Multiple Satellite Research Articles

Multiple Satellite Observations of the High‐Latitude Cusp Aurora During Northward IMF Conditions

AbstractCusp auroras poleward of the typical auroral oval are ascribed to high‐latitude lobe reconnection when the Interplanetary Magnetic Field (IMF) Bz is predominantly northward. In this study, we further investigate the ionospheric characteristics of a unique high‐latitude cusp region employing multiple satellite observations. A cusp aurora event with wide spatial spread was observed in the postnoon polar cap region. It was found to be associated with northward IMF Bz and positive By components. The cusp aurora was located from 68° to 86° in magnetic latitude and within 15–17 hr in magnetic local time. This broad coverage in the polar cap indicates direct precipitating particles from the magnetosheath. Particle energy is different between the equatorward and poleward edges of the cusp aurora. The precipitating ions at the equatorward side maintain magnetosheath particle characteristics as expected, while ions with higher energies occurred in the poleward side. Further, the poleward edge of the cusp aurora was nearly situated in the center of a convection shear and was associated with an upward field‐aligned current. These observations suggest a lobe cell circulation, hence we attribute the formation of the cusp aurora to the high‐latitude lobe reconnection. Simultaneous observations in the southern hemisphere indicate the absence of cusp aurora. The auroral presence only in the northern hemisphere is probably due to the combination of large dipole tilt angle and positive IMF Bz, which facilitates the lobe reconnection.

How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.

A comparative assessment of five precipitation products in the Saharan desert of Morocco: Sakia El Hamra basin case study.

This study evaluates the performance of five satellite precipitation products (GPM IMERG, TRMM 3B42, ERA5, PERSIANN, and CHIRPS) compared to monthly observations from two weather stations (Laayoune and Essmara) over 2001-2017 using statistical metrics including correlation coefficient (CC) and mean square error (MSE). The results reveal notable differences between products. On a monthly timescale, GPM IMERG shows the best overall accuracy with a MSE of 16.8mm/month. However, TRMM 3B42 exhibits higher temporal correlations with a CC around 0.83. The analysis provides insights into product capabilities and limitations for hydrological monitoring in data-sparse regions. Key findings include the superior performance of latest generation datasets like GPM alongside biases requiring localized calibration. The study delivers an assessment framework to guide integration of multiple satellite estimates for enhanced precipitation quantification and hydroclimatic modeling in water-stressed environments.

Comparisons of Greenhouse Gas Observation Satellite Performances Over Seoul Using a Portable Ground‐Based Spectrometer

AbstractSatellites provide global coverage for monitoring atmospheric greenhouse gases, crucial for understanding global climate dynamics. However, their temporal and spatial resolutions fall short in detecting urban‐scale variations. To enhance satellite reliability over urban areas, this study presents the first comprehensive analysis of long‐term observations of column‐averaged dry air mole fractions of CO2, CH4, and CO (XCO2, XCH4, XCO) using two ground‐based fourier transform infrared spectrometers, EM27/SUNs, in a megacity. With over 2 years of observations, our study shows that EM27/SUN measurements can effectively capture the daily and seasonal variability of XCO2, XCH4, and XCO over Seoul, a megacity with complex topography and various emission sources. In addition, we use the advantage of having multiple greenhouse gas satellites targeting Seoul to compare with the EM27/SUNs. Our study highlights the importance of EM27/SUN observations in Seoul to identify the need for improvements in satellites to monitor greenhouse gas behaviors and emissions in urban areas.

Mapping vertical and horizonal deformation of the newly reclaimed third runway at Hong Kong International Airport with PAZ, COSMO-SkyMed, and Sentinel-1 SAR images

In order to meet long-term air transportation needs, Hong Kong International Airport (HKIA) has been constructing the third runway system (3RS) through land reclamation projects since 2016. However, the third runway and taxiway, which were paved in September 2021, suffered from severe surface settlement. Therefore, we attempted to reveal the settlement pattern within the 3RS and analyze the factors contributing to this displacement. Employing the multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique, we utilized PAZ, COSMO-SkyMed, and Sentinel-1 satellite images to extract ground displacements along the radar line of sight. The results show that, compared with COSMO-SkyMed and Sentinel-1 images, PAZ demonstrates superior performance in the deformation monitoring of the HKIA, with more deformation details and larger displacements. The maximum settlement rate derived by PAZ on the 3RS exceeds 110 mm/year. To ensure the reliability of the derived displacements along the LOS, we validated them through cross-validation, comparing the outcomes from different SAR data with data from GPS stations. In order to gain a detailed deformation information, we derived the vertical and horizontal (ground range direction) displacements by utilizing deformation measurements along the LOS of multiple SAR satellites and the geometric information, and the maximum values recorded for vertical and E-W displacements reached 88.31 mm/y and 41.91 mm/y, respectively. Furthermore, our investigation revealed that the significant local deformation observed on the taxiway and third runway was predominantly attributed to various factors including the creep of soft soil, consolidation of filling materials, characteristics of road surface materials, and distinct stages of construction.

Multiple Nodes Co-Carrier Cooperative Transmission in LEO Communication Networks: Developing the Diversity Gain of Satellites.

Low Earth orbit (LEO) satellite communication (SATCOM) networks have gradually been recognized as an efficient solution to enhance ground-based wireless networks. As one of the main characteristics of LEO SATCOM, the beam-edge area could be covered by multiple satellite nodes. In this case, user terminals (UTs) located at the beam-edge have the chance to connect one or more LEO satellites. To develop the diversity gain of multiple nodes in the overlapping area, we propose two high spectral efficiency cooperative transmission strategies, i.e., directly combining (DC) and selection combining (SC). In the DC scheme, signals arrived at the UT simultaneously could be combined into one enhanced signal. For downlink time division multiplexing, the SC scheme enables the UT to select the strongest signal path. Further, as there exists a significant channel gain difference of the beam-center and beam-edge areas, UTs in these two areas can be allocated in one resource block. In this case, we derive co-carriers based on DC and SC, respectively. To deeply analyze the novel methods, we study the ergodic sum-rate and outage probability while the outage diversity gain is further provided. Simulation results show that the co-carrier-based DC method has the ability to provide a higher ergodic sum-rate while the SC method performs better in terms of the outage probability.

Analysis of solar plasma parameters during intense geomagnetic storm (SYM/H

We utilize numerous solar plasma and field parameters with geomagnetic indices for the geomagnetic storm in 25 solar cycles. Using Planetary index (Kp) having 3 h data, Dst index hourly data, Symmetric horizontal field (SYM/H) minute resolution data and solar plasma measurements from multiple satellites, examine the chain of significant events that occurred in the year 2023. Statical analysis of geomagnetic provisional activity Sym/H has better results than Kp index during various main phases of geomagnetic activity. The statistical correlation technique is used to examine the relationship between Interplanetary parameters and geomagnetic indices during major geomagnetic storms. This research improves our comprehension of solar–terrestrial interactions and provides valuable insights for enhancing space weather forecasting models.

Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS

Long-term, accurate, stable, and continuous aerosol records from space are a major requirement for climate and atmospheric environment research. Due to the limited period span of the single satellite mission, solving the problem requires the combination of multiple satellite missions. In this study, a novel aerosol retrieval algorithm, named enhanced Land General Aerosol Retrieval (e-LaGA), for Visible/Infrared Imager Radiometer Suite (VIIRS) was developed to continue Moderate-resolution Imaging Spectro-radiometer (MODIS) aerosol retrieval. e-LaGA utilizes a Surface Reflectance (SR) relationship model map to more accurately estimate the SR parameter, optimizes the priori aerosol-type map, and improves the multi-band retrieval strategy using the residual-interpolation approach. Additionally, e-LaGA expands the retrieval ability of the traditional Dark Target (DT) algorithm over bright surfaces, and can more accurately retrieve Aerosol Optical Depth (AOD), Fine-mode AOD (AODF), and Fraction (FMF). The validation using global 533 AERONET sites shows that the volume of matchups of AOD (AODF) is approximately 80000, the correlation coefficient (R) is 0.902 (0.879) and the fraction of meeting the expected error envelope (±0.05 ± 0.15τ) is 0.806 (0.804). The inter-comparison indicates that the accuracy of e-LaGA AOD retrievals is comparable to seven commonly used AOD products. The validation performance and spatial distribution pattern of e-LaGA AODF and FMF are in good agreement with the POLDER (Polarization and Directionality of the Earth's Reflectances) products. e-LaGA is also applied to MODIS sensors. The VIIRS e-LaGA AOD, AODF, and FMF retrievals have high consistency with MODIS e-LaGA. Their average bias of AOD is 0.006, which is smaller than 0.024 for the Deep Blue and 0.011 for the DT. These preliminary results demonstrate the robustness of the e-LaGA algorithm and its potential for establishing a long-term climate record of total and fine-mode aerosol by combining multiple satellite missions, which is expected to reduce the uncertainty of climate change research.

MT-InSAR Optimisation for Structural Health Monitoring

The implementation of effective and sustainable Structural Health Monitoring (SHM) systems for the evaluation of infrastructure conditions is critical to address the deterioration and damage experienced by structures worldwide. Given the vast number of structures involved, resorting to traditional in-situ visual inspections and data gathering methods is becoming increasingly unfeasible. Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has recently gained attention as a viable solution for long-term SHM. This remote sensing technique combines multiple satellite radar images to measure changes in the Earth’s surface over time. Unlike conventional techniques, MT-InSAR does not require in-situ installations and offers extensive coverage, enabling observations across diverse location and structures. However, the applicability of MT-InSAR monitoring depends on the relatively unpredictable distribution and location of permanent scatterers (PSs), which are influenced by surface characteristics and vegetation changes. Evaluating the reliability and capacity of MT-InSAR is therefore crucial to enhance its effectiveness in assessing the location and extent of structural damage. In this study, we present an effective approach to determine the optimal number and position of PSs for detecting different structural damage mechanisms. The approach is exemplified through a case study of a quay wall in Amsterdam, with data inputs simulated using the Finite Element Method. The proposed method has the potential to evaluate the feasibility of MT-InSAR for a broader range of scenarios, enabling to detect specific structural conditions.

Automating surgical procedure extraction for society of surgeons adult cardiac surgery registry using pretrained language models.

Surgical registries play a crucial role in clinical knowledge discovery, hospital quality assurance, and quality improvement. However, maintaining a surgical registry requires significant monetary and human resources given the wide gamut of information abstracted from medical records ranging from patient co-morbidities to procedural details to post-operative outcomes. Although natural language processing (NLP) methods such as pretrained language models (PLMs) have promised automation of this process, there are yet substantial barriers to implementation. In particular, constant shifts in both underlying data and required registry content are hurdles to the application of NLP technologies. In our work, we evaluate the application of PLMs for automating the population of the Society of Thoracic Surgeons (STSs) adult cardiac surgery registry (ACS) procedural elements, for which we term Cardiovascular Surgery Bidirectional Encoder Representations from Transformers (CS-BERT). CS-BERT was validated across multiple satellite sites and versions of the STS-ACS registry. CS-BERT performed well (F1 score of 0.8417 ± 0.1838) in common cardiac surgery procedures compared to models based on diagnosis codes (F1 score of 0.6130 ± 0.0010). The model also generalized well to satellite sites and across different versions of the STS-ACS registry. This study provides evidence that PLMs can be used to extract the more common cardiac surgery procedure variables in the STS-ACS registry, potentially reducing need for expensive human annotation and wide scale dissemination. Further research is needed for rare procedural variables which suffer from both lack of data and variable documentation quality.

Comparison of the Water Vapor Budget Evolution of Developing and Non-Developing Disturbances over the Western North Pacific

Tropical cyclone (TC) genesis prediction remains a major operational challenge. Using multiple satellite datasets and a state-of-the-art reanalysis dataset, this study identifies developing and non-developing tropical disturbances over the western North Pacific from June to November of 2000–2019 and conducts composite analyses of their water vapor budget components and relevant dynamic–thermodynamic parameters in the Lagrangian framework following three-day disturbance tracks. Both groups of disturbances have a similar initial 850 hPa synoptic-scale relative vorticity, while the water vapor budget of developing disturbances exhibits distinct stage-wise evolution characteristics from non-developing cases. Three days prior to TC genesis, developing cases are already associated with significantly higher total precipitable water (TPW), vertically integrated moisture flux convergence (VIMFC), and precipitation, of which TPW is the most important parameter to differentiate two groups of disturbances. One day later, all the water vapor budget components (i.e., TPW, VIMFC, precipitation, and evaporation) strengthened, linked with the enhancement of the mid-to lower-tropospheric vortices. A negative radial gradient of evaporation occurs, suggesting the beginning of the wind−evaporation feedback. On the day prior to TC genesis, the water vapor budget components, as well as the mid-to lower-tropospheric vortices, continue to intensify, eventually leading to TC genesis. By contrast, non-developing disturbances are associated with a drier environment and weaker VIMFC, precipitation, and evaporation during the three-day evolution. All these factors are not favorable for the intensification of the mid-to lower-tropospheric vortices; thus, the disturbances fail to upgrade to TCs. The results may shed light on TC genesis prediction.

G.I.S. SEARCH ENGINE

Abstract– This paper presents the development of a Geographic Information System (GIS) search engine leveraging Django, OpenLayers, MySQL, and GDAL. The application enables users to perform spatial searches and visualize geographic data interactively. This research details the system architecture, implementation process, and evaluation of performance and usability. The scope of the project encompasses the development of advanced tools for data integration, real-time processing, visualization, and the dissemination of insights derived from multiple satellite sources. By providing an infrastructure that supports the seamless flow and interpretation of satellite data, SDIS aims to enhance decision-making processes and foster innovative applications across different sectors. Keywords: GIS, Search Engine, QGIS, Spatial data, OpenLayers, GDAL

G.I.S. SEARCH ENGINE

Abstract– This paper presents the development of a Geographic Information System (GIS) search engine leveraging Django, OpenLayers, MySQL, and GDAL. The application enables users to perform spatial searches and visualize geographic data interactively. This research details the system architecture, implementation process, and evaluation of performance and usability. The scope of the project encompasses the development of advanced tools for data integration, real-time processing, visualization, and the dissemination of insights derived from multiple satellite sources. By providing an infrastructure that supports the seamless flow and interpretation of satellite data, SDIS aims to enhance decision-making processes and foster innovative applications across different sectors. Keywords: GIS, Search Engine, QGIS, Spatial data, OpenLayers, GDAL

G.I.S. SEARCH ENGINE

Abstract– This paper presents the development of a Geographic Information System (GIS) search engine leveraging Django, OpenLayers, MySQL, and GDAL. The application enables users to perform spatial searches and visualize geographic data interactively. This research details the system architecture, implementation process, and evaluation of performance and usability. The scope of the project encompasses the development of advanced tools for data integration, real-time processing, visualization, and the dissemination of insights derived from multiple satellite sources. By providing an infrastructure that supports the seamless flow and interpretation of satellite data, SDIS aims to enhance decision-making processes and foster innovative applications across different sectors. Keywords: GIS, Search Engine, QGIS, Spatial data, OpenLayers, GDAL

Atmospheric and Radiometric Normalization of Satellite Images for Landscape-Level Environmental Monitoring: The Case of The Mediterranean Region

Ensuring atmospheric and radiometric consistency among the frameworks of satellite data used in regional studies is a critical requirement for change detection studies employed in regional planning monitoring. The purpose of this article is to provide a guide for the necessary atmospheric correction and radiometric normalization processes required in generating environmental data at the landscape level for physical planning. In this context, adjustments were made to remove atmospheric effects before merging multiple ASTER satellite image frames used in a project supported by TÜBİTAK, covering landscape-level environmental inventory and monitoring. The Dark Object Subtraction method with the Cos(t) model was utilized in the atmospheric correction process. Subsequently, separate regression relationships were computed for each band by considering overlapping areas on adjacent tracks of ASTER data, and radiometric normalization was performed based on these regression equations. Thus, differences between satellite images used in monitoring land changes and affecting multiple frames were minimized.

Pre-Launch Polarization Assessment of JPSS-3 and -4 VIIRS VNIR Bands and Comparison with Previous Builds

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, deployed on multiple satellites including the Suomi National Polar-orbiting Partnership (S-NPP), National Oceanic and Atmospheric Administration 20 (NOAA-20), NOAA-21, Joint Polar Satellite System (JPSS-3), and JPSS-4 spacecraft, with launches in 2011, 2017, 2022, 2032, and 2027, respectively, has polarization sensitivity that affects the at-aperture radiometric Sensor Data Record (SDR) calibration in the Visible Near InfraRed (VNIR) spectral region. These SDRs are key inputs into the VIIRS atmospheric, land, and water Environmental Data Records (EDRs) that are integral to weather and climate applications. If the polarization sensitivity of the VIIRS instrument is left uncorrected, EDR quality will degrade, causing diminished quality of weather and climate data. Pre-launch characterization of the instrument’s polarization sensitivity was performed to mitigate this on-orbit calibration effect and improve the quality of the EDRs. Specialized ground test equipment, built specifically for the VIIRS instrument, enabled high-fidelity characterization of the instrument’s polarization performance. This paper will discuss the polarization sensitivity characterization test approach, methodology, and results for the JPSS-3 and -4 builds. This includes a description of the ground test equipment, instrument requirements, and how the testing was executed and analyzed. A comparison of the polarization sensitivity results of the on-orbit S-NPP, NOAA-20, and -21 instruments with the JPSS-3 and -4 VIIRS instruments will be discussed as well.

Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change

Extended growing season lengths under climatic warming suggest increased time for plant growth. However, research has focused on climatic impacts to the timing or duration of distinct phenological events. Comparatively little is known about impacts to the relative time allocation to distinct phenological events, for example, the proportion of time dedicated to leaf growth versus senescence. We use multiple satellite and ground-based observations to show that, despite recent climate change during 2001 to 2020, the ratio of time allocated to vegetation green-up over senescence has remained stable [1.27 (± 0.92)] across more than 83% of northern ecosystems. This stability is independent of changes in growing season lengths and is caused by widespread positive relationships among vegetation phenological events; longer vegetation green-up results in longer vegetation senescence. These empirical observations were also partly reproduced by 13 dynamic global vegetation models. Our work demonstrates an intrinsic biotic control to vegetation phenology that could explain the timing of vegetation senescence under climate change.

Impact of seasonal variations on low and mid-latitudes ionospheric electric field satellite measurements

Ionospheric electric field measurements are provided by the Langmuir probe onboard the DEMETER satellite. Because there are four probes, various combinations of them form double probes that yield measurements. However, only the combination of probe antennas (E3) and (E4) enables possible measurements of the ambient (natural) ionospheric electric field. For other combinations, the measured field is almost identical to the induced field resulting from satellite motion. On the basis of this, we conducted comparative studies of the measured and induced electric fields during different seasons (summer, autumn, winter, and spring) to determine the best period for ionospheric electric field measurements. Multiple orbits were selected during different periods when the satellite was exposed to solar radiation. In all seasons except winter, the profiles of the induced and measured ionospheric electric field are similar. This masking effect leads to an underestimation of the ambient electric field. However, the highest accuracy for the ambient field can be obtained during winter, when solar flux radiation is weaker than that during other seasons. The probe position and orbital altitude also play important roles in the behavior of the induced electric field.

Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach

Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic variation and anthropogenic actions. Conservation of biodiversity is important because millions of people are directly and indirectly dependent on vegetation (forest and crop) and its associated secondary products. United Nations Sustainable Development Goals (SDGs) propose to quantify the proportion of vegetation as a proportion of total land area of all countries. Satellite images form as one of the main sources of accurate information to capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degradation. Open source Cloud computing system Google Earth Engine (GEE) was used to systematically monitor vegetation degradation and evaluate the potential of multiple satellite data with variable spatial resolutions. Hotspots were demarcated using machine learning techniques to identify the greening and the browning effect of vegetation using coarse resolution Normalized Difference Vegetation Index (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for the period 2000–2022 were also used to find rainfall anomaly in the region. Furthermore, hotspot areas were identified using high-resolution datasets in major vegetation degradation areas based on long-term vegetation and rainfall analysis to understand and verify the cause of change whether anthropogenic or climatic in nature. This study is important for several State/Central Government user departments, Universities, and NGOs to lay out managerial plans for the protection of vegetation/forests in India.

Cuprate-like electronic structures in infinite-layer nickelates with substantial hole dopings.

Superconducting infinite-layer (IL) nickelates offer a new platform for investigating the long-standing problem of high-temperature superconductivity. Many models were proposed to understand the superconducting mechanism of nickelates based on the calculated electronic structure, and the multiple Fermi surfaces and multiple orbitals involved create complications and controversial conclusions. Over the past five years, the lack of direct measurements of the electronic structure has hindered the understanding of nickelate superconductors. Here we fill this gap by directly resolving the electronic structures of the parent compound LaNiO2 and superconducting La0.8Ca0.2NiO2 using angle-resolved photoemission spectroscopy. We find that their Fermi surfaces consist of a quasi-2D hole pocket and a 3D electron pocket at the Brillouin zone corner, whose volumes change upon Ca doping. The Fermi surface topology and band dispersion of the hole pocket closely resemble those observed in hole-doped cuprates. However, the cuprate-like band exhibits significantly higher hole doping in superconducting La0.8Ca0.2NiO2 compared to superconducting cuprates, highlighting the disparities in the electronic states of the superconducting phase. Our observations highlight the novel aspects of the IL nickelates, and pave the way toward the microscopic understanding of the IL nickelate family and its superconductivity.