Observation Missions Research Articles

Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology

Timely crop yield information is needed for agricultural land management and food security. We investigated using remote sensing data from the Earth observation mission Sentinel-2 to monitor the crop phenology and predict the crop yield of sunflowers at the field scale. Ten sunflower fields in Mezőhegyes, southeastern Hungary, were monitored in 2021, and the crop yield was measured by a combine harvester. Images from Sentinel-2 were collected throughout the monitoring period, and vegetation indices (VIs) were extracted to monitor the crop growth. Multiple linear regression and two different machine learning approaches were applied to predicting the crop yield, and the best-performing one was selected for further analysis. The results were as follows. The VIs showed the highest correlation with the crop yield (R > 0.6) during the inflorescence emergence stage. The most suitable time for predicting the crop yield was 86–116 days after sowing. Random forest regression (RFR) was the best machine learning approach for predicting field-scale variability of the crop yield (R2 ∼ 0.6 and RMSE 0.284–0.473 t/ha). Our results can be used to develop a timely and robust prediction method for sunflower crop yields at the field scale to support decision-making by policymakers regarding food security.

Survey of Deep Learning for Autonomous Surface Vehicles in Marine Environments

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially in aging but still functional fleets, is described as being in a very early and emerging phase. This presents great challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This article reviews existing work on the implementation of DL methods in fields related to ASV. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations are presented. Finally, this survey is completed by highlighting current challenges and future research directions.

Optimising General Configuration of Wing-Sailed Autonomous Sailing Monohulls Using Bayesian Optimisation and Knowledge Transfer

Wing-sailed autonomous sailing monohulls are promising platforms used in various scenarios to provide data for marine science research. These platforms need to operate long-term in changing seas; their general configurations (size matching between sail, hull, and keel) necessitate careful trade-offs to balance safety and efficiency. Since autonomous sailboats are often designed for different observation missions, scientific pay-loads and target areas, their design space is considerably large. It is also challenging to obtain prior performance estimation from historical designs. Therefore, traditional offline surrogate-based simulation-driven design frameworks suffer from a large amount of sampling required, the computational cost of which remains too expensive for such ad hoc design tasks. This paper proposes an innovative, generalised simulation-driven framework combining Bayesian optimisation and knowledge transfer. It allows for high-quality, low-cost optimisation of autonomous sailing monohulls’ general configuration without initial design and prior performance estimation. The proposed optimisation framework has been used to optimise the ‘Seagull’ prototype within the design constraints. The optimised design exhibits significant performance improvements. At the same time, the results show that the present method is significantly superior to traditional offline methods. The authors believe that the proposed framework promises to provide the autonomous sailing community with a solution for a general design methodology.

Imaging effects due to pixel distortions in CdZnTe detectors—results from the HREXI Calibration Facility

ProtoEXIST2 (P2) was a prototype imaging X-ray detector plane developed for wide-field Time Domain Astrophysics (TDA) in the 5 - 200 keV energy band. It was composed of an 8 $\times$ 8 array of 5 mm thick, 2cm $\times$ 2cm pixelated (32 $\times$ 32) CdZnTe (CZT) detectors with a 0.6 mm pitch that utilize the NuSTAR ASIC(NuASIC) for readout. During the initial detector development process leading up to post-flight examination of the entire detector plane, distortions in expected pixel positions and shapes were observed in a significant fraction of the detectors. The HREXI (High Resolution Energetic X-ray Imager) Calibration Facility (HCF) was designed and commissioned to improve upon these early experiments and to rapidly map out and characterize pixel non-uniformities and defects within CZT detector planes at resolutions down to 50 $\rm \mu$m. Using this facility, the sub-pixel level detector response of P2 was measured at 100 $\rm \mu$m resolution and analyzed to extract and evaluate the area and profile of individual pixels, their morphology across the entire P2 detector plane for comparison with previous measurements and to provide additional characterization. In this article, we evaluate the imaging performance of a coded-aperture telescope using the observed pixel morphology for P2 detectors. This investigation will serve as an initial guide for detector selection in the development of HREXI detector planes, for the future implementation of the 4pi X-Ray Imaging Observatory (4piXIO) mission which aims to provide simultaneous and continuous imaging of the full sky ($\rm 4\pi$ sr) in the 3-200 keV energy band with $\rm \simeq$ 2 arcmin angular resolution and $\simeq$ 10 arcsec source localization, as well as other, future coded-aperture instruments.

Large-Scale Estimation of Hourly Surface Air Temperature Based on Observations from the FY-4A Geostationary Satellite

Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth’s surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China’s new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65–2.08 K for sample-based cross-validation, and 2.22–2.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets.

Guidance for autonomous spacecraft repointing under attitude constraints and actuator limitations

Current and future space observation missions need to perform many large-angle, multi-axis slew maneuvers between observations while keeping the scientific instrument’s attitude in a safe region. The state-of-practice typically divides each multi-axis maneuver into a series of single-axis sub-maneuvers, each of which is computed by restricting its guidance solution to the exact spacecraft momentum capacity. This ensures that the constraints are explicitly considered and results in a simple on-board implementation of the guidance algorithm, but is time-consuming and non-optimal for the whole multi-axis maneuver. Addressing this issue, this article presents a novel analytical guidance approach that relies on the convexity of the permissible attitude zone. The proposed guidance is time-optimal for a given spacecraft design and set of admissible observation targets. Both guidance approaches are compared using a multi-body/multi-actuator benchmark spacecraft, whose complex repointing phase requires an autonomous on-board guidance computation. It is shown that the proposed approach is systematic and that the reduction in maneuver time, compared to the state-of-practice approach, is considerable.

Reasoning-Based Scheduling Method for Agile Earth Observation Satellite with Multi-Subsystem Coupling

With the rapid development of agile Earth observation satellites (AEOSs), these satellites are able to conduct more high-quality observation missions. Nevertheless, while completing these missions takes up more data transmission and electrical energy resources, it also increases the coupling within each satellite subsystem. To address this problem, we propose a reasoning-based scheduling method for an AEOS under multiple subsystem constraints. First, we defined the AEOS mission scheduling model with multi-subsystem constraints. Second, we put forward a state variable prediction method that reflects the different coupling states of a satellite after analyzing the coupling relationships between various subsystems and identifying the primary limiting coupling states for each subsystem. Third, we established the reasoning rules corresponding to the planning strategies of different coupling states of the satellite by adding two planning strategies based on the planning strategies of existing planning methods. By comparing the proposed method to three heuristic scheduling methods and a meta-heuristic scheduling method, the results show that our method has better performance in terms of scheduling results and efficiency.

Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm

The increasing demand for low-cost space-borne Earth observation missions has led to small satellite constellation systems development. CubeSat platforms can provide a cost-effective multiple-mission space system using state-of-the-art technology. This paper presents a new approach to CubeSat constellation design for multiple missions using a multi-objective genetic algorithm (MOGA). The CubeSat constellation system is proposed to perform multi-missions that should satisfy global Earth observation and regional disaster monitoring missions. A computational approach using a class of MOGA named non-dominated sorting genetic algorithm II is implemented to optimize the proposed system. Pareto optimal solutions are found that can minimize the number of satellites and the average revisit time (ART) for both regional and global coverage while maximizing the percentage coverage. As a result, the study validates the feasibility of implementing the CubeSat constellation design with an acceptable level of performance in terms of ART and percentage coverage. Moreover, the study demonstrates CubeSat’s ability to perform a multi-missions.

Evaluation of Polarization Observation Accuracy of SGLI VNR-PL Using In-Orbit Calibration Data

The Second Generation Global Imager (SGLI) on the Global Change Observation Mission—Climate (GCOM-C) “SHIKISAI” has polarization observation channels at wavelengths of red (673.5 nm—P1) and near infrared (868.5 nm—P2), and it is expected to extract information of aerosols on land with higher accuracy than conventional observation methods by utilizing the scattering by atmospheric particles obtained by polarization observation. The polarization observation of SGLI adopts a method to derive the Stokes parameters I, Q, and U by observing three polarized azimuths. In this paper, the polarization observation accuracy means the polarization degree accuracy and polarization azimuth accuracy, which can be expressed as a relative value of I, Q, and U, and does not include the accuracy of radiance. In order to evaluate the polarization observation accuracy of SGLI on orbit, variations of the Q/I and U/I have been investigated using three kinds of calibration data. The effects of calibration methods and aging have been successfully eliminated by comparing independent evaluations of three kinds of calibration data. As a result, it was concluded that the variations of Q/I and U/I are achieved within a slight variation range of ±0.07% at the P1 telescope and ±0.04% at the P2 telescope.

Surface Biology and Geology imaging spectrometer: A case study to optimize the mission design using intrinsic dimensionality

The information content that can be derived from spectroscopic imagery tends to increase with finer ground sampling distance, finer spectral sampling, more frequent revisit, and higher signal-to-noise ratios (SNRs). However, these parameters are not independent, and it is thus impossible to design a space-borne imaging spectrometer to maximize all of them simultaneously. We present an instrument model and simulation environment that enable us to find the optimal combination of these four mission design parameters, using intrinsic dimensionality (ID) as the metric. ID is the size of the signal subspace – the maximum degrees of freedom when noise can be disregarded – and is a metric that is independent of any one particular algorithm or application area. This study is important for upcoming missions such as NASA's Earth System Observatory mission to study the Earth's Surface Biology and Geology (SBG), which will comprise a visible to shortwave infrared spectrometer in addition to a multi-channel thermal radiometer on a separate platform. When evaluating a desert site and a tropical forested site, we find that spectral resolution drives information content, with a significant drop in normalized ID (15–45% decrease) when simulating 15 nm spectral sampling as opposed to 10 nm spectral sampling. However, there was some variation between sites, with the forested site benefiting from 5 nm spectral sampling, whereas the desert site had poorer results at this resolution, due to the impact on noise. At 10 nm spectral sampling, ground sampling distances in the range 30–50 m provided the optimal balance between spatial resolution and SNR, although more frequent revisit, potentially by combining data from multiple missions, would maximize total information content.

What is in a Mandate? Introducing the UN Peace Mission Mandates Dataset.

UN peace missions are constantly evolving. Yet, we lack a detailed understanding of the shifting types and objectives of peace missions beyond broad categorizations that distinguish for instance between observer, traditional, multidimensional, and peace enforcement missions. To address this gap, we present the UN Peace Mission Mandates (UNPMM) dataset. With global coverage, 30years of data between 1991 and 2020, a broad scope that includes peacekeeping and political missions, and information on 41 mandate tasks, the UNPMM represents one of the most detailed and up-to-date datasets on UN peace mission mandates. We use it to highlight how mission types, objectives, and specific tasks have changed since the end of the Cold War, and to analyze what factors influence the kind of missions the UN is willing to authorize. The descriptive statistics and empirical analysis reaffirm the need for a greater disaggregation of data on UN peace missions and their mandates.

Optimal constellation design based on satellite ground tracks for Earth observation missions

In this study, we propose an optimal constellation determination method for Earth observation satellites. First, orbit requirements for the interferometric synthetic aperture radar (InSAR) observation are mathematically formulated. From these requirements, we can obtain nadir trajectories (ground tracks) of the chief satellites of the constellation. The optimal constellation determination problem is modeled as a multi-objective mixed integer programming problem. Then, a metaheuristic optimization method is applied to the resultant problem to optimize the number and configuration of satellite ground tracks and orbital planes. The proposed method enables us to design optimal satellite constellation configurations according to the mission conditions such as the observation area and the acceptable altitude range.

Online Terrain Classification Using Neural Network for Disaster Robot Application

A disaster robot is used for crucial rescue, observation, and exploration missions. In the case of implementing disaster robots in bad environmental situations, the robot must be equipped with appropriate sensors and good algorithms to carry out the expected movements. In this study, a neural network-based terrain classification that is applied to Raspberry using the IMU sensor as input is developed. Relatively low computational requirements can reduce the power needed to run terrain classification. By comparing data from the Accelerometer, Gyroscope, and combined Accelero-Gyro using the same neural network architecture, the tests were carried out in a not moving position, indoors, on asphalt, loose gravel, grass, and hard ground. In its implementation, the mobile robot runs over the field at a speed of about 0,5 m/s and produces predictive data every 1,12s. The prediction results for online terrain classification are above 93% for each input tested.

SAR Radiometric Calibration Based on Differential Geometry: From Theory to Experimentation on SAOCOM Imagery

The modeling and simulation of topography-induced imaging distortions are crucial for consistent radiometric information exploitation in current and forthcoming SAR-based Earth observation missions with a high spatial and temporal resolution, with relevance in several applications. In this paper, for the first time, we specifically investigate the compensation of topography-induced radiometric distortions affecting SAR images acquired by the L-band Argentinian satellite SAOCOM. We adopt a recently developed calibration method relying on an analytical formulation derived in the rigorous framework of the differential geometry of surfaces. We first provide an original interpretation of the analytical formulation, thus providing further insights into the relevant area-stretching-based formalism. Then, the numerical implementation of the method is specialized to systematically process the data acquired by SAOCOM sensors; hence, the resulting sensor-specific prototype solution processor is employed in this study. Finally, experiments performed over a real scenario in the southern part of Italy, characterized by large topography variations, are presented and discussed, thus elucidating the effectiveness of the adopted method applied to SAOCOM images. The adopted effective SAR calibration strategy opens up the way to its operational use in large-scale SAOCOM data processing.

An investigation of the relationship between the CME and the Geomagnetic Storm

The primary objective of this work is to look for any conceivable link between the geomagnetic storm and coronal mass ejection (CME) events. The relevant data for both CME and geomagnetic storm occurrences have been obtained from the Solar and Heliospheric Observatory mission’s LASCO and the NOAA Space Weather Prediction Center, respectively, for the same time span (February 1999 to December 2007). We performed Multivariate Singular Spectral Analysis (MSSA), Semblance Analysis, and Multifractal cross-correlation detrended fluctuation (MFXDFA) analysis to achieve our target. We also applied MSSA to investigate the causal link between the two data sets mentioned above. The analysis identifies the supportiveness factor between the data series and the possibility of a one-way statistical relationship between the data series is also verified. Then, Semblance and MFXDFA were used to detect the presence of any conceivable local phase relationship (time and frequency-dependent) and cross-correlation in the data sets. To make our analysis more persuasive, we must use all three methods. The Semblance analysis reveals positive and negative phase correlation between the signals under investigation at different time sub-intervals, whilst the MFXDFA validates the long-term power-law cross-correlations.

Fully Autonomous Orbit Determination and Synchronization for Satellite Navigation and Communication Systems in Halo Orbits

This paper presents a solution for autonomous orbit determination and time synchronization of spacecraft in Halo orbits around Lagrange points using inter-satellite links. Lagrange points are stable positions in the gravitational field of two large bodies that allow for a sustained presence of a spacecraft in a specific region. However, a challenge in operating at these points is the lack of fixed landmarks for orbit determination. The proposed solution involves using inter-satellite links to perform range and range-rate measurements, allowing for accurate computation of the spacecraft’s orbit parameters without the need for any facilities on Earth. Simulations using a fleet of three satellites in Near Rectilinear Halo Orbits around the Earth–Moon Lagrange point, proposed for the Lunar Gateway stations, were conducted to demonstrate the feasibility of the approach. The results show that inter-satellite links can provide reliable and accurate solutions for orbit determination with a DRMS error lower than one meter (90th percentile) and synchronization errors of around one nanosecond. This solution paves the way for a fully autonomous fleet of spacecraft that can be used for observation, telecommunication, and navigation missions.

HS2P: Hierarchical spectral and structure-preserving fusion network for multimodal remote sensing image cloud and shadow removal

Optical remote sensing images are often contaminated by clouds and shadows, resulting in missing data, which greatly hinders consistent Earth observation missions. Cloud and shadow removal is one of the most important tasks in optical remote sensing image processing. Due to the characteristics of active imaging that enable synthetic aperture radar (SAR) to penetrate cloud cover and other climatic conditions, SAR data are extensively utilized to guide optical remote sensing image cloud and shadow removal. Nevertheless, SAR data are highly corrupted by speckle noise, which generates artifact pollution to spectral features extracted from optical images and makes SAR-optical fusion ill-posed to generate cloud and shadow removal results while retaining high spectral fidelity and reasonable spatial structures. To overcome the aforementioned drawbacks, this paper presents a novel hierarchical spectral and structure-preserving fusion network (HS2P), which can recover cloud and shadow regions in optical remote sensing imagery based on the hierarchical fusion of optical and SAR remote sensing imagery. In HS2P, we present a deep hierarchical architecture with stacked residual groups (ResGroups), which progressively constrains the reconstruction. To pursue the adaptive selection of more informative features for fusion and reduce attention to the features with artifacts brought by clouds and shadows in optical data or speckle noise in SAR data, residual blocks with a channel attention mechanism (RBCA) are recommended. Additionally, a novel collaborative optimization loss function is proposed to preserve spectral features while enhancing structural details. Extensive experiments on the publicly open dataset (i.e., SEN12MS-CR) demonstrate that the proposed method can robustly recover diverse ground information in optical remote sensing imagery with various cloud types. Compared with the state-of-the-art cloud and shadow removal methods, our HS2P achieves significant improvements in terms of quantitative and qualitative results. The source code is publicly available at https://github.com/weifanyi515/HS2P.

Can domestic observers serve as impartial arbiters?: evidence from Zambia’s 2021 elections

ABSTRACT With international election observation subject to increasing criticism, this article evaluates how effectively domestic observers can play the role of impartial arbiters relative to their international counterparts. It reviews academic arguments about the strengths and weaknesses of domestic and international observers, with a focus on 1) their methodologies, resources, and reporting practices; and 2) their credibility. It presents a case study of Zambia’s 2021 elections, detailing the major observation missions and their activities. It then compares the media coverage and popular perceptions of domestic and international observers, showing that many Zambian citizens still have reservations about the partiality of domestic election observation initiatives. The article concludes by theorizing the conditions in which domestic election observation can 1) provide impartial evaluations of election conduct; and 2) be perceived as trustworthy sources of these evaluations.

A Bispectral Approach for Destriping and Denoising the Sea Surface Temperature from SGLI Thermal Infrared Data

Abstract Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation. Significance Statement This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement.

Energy Efficient Maximal Throughput Resource Scheduling Strategy in Satellite Networks

In this letter, a greedy routing strategy is proposed for resource scheduling in Earth observation mission to achieve the maximum throughput and improve the energy efficiency. Resource time-expanded graph (RTEG) is introduced to depict the time-evolving topology and resources of satellite networks. Based on RTEG, an energy efficient resource allocation algorithm (EERA) is designed to reduce the total energy consumption and maximize the throughput in data transmission by properly scheduling the transceiver resource, storage resource and power resource of satellites. Simulation results show that EERA achieves preferred energy efficiency when reaching the maximum throughput.