Satellite Constellation Research Articles

Italian COSMO-SkyMed atlas: R-Index and the percentage of measurability of movement

ABSTRACT SAR (Synthetic Aperture Radar) satellite interferometry is a helpful remote sensing technique for large areas analyses and monitoring, especially where the study area is difficult to access for practical or for legal reasons. As a result, the use of these techniques has significantly increased over the past three decades. Among the available different satellite constellations displaying different spatial and temporal resolutions, COSMO-SkyMed of the Italian Space Agency (ASI) represents a cutting-edge reality. COSMO-SkyMed constellation, launched in 2007 by ASI, is a valuable Earth observation tool that provides all-weather, day-and-night imaging capabilities with high resolution and a short revisit time. In this study, we produced an atlas for the entire Italian peninsula using two parameters (R-Index and Percentage of measurability of movement), in order to evaluate the quality and a-priori applicability of satellite interferometry data collected by the COSMO-SkyMed constellation. The atlas was obtained by means of the implementation of different model builders in the GIS (Geographical Information Systems) environment, providing a semi-automatic way to generate the above-mentioned outputs. The R-Index describes the likelihood of detecting Permanent Scatterers in mountainous areas, while the Percentage of measurability of movement indicates the percentage of real motion that interferometry can detect at a certain point in the analyzed region. A high-detail Digital Terrain Model (DTM) has been used to identify the most suitable areas for satellite interferometry monitoring and studying. The results of our analysis showed that the R-Index and the Percentage of measurability of movement could be used to pre-evaluate the quality of satellite interferometry data collected by the COSMO-SkyMed constellation. This research has important implications for disaster response, environmental monitoring, and scientific research and is one of a few cases in the world in which a unified representation for an entire country is provided.

A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing

Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map.

Underestimated Dry Season Methane Emissions from Wetlands in the Pantanal.

Tropical wetlands contribute ∼30% of the global methane (CH4) budget. Limited observational constraints on tropical wetland CH4 emissions lead to large uncertainties and disparities in representing emissions. In this work, we combine remote sensing observations with atmospheric and wetland models to investigate dry season wetland CH4 emissions from the Pantanal region of South America. We incorporate inundation maps generated from the Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation together with traditional inundation maps to generate an ensemble of wetland CH4 emission realizations. We challenge these realizations with daily satellite observations for May-July when wetland CH4 emission predictions diverge. We find that the CYGNSS inundation products predict larger emissions in May, in better agreement with observations. We use the model ensemble to generate an empirical observational constraint on CH4 emissions independent of choice of inundation map, finding large dry season wetland CH4 emissions (31.7 ± 13.6 and 32.0 ± 20.2 mg CH4/m2/day in May and June/July during 2018/2019, respectively). These May/June/July emissions are 2-3 times higher than current models, suggesting that annual wetland emissions may be higher than traditionally simulated. Observed trends in the early dry season indicate that dynamics during this period are of importance in representing tropical wetland CH4 behaviors.

Towards Client Selection in Satellite Federated Learning

Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these data. However, when applying FL in satellite networks, it is necessary to consider the unique challenges brought by satellite networks, which include satellite communication, computational ability, and the interaction relationship between clients and servers. This study focuses on the siting of parameter servers (PSs), whether terrestrial or extraterrestrial, and explores the challenges of implementing a satellite federated learning (SFL) algorithm equipped with client selection (CS). We proposed an index called “client affinity” to measure the contribution of the client to the global model, and a CS algorithm was designed in this way. A series of experiments have indicated the advantage of our SFL paradigm—that satellites function as the PS—and the availability of our CS algorithm. Our method can halve the convergence time of both FedSat and FedSpace, and improve the precision of the models by up to 80%.

Doppler effect‐based low‐Earth orbit satellite constellation parameters

AbstractThe present study focuses on the manifestation of the Doppler effect in low‐Earth orbit (LEO) satellite constellations with laser inter‐satellite links (LISLs). In this letter, the LEO satellite constellation parameters that influence the variation of the Doppler effect in LISLs are presented. The LEO satellite constellation configurations that perform LISLs with less possible Doppler effect could be designed based on the tendency of the Doppler wavelength shift (DWS) as a function of the LEO satellite constellation's parameters. The paper presents the main LEO satellite constellation parameters that involve the variation of the Doppler effect, such as the altitude of the satellites, the orbital plane number within the constellation, and the inclination angle of the orbital plane. In addition to the operating laser wavelength that is employed as a data carrier. These parameters could be helpful to control the Doppler effect in LISL before building an LEO constellation.

Explicit Mathematical Formulation and Coverage Consistency of Satellite Constellation Designs for Repeating Ground Track Orbits

Explicit Mathematical Formulation and Coverage Consistency of Satellite Constellation Designs for Repeating Ground Track Orbits

A Low-Power Radiation-Hardened Ka-Band CMOS Phased-Array Receiver for Small Satellite Constellation

A Low-Power Radiation-Hardened <i>Ka</i>-Band CMOS Phased-Array Receiver for Small Satellite Constellation

Offshore methane detection and quantification from space using sun glint measurements with the GHGSat constellation

Abstract. ​​​​​​​The ability to detect and quantify methane emissions from offshore platforms is of considerable interest in providing actionable feedback to industrial operators. While satellites offer a distinctive advantage for remote sensing of offshore platforms which may otherwise be difficult to reach, offshore measurements of methane from satellite instruments in the shortwave infrared are challenging due to the low levels of diffuse sunlight reflected from water surfaces. Here, we use the GHGSat satellite constellation in a sun glint configuration to detect and quantify methane emissions from offshore targets around the world. We present a variety of examples of offshore methane plumes, including the largest single emission at (84 000 ± 24 000) kg h−1 observed by GHGSat from the Nord Stream 2 pipeline leak in 2022 and the smallest offshore emission measured from space at (180 ± 130) kg h−1 in the Gulf of Mexico. In addition, we provide an overview of the constellation's offshore measurement capabilities. We measure a median column precision of 2.1 % of the background methane column density and estimate a detection limit, from analytical modelling and orbital simulations, that varies between 160 and 600 kg h−1 depending on the latitude and season.

Last Deglacial Environmental Change in the Tropical South Pacific From Tahiti Corals

AbstractOn glacial‐interglacial time scales, changes in the Earth's orbital configuration control climate seasonality and mean conditions. Tropical coral skeletons can be sampled at a sufficient resolution to reconstruct past seasonality. Here, last deglacial Porites skeletons from Integrated Ocean Drilling Program Expedition 310 to Tahiti are investigated and, supported by a modern calibration, monthly resolved time series in geochemical proxies (Sr/Ca, δ18O, δ13C) are constructed. For most of the deglaciation, Sr/Ca seasonality was similar to modern (0.139 ± 0.010 mmol mol−1; 2.8 ± 0.2°C) reflecting the small change in insolation seasonality. However, during the Younger Dryas, high values in Sr/Ca seasonality (0.171 ± 0.017 mmol mol−1; 3.4 ± 0.3°C) suggest a reduced mixed layer depth and enhanced influence of the South Pacific Subtropical Gyre due to South Pacific Convergence Zone (SPCZ) inactivity. Furthermore, high amplitudes in Younger Dryas skeletal δ18O (0.40 ± 0.22 ‰) and δ13C (0.86 ± 0.22 ‰) seasonality compared to modern (δ18O = 0.29 ± 0.08 ‰; δ13C = 0.27 ± 0.08 ‰) point to elevated winter‐summer discrepancies in rainfall and runoff. Mean coral Sr/Ca variability suggests an influence of Northern Hemisphere climate events, such as the Younger Dryas cooling (+0.134 ± 0.012 mmol mol−1;−2.6 ± 0.2°C), or the Bølling–Allerød warming (+0.032 ± 0.040 mmol mol−1; −0.6 ± 0.4°C). Deglacial mean coral Δδ18O (δ18Oseawater contribution to skeletal δ18O), corrected for the ice volume effect, was elevated pointing to more saline, thus dryer conditions, likely due to a northward migration of the SPCZ. Seasonal cycles in coral δ13C were likely caused by variations in linear extension rates that were reduced during the last deglaciation (1.00 ± 0.6 cm year−1) compared to today (1.6 ± 0.3 cm year−1).

Evaluating the benefits of dark and quiet skies in an age of satellite mega-constellations

In an age of proliferating satellite constellations and congested Earth orbits, what is the status and value of dark and quiet skies? Satellite mega-constellations such as Starlink are illuminating the night sky with unnatural light and making astronomical observations—both professional and recreational—increasingly difficult. Radio frequency interference (RFI) from these constellations also adds obtrusive “noise,” impacting sensitive astronomical observations in the radio portion of the electromagnetic spectrum. Scientists predict that the growing commercial space sector will add more than 100,000 satellites to Earth's orbit by the end of the decade, raising concerns from numerous scientists as to the future of dark skies. Astronomy, in particular, is inevitably affected by the impending disappearance of dark and quiet skies, which are necessary conditions for many aspects of scientific research. This topic has also gained attention, at both the international level and national level, within the United States. Nonetheless, contributions to the conversation of mitigating light/noise pollution predominantly focus on the likely adverse effects of artificial sky brightening from satellites. A holistic discussion is also warranted on the benefits of existing night skies and their significance for society.This paper addresses the positive value of dark skies and the significance of protecting astronomy through current governing and analogous frameworks. First, this paper evaluates dark skies’ application under existing international and national law and policy, highlighting lacunae in governance and outlining convoluted issues for Earth-based science (astronomy) versus the lawful use of space (satellites). U.S. space law is used as a case study given the extensive history of the United States in space, comprehensive U.S. space political infrastructures, and the burgeoning domestic satellite mega-constellation industry. Second, relevant ethical values are identified to clarify inherent moral considerations that can guide policymaking and serve to establish practical measures and approaches to balance the conflicting interests of astronomers and satellite operators. Significantly, this paper contributes to the topic by explaining how the advent of technological change creates new conflicts on Earth and expounds on how and why these issues are lacking or are inadequate under current international frameworks. Moreover, this work explores key ethical values on this topic for informing public policy on dark and quiet skies and highlights some mechanisms for resolving these conflicts. In conclusion, the recession of dark skies is already occurring, and the likely impacts require transparency, good will, and interdisciplinary compromise between scientists, policymakers, and the space industry.

Post-Newtonian Orbital Effects Induced by the Mass Quadrupole and Spin Octupole Moments of an Axisymmetric Body

The post-Newtonian orbital effects induced by the mass quadrupole and spin octupole moments of an isolated, oblate spheroid of constant density that is rigidly and uniformly rotating on the motion of a test particle are analytically worked out for an arbitrary orbital configuration and without any preferred orientation of the body’s spin axis. The resulting expressions are specialized to the cases of (a) equatorial and (b) polar orbits. The opportunity offered by a hypothetical new spacecraft moving around Jupiter along a Juno-like highly elliptical, polar orbit to measure them is preliminarily studied. Although more difficult to be practically implemented, also the case of a less elliptical orbit is considered since it yields much larger figures for the relativistic effects of interest. The possibility of using the S-stars orbiting the supermassive black hole in Sgr A* at the Galactic Center as probes to potentially constrain some parameters of the predicted extended mass distribution surrounding the hole by means of the aforementioned orbital effects is briefly examined.

Improved Simulated Annealing Algorithm on the Design of Satellite Orbits for Common-View Laser Time Transfer

Laser Time Transfer (LTT) has proven to be able to improve remote time transfer accuracy compared to microwave technology. The impact of satellite clock errors and atmospheric delays during LTT will be further reduced in the common-view mode. The challenge is presented as an optimization problem that is limited by satellite trajectories. This paper introduces an improved simulated annealing algorithm designed to maximize the common-view possibility among various station pairs within regional Satellite Laser Ranging (SLR) networks by optimizing satellite orbit trajectories. The study proposes a system model that integrates LTT principles with satellite visibility considerations. The simulated annealing algorithm is improved with new annealing strategies that incorporate control strategies, and modify the cooling function. Comparative simulation analyses demonstrate the effectiveness of the algorithm, resulting in a significant reduction in computation time by over 10 times. The optimized orbits exhibit common-view windows between 3.337 and 8.955 times longer than existing orbits. Further simulations are conducted to optimize the orbits, and common-view models are established for 45 pairs among 10 stations. The optimizations result in common-view times ranging from 6.183 to 60.633 min in the Asia-Pacific region and from 5.583 to 61.75 min in the Europe-to-Asia region. This can provide valuable references for designing satellite constellations.

Investigating Regional Communication Network Robustness of an Asymmetric String-of-Pearls Satellite Constellation Design Framework

Appropriate selection of a satellite constellation design framework for a particular mission set requires a priori knowledge about the relative merits and shortcomings of different frameworks. Symmetric satellite constellation frameworks exhibit good properties for missions requiring continuous global coverage, whereas asymmetric satellite constellation frameworks benefit missions focusing on regional coverage. This research compares the performance of an asymmetric “string-of-pearls” common repeating ground track constellation design framework against a Walker constellation design framework for maintaining continuous connectivity between regions of interest. Several examples illustrate that the asymmetric string-of-pearls constellation framework appears to require an average of approximately 1.25 fewer satellites than the Walker constellation, whereas the Walker constellation appears approximately 7.86% more robust to satellite failures.

Jahn–Teller Distortions Induced by in situ Li Migration in λ‐MnO2 for Boosting Electrocatalytic Nitrogen Fixation

AbstractLithium‐mediated electrochemical nitrogen reduction reaction (Li‐NRR) completely eschews the competitive hydrogen evolution reaction (HER) occurred in aqueous system, whereas the continuous deposition of lithium readily blocks the active sites and further reduces the reaction kinetics. Herein, we propose an innovative in situ Li migration strategy to realize that Li substitutes Mn sites in λ‐MnO2 instead of evolving into the dead Li. Comprehensive characterizations corroborate that the intercalation of Li+ at high voltage breaks the structural integrity of MnO6 octahedron and further triggers unique Jahn–Teller distortions, which promotes the spin state regulation of Mn sites to generate the ameliorative eg orbital configuration and accelerates N≡N bond cleavage via eg‐σ and eg‐π* interaction. To this end, the resulted cationic disordered LiMnO4 delivers the recorded highest NH3 yield rate of 220 μg h−1 cm−2 and a Faradaic efficiency (FE) 83.80 % in organic electrolyte.

Jahn-Teller Distortions Induced by in situ Li Migration in λ-MnO2 for Boosting Electrocatalytic Nitrogen Fixation.

Lithium-mediated electrochemical nitrogen reduction reaction (Li-NRR) completely eschews the competitive hydrogen evolution reaction (HER) occurred in aqueous system, whereas the continuous deposition of lithium readily blocks the active sites and further reduces the reaction kinetics. Herein, we propose an innovative in situ Li migration strategy to realize that Li substitutes Mn sites in λ-MnO2 instead of evolving into the dead Li. Comprehensive characterizations corroborate that the intercalation of Li+ at high voltage breaks the structural integrity of MnO6 octahedron and further triggers unique Jahn-Teller distortions, which promotes the spin state regulation of Mn sites to generate the ameliorative eg orbital configuration and accelerates N≡N bond cleavage via eg -σ and eg -π* interaction. To this end, the resulted cationic disordered LiMnO4 delivers the recorded highest NH3 yield rate of 220 μg h-1 cm-2 and a Faradaic efficiency (FE) 83.80 % in organic electrolyte.

Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands

Abstract. The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017–2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation.

LEOCN: Real-time and complete network simulation framework for LEO constellation networks1

With the development of the space industry, LEO simulation platforms are increasingly important. Prior work mainly focused on the dynamics of constellation topology, routing algorithms against the topological motion, and the behavior of LEO constellation networks, but neglected real-time and completeness properties. In our work, we design a real-time and complete network simulation framework, named LEOCN, that is more meaningful and detailed for researchers than previous work on LEO satellite constellation simulations. LEOCN is supported by Linux System, which makes the framework more realistic and relieves the computational burden. Meanwhile, LEOCN deeply considers topology and routing algorithms, visualization, satellite mobility, communication protocols, and network traffic problems. On LEOCN, we can deploy routing algorithm experiments via SDN. We evaluate the performance of LEOCN in large-scale constellation scenarios, and the results demonstrate the superiority of the real-time and complete network simulation framework.

Exploiting Satellite Doppler for Reliable and Faster Data Download in IoT Satellite Networks

Low Earth Orbit (LEO) satellite constellations are enhancing IoT device connectivity with cost-effective, easily deployable picosatellites. These IoT satellite constellations bypass the need for Earth-based gateways, offering global coverage. This article delves into communication challenges encountered when deploying an IoT satellite network. Shifting the classic perspective on Doppler from a hindrance to an advantage, we present novel techniques leveraging Doppler shift for reliable packet detection and decoding, particularly in noisy environments. Integrated into our system, Spectrumize [1], these techniques offer a substantial performance boost compared to conventional methods.

Self-Position Awareness Based on Cascade Direct Localization Over Multiple Source Data

The global positioning system (GPS), which provides ubiquitous location-awareness with a constellation of satellites, has become an instrumental function of multiple mass-market applications. Satellite signals, however, may not be capable of penetrating obstacles in harsh environments (e.g., urban canyons, tree canopies, and flyovers). Hence, GPS may not provide adequate localization accuracy for applications like autonomous vehicles. Resorting to data fusion of heterogeneous signals emanating from multiple anchors, we advocate a self-localization method that provides accurate estimates of the vehicle position. To be more specific, several heterogenous emitters whose positions are known are used as anchors to determine the vehicle's position based on the weighted direct position determination (DPD) method that eliminates nonhomogeneity among different emitters. However, the weighted DPD method requires an exhaustive search of the parameter search space and is thus time-consuming. To reduce the computational burden, we propose a weighted cascade compensation estimator (WCCE) that is tailored for real-time tracking and self-localization. The proposed WCCE outperforms traditional DPD methods in terms of computational complexity while achieving nearly comparable localization accuracy. The effectiveness of the proposed method is corroborated by extensive simulated examples.

Configuration Design Method of Mega Constellation for Low Earth Orbit Observation

The configuration optimization design of Low Earth Orbit observation mega constellation in complex space environment is a nonlinear problem that is difficult to solve analytically. In this paper, a constellation design method is proposed, considering satellite imaging width, formation flying of subgroup satellites, and global uniform coverage by payloads. Firstly, a configuration of satellites with the same subsatellite trajectory is proposed, and its orbital analytical expression under J2 perturbation is provided. Then, the relative motion feature points are extracted near the orbit of each satellite, and a group of uniform natural accompanying satellites are set to corresponding points. Afterwards, the orbit parameters of satellite and its companions are set as initial values, and the precise orbits under the High Precision Orbit Propagator model are solved in the neighborhood by using the Nondominated Sort Particle Swarm Optimization algorithm. Finally, the correctness of the configuration design method is verified by numerical simulation.