Earth Observing System Satellite Research Articles

On-orbit geometric calibration and preliminary accuracy verification of GaoFen-14 (GF-14) optical two linear-array stereo camera

ABSTRACT The GaoFen-14 (GF-14) satellite is China’s most recent high-resolution earth observation satellite system. It is equipped with a two linear-array stereo camera and is intend for topographic mapping at 1:10,000-scale without ground control points (GCPs). The technical parameters of payloads will change once the satellite enters orbit. As a result, strict on-orbit geometric calibration is necessary. This study performs the on-orbit geometric calibration of the GF-14 stereo camera using ground calibration data. The exterior orientation errors are corrected by a comprehensive bias matrix, internal orientation errors are described by a fifth order polynomial. The results of Yinchuan calibration field show that the planar accuracy is better than 286 m (RMS) before calibration, and improved to 2.11 m (front camera) and 1.51 m (back camera) by external orientation, and further improved to 1.46 m (front camera) and 0.99 m (back camera) after internal calibration. The front intersection accuracy reaches 0.67 m in plane (RMS) and 1.10 m in elevation (RMS), respectively. Checking by multiple global check points (CKPs), the planar and elevation RMS reaches 2.38 m (5.09 m in CE90) and 2.08 m (3.43 m in LE90), respectively.

Integrating Earth observation IMINT with OSINT data to create added-value multisource intelligence information: A case study of the Ukraine–Russia war

The Russian invasion of Ukraine on 24 February 2022 heralded a new “social media war” era. This “hybrid warfare” extends beyond the military landscape and includes attacks in cyberspace and fake news with the aim of destabilising governments. The goal of this paper is to present a high-level of architecture based on imagery intelligence (IMINT) and geospatial intelligence (GEOINT) using geographic datasets and state-of-the-art methods. Integration with intelligence information (like Open-Source Intelligence [OSINT]) produces multiintelligent knowledge for security and defence decision-making end users. The results depict a harmonious and creative collaboration between IMINT, OSINT, and GEOINT. OSINT data helps to identify and describe the meteorological conditions that are present, contributing to the procedure’s responsiveness. Weather and dense cloud cover above Ukraine poses a challenge for optical imaging satellites, but synthetic aperture radar (SAR) sensor satellites can operate at night and overcome the problem. We carried out OSINT and IMINT analysis, monitoring the situation shortly after the invasion. OSINT data helped in the choice of an appropriate area of interest. Using the right Earth observation satellite system and artificial intelligence/machine learning algorithms is the best way to keep an eye on many different sites over long periods, send out alerts about unusual activity, and find new places where incoherent changes might be happening.

Exploring the relationship between soil chemical composition and NDVI index using AI

This scientific article presents the results of research focused on developing a method for predicting the Normalized Difference Vegetation Index (NDVI) based on soil chemical composition using a multilayer artificial intelligence (AI) model. This method aims to improve the accuracy and predictive capability of land resource assessment, as well as the impact of chemical factors on vegetation. The study involved collecting soil chemical composition data in various conditions, providing a wide range of information for analysis. For NDVI assessment, a key indicator of vegetation condition, data from modern Earth observation satellite systems were used. The central aspect of the research is the multilayer AI model based on the Rosenblatt perceptron, capable of detecting complex nonlinear relationships between soil chemical parameters and NDVI. The training algorithm was tuned for maximum accuracy and generalization of results. The results show that the developed model provides high accuracy in NDVI predictions, making it an important tool for agriculture, ecology, and sustainable land use. These findings highlight the potential of using AI and soil data to optimize agricultural production, monitor ecosystems, and manage land resources.

Design and Fabrication of an X-Band Low Noise Amplifier Using FR-4 for Military Radar and Ground Station Receiver Applications

The low noise amplifier (LNA) plays an important role in the radiofrequency receiver front-ends, its main function is to amplify the weak receiving signal from the ground noise, as well as improving the receiver sensitivity. For LNAs which operate in the frequencies higher than the S-band, the printed circuit board (PCB) with high cost substrate materials have been used in almost designs so far, thus increasing the total price of the entire receiving unit. This paper introduces a new approach, in which a LNA has been designed using the FR-4 material, a common, low-cost substrate in PCB fabrication. The proposed LNA will maintain the quality of all of the important parameters such as gain, noise figure in compared with the LNAs designed by high cost material substrates. The stepped impedance matching technique is used in order to reach a balance between the circuit dimension and its efficiency. The frequency range of the proposed LNA lies within the X-band, which is a suitable range for military RADAR applications. Furthermore, it is possible to apply the desired LNA in the ground station receiver front-ends of a Low Earth Orbit (LEO) Earth Observation Satellite system

BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING

Abstract. Global warming modifies the climate balance. Warming parameters are observed by many Earth Observation satellite systems, and the huge amount of data modifies the way to process them. This paper presents a few studies relative to damage detection on buildings, occurred during natural disasters. Recent advances in deep learning techniques are used for the building detection such as EfficientNet networks. Additional networks as Siamese models are used to evaluate the damage level with pre- and post-event images. Different techniques to merge detection masks are described and compared to a multiclass segmentation network. Results are presented and performances of the different solutions are compared.

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

Earth observation satellite (EOS) systems often encounter emergency observation tasks oriented to sudden disasters (e.g., earthquake, tsunami, and mud-rock flow). However, EOS systems may not be able to provide feasible coverage time windows for emergencies, which requires that an appropriately selected satellite transfers its orbit for better observation. In this context, we investigate the orbit maneuver optimization problem. First, by analyzing the orbit coverage and dynamics, we construct three models for describing the orbit maneuver optimization problem. These models, respectively, consider the response time, ground resolution, and fuel consumption as optimization objectives to satisfy diverse user requirements. Second, we employ an adaptive differential evolution (DE) integrating ant colony optimization (ACO) to solve the optimization models, which is named ACODE. In ACODE, key components (i.e., genetic operations and control parameters) of DE are formed into a directed acyclic graph and an ACO is appropriately embedded into an algorithm framework to find reasonable combinations of the components from the graph. Third, we conduct extensive experimental studies to show the superiority of ACODE. Compared with three existing algorithms (i.e., EPSDE, CSO, and SLPSO), ACODE can achieve the best performances in terms of response time, ground resolution, and fuel consumption, respectively.

Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition?

Abstract. Improvements in air quality and Earth's climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine aerosol composition measurements from ground-based networks are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, an airborne field campaign carried out over the US during the summer of 2013). We find distinct optical signatures for AMTs such as biomass burning (from agricultural or wildfires), biogenic and polluted dust. We find that all four AMTs, studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from a few combinations of airborne in situ aerosol optical properties (e.g., extinction Ångström exponent, absorption Ångström exponent and real refractive index). However, we find that the optically based classifications for biomass burning from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results related to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol dataset to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosols, Cloud, Convection and Precipitation (ACCP) designated observables).

Automatic generation of event sequence diagrams for guiding simulation based dynamic probabilistic risk assessment (SIMPRA) of complex systems

Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past decades to evaluate the risks associated with complex systems such as nuclear power plants, space missions, chemical plants, and military systems. A critical step in DPRA is generating risk scenarios which are used to enumerate and assess the probability of different outcomes. The classical approach to generating risk scenarios is not, however, sufficient to deal with the complexity of the above-mentioned systems.The primary contribution of this study is in offering a new method for capturing different types of engineering knowledge and using them to automatically generate risk scenarios, presented in the form of generalized event sequence diagrams, for dynamic systems. In this study, guidelines of this new method are explained in detail. In addition, in order to show the procedure of applying the proposed method on a complex system with industrial application, earth observation satellite systems are studied, and a planner is developed for these satellites. This planner is applied to a lunar reconnaissance orbiter satellite, which is an example of a earth observation satellite system, and risk scenarios are generated.

Capabilities of Commercial Satellite Earth Observation Systems and Applications for Nuclear Verification and Monitoring

A growing number of commercial Earth observation satellite systems provide capabilities with significant application in nuclear verification, monitoring, and proliferation analysis. This article provides some relevant examples and a case study describing the importance of spatial, spectral, and temporal resolution on detectability of ground targets and monitoring of activity. The article also provides an overview of 300 operational (as of September 2021) optical and radar systems with a ground resolution of 5 m or better, whose imagery is available to the public. By merging all satellites into one super-constellation, a simulation was performed to describe its potential coverage. The analysis suggests that with current commercial capabilities it would be possible to image newly discovered alleged ICBM fields in China every few hours with a ground resolution sufficient to detect new construction and missile uploading.

EARTH OBSERVATION AND GLOBAL NAVIGATION SATELLITE SYSTEMS ANALITICAL REPORT PART II (TIMING & SYNCHRONISATION OF TELECOMMUNICATION NETWORKS, MARITIME AND INLAND WATERWAYS, RAIL AND AUTOMOTIVE TRANSPORT)

The EU Space Programme is a business growth enabler that stimulates the economy and pushes the bar of innovation. The EUSPA EO & GNSS Market Report is the ultimate guide to anyone who seeks to make the EU Satellite Navigation and Earth Observation technologies part of their business plan and develop new space downstream applications. More than ever society relies on innovative solutions to deal with the big data paradigm. Earth Observation (EO) and Global Navigation Satellite System (GNSS) data is becoming increasingly important to these innovative solutions through dozens of applications that are emerging or already in use by citizens, businesses, governments, industry, international organisations, NGOs and researchers around the world. The study provides analytical information on the dynamic GNSS and EO markets, along with indepthanalyses of the latest global trends and developments through illustrated examples and use cases. Using advanced econometric models, it also offers market evolution forecasts of GNSS shipments or EO revenues spanning to 2031. This article represent the brief overview essential role of space data across market segments including, timing & synchronisation of telecommunication networks, maritime and inland waterways, rail and automotive transport.

EARTH OBSERVATION AND GLOBAL NAVIGATION SATELLITE SYSTEMS ANALITICAL REPORT PART I (AVIATION AND SPACE)

The EU Space Programme is a business growth enabler that stimulates the economy and pushes the bar of innovation. The EUSPA EO & GNSS Market Report is the ultimate guide to anyone who seeks to make the EU Satellite Navigation and Earth Observation technologies part of their business plan and develop new space downstream applications. More than ever society relies on innovative solutions to deal with the big data paradigm. Earth Observation (EO) and Global Navigation Satellite System (GNSS) data is becoming increasingly important to these innovative solutions through dozens of applications that are emerging or already in use by citizens, businesses, governments, industry, international organisations, NGOs and researchers around the world. The study provides analytical information on the dynamic GNSS and EO markets, along with indepth analyses of the latest global trends and developments through illustrated examples and use cases. Using advanced econometric models, it also offers market evolution forecasts of GNSS shipments or EO revenues spanning to 2031. With a focus on Galileo/EGNOS and Copernicus, the report highlights the essential role of space data across 17 market segments including, Agriculture; Aviation and Drones; Biodiversity, Ecosystems and Natural Capital; Climate Services; Consumer Solutions, Tourism, and Health; Emergency Management and Humanitarian Aid; Energy and Raw Materials; Environmental Monitoring; Fisheries and Aquaculture; Forestry; Infrastructure; Insurance and Finance; Maritime and Inland Waterways; Rail; Road and Automotive; Urban Development and Cultural Heritage; and Space. This article represent the brief overview essential role of space data across 2 market segments including, Aviation and Drones and Rail, Road and Automotive.

System demonstrations of Ka‐band 5‐Gbps data transmission for satellite applications

SummarySatellite‐ground‐link high‐rate high‐efficiency data transmission technique is one of the key supports for the development of high‐resolution low‐earth‐orbit (LEO) earth observation satellite systems. Ka‐band, frequency ranging from 24.6 to 40 GHz, shares a wide available bandwidth, high frequency reuse rate, and strong ability of anti‐jamming. Satellite operators have utilized the Ka‐band for satellite‐earth data transmission, which requires higher traffic capacity, and even have planned to launch new satellites with Ka‐band capacity. In this paper, we propose and experimentally demonstrate a 5‐Gbps uncoded data rate (corresponding to the channel rate of 6 Gbps) of Ka‐band data‐transmission system with dual‐circular polarization and frequency reuse for satellite‐earth applications. The objective of the experiment is gathering experimental data to validate the performance of the proposed four channel systems, including bit error ratio (BER) and link budget. Our work could support future satellite communication applications and meet the imperative demands of high rate transmission to earth.

A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism.

While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.

NO2 anomalies - Economy attribution and rapid climate response

Close to two decades of NASA Earth Observing System satellite data and algorithms refinements present unique opportunity to revisit the attributions of variability of tropospheric pollutants to economic activity, as well as pollutants contribution to the anthropogenic radiative forcing. The nitrogen dioxide (NO2) is one of the top pollutants produced by industrial and power plants and traffic. That long data record now covers events of economic variability like the recession of 2008/2009, as well as the effects of COVID-19 closures of 2019/2020. In particular, the COVID-19 lockdowns in the winter of 2019/2020 and the spring of 2020 resulted in contracting economies around the world. While numerous imageries of reduced nitrogen dioxide (NO2) appeared to be popular, few studied gave numerical assessment of how much of the anomalies is attributable to the economic slowdown. Furthermore, few studies touched on the possible climate offsets under the reduced emissions. Using principal component (PC) analysis of 16 years of NO2 monthly series data from the Ozone Monitoring Instrument on the Aura satellite, we show that it is the third PC (PC3) from the deseasonalized hierarchy of principal modes that is best coupled with the economic indicators. This coupling is positive, i.e. PC3 and economic indicators manifest positive covariance. However, the economic variability can explain only 40% of the information in PC3. Furthermore, this mode by itself explains only 3% of the total deseasonalized NO2 variability. We therefore conclude that, while having an unambiguous impact, the economy can be awarded at best third order of importance in the NO2 departures from the seasonal averages.Once we identified PC3 as the NO2 mode that is coupled with the economic variability, we use this mode as an indicator and look for rapid climate adjustments to that part of the NO2 variability that we are confident is coupled with the economic variability. We focus on observational data from the Atmospheric Infrared Sounder (AIRS) on board the NASA Aqua satellite, decompose the series of surface skin temperature and clear-sky outgoing longwave radiances (OLR) into principal components, and identify potential impacts of the NO2 PC3 on these climate variables.

Model for selecting the Earth observation satellite systems by object recognition probability

Model for selecting the Earth observation satellite systems by object recognition probability

Evaluation of satellite-based aerosol datasets and the CAMS reanalysis over the ocean utilizing shipborne reference observations

Abstract. Reliable reference measurements over the ocean are essential for the evaluation and improvement of satellite- and model-based aerosol datasets. Within the framework of the Maritime Aerosol Network, shipborne reference datasets have been collected over the Atlantic Ocean since 2004 with Microtops Sun photometers. These were recently complemented by measurements with the multi-spectral GUVis-3511 shadowband radiometer during five cruises with the research vessel Polarstern. The aerosol optical depth (AOD) uncertainty estimate of both shipborne instruments of ±0.02 can be confirmed if the GUVis instrument is cross calibrated to the Microtops instrument to account for differences in calibration, and if an empirical correction to account for the broad shadowband as well as the effects of forward scattering is introduced. Based on these two datasets, a comprehensive evaluation of aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) flown on NASA's Earth Observing System satellites, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat satellite, and the Copernicus Atmosphere Monitoring Service reanalysis (CAMS RA) is presented. For this purpose, focus is given to the accuracy of the AOD at 630 nm in combination with the Ångström exponent (AE), discussed in the context of the ambient aerosol type. In general, the evaluation of MODIS AOD from the official level-2 aerosol products of C6.1 against the Microtops AOD product confirms that 76 % of data points fall into the expected error limits given by previous validation studies. The SEVIRI-based AOD product exhibits a 25 % larger scatter than the MODIS AOD products at the instrument's native spectral channels. Further, the comparison of CAMS RA and MODIS AOD versus the shipborne reference shows similar performance for both datasets, with some differences arising from the assimilation and model assumptions. When considering aerosol conditions, an overestimation of AE is found for scenes dominated by desert dust for MODIS and SEVIRI products versus the shipborne reference dataset. As the composition of the mixture of aerosol in satellite products is constrained by model assumptions, this highlights the importance of considering the aerosol type in evaluation studies for identifying problematic aspects.

Online scheduling of distributed Earth observation satellite system under rigid communication constraints

This paper focused on online scheduling of distributed Earth observation satellite system in a dynamic environment. The objective was to maximize the total profit of the overall system by efficiently coordinating the different satellites with stochastic arrival of urgent tasks, subject to rigid communication and observation time window constraints. We formulated this problem as a single-task, single-robot, time-extended assignment problem with intra-schedule dependency using the multi-robot task assignment taxonomy and formulated the sub-problem after releasing urgent tasks in a mixed-integer linear programming model. We first described the online scheduling algorithm for a single satellite, then we proposed the modified consensus-based bundle algorithm (m-CBBA) and modified asynchronous consensus-based bundle algorithm (m-ACBBA) with synchronous and asynchronous communication, respectively. Compared with initial versions of CBBA and ACBBA, the modified versions added the communication loop prediction phases to efficiently utilize scarce communication opportunities and reduce the communication requirements. Additionally, we introduce two contract net protocol (CNP)-based algorithms for comparison, respectively SingleItem-CNP-based (SI-CNP) algorithm and Batch-CNP-based (BA-CNP) algorithm. Computational experiments indicated that both the total profit and percentage of scheduled urgent tasks achieved by the m-ACBBA and m-CBBA algorithms were much higher than those achieved by both SI-CNP and BA-CNP. Additionally, the number of communications needed by either m-ACBBA or m-CBBA algorithm was lower than that by SI-CNP. When the communication cost in the system is high, the m-CBBA algorithm is preferred because it balances the profit and the required number of communications. When the communication cost is low, the m-ACBBA algorithm is preferred because it achieves high total profit and high percentage of scheduled urgent tasks.

Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting

Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.

Research Progress on Requirements Integrated Preprocessing and Mission Planning for Earth Observation Satellites

The scale of Earth observation satellite system continues to expand and its application is becoming more and more extensive, which puts forward higher requirements for satellite mission planning system. This paper summarizes and analyzes some key issues in the mission planning of earth observation satellites. Firstly, the development status and the trend of earth observation satellites are introduced. Secondly, we summarize and analyze complex requirements of observation decomposition method, the priority of requirements determination method, the model construction and solving methods of single-satellite scheduling, multi-satellite integrated scheduling and multi-satellite dynamic scheduling in the process of requirements Integrated Preprocessing and mission planning. Finally, we discuss the problems that still need to be solved and the direction of research.

VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long‐Term Aerosol Data Records

AbstractA primary goal of the Deep Blue (DB) project is to create consistent long‐term aerosol data records, suitable for climate studies, using multiple satellite instruments. In order to continue Earth Observing System (EOS)‐era aerosol products into the Joint Polar Satellite System era, we have successfully ported the DB algorithm to process data from the Visible Infrared Imaging Radiometer Suite (VIIRS). Although the basic structure of the VIIRS algorithm is similar to that for the Moderate Resolution Imaging Spectroradiometer (MODIS), many enhancements have been made compared to the MODIS collection 6 (C6) version. Most have also been implemented in the latest MODIS Collection 6.1 (C6.1). For example, a new smoke mask was developed based on the spectral curvature of measured reflectance to distinguish biomass burning smoke from weakly absorbing urban/industrial aerosols. Consequently, a new aerosol‐type flag was added into the VIIRS DB data set. In addition, new dust models have been developed to account for the nonsphericity of mineral dust. As a result, a discontinuity in the retrieved aerosol optical depth (AOD) of Saharan dust plumes seen in MODIS C6 products near the boundary between North Africa and the Atlantic has been much reduced. We have also evaluated the VIIRS and MODIS Terra/Aqua C6.1 AOD against Aerosol Robotic Network data. VIIRS and MODIS retrievals show similar performance; around 80% of matchups agree with Aerosol Robotic Network within the expected error of ±(0.05 + 20)%, indicating that DB can provide consistent AOD through the historical EOS and present Joint Polar Satellite System eras.