We found that 2020 was an exceptional year for cyanobacteria blooms in the Okavango Delta region compared to the past three years (2017–2019). Bloom phenology indicated that the cyanobacteria blooms initiated in September-October 2019, experienced an exponential growth reaching peak in January-February 2020, and eventually senescing in June 2020. This being a notoriously data-scarce region of the world, we did not have any means to perform site-specific validation of the models. Although magnitude and timeline of the blooms coincided with the timeline of elephant death reports, our study do not confirm it to be the trigger. For the first time, we show the widespread nature of these blooms across the landscape, which may have increased the toxin exposure for elephants. We theorize that 2020 might have been the first year for such a mass die-off event, but it will certainly not be the last because warming trends under changing climate are creating increasingly suitable conditions for these blooms to be pervasive and ubiquitous. Through this preliminary study, we demonstrate the critical need for frequent and comprehensive monitoring of toxic cyanobacterial blooms in the Delta to avoid another such event in the future.

Flood damage assessment with Sentinel-1 and Sentinel-2 data after Sardoba dam break with GLCM features and Random Forest method

Accurate mapping and monitoring of flooded areas are immensely required for disaster management purposes, such as for damage assessment and mitigation. In this study, the flood damage mapping performances of two satellite Earth Observation sensors, i.e., European Space Agency's Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral optical instruments, were evaluated using the Random Forest (RF) supervised classification method and various feature types. The study area was Sardoba Reservoir (Uzbekistan) and its surroundings, in which a disastrous dam failure occurred on May 1, 2020. After the failure of a part of the earthfill dam, a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. S1 and S2 cloudless data with a short temporal interval acquired soon after the event were available for the area. Four different data availability scenarios, such as (i) only S1 pre- and post-flood data; (ii) only S2 pre- and post-flood data; (iii) S1 pre- and post-flood and S2 pre-flood data; and (iv) S1 and S2 pre- and post-flood data were evaluated in terms of classification accuracy. In addition to the polarization information of S1 and the intensity values of S2 bands, feature maps produced from these datasets, such as vegetation and water indices, textural information obtained from gray level co-occurrence matrix (GLCM), and the principal component analysis (PCA) bands were employed in the RF method. The results show that the fusion of S1 and S2 data exhibit very high classification accuracy for the flooded areas and can separate the inundated vegetation as well. The use of S2 pre-event data together with the S1 pre- and post-event data is recommended for obtaining high accuracy even when post-event optical data is not available.

SENTINEL-2 ғарыштық суреттерін пайдаланып ауыл шаруашылығы алқаптарын бақылау

The article presents the issues of monitoring and analysis of agricultural land using satellite imagery data. Images from the European Space Agency's Sentinel-2 satellite were used as the main research material. This satellite provides optical data that is used for many areas, such as monitoring for floods, landslides, fires, agricultural crops, etc. The area of the Northern Economic Zone of Kazakhstan, the mouth of the Yessil River, was used as the object of the study. The vegetation index NDVI (Normalized Difference Vegetation Index) was used for monitoring, and the basic principles of its application were explained. The analysis of the obtained result is presented and the diagrams show changes in the vegetation background of the studied territory. Special recommendations were given for further improvement of research methods and its effectiveness was justified.

Modeling water quality impacts from hurricanes and extreme weather events in urban coastal systems using Sentinel-2 spectral data.

Conventional water quality measurements are nearly impossible during and immediately after extreme storms due to dangerous conditions. In this study, remotely sensed reflectance is used to develop a regression equation that quantifies total suspended solids (TSS) in near real-time after Hurricane Harvey. The application focused specifically on sediment loading and deposition and its potential impacts on the Houston Ship Channel and Galveston Bay riverine-estuarine system. The European Space Agency's Sentinel-2 satellite captured images at critical points in the storm's progression, necessitating the development of a new algorithm for this relatively new satellite mission. Several linear regressions were analyzed with the goal of developing a simple one- or two-band equation, and the final model uses the red and near infrared bands (R2 = 0.74). Results show that record flows during Harvey delivered unprecedented suspended sediment loads to the Gulf of Mexico at concentrations above 125mg/L with a mean concentration of 43mg/L across the bay. The study findings demonstrated that it took up to 11days after the storm for sediment transport to abate.

Tracing the Pre-failure Two-dimensional Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry雷达干涉测量对甘肃南峪乡滑坡灾前二维形变追溯

The pre-failure evolution of landslides is of great value for the analysis of landslide triggering factors and post-disaster stability assessment. At present, optical images are commonly employed to analyze the pre-failure evolution, but it is well-known that their data availability could be highly limited due to the presence of clouds. With the advance in radar remote sensing and interferometric synthetic aperture radar(InSAR), it could provide a new technical approach for landslide pre-failure detection. In this paper, the 2018 Nanyu landslide in Gansu Province is utilized to demonstrate the capability of InSAR to trace its pre-failure surface displacements using European Space Agency's Sentinel-1 radar data with a temporal interval of 12 days in different tracks. The results show that the landslide began to deform in June of 2017, and the maximum cumulative deformation reached up to 77 mm in the 13 months before the occurrence of the landslide. The time series InSAR derive displacements and the rainfall data is consistent, suggesting that the rainfall should be one of the triggering factors for the landslide. The study demonstrates the potential of radar interferometry for landslide detection, which can provide insights on landslide triggering factors, landslide disaster prevention and mitigation, and even landslide monitoring and early warning work in the future.

Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest

National Forest Inventories (NFI) are key data and tools to better understand the role of forests in the global carbon budget. Traditionally inventories have been carried out as field work, which makes them laborious and expensive. In recent years, the development of various remote sensing techniques to improve the cost-efficiency of the NFIs has accelerated. The goal of this study is to determine the usability of open and free multitemporal multispectral satellite images from the European Space Agency's Sentinel-2 satellite constellation and to compare their usability in forest inventories against airborne laserscanning (ALS) and three-dimensional data obtained with high-resolution optical satellite images from WorldView-2 and Synthetic Aperture Radar (SAR) stereo data from TerraSAR-X. Ground reference consisted of field data collected over 74 boreal forest plots in Southern Finland in 2014 and 2016. Features utilizing both single- and multiple-date information were designed and tested for Sentinel-2 data. Due to high cloud cover, only four Sentinel-2 images were available for the multitemporal feature analysis of all reference plots within the monitoring window. Random Forest technique was used to find the best descriptive feature sets to model five forest inventory parameters (mean height, mean diameter at breast height, basal area, volume, above-ground biomass) from all input remote sensing data. The results confirmed that the higher spatial resolution input data correlated with more accurate forest inventory parameter predictions, which is in line with other results presented in literature. The addition of temporal information to the Sentinel-2 results showed limited variation in prediction accuracy between the single and multidate cases ranging from 0.45 to 1.5 percentage points, whereof mean height, basal area and aboveground biomass are lower for single date with relative RMSEs of 14.07%, 20.66% and 24.71% respectively. Diameter at breast height and volume are lower for multi date feature combination with relative RMSEs of 18.38% and 27.21%. The results emphasize the importance of obtaining more evenly distributed data acquisitions over the growing season to fully exploit the potential of temporal features.

Decision tree for mapping of halophyte cover around Ghannouch, Tunisia.

Environment of Ghannouch in the south-east of Tunisia is characterized by the wide-spread hypersaline soils, typically colonized by halophytes. The study of their distribution is required in order to reveal the extent of salinization and its dynamic. Mapping and monitoring with a remote sensing approach are foreseen as the ways to trace the spatial and temporal dimensions of the phenomenon. The identification of halophyte vegetation can take advantage by analyzing optical remote sensing data. Here, we propose using a decision tree approach applied to European Space Agency Sentinel-2 imagery, for an accurate land cover mapping of Ghannouch district in Gabès governorate. Data pre-processing was carried out using the European Space Agency's Sentinel Application Platform and the SEN2COR toolboxes. The mapping approach combines the spectral information in several channels of the visible-near-infrared spectrum. The land cover identification was performed following a spectral classification approach, exploiting several optical indices, normalized difference water index, normalized difference vegetation index, and several soil salinity index, in order to elaborate a decision tree algorithm. As a result, for an area of interest of 50 × 50km2, at least 68% was classified as halophyte land cover. This mapping exercise represents an important step toward improved halophytes mapping in Tunisia and could be used to monitor the status of other salinity prone regions in the world.

Trends and perspectives of space-borne SAR remote sensing for archaeological landscape and cultural heritage applications

This paper provides an overview of the opportunities that image analysts, archaeologists and conservation scientists currently have to use space-borne Synthetic Aperture Radar (SAR) imagery for prospection of cultural landscapes and investigation of environmental, land surface and anthropogenic processes that can alter the condition of heritage assets. The benefits of the recent developments in SAR satellite sensors towards higher resolution (up to less than 1m) and shorter revisiting times (up to a few days) are discussed in relation to established techniques using the two key SAR parameters – amplitude and phase. Selected case studies from Middle East to South America illustrate how SAR can be effectively used to detect subtle archaeological features in modern landscapes, monitor historic sites and assess damage in areas of conflict. These examples form the basis to highlight the current trends in archaeological remote sensing based on space-borne SAR data in the era of the European Space Agency's Sentinel-1 constellation and on-demand high resolution space missions such as TerraSAR-X.

Radar Technique Shows Magma Flow in 2014 Cape Verde Eruption

The European Space Agency's Sentinel-1 satellite captures volcanic surface changes that reveal the flow below.

Emulating Sentinel-1 Doppler Radial Ice Drift Measurements Using Envisat ASAR Data

Using data from the Envisat Advanced Synthetic Aperture Radar (ASAR) instrument, this paper demonstrates how the high-precision radial surface velocity product, which will become available with the European Space Agency's Sentinel-1 satellite, can complement the analysis of sea ice motion. High-resolution Doppler frequency measurements are used to estimate the subsecond line-of-sight motion of drifting sea ice in Fram Strait. We compare the method with buoy measurements and a recent cross-correlation algorithm for tracking ice between pairs of images. Maximum speeds measured from the time series were on the order of 20 cm/s. Using our method, we measured instantaneous speeds reaching 40–60 cm/s.

Global representation of aerosol vertical profiles by sums of lognormal modes: Consequences for the passive remote sensing of aerosol heights

AbstractAerosol extinction profiles from a climatology of CALIPSO measurements are analyzed with respect to their representation by truncated sums of up to three lognormal functions. Discussed are results for the climatological mean and a realistic profile, which is constructed from the climatological mean and its standard deviation. The realistic profile shows, in general, a more feature‐rich structure and is used as proxy for actual measurements. It was found that a single lognormal mode is on global scale able to explain 59% of the vertical profile, while a sum of two modes is able to explain 74%. Including a third lognormal mode leads only to a minor improvement to 79%. The strong regional and weaker seasonal dependency of the reconstruction accuracy with respect to the number of used lognormal modes is presented and discussed in detail. These findings have implications for the design of future operational aerosol height retrievals for upcoming instruments such as Tropospheric Monitoring Instrument on board European Space Agency's Sentinel 5 precursor or Sentinel 4.

Automated global water mapping based on wide-swath orbital synthetic-aperture radar

Abstract. This paper presents an automated technique which ingests orbital synthetic-aperture radar (SAR) imagery and outputs surface water maps in near real time and on a global scale. The service anticipates future open data dissemination of water extent information using the European Space Agency's Sentinel-1 data. The classification methods used are innovative and practical and automatically calibrated to local conditions per 1 × 1° tile. For each tile, a probability distribution function in the range between being covered with water or being dry is established based on a long-term SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classification mode, the probability of water coverage per pixel of 1 km × 1 km is calculated with the input of the current backscatter – incidence angle combination. The overlap between the probability distributions of a pixel being wet or dry is used as a proxy for the quality of our classification. The service has multiple uses, e.g. for water body dynamics in times of drought or for urgent inundation extent determination during floods. The service generates data systematically: it is not an on-demand service activated only for emergency response, but instead is always up-to-date and available. We validate its use in flood situations using Envisat ASAR information during the 2011 Thailand floods and the Pakistan 2010 floods and perform a first merge with a NASA near real time water product based on MODIS optical satellite imagery. This merge shows good agreement between these independent satellite-based water products.

Feasibility of Along-Track Displacement Measurement From Sentinel-1 Interferometric Wide-Swath Mode

The European Space Agency's Sentinel-1, a C-band imaging radar mission to be launched in mid-2013, will provide a continuity of radar data for monitoring the changing Earth. The azimuth resolution of Sentinel-1's background mode, interferometric wide-swath (IW) mode, is four times lower than that of European remote-sensing satellite (ERS) and Envisat systems. Therefore, the measurement accuracy of along-track displacement from Sentinel-1 IW images presumably will be significantly reduced. In this paper, we test the feasibility of along-track displacement measurement from Sentinel-1 IW mode. We simulate Sentinel-1 IW synthetic aperture radar (SAR) images from the ERS raw data that captured the coseismic deformation of the 1999 Hector Mine earthquake in California. Along-track displacement maps are generated using multiple-aperture interferometric SAR (MAI) and intensity tracking techniques, respectively, and are compared with GPS measurements. The root-mean-square (rms) error between the synthetic Sentinel-1 MAI and GPS measurements is about 9.6 cm, which corresponds to only 0.5 % of the azimuth resolution. The rms error between the along-track displacements from synthetic Sentinel-1 offset tracking and GPS is about 27.5 cm, which is about 1.4 % of the azimuth resolution. These results suggest that the MAI method will still be useful to measure along-track displacements from Sentinel-1 IW InSAR imagery and that it would be difficult to effectively measure the along-track displacements by the Sentinel-1 offset tracking method.

Mapping of Different Sea Ice Regimes Using Images From Sentinel-1 and ALOS Synthetic Aperture Radar

Airborne C- and L-band synthetic aperture radar (SAR) images were acquired for three test sites over different sea ice regimes around Svalbard in March 2007, complemented by optical imagery, environmental data, and ice observations. One objective was to use the high-resolution low-noise radar data for investigations on the technical performance of European Space Agency's Sentinel-1 mission for sea ice mapping, the other to assess the potential gain of additional use of L-band SAR data currently available from Advanced Land Observing Satellite (ALOS). The airborne SAR images were employed to simulate data products resembling the interferometric wide-swath mode (IWSM) of Sentinel-1 and ALOS PALSAR fine resolution mode (FRM) in order to quantify the information loss due to the higher noise level and coarser spatial resolution. At the IWSM noise level, zones of deformation, level ice, and new ice can be well separated at like-polarization. At cross-polarization, the noise level is too high for a robust automated classification including thin ice, but deformed and level ice can be discriminated. PALSAR FRM is on average better suited to distinguish deformed and level ice over all ice regimes. It was worse for separating new from thicker level ice at two test sites. For the third test site with very few patches of new ice, the PALSAR FRM showed larger intensity contrasts between new and level ice than Sentinel-1 IWSM. The spatial resolution provided by the IWSM and FRM is sufficient to identify most ice features and types without ambiguities. Typical characteristics of the imaged ice regimes are described.