Fleet Of Satellites Research Articles

PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites

AbstractUltra‐relativistic electrons with energies greater than or equal to two megaelectron‐volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium‐Earth‐orbits (MEO), is called PREdictive MEV Electron (PreMevE)‐MEO model that nowcasts ultra‐relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi‐Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long‐term in situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE‐MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE‐MEO model demonstrates the feasibility of making high‐fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.

A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020)

A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020)

Opportunities and challenges of on-board AI-based image recognition for small satellite Earth observation missions

The satellite industry is rapidly growing. There has been a significant increase in the number of new small satellites that are launched, which is complemented by the rapid pace of the development of image recognition algorithms. Convolutional neural networks (CNNs) in particular, have achieved state-of-the-art performance in computer vision related applications. Combining both and running an AI algorithm on-board the satellite to observe and recognize any natural disaster directly from the orbit is an important opportunity. This paper presents notable challenges that are generally involved in an Earth Observation small satellite mission and further challenges that are posed by combining it with AI-based image recognition on-board the satellite. This study discusses an approach that is feasible mainly for a fleet of small satellites.

The future of coastal monitoring through satellite remote sensing

AbstractSatellite remote sensing is transforming coastal science from a “data-poor” field into a “data-rich” field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change have outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, that is, (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer vision to fully leverage this massive treasure trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earth-observing satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems.

An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management

AbstractThe repetitive and destructive nature of floods across the globe causes significant economic damage, loss of human lives, and leaves the people living in flood-prone areas with fear and insecurity. With enough literature projecting an increase in flood frequency, severity, and magnitude in the future, there is a clear need for effective flood management strategies and timely implementation. The earth observatory satellites of the European Space Agency’s Sentinel series, Sentinel-1, Sentinel-2, and Sentinel-3, have a great potential to combat these disastrous floods by their peerless surveillance capabilities that could assist in various phases of flood management. In this article, the technical specifications and operations of the microwave synthetic aperture radar (SAR) onboard Sentinel-1, optical sensors onboard Sentinel-2 (Multispectral Instrument) and Sentinel-3 (Ocean and Land Color Instrument), and SAR altimeter onboard Sentinel-3 are described. Moreover, the observational capabilities of these three satellites and how these observations can meet the needs of researchers and flood disaster managers are discussed in detail. Furthermore, we reviewed how these satellites carrying a range of technologies that provide a broad spectrum of earth observations stand out among their predecessors and have bought a step-change in flood monitoring, understanding, and management to mitigate their adverse effects. Finally, the study is concluded by highlighting the revolution this fleet of Sentinel satellites has brought in the flood management studies and applications.

Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research

Planet, a commercial company, has achieved a key milestone by launching a large fleet of small satellites (smallsats) that provide high spatial resolution imagery of the entire Earth’s surface on a daily basis with its PlanetScope sensors. Given the potential utility of these data, this study explores the use for fine particulate matter (PM2.5) air quality applications. However, before these data can be utilized for air quality applications, key features of the data, including geolocation accuracy, calibration quality, and consistency in spectral signatures, need to be addressed. In this study, selected Dove-Classic PlanetScope data is screened for geolocation consistency. The spectral response of the Dove-Classic PlanetScope data is then compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data over different land cover types, and under varying PM2.5 and mid visible aerosol optical depth (AOD) conditions. The data selected for this study was found to fall within Planet’s reported geolocation accuracy of 10 m (between 3–4 pixels). In a comparison of top of atmosphere (TOA) reflectance over a sample of different land cover types, the difference in reflectance between PlanetScope and MODIS ranged from near-zero (0.0014) to 0.117, with a mean difference in reflectance of 0.046 ± 0.031 across all bands. The reflectance values from PlanetScope were higher than MODIS 78% of the time, although no significant relationship was found between surface PM2.5 or AOD and TOA reflectance for the cases that were studied. The results indicate that commercial satellite data have the potential to address Earth-environmental issues.

Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?

Non-orthogonal multiple access (NOMA) technologies are considered key technologies for terrestrial 5G massive machine-type communications (mMTC) applications. It is less known that NOMA techniques were pioneered about ten years ago in the satellite domain to match the growing demand for mMTC services. This paper presents the key features of the first NOMA-based satellite network, presenting not only the underlying technical solutions and measured performance but also the related deployment over the Eutelsat satellite fleet. In particular, we describe the specific ground segment developments for the user terminals and the gateway station. It is shown that the developed solution, based on an Enhanced Spread ALOHA random access technique, achieves an unprecedented throughput, scalability and service cost and is well matched to several mMTC satellite use cases. The ongoing R&D lines covering both the ground segment capabilities enhancement and the extension to satellite on-board packet demodulation are also outlined. These pioneering NOMA satellite technology developments and in-the-field deployments open up the possibility of developing and exploiting 5G mMTC satellite- and terrestrial-based systems in a synergic and interoperable architecture.

Framework for Modeling and Optimization of On-Orbit Servicing Operations Under Demand Uncertainties

This paper develops a framework that models and optimizes the operations of complex on-orbit servicing infrastructures involving one or more servicers and orbital depots to provide multiple types of services to a fleet of geostationary satellites. The proposed method extends the state-of-the-art space logistics technique by addressing the unique challenges in on-orbit servicing applications and integrates it with the Rolling Horizon decision-making approach. The space logistics technique enables modeling of the on-orbit servicing logistical operations as a Mixed-Integer Linear Program whose optimal solutions can efficiently be found. The Rolling Horizon approach enables the assessment of the long-term value of an on-orbit servicing infrastructure by accounting for the uncertain service needs that arise over time among the geostationary satellites. Two case studies successfully demonstrate the effectiveness of the framework for 1) short-term operational scheduling and 2) long-term strategic decision making for on-orbit servicing architectures under diverse market conditions.

The Moon at thermal infrared wavelengths: a benchmark for asteroid thermal models

Thermal-infrared measurements of asteroids, satellites, and distant minor bodies are crucial for deriving the objects’ sizes, albedos, and in some cases, also the thermophysical properties of the surface material. Depending on the available measurements and auxiliary data, such as visual light curves, spin and shape information, or direct size measurements from occultations or high-resolution imaging techniques, a range of simple to complex thermal models are applied to achieve specific science goals. However, testing these models is often a difficult process and the uncertainties of the derived parameters are not easy to estimate. Here, we make an attempt to verify a widely accepted thermophysical model (TPM) against unique thermal infrared (IR), full-disk, and well-calibrated measurements of the Moon. The data were obtained by the High-resolution InfraRed Sounder (HIRS) instruments on board a fleet of Earth weather satellites that serendipitously scan the surface of the Moon. We found 22 Moon intrusions, taken in 19 channels between 3.75 μm and 15.0 μm, and over a wide phase angle range from −73.1° (waxing Moon) to +73.8° (waning Moon). These measurements include the entire Moon in a single pixel, seen almost simultaneously in all bands. The HIRS filters are narrow and outside the wavelength regime of the Christiansen feature. The similarity between these Moon data and typical asteroid spectral-IR energy distributions allows us to benchmark the TPM concepts and to point out problematic aspects. The TPM predictions match the HIRS measurements within 5% (10% at the shortest wavelengths below 5 μm) when using the Moon’s known properties (size, shape, spin, albedo, thermal inertia, roughness) in combination with a newly established wavelength-dependent hemispherical emissivity. In the 5–7.5 μm and in the 9.5–11 μm ranges, the global emissivity model deviates considerably from the known lunar sample spectra. Our findings will influence radiometric studies of near-Earth and main-belt asteroids in cases where only short-wavelength data (from e.g., NEOWISE, the warm Spitzer mission, or ground-basedM-band measurements) are available. The new, full-disk IR Moon model will also be used for the calibration of IR instrumentation on interplanetary missions (e.g., for Hayabusa-2) and weather satellites.

NASA’s new fleet of satellites will offer insights into the wild cards of climate change

NASA’s new fleet of satellites will offer insights into the wild cards of climate change

Shifting ground.

Fleets of radar satellites are measuring movements on Earth like never before.

Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions

Constellations of satellites are being proposed in large numbers; most of them are expected to be in orbit within the next decade. They will provide communication to unserved and underserved communities, enable global monitoring of Earth and enhance space observation. Mostly enabled by technology miniaturization, satellite constellations require a coordinated effort to face the technological limits in spacecraft operations and space traffic. At the moment in fact, no cost-effective infrastructure is available to withstand coordinated flight of large fleets of satellites. In order for large constellations to be sustainable, there is the need to efficiently integrate and use them in the current space framework. This review paper provides an overview of the available experience in constellation operations and statistical trends about upcoming constellations at the moment of writing. It highlights also the tools most often proposed in the analyzed works to overcome constellation management issues, such as applications of machine learning/artificial intelligence and resource/infrastructure sharing. As such, it is intended to be a useful resource for both identifying emerging trends in satellite constellations, and enabling technologies still requiring substantial development efforts.

Homogenization of Geostationary Infrared Imager Channels for Cold Cloud Studies Using Megha-Tropiques/ScaRaB

Infrared (IR) observations from the fleet of multiagencies meteorological geostationary satellites have a great potential to support scientific and operational investigations at a quasi-global scale. In particular, such a data record, defined as the GEOring data set, is well suited to document the tropical convective systems life cycles by applying cloud tracking algorithms. Yet, this GEOring data set is far from being homogeneous, preventing the realization of its potential. A number of sources of inhomogeneities are identified ranging from spatiotemporal resolutions to spectral characteristics of the IR channels and calibration methodologies. While previous efforts have attempted to correct such issues, the adjustment of the cold part of the IR spectrum remains unfit for cold cloud studies. Here, a processing method is introduced to minimize the inhomogeneities against a reference observational data set from the Scanner for Radiation Budget (ScaRaB) instrument onboard the Megha-Tropiques satellite. The method relies on the collocations between the geostationary observations and the reference. The techniques exhibit significant sensitivity to the selection of the relevant pairs of observations requiring a dedicated filtering of the data. A second effort is then proposed to account for the limb-darkening effect and a method is developed to correct the brightness temperature (BT) dependence on the geostationary viewing zenith angle (VZA). Overall, results show a residual after the processing of 0 K between any of the geostationary data and the ScaRaB reference. The final calibrated and limb-adjusted IR observations are then homogeneous for cold BT lower than 240 K with a standard deviation lower than 1.5 K throughout the GEOring.

Iridium deorbit strategy, execution, and results

Debris remediation starts with a responsible operational posture and completes with end-of-life disposal. Iridium has just completed the largest technology refresh in space by replacing their entire fleet of LEO satellites. As part of that effort, Iridium developed and then implemented a deorbit program to ensure space was safe for future generations by meticulously ensuring each satellite being replaced was deorbited in an efficient, cooperative, and responsible manner. This paper addresses the efforts and status of the Iridium deorbit program and provides lessons learned that should be applied as best practices for future space assets.

Entanglement demonstration on board a nano-satellite

Global quantum networks for secure communication can be realized using large fleets of satellites distributing entangled photon pairs between ground-based nodes. Because the cost of a satellite depends on its size, the smallest satellites will be most cost-effective. This Letter describes a miniaturized, polarization entangled, photon-pair source operating on board a nano-satellite. The source violates Bell’s inequality with a Clauser–Horne–Shimony–Holt parameter of 2.60 ± 0.06 . This source can be combined with optical link technologies to enable future quantum communication nano-satellite missions.

Legal Aspects of Satellite Constellations

In recent years a few entrepreneurs proposed launching into space large fleets of satellites, consisting of hundreds of satellites, often referred to as ‘constellations’. Their role is to provide satellite services to the largest possible number of users on Earth. This article provides a review of key legal issues associated with satellite constellations, including responsibility and liability for potential damages caused by satellites being a part of a constellation, insurance, registration of space objects, allocation of radio frequencies and orbital slots, and space debris. satellite constellations, space law, space insurance, liability, space debris

Wanted: A fallback for GPS: The U.S. government seeks tech that could step in if its global satellite fleet were to fail - [News

The U.S. Global Positioning System fleet of satellites provides critical data for navigation apps, banks, power grids, and other commercial and government infrastructure. But for the past decade, it has operated without a safety net, with no backup system in place. Now, two U.S. federal agencies want to change that, and they could select one or more alternatives by September.

The Automated System of Optical Observation and Tracking of Space Objects

AbstractHaving a fleet of artificial satellites necessitates the construction of a system that will enable obtaining information on the situation in the Earth orbit (Space Situation Awareness - SSA). Such systems are built either as active radar systems or as passive optical systems. The automated system of optical observation and tracking of space objects (ASOPEK) consists of two optical systems (wide-angle and narrow-angle) and software enabling: detection of artificial satellites in the field of view of the system, identification of detected objects, updating the satellite database with unknown objects, updating orbits of observed objects. The ASOPEK system was created as part of a project financed by the National Center for Research and Development No. DOB-BIO7/25/02/2015.

Spectral sizing of a coarse-spectral-resolution satellite sensor for XCO<sub>2</sub>

Abstract. Verifying anthropogenic carbon dioxide (CO2) emissions globally is essential to inform about the progress of institutional efforts to mitigate anthropogenic climate forcing. To monitor localized emission sources, spectroscopic satellite sensors have been proposed that operate on the CO2 absorption bands in the shortwave-infrared (SWIR) spectral range with ground resolution as fine as a few tens of meters to about a hundred meters. When designing such sensors, fine ground resolution requires a trade-off towards coarse spectral resolution in order to achieve sufficient noise performance. Since fine ground resolution also implies limited ground coverage, such sensors are envisioned to fly in fleets of satellites, requiring low-cost and simple design, e.g., by restricting the spectrometer to a single spectral band. Here, we use measurements of the Greenhouse Gases Observing Satellite (GOSAT) to evaluate the spectral resolution and spectral band selection of a prospective satellite sensor with fine ground resolution. To this end, we degrade GOSAT SWIR spectra of the CO2 bands at 1.6 (SWIR-1) and 2.0 µm (SWIR-2) to coarse spectral resolution, without a further addition of noise, and we evaluate single-band retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) by comparison to ground truth provided by the Total Carbon Column Observing Network (TCCON) and by comparison to global “native” GOSAT retrievals with native spectral resolution and spectral band selection. Coarsening spectral resolution from GOSAT's native resolving power of >20 000 to the range of 700 to a few thousand makes the scatter of differences between the SWIR-1 and SWIR-2 retrievals and TCCON increase moderately. For resolving powers of 1200 (SWIR-1) and 1600 (SWIR-2), the scatter increases from 2.4 (native) to 3.0 ppm for SWIR-1 and 3.3 ppm for SWIR-2. Coarser spectral resolution yields only marginally worse performance than the native GOSAT configuration in terms of station-to-station variability and geophysical parameter correlations for the GOSAT–TCCON differences. Comparing the SWIR-1 and SWIR-2 configurations to native GOSAT retrievals on the global scale, however, reveals that the coarse-resolution SWIR-1 and SWIR-2 configurations suffer from some spurious correlations with geophysical parameters that characterize the light-scattering properties of the scene such as particle amount, size, height and surface albedo. Overall, the SWIR-1 and SWIR-2 configurations with resolving powers of 1200 and 1600 show promising performance for future sensor design in terms of random error sources while residual errors induced by light scattering along the light path need to be investigated further. Due to the stronger CO2 absorption bands in SWIR-2 than in SWIR-1, the former has the advantage that measurement noise propagates less into the retrieved XCO2 and that some retrieval information on particle scattering properties is accessible.

Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model

The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band’s settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.