Geostationary Research Articles

A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning

A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning

A stepwise unmixing model to address the scale gap issue present in downscaling of geostationary meteorological satellite surface temperature images

Land surface temperature (LST) is a critical parameter that drives the response of a variety of ecosystems to environmental and climatic changes. The geostationary satellite brings unique opportunities to monitor the LST at a hemispheric scale with temporal resolutions of up to 5 min. However, the ultra-coarse spatial resolutions ranging from 2 km to 5 km limit its application at local spatial scales. Downscaling the geostationary satellite LST image with the high-resolution low-Earth-orbit satellite images is a cost-effective way to circumvent this dilemma. Yet, the big gap between the observation scales of these satellite data poses a challenge for accurate downscaling. To address this problem, we proposed a stepwise temperature unmixing (TUM) model called ‘UnmixGO’, which downscales hourly LST images of Geostationary Operational Environmental Satellites (GOES-R) from 2 km to 100 m resolution. The spatially adaptive endmembers and the constrained solution space of the TUM model keep the errors in downscaled LSTs from being over-amplified in the stepwise data treatment. We validated the algorithm in six experimental areas and at five flux tower sites across the contiguous United States, revealing that UnmixGO outperformed conventional methods in accuracy by 0.49 K and 1.11 K on average for downscaling the simulated and real GOES-R LST images, respectively. Furthermore, the technical framework employed by UnmixGO is compatible with multi-source satellite images, enhancing the added value of our study in a future where various remote sensing data is increasingly accessible.

Urgensi Sui Generis Regime Permintaan Slot Geostationary Orbit melalui Perspektif Negara Khatulistiwa Khususnya Indonesia

Indonesia is one of the countries whose position is crossed by the equator, which is parallel to the Geostationary Orbit (GSO). The passing of Law No. 16 of 2002 marks Indonesia’s binding to the Outer Space Treaty, which encourages Indonesia to have regulations regarding the resolution of issues relating to the use of GSO slots in the national interest. Indonesia proposes a special legal regime that recognizes GSO as an independent territorywithout disturbing existing rules. This research emphasizes the urgency of creating a fair and equitable legal framework in utilizing GSO, especially for developing countries. This research aims to explore legal certainty in the context of space utilization and exploration, especially Geostationary Orbit (GSO). This research uses a normative juridical method with the main approaches, namely legislation and conceptual. The results show the needfor a special regime that regulates the utilization of GSO so that there is no gap between countries with the principles of maintaining peace, justice and mutual benefit, with the hope of providing clarity of rules and guidelines in exploring and utilizing space safely and fairly.

Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience

Satellite communications systems provide a means to connect people and devices in hard-to-reach locations. Traditional geostationary orbit (GEO) satellite systems and low Earth orbit (LEO) constellations, having their own strengths and weaknesses, have been used as separate systems serving different markets and customers. In this article, we analyze how satellite systems in different orbits could be integrated together and used as a multi-layer satellite system (MLSS) to improve communication services. The optimization concerns combining the strengths of different layers that include a larger coverage area as one moves up by each layer of altitude and a shorter delay as one moves down by each layer of altitude. We review the current literature and market estimates and use the information to provide a thorough assessment of the economic, regulatory, and technological enablers of the MLSS. We define the MLSS concept and the architecture and describe our testbed and the simulation tools used as a comprehensive engineering proof-of-concept. The validation results confirm that the MLSS approach can intelligently exploit the smaller jitter of GEO and shorter delay of LEO connections, and it can increase the availability and resilience of communication services. As a main conclusion, we can say that multi-layered networks and the integration of satellite and terrestrial segments seem very promising candidates for future 6G systems.

Hue Channel Mesoscale Convective System Identification Techniques using Kalpana Geostationary Satellite Observations

Climate change is leading to sudden extreme weather events like floods and heavy rain to occur. One of the most significant rain-bearing systems, Mesoscale Convective Systems (MCSs), is in charge of catastrophic rainfall and flood events that may cause loss of life and property. MCSs are one of the vital components of the climate system on Earth. Many studies contributed to two significant steps, MCS identification and tracking. There are a variety of algorithms focused on tracking whereas for MCS identification only limited methods are present. They contribute to global as well as regional climate patterns by transporting heat and moisture. It is necessary to identify MCS properly to track MCS occurrences over time and comprehend their typical lifecycle to get early warnings of their existence. Three MCS identification techniques based on the Hue channel of the Hue Saturation Value (HSV) color model are implemented in this research, and their effectiveness is assessed. These techniques execute segmentation based on Hue channel Thresholding (HT), K means clustering combined with Hue channel Thresholding (KMCHT) and the modified Source Apportionment Technique combined with Hue channel Thresholding (SATHT). Image pixel values are used to depict infrared brightness temperature data obtained from the Indian geostationary satellite Kalpana-1. The generated ground truth images and performance measurements are used to assess the effectiveness of the methods. The proposed SATHT method for multiple cloud segmentation results in superior performance metrics than the HT and KMCHT approaches.

Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI

Surface upward longwave radiation (SULR) is one of the four components of surface net radiation. Geostationary satellites can provide high temporal but coarse spatial resolution SULR products. Downscaling coarse SULR to a higher resolution is important for fine-scale thermal condition monitoring. Statistical regression downscaling is widely used due to its simplicity and is built on the assumption that the thermal parameter like land surface temperature (LST) or SULR has a relationship with the related surface factors like the normalized difference vegetation index (NDVI), and the relationship remains unchanged in any scales. In this study, to establish the relationship between SULR and the related surface factors, we chose the multiple linear regression (MLR) model and five surface factors (i.e., the modified normalized difference water index (MNDWI), normalized difference built-up and soil index (NDBSI), NDVI, normalized moisture difference index (NMDI), and urban index (UI)) to drive the downscaling process. Additionally, a step-by-step downscaling strategy was applied to reach the 100-fold increase in spatial resolution, transitioning the estimated SULR from 4 km of the advanced geostationary radiation imager (AGRI) onboard FengYun-4B (FY-4B) satellite to 40 m of the visual and infrared multispectral imager (VIMI) in infrared spectrum onboard GaoFen5-02 (GF5-02). Finally, we evaluated the downscaling results by comparing the downscaled SULR values with the in situ measured SULR and GF5-02-calculated SULR, and the root mean square errors (RMSEs) were 19.70 W/m2 and 24.86 W/m2, respectively. Throughout this MLR-based step-by-step downscaling method (high-frequency data from FY-4B and high spatial resolution data from GF5-02), high spatiotemporal SULR (15 min temporal resolution, 40 m spatial resolution) were successfully generated instead of coarse spatial resolution ones from the FY-4B satellite or a coarse temporal resolution one from the GF5-02 satellite, relieving the above-mentioned conflict to some extent.

Semidiurnal Non‐Migrating Tides in the Middle Thermosphere From Far Ultraviolet Observations

AbstractMuch of the longitude/local time dependence of the thermosphere is controlled by non‐migrating tides. Observations of semidiurnal (12‐hr) tides between 120 and 200 km altitude, that is, the middle thermosphere, are rare owing to the lack of systematic measurements in this region. Since late 2018, the Global‐scale Observations of the Limb and Disk (GOLD) Mission has provided unique measurements of thermospheric disk temperature and the column density ratio of atomic oxygen to molecular nitrogen ratio (ΣO/N2) from geostationary orbit. In this paper, we present an approach to deduce the strongest semidiurnal non‐migrating tides in the middle thermosphere by adapting the method of Krier et al. (2021, https://doi.org/10.1029/2021ja029563) that deduces diurnal non‐migrating tides in simultaneous observations of temperature and ΣO/N2 made by GOLD. Testing of this approach suggests that the principal sources of uncertainties in the derived semidiurnal non‐migrating tides are the limitation on the longitudes sampled, such that uncertainties are higher for tides with longer horizontal wavelength, and contamination of the local time sums by stationary planetary waves, which causes amplitudes to be overestimated. Our approach is applied to GOLD data during solstice conditions in 2019–2021. Comparison to models yield disagreements which are likely due to uncertainties intrinsic to the method and/or misrepresentation of tidal dynamics in the models. These results are the first observations of semidiurnal non‐migrating tides in the middle thermosphere from a geostationary observational platform.

Void Formation and Radiation‐Induced Ion Migration in Perovskite Solar Cells under 10 MeV Proton Radiation

Perovskite solar cell (PSC) technology is a promising candidate for space applications because of its high power‐to‐weight ratio, low‐cost fabrication process, and good tolerance to high‐energy particle radiation. In this work, perovskite films and resultant high‐efficiency PSCs are assessed under 10 MeV proton radiation at fluences in the range 1e12–1e14 p cm−2, which are equivalent to 1 to 100 years in geostationary orbit (GEO) without any shielding or cover. For the first time, void formation and material ablation are detected on perovskite films, indicating structural damage of the materials under the proton radiation. Furthermore, ions inside the devices especially Au and Pb ions are displaced to underlying layers under the proton bombardment. These lead to the degradation of PSCs to ≈89% of the initial performance (from 24.1% to 21.4%) at the highest dose. The experimental results are supported by previous simulation works with a good fit in all optoelectronic parameters. This study provides insights into the degradation mechanism of PSCs under proton radiation and paves the way for the utilization of PSCs in space applications.

A GEO-GEO Stereo Observation of Diurnal Cloud Variations over the Eastern Pacific

Fast atmospheric processes such as deep convection and severe storms are challenging to observe and understand without adequate spatiotemporal sampling. Geostationary (GEO) imaging has the advantage of tracking these fast processes continuously at a cadence of the 10 min global and 1 min mesoscale from thermal infrared (TIR) channels. More importantly, the newly-available GEO-GEO stereo observations from our 3D-Wind algorithm provide more accurate height assignment for atmospheric motion vectors (AMVs) than those from conventional TIR methods. Unlike the radiometric methods, the stereo height is insensitive to radiometric TIR calibration of satellite sensors and can assign the feature height correctly under complex situation (e.g., multi-layer clouds and atmospheric inversion). This paper shows a case study from continuous GEO-GEO stereo observations over the Eastern Pacific during 1–5 February 2023, to highlight diurnal variations of clouds and dynamics in the planetary boundary layer (PBL), altocumulus/congestus, convective outflow and tropical tropopause layer (TTL). Because of their good vertical resolution, the stereo observations often show a wind shear in these cloud layers. As an example, the stereo winds reveal the classic Ekman spiral in marine PBL dynamics with a clockwise (counterclockwise) wind direction change with height in the Northern (Southern) Hemisphere subtropics. Over the Southeastern Pacific, the stereo cloud observations show a clear diurnal variation in the closed-to-open cell transition in the PBL and evidence of precipitation at a lower level from broken stratocumulus clouds.

Transformer-based self-supervised image super-resolution method for Rotating Synthetic Aperture system via multi-temporal fusion

Rotating Synthetic Aperture (RSA) technology is one of the distinctly advantageous Earth geostationary orbit optical remote sensing technologies. However, the continuous rotation of the RSA system’s rectangular primary mirror results in a discernible drop in resolution along the shorter side of the mirror. Additionally, the captured images exhibit periodic and time-varying characteristics. To improve the image quality to meet interpretation needs, we first delineate the imaging process of the rotating primary mirror and analyze the characteristics of image degradation based on the system’s imaging mechanism. Then, we propose a dual super-resolution (SR) framework based on Swin Transformer and introduce a self-supervised learning method for jointly training the unified SR network using wavelet fusion. The self-supervised learning method effectively utilizes the spatiotemporal correlation of the information contained in images captured at different rotation directions of the rectangular pupil. Moreover, the attention mechanism in Transformer can adopt a global perspective and utilize content-based interactions between image content and attention weights to model strong long-range dependencies in remote sensing images. This approach significantly enhances image quality along the pupil’s shorter side, consequently yielding superior results. Extensive digital and semi-physical imaging experiments, involving six aspect ratios of the primary mirror, demonstrate that our SR method surpasses state-of-the-art methods. The work in this paper can serve as a valuable reference for future space applications of the RSA technology.

Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNet

Solar energy has emerged as a key industry in the field of renewable energy due to its universality, harmlessness, and sustainability. Accurate prediction of solar radiation is crucial for optimizing the economic benefits of photovoltaic power plants. In this paper, we propose a novel spatiotemporal attention mechanism model based on an encoder-translator-decoder architecture. Our model is built upon a temporal AttUNet network and incorporates an auxiliary attention branch to enhance the extraction of spatiotemporal correlation information from input images. And utilize the powerful ability of edge intelligence to process meteorological data and solar radiation parameters in real-time, adjust the prediction model in real-time, thereby improving the real-time performance of prediction. The dataset utilized in this study is sourced from the total surface solar incident radiation (SSI) product provided by the geostationary meteorological satellite FY4A. After experiments, the SSIM has been improved to 0.86. Compared with other existing models, our model has obvious advantages and has great prospects for short-term prediction of surface solar incident radiation.

Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data

ABSTRACT Geostationary satellites have the capability to offer AOD products with a higher frequency of observation within a given period, thus improving the geographical coverage of averaged AOD products compared to polar satellites. Moreover, the averaged AOD of geostationary satellites is more reflective of the average aerosol load as compared to AOD products derived from polar orbit satellites. Despite this, there is still an absence of comprehensive research on the comparative representativeness of AOD mean products from geostationary and polar orbit satellites, and most current research only focuses on retrieval accuracy. This paper compares the geographical coverage of averaged AOD products released by the Advanced Baseline Imager (ABI) sensor on the Geostationary Operational Environmental Satellite-R (GOES-R) (09:00 UTC-23:00 UTC) with AOD products released by the Moderate-resolution Imaging Spectroradiometer (MODIS) (16:00 UTC and 19:00 UTC) on hourly, daily, semi-monthly and monthly scales. Moreover, the Aerosol Robotic Network datasets were used to evaluate the accuracy of ABI AOD and to propose usage suggestions. In terms of daily AOD products, the AOD daily mean generated by the geostationary satellite GOES-R/ABI consistently outperforms the AOD daily mean generated by MODIS AOD in terms of spatial coverage. However, on monthly scales, the difference is no longer significant. With regard to accuracy, it is proved that when the time scale of averaging is gradually expanded, root mean square error (RMSE) and mean absolute error (MAE) gradually decrease. On the season scale, ABI AOD exhibits the highest level of accuracy during the autumn season (September, October, November); on the spatial scale, ABI AOD exhibits the best accuracy in North America. Therefore, if the daily, semi-monthly or monthly averaged ABI AOD datasets are used, the recommended time is autumn, and the study area is North America.

Improving the Estimation of PM2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest

Fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) profoundly affects environmental systems, human health and economic structures. Multi-source data and advanced machine or deep-learning methods have provided a new chance for estimating the PM2.5 concentrations at a high spatiotemporal resolution. In this paper, the Random Forest (RF) algorithm was applied to estimate hourly PM2.5 of the North China area (Beijing–Tianjin–Hebei, BTH) based on the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) aerosol optical depth (AOD) products. To improve the estimation of PM2.5 concentration across large areas, we construct a method for co-weighting the environmental similarity and the geographical distances by using an attention mechanism so that it can efficiently characterize the influence of spatial–temporal information hidden in adjacent ground monitoring sites. In experiment results, the hourly PM2.5 estimates are well correlated with ground measurements in BTH, with a coefficient of determination (R2) of 0.887, a root-mean-square error (RMSE) of 18.31 μg/m3, and a mean absolute error (MAE) of 11.17 µg/m3, indicating good model performance. In addition, this paper makes a comprehensive analysis of the effectiveness of multi-source data in the estimation process, in this way, to simplify the model structure and improve the estimation efficiency of the model while ensuring its accuracy.

Optimizing Propellant Distribution for Interorbital Transfers

The advent of space exploration missions, especially those aimed at establishing a sustainable presence on the Moon and beyond, necessitates the development of efficient propulsion and mission planning techniques. This study presents a comprehensive analysis of chemical and electric propulsion systems for spacecraft, focusing on optimizing propellant distribution for missions involving transfers from Low-Earth Orbit (LEO) to Geostationary Orbit (GEO) and the Lunar surface. Using mathematical modeling and optimization algorithms, we calculate the delta-v requirements for key mission segments and determine the propellant mass required for each propulsion method. The results highlight the trade-offs between the high thrust of chemical propulsion and the high specific impulse of electric propulsion. An optimization model is developed to minimize the total propellant mass, considering a hybrid approach that leverages the advantages of both propulsion types. This research contributes to the field of aerospace engineering by providing insights into propulsion system selection and mission planning for future exploration missions to the Moon, Mars, and Venus.

Performance of the QUIC transport protocol over geostationary satellite links

Performance of the QUIC transport protocol over geostationary satellite links

AI-optimised site diversity switching for geostationary satellite links

AI-optimised site diversity switching for geostationary satellite links

Effect of satellite availability and time delay of corrections on position accuracy of differential NavIC

Abstract The position accuracy of standalone Navigation with Indian Constellation (NavIC) may not be met for certain applications like civil aviation. To improve the position accuracy of the user receiver, the technique used is Differential NavIC, which makes use of Differential corrections. The position accuracy of the user receiver also depends on the satellite availability (i.e. number of satellites available at a certain time instant to estimate the user position) and delay in transmission of differential corrections. In this article, the analysis of differential NavIC using different numbers of visible satellites (satellite availability) and different time delays for transmission of corrections is carried out. For this analysis, three cases are considered, Case I is when all (six) NavIC satellites are visible, Case II is when five satellites (3 Geosynchronous Orbit (GSO) and 2 Geostationary Earth Orbit (GEO)) are visible, and Case III is when five satellites (3 GEO and 2 GSO) are visible. The comparative analysis for these three cases is carried out with respect to the position accuracy parameters and Geometric Dilution of Precision. It is observed that, with the satellite availability in case I and case III, the user receiver accuracy is approximately the same. In case III, the accuracy of the user receiver (3.08 m) is similar to the accuracy (3.09 m) in case I. For time delay in transmission of corrections, different time delays (0, 5, 10, 20, …, 300 s) are considered to observe the effect on the positional accuracy of the user receiver. Due to the increase in this time delay, there is a significant degradation in the user receiver position accuracy of differential NavIC.

Multiband photometric observations of GEO objects through Sloan filters

With the precipitous growth of the population of objects in Earth's orbit, all major space agencies and companies have made space surveillance a priority to ensure the safety of space missions. With the application of the methods of space surveillance, this article aims to characterize the population of objects in the Geostationary Earth Orbit (GEO) using optical measurements across multiple spectral bands. The study takes place in a context in which the existence of space debris poses a continuous hazard to the operations of active satellites in GEO. Photometric observations of GEO objects using Sloan filters were obtained with SCUDO, a small-aperture telescope part of the Sapienza network. Observations are divided into categories, including 11 objects: rocket bodies (4), defunct satellites (3), and active satellites (4). The spectral response of these objects has been analyzed leading to the identification of a photometric signature which was repeated for two Eutelsat satellites observed under same conditions. All the observed targets resulted in a lower magnitude in the near-infrared. Color indexes also indicate that they are redder than the Sun. This is particularly evident for SL-12 rocket bodies, whose g’-z’ reaches 1.5.

Systematically tracking the hourly progression of large wildfires using GOES satellite observations

Abstract. In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develop the Geostationary Operational Environmental Satellites (GOES)-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a product of hourly fire perimeters, active-fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019 to 2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters using 12 h data from the Visible Infrared Imaging Radiometer Suite (VIIRS)-derived Fire Event Data Suite (FEDS) and final fire perimeters from the California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has a coarser resolution (2 km at the Equator), the final fire perimeters from GOFER correspond reasonably well to those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP; the IoU indicates the area of overlap over the area of the union relative to the reference perimeters, in which 0 is no agreement and 1 is perfect agreement. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery, where perimeters are available at intervals of 12 h or longer or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km h−1. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in the rates of fire spread on diurnal timescales. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates (https://doi.org/10.5281/zenodo.8327264, Liu et al., 2023).

Drought triggers and sustains overnight fires in North America.

Overnight fires are emerging in North America with previously unknown drivers and implications. This notable phenomenon challenges the traditional understanding of the 'active day, quiet night' model of the diurnal fire cycle1-3 and current fire management practices4,5. Here we demonstrate that drought conditions promote overnight burning, which is a key mechanism fostering large active fires. We examined the hourly diurnal cycle of 23,557 fires and identified 1,095 overnight burning events (OBEs, each defined as a night when a fire burned through the night) in North America during 2017-2020 using geostationary satellite data and terrestrial fire records. A total of99% of OBEs were associated with large fires (>1,000 ha) and at least one OBE was identified in 20% of these large fires. OBEs were early onset after ignition and OBE frequency was positively correlated with fire size. Although warming is weakening the climatological barrier to night-time fires6, we found that the main driver of recent OBEs in large fires was the accumulated fuel dryness and availability (that is, drought conditions), which tended to lead to consecutive OBEs in a single wildfire for several days and even weeks. Critically, we show that daytime drought indicators can predict whether an OBE will occur the following night, which could facilitate early detection and management of night-time fires. We also observed increases in fire weather conditions conducive to OBEs over recentdecades, suggesting an accelerated disruption of the diurnal fire cycle.