Multiple Satellite Research Articles

Evaluation and fusion of multi-source sea ice thickness products with limited in-situ observations

Sea ice thickness (SIT) is a critical and sensitive parameter in the climate system, with its dynamic changes profoundly influencing global climate models, navigational routes, and the potential for Arctic resource development. Given the widespread application of current satellite remote sensing technology in monitoring SIT, significant uncertainties remain. This study first underscores the importance of in-situ observations as a direct measurement method for SIT. However, the limitations of in-situ data in terms of acquisition cost, spatiotemporal coverage continuity, and distribution uniformity significantly hinder the effective evaluation of multi-source SIT products. To address this, the study innovatively introduces the Triple Collocation (TC) method, which effectively mitigates the impact of errors from individual data sources on the overall evaluation results through a mutual validation mechanism among multiple satellite data sources. This allows for a scientific assessment of multi-source SIT products even in the context of scarce in-situ observations. The findings indicate that the TC method not only successfully resolves the challenges of multi-source data evaluation but also facilitates data integration among these products, significantly enhancing the overall accuracy and spatiotemporal consistency of SIT data.

Interplay Between Atmospheric Correction and Fusion Techniques Enhances the Quality of Remote Sensing Image Fusion

Remote sensing image fusion technology integrates observational data from multiple satellite platforms to leverage the complementary advantages of the different types of remote sensing images. High-quality fused remote sensing images provide detailed information on surface radiation, climate, and environmental conditions, thereby supporting governmental policies on environmental changes. Improving the quality and quantitative accuracy of fused images is a crucial trend in remote sensing image fusion research. This study investigates the impact of atmospheric correction and five widely applied fusion techniques on remote sensing image fusion. By constructing four fusion frameworks, it evaluates how the choice of fusion method, the implementation of atmospheric correction, the synchronization of atmospheric parameters, and the timing of atmospheric correction influence the outcomes of remote sensing image fusion. Aerial flights using remote sensors were conducted to acquire atmospheric parameter distribution images that are strictly synchronous with the remote sensing images. Comprehensive and systematic evaluations of the fused remote sensing images were performed. Experiments show that for the remote sensing images used, selecting the appropriate fusion method can improve the spatial detail evaluation metrics of the fused images by up to 2.739 times, with the smallest deviation from true reflectance reaching 35.02%. Incorporating synchronous atmospheric parameter distribution images can enhance the spatial detail evaluation metrics by up to 2.03 times, with the smallest deviation from true reflectance reaching 5.4%. This indicates that choosing an appropriate fusion method and performing imaging-based synchronous atmospheric correction before fusion can maximize the enhancement of spatial details and spectral quantification in fused images.

Investigating the vertical extent of the 2023 summer Canadian wildfire impacts with satellite observations

Abstract. Pyrocumulonimbus clouds (pyroCbs) generated by intense wildfires can serve as a direct pathway for the injection of aerosols and gaseous pollutants into the lower stratosphere, resulting in significant chemical, radiative, and dynamical changes. Canada experienced an extremely severe wildfire season in 2023, with a total area burned that substantially exceeded those of previous events known to have impacted the stratosphere (such as the 2020 Australian fires). This season also had record-high pyroCb activity, which raises the question of whether the 2023 Canadian event resulted in significant stratospheric perturbations. Here, we investigate this anomalous wildfire season using retrievals from multiple satellite instruments, ACE-FTS (Atmospheric Chemistry Experiment – Fourier transform spectrometer), OMPS LP (Ozone Mapping and Profiler Suite Limb Profiler), and MLS (Microwave Limb Sounder), to determine the vertical extents of the wildfire smoke along with chemical signatures of biomass burning. These data show that smoke primarily reached the upper troposphere, and only a nominal amount managed to penetrate the tropopause. Only a few ACE-FTS occultations captured elevated abundances of biomass-burning products in the lowermost stratosphere. OMPS LP aerosol measurements also indicate that any smoke that made it past the tropopause did not last long enough or reach high enough to significantly perturb stratospheric composition. While this work focuses on Canadian wildfires given the extensive burned area, pyroCbs at other longitudes (e.g., Siberia) are also captured in the compositional analysis. These results highlight that despite the formation of many pyroCbs in major wildfires, those capable of penetrating the tropopause are extremely rare; this in turn means that even a massive area burned is not necessarily an indicator of stratospheric effects.

Design and On-Orbit Performance of Ku-Band Phased-Array Synthetic-Aperture Radar Payload System

The current emphasis in the advancement of space-based synthetic-aperture radar (SAR) is on lightweight payloads under 100 kg with resolutions surpassing 1 m. This focus is directed toward meeting the launch criteria for multiple satellites on a single rocket and cutting costs. This article discusses the creation and progress of a Ku-band SAR payload for the Taijing-4(03) satellite, launched on 23 January 2024 and accompanied by four other satellites. The SAR payload design was customized to meet the demands of a micro-nano satellite platform, resulting in a lightweight, flat design weighing less than 80 kg, seamlessly integrated with the plate-shaped satellite platform. The article also introduces a beam optimization strategy for the phased array SAR antenna, significantly boosting the SAR system’s performance. The SAR payload provides various operating modes like slide-spot, strip, Scan 1, Scan 2, and others, with a maximum achievable resolution exceeding 1 m. Extensive in-orbit testing of the payload produced numerous high-quality SAR images with potential uses in emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, and more.

Flow state transition induced by emergence of orbiting satellite eddies in two-dimensional turbulent Rayleigh–Bénard convection

We report a numerical investigation of a previously noticed but less explored flow state transition in two-dimensional turbulent Rayleigh–Bénard convection. The simulations are performed in a square domain over a Rayleigh number range of $10^7 \leq Ra \leq 2 \times 10^{11}$ and a Prandtl number range of $0.25 \leq Pr \leq 20$ . The transition is characterized by the emergence of multiple satellite eddies with increasing $Ra$ , which orbit around and interact with the main vortex roll in the system. Consequently, the main roll is squeezed to a smaller size compared with the domain and wanders around in the bulk region irregularly and extensively. This is in sharp contrast to the flow state before the transition, which is featured by a domain-sized circulatory roll with its vortex centre ‘condensed’ near the domain's centre. Detailed velocity field analysis reveals that there exists an abrupt increase in the energy fluctuations of the Fourier modes during the transition. Based on this phase-transition-like signal, the critical condition for the transition is found to follow a scaling relation as $Ra_t \sim Pr^{1.41}$ where $Ra_t$ is the critical Rayleigh number for the transition. This scaling relation is quantitatively explained by a phenomenological model grounded on the bistability behaviour (i.e. spontaneous and stochastic switching between the two flow states) observed at the edge of the transition. The model can also account for the effects of aspect ratio on the transition reported in the literature (van der Poel et al., Phys. Fluids, vol. 24, 2012).

Decision Transformer-Based Efficient Data Offloading in LEO-IoT.

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people's attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%.

Estimation of surface soil moisture from Sentinel-1 synthetic aperture radar imagery using machine learning method

Surface soil moisture (SM) is a crucial variable representing the water content in soil at the topmost soil layer. Accurate data of SM are essential for various purposes, including the monitoring of drought, vegetation modeling, weather forecasting, and agriculture management. Over the past decades, microwave remote sensing has been employed to estimate SM at large scales, with the Sentinel-1 satellite mission recently offering Synthetic Aperture Radar (SAR) data and enabling to estimate SM at high spatial resolution. Machine learning (ML) techniques have further enhanced these estimations, with random forest (RF) emerging as a promising method due to its strong capabilities. However, the performance of RF in estimating SM with satellite data has not yet been thoroughly evaluated across large areas. This study presents a new RF-based model for estimating SM over Europe with SAR data from Sentinel-1, along with the climate and terrain data. The predictions from the RF model were compared against the ground measurements from the International Soil Moisture Network (ISMN), Integrated Carbon Observation System (ICOS), and official SMAP/Sentinel-1 SM products. The results demonstrated that the RF model yielded accurate predictions with a Pearson's correlation coefficient (R) of 0.847 outperforming the official SMAP/Sentinel-1 product at spatial resolutions of 1 km and 3 km, which achieved R values of 0.599 and 0.616, respectively. Additionally, the impact of vegetation cover that is represented by using multiple satellite vegetation products on the RF model was investigated. Despite a moderate correlation between backscattering coefficients and vegetation cover, no correlation was found between the RF model's errors and vegetation cover. The results suggest that the RF model and Sentinel-1 SAR data can be used to provide SM estimations at high spatial resolution, characterizing the fine-scale variations in SM, to support various applications such as precision agriculture at the small, localized scales.

Spatiotemporal performance evaluation of high-resolution multiple satellite and reanalysis precipitation products over the semiarid region of India.

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions.

Cross-satellite atmospheric correction for consistent remote sensing reflectance from multiple ocean color satellites: Concept and demonstrations

Cross-satellite atmospheric correction for consistent remote sensing reflectance from multiple ocean color satellites: Concept and demonstrations

Improved Adaptive Large Neighborhood Search Algorithm Based on the Two-Stage Framework for Scheduling Multiple Super-Agile Satellites

Improved Adaptive Large Neighborhood Search Algorithm Based on the Two-Stage Framework for Scheduling Multiple Super-Agile Satellites

Routing cost-integrated intelligent handover strategy for multi-layer LEO mega-constellation networks

Low Earth Orbit (LEO) mega-constellation networks, exemplified by Starlink, are poised to play a pivotal role in future mobile communication networks, due to their low latency and high capacity. With the massively deployed satellites, ground users now can be covered by multiple visible satellites, but also face complex handover issues with such massive high-mobility satellites in multi-layer. The end-to-end routing is also affected by the handover behavior. In this paper, we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks. Firstly, an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model. Subsequently, an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network (D3QN)-based deep reinforcement learning algorithm for single-layer constellations. Moreover, an optimal cross-layer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead. Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation, showcasing reductions of up to 8.2% and 59.5% in end-to-end latency and jitter respectively, when compared to the existing handover strategies.

Numerical study of two equal-sized miscible drops undergoing head-on collision

The numerical analysis focuses on investigating head-on collisions between two miscible drops composed of distinct fluids, specifically ethanol and water. The simulations are performed using a coupled level set and volume of fluid approach with different Weber numbers to study the effect of drop inertia. The code is validated against experimental and numerical results from earlier investigations. Additionally, a comparative study involving both water drops and ethanol–water drops is conducted to explore the impact of varying surface tension ratios on collision outcomes. Results show that when miscible drops collide, the merged liquid drop exhibits asymmetric behavior, such as an asymmetric combined drop shape in cases of permanent coalescence or an asymmetric end droplet breakup in cases of reflexive separation. The collision outcome undergoes significant variation as the Weber number changes. At lower Weber numbers, permanent coalescence is observed, while at medium Weber numbers, reflexive separation occurs without the formation of a secondary drop. For medium to large Weber numbers, reflexive separation with the generation of one secondary drop becomes prominent, and in the case of very large Weber numbers, multiple satellite drops form. The maximum vertical elongation of the merged drop and corresponding surface energy increase as the surface tension ratio rises, irrespective of the Weber number. However, for a fixed surface tension ratio, the maximum vertical elongation and associated surface energy vary with an increase in the Weber number. The findings also shed light on the enhanced internal mixing arising from the mismatched surface tension of the colliding drops as the Weber number increases. Furthermore, the study explores the effect of drop inertia on various aspects, including asymmetric collision behavior, energy budget, and mixing index.

Interorbital antisymmetric hopping generated flat bands on kagome and pyrochlore Lattices

Flat bands are intriguing platforms for correlated and topological physics. Various methods have been developed to create flat bands utilizing lattice geometry, but the investigation of orbital symmetry in multiorbital materials is a new area of focus. Here, we introduce a site symmetry-based approach to emerging multiorbital 2D and 3D flat bands on the kagome and pyrochlore lattices. As a conceptual advance, the one-orbital flat bands are shown to originate as mutual eigenstates of isolated molecular motifs. Further developing the mutual eigenstate method for multiple orbitals transforming differently under the site symmetries, we derive interorbital hopping generated flat bands from the antisymmetric interorbital Hamiltonian and introduce group-theoretic descriptions of the flat band wavefunctions. Realizations of multiorbital flat bands in realistic materials are shown to be possible in the Slater-Koster formalism. Our findings provide new directions for exploring flat-band electronic structures for novel correlated and topological quantum states.

A Multi-Pointer Network for Multiple Agile Optical Satellite Scheduling Problem

With the rapid growth in space-imaging demands, the scheduling problem of multiple agile optical satellites has become a crucial problem in the field of on-orbit satellite applications. Because of the considerable solution space and complicated constraints, the existing methods suffer from a huge computation burden and a low solution quality. This paper establishes a mathematical model of this problem, which aims to maximize the observation profit rate and realize the load balance, and proposes a multi-pointer network to solve this problem, which adopts multiple attention layers as the pointers to construct observation action sequences for multiple satellites. In the proposed network, a local feature-enhancement strategy, a remaining time-based decoding sorting strategy, and a feasibility-based task selection strategy are developed to improve the solution quality. Finally, extensive experiments verify that the proposed network outperforms the comparison algorithms in terms of solution quality, computation efficiency, and generalization ability and that the proposed three strategies significantly improve the solving ability of the proposed network.

Version 1 NOAA-20/OMPS Nadir Mapper total column SO2 product: continuation of NASA long-term global data record

Abstract. For nearly 2 decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version-1 NOAA-20 (N20)/OMPS SO2 product (https://doi.org/10.5067/OMPS/OMPS_N20_NMSO2_PCA_L2_Step1.1, Li et al., 2023), aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as Kilauea in 2018 and Raikoke in 2019. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCDs) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated, but N20/OMPS SO2 is biased low, especially for India and the Middle East, where the differences reach ∼ 20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ∼ 10 %–15 %) is found over degassing volcanoes. SO2 emissions (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022) from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and the TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients >0.98 and overall differences <10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios of all four sensors, the ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version-1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites.

Dynamic low earth orbit multi satellite hopping beam resource allocation considering load and dynamic coverage balance (LDCB-MSHBRA)

A dynamic low earth orbit multi satellite hopping beam resource allocation method based on time slicing is proposed to address the issue of changing coverage and beam interference caused by satellite mobility in multi satellite and multi beam communication scenarios. The method involves six processes: (1) Establishing a low earth orbit satellite system with three identical configuration parameters and motion speed parameters; (2)Assign a value to the time slicing weight coefficient and use the principle of load balancing to minimize the difference in demand for dual star services as the optimization objective; (3) Obtain initial power resource allocation with maximum system capacity as the optimization objective; (4) Considering the mobility of low earth orbit satellites, updating weight coefficients and dynamically allocating power resources, a time resource dynamic allocation matrix and the optimal service cell set are obtained; (5) Update the independent coverage cell set of the third low earth orbit satellite, use the Lagrange equation of the weighted fair objective function to obtain the time resource allocation optimization matrix, and use one-way search to determine the optimal service cell set; (6) With the goal of maximizing system capacity, the optimal solution of the power resource allocation matrix for the third low earth orbit satellite is obtained. The system throughput is introduced to intuitively measure system performance, and adjustments are made based on the overlap of Samsung coverage. The resource allocation method is based on a fairness model, considering load balancing and dynamic coverage range changes. On the basis of improving the overall system throughput, it ensures the fairness of resource allocation and allocates time, frequency, and power resources to multiple satellites with overlapping coverage, which helps to improve the throughput and resource utilization of satellite systems.

Multi-objective joint optimization of task offloading based on MADRL in internet of things assisted by satellite networks

The Internet of Things (IoT) integrates a large number of heterogeneous terminals and systems, possessing ubiquitous sensing and computing capabilities. Satellite networks are the crucial supplement to terrestrial networks, particularly in remote areas where network infrastructures are sparingly distributed or unavailable. Combining edge computing with satellite networks provides on-orbit computing capabilities for IoT applications, reducing service delay and enhancing service quality. Due to the resource constraints of satellites, achieving collaborative services through task offloading among multiple satellites becomes essential. Both the privacy leakage risk arising from frequent data interactions and the load imbalance resulting from offloading preferences cannot be overlooked. The key challenge of task offloading is to safeguard the privacy of offloaded data and ensure the system’s load balance while minimizing the delay and energy consumption. In this paper, the task offloading problem is formulated as a Partially Observable Markov Decision Process (POMDP), and a task offloading algorithm based on multi-objective joint optimization using multi-agent deep reinforcement learning in a distributed architecture is proposed. The simulation results validate the efficacy of our model and algorithm, demonstrating that our proposed algorithm achieves better performance in minimizing comprehensive offloading costs.

Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring

Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring

Enhancing Surface Water Monitoring through Multi-Satellite Data-Fusion of Landsat-8/9, Sentinel-2, and Sentinel-1 SAR

Long revisit intervals and cloud susceptibility have restricted the applicability of earth observation satellites in surface water studies. Integrating multiple satellites offers potential for more frequent observations, yet combining different satellite sources, particularly optical and SAR satellites, presents complexities. This research explores the data-fusion potential and limitations of Landsat-8/9 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture (SAR) satellites to enhance surface water monitoring. By focusing on segmented surface water images, we demonstrate that combining optical and SAR data is generally effective and straightforward using a simple statistical thresholding algorithm. Kappa coefficients(κ) ranging from 0.80 to 0.95 indicate very strong harmony for integration across reservoirs, lakes, and river environments. In vegetative environments, integration with S1SAR shows weak harmony, with κ values ranging from 0.27 to 0.45, indicating the need for further studies. Global revisit interval maps reveal significant improvement in median revisit intervals from 15.87 to 22.81 days using L8/9 alone, to 4.51 to 7.77 days after incorporating S2, and further to 3.48 to 4.62 days after adding S1SAR. Even during wet season months, multi-satellite fusion maintained the median revisit intervals to less than a week. Maximizing all available open-source earth observation satellites is integral for advancing studies requiring more frequent surface water observations, such as flood, inundation, and hydrological modeling.

Removal of levofloxacin by H2O2 and PMS co-activation by sulfide-supported oxalate zero-valent iron enhanced with simultaneous catalysis of SO4-• and 1O2: Major free radicals, synergistic effects and mechanism exploration

It is reported in the literature that sulfide and oxalate are promising ways to modify zero-valent iron (ZVI) for the degradation of antibiotics, but the synergistic activation mechanism of H2O2 and PMS is still unclear. In order to explore this effect, a new type of oxalated and sulfided ZVI (OA-S-ZVI) was synthesized in this study, and levofloxacin (LEV) was degraded in H2O2, PMS and H2O2/PMS systems. The batch experiments confirmed that the removal ability of OA-S-ZVI/H2O2/PMS was 1.65–10 times that of other systems. This result was achieved by modifying ZVI surface catalytic sites and cooperating with H2O2/PMS. At the same time, the synergistic factor S (1.74) confirmed that the composite system H2O2/PMS can capture electrons from the catalyst Fe0 to Fe2+ and adsorb active sites of the catalyst, such as ≡Fe-C2O4-, ≡Fe-OH/OOH and ≡Fe-S. Further calculation and analysis revealed that the binding energy of singlet oxygen (1O2) and sulfate radical (SO4-•) in the composite system decreased and multiple molecular orbitals participated in the removal process. The characterization experiments and EPR experiments explained the transfer free radicals to non-free radicals from a single system to a composite system. This study provides a new perspective on modifying ZVI and utilizing multi-system synergy to effectively remove organic micropollutants from water.