Earth Observation Satellites Research Articles

Air quality in the New Delhi metropolis under COVID-19 lockdown

Air pollution has been on continuous rise with increase in industrialization in metropolitan cities of the world. Several measures including strict climate laws and reduction in the number of vehicles were implemented by several nations. The COVID-19 pandemic provided a great opportunity to understand the daily human activities effect on air pollution. Majority nations restricted industrial activities and vehicular traffic to a large extent as a measure to restrict COVID-19 spread. In this paper, we analyzed the impact of such COVID19-induced lockdown on the air quality of the city of New Delhi, India. We analyzed the average concentration of common gaseous pollutants viz. sulfur dioxide (SO), ozone (O), nitrogen dioxide (NO), and carbon monoxide (CO). These concentrations were obtained from the tropospheric column of Sentinel-5P (an earth observation satellite of European Space Agency) data. We observed that the city observed a significant drop in the level of atmospheric pollutant’s concentration for all the major pollutants as a result of strict lockdown measure. Such findings are also validated with pollutant data obtained from ground based monitoring stations. We observed that near-surface pollutant concentration dropped significantly by 50% for PM2.5, 71.9% for NO, and 88% for CO, after the lockdown period. Such studies would pave the path for implementing future air pollution control measures by environmentalists.

Object Tracking in Satellite Videos Based on Correlation Filter with Multi-Feature Fusion and Motion Trajectory Compensation

As a new type of earth observation satellite approach, video satellites can continuously monitor an area of the Earth and acquire dynamic and abundant information by utilizing video imaging. Hence, video satellites can afford to track various objects of interest on the Earth's surface. Inspired by the capabilities of video satellites, this paper presents a novel method to track fast-moving objects in satellite videos based on the kernelized correlation filter (KCF) embedded with multi-feature fusion and motion trajectory compensation. The contributions of the suggested algorithm are multifold. First, a multi-feature fusion strategy is proposed to describe an object comprehensively, which is challenging for the single-feature approach. Second, a subpixel positioning method is developed to calculate the object’s position and overcome the poor tracking accuracy difficulties caused by inaccurate object localization. Third, introducing an adaptive Kalman filter (AKF) enables compensation and correction of the KCF tracker results and reduces the object’s bounding box drift, solving the moving object occlusion problem. Based on the correlation filtering tracking framework, combined with the above improvement strategies, our algorithm improves the tracking accuracy by at least 17% on average and the success rate by at least 18% on average compared to the KCF algorithm. Hence, our method effectively solves poor object tracking accuracy caused by complex backgrounds and object occlusion. The experimental results utilize satellite videos from the Jilin-1 satellite constellation and highlight the proposed algorithm's appealing tracking results against current state-of-the-art trackers regarding success rate, precision, and robustness metrics.

HiPGen: An approach for fast generation of multi-satellite observation plans via a hierarchical multi-channel transformer network

Effective and fast Earth Observation Satellite (EOS) scheduling plays an essential role in new generation space-based information services, especially in emergency scenarios. Most existing research regards the EOS scheduling as a combinatorial optimization problem and adopts metaheuristic local search-based methods to solve it in a batch-wise fashion. To the best of our knowledge, no practical scheduling approach has been able to generate an optimized multi-EOS observation plan in an immediate response style. This paper proposes a novel real-time multi-satellite scheduling method consisting of a machine learning-based hierarchical prediction model and a heuristic local search algorithm. Firstly, the hierarchical prediction model based on a stacked multi-channel transformer network can learn from existing historical multi-EOS observation plans and generate a high-quality initial solution for the current scheduling scenario. Then, the local search algorithm based on Random Hill Climbing does further improvement and heuristic constraint handling on the initial solution and generates the final solution. To verify the effectiveness of the proposed method, simulation experiments are carried out. The results show that the method proposed in this paper consumes a short calculation time and has high planning profits compared with the state-of-the-art approaches.

Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems

The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.

A comprehensive geometric quality assessment approach for MSG SEVIRI imagery

Geometric Quality Assessment (GQA) of Earth Observation satellites is crucial for monitoring the system performance and anticipate the accuracy of their products. The Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensors aboard the geostationary Meteosat satellites of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) acquire multispectral images at 5 min (rapid-scan) and 15 min (full-disk scan) intervals in whiskbroom fashion. The existing GQA method used for the SEVIRI images involves matching of landmark points located on the shorelines, which prevents from comprehensive analysis inland. Limitations on detecting further landmarks (keypoints) in the images arise from low spatial resolution (1 and 3 km), large radiometric differences between the spectral bands and low textural content, which is even poorer for night time thermal infrared images. Within the present study initiated by EUMETSAT, a new approach for SEVIRI Level 1.5 imagery was developed for the relative, absolute and interband GQA by using dense image matching with sub-pixel precision. In the study, various image filtering and texture extraction methods were evaluated to obtain dense and reliable keypoints over the full-disk image area. Global image mosaics with superior spatial resolution and accuracy were employed as external reference. 2D shifts between search and reference images were determined the using a sub-pixel image matching technique together with a robust outlier elimination strategy. The results show that the textural information in SEVIRI images can be enhanced greatly by using various filtering methods, such as Laplacian, Sobel, Wallis and Local Binary Patterns. The Laplacian filter was found more suitable for the absolute GQA of infrared band images, which was the most difficult to band to assess. Depending on the cloud cover, thousands of well-distributed keypoints can be detected and employed as independent check points in the absolute GQA process. With the developed GQA Tool, new acquisitions can be assessed rapidly and various accuracy measures on the image geometric quality can also be achieved.

Assessing Education from Space: Using Satellite Earth Observation to Quantify Overcrowding in Primary Schools in Rural Areas of Nigeria

Nigeria is a country with a rapidly growing youthful population and the availability of good quality education for all is a key priority in the sustainable development of the country. An important element of this is the need to improve access to high-quality primary education in rural areas. A key indicator for progress on this is the provision of adequate classroom space for the more than 20 million learners in Nigerian public schools because overpopulated classrooms are known to have a strong negative impact on the performance of both pupils and their teachers. However, it can be challenging to rapidly monitor this indicator for the over 60 thousand primary schools, especially in rural areas. In this research, we used satellite Earth Observation (EO) and Nigerian government data to determine the size of available teaching spaces and evaluate the degree of overcrowding in a sample of 1900 randomly selected rural primary schools across 19 Nigerian states spanning all regions of the country. Our analysis shows that 81.4% of the schools examined were overcrowded according to the minimum standard threshold for school size of at least 1.2 m2 of classroom space per pupil defined by the Federal Government of Nigeria. Such overcrowding can be expected to have a negative impact on educational performance, on achieving universal basic education and UN Sustainable Development Goal (SDG) 4 (Quality Education), and it can lead to poverty. While measuring floor area can be performed manually on site, collecting, and reporting such data for the number of rural primary schools in a large and populous country such as Nigeria is a serious, time-consuming administrative task with considerable potential for errors and data gaps. Satellite EO data are readily available including for remote areas, are reproducible and are easy to update over time. This paper provides a proof-of-concept example of how such EO data can contribute to addressing this socio-economic dimension of the SDGs framework.

Lack of vegetation exacerbates exposure to dangerous heat in dense settlements in a tropical African city

Both climate change and rapid urbanization accelerate exposure to heat in the city of Kampala, Uganda. From a network of low-cost temperature and humidity sensors, operational in 2018–2019, we derive the daily mean, minimum and maximum Humidex in order to quantify and explain intra-urban heat stress variation. This temperature-humidity index is shown to be heterogeneously distributed over the city, with a daily mean intra-urban Humidex Index deviation of 1.2 ∘C on average. The largest difference between the coolest and the warmest station occurs between 16:00 and 17:00 local time. Averaged over the whole observation period, this daily maximum difference is 6.4 ∘C between the warmest and coolest stations, and reaches 14.5 ∘C on the most extreme day. This heat stress heterogeneity also translates to the occurrence of extreme heat, shown in other parts of the world to put local populations at risk of great discomfort or health danger. One station in a dense settlement reports a daily maximum Humidex Index of 40 ∘C in 68% of the observation days, a level which was never reached at the nearby campus of the Makerere University, and only a few times at the city outskirts. Large intra-urban heat stress differences are explained by satellite earth observation products. Normalized Difference Vegetation Index has the highest (75%) power to predict the intra-urban variations in daily mean heat stress, but strong collinearity is found with other variables like impervious surface fraction and population density. Our results have implications for urban planning on the one hand, highlighting the importance of urban greening, and risk management on the other hand, recommending the use of a temperature-humidity index and accounting for large intra-urban heat stress variations and heat-prone districts in urban heat action plans for tropical humid cities.

Developing capacity for impactful use of Earth Observation data: Lessons from the AfriCultuReS project

An increasing number of products and services based on satellite Earth Observation (EO) data are being developed for use by decision-makers in African agricultural contexts, providing information such as weather and climate forecasts, crop yields and water availability. Capacity development to support impactful use of EO data is a key component of many EO-for-development initiatives, but there is little consensus over where or how capacity should be developed. Our goal in this piece is to provide a critical perspective on the capacity development required to support the creation of more impactful EO data services. Drawing on a capacity needs assessment carried out as part of the AfriCultuReS project (a major EO-for-development initiative), we identify proximate factors which inhibit the success of EO data services such as flawed communication strategies, low relevance in African agricultural contexts, duplication of existing products, and lack of financial sustainability. We link these proximate challenges to deeper issues such as unequal access to funding and resources, fragmentation in the EO field, and relational asymmetries of power, all of which combine to exclude important forms of knowledge from decision-making. Based on this needs assessment, we argue that capacity development requires broader systems-based approaches which develop the capacities of all actors (including those in the Global North) to respect different forms of knowledge, use and participate in co-design approaches, and recognise and challenge the asymmetries of power which currently limit the involvement of certain groups in processes of EO data service design.

A Novel Method of Monitoring Surface Subsidence Law Based on Probability Integral Model Combined with Active and Passive Remote Sensing Data

For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2–1.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision.

Machine learning improves satellite imagery analysis of North Korean nuclear activity

ABSTRACT Much of the available data on nuclear activities in North Korea comes from Earth observation satellites. However, with increased coverage and higher resolution satellite images, analysts face challenges in systematically analyzing large-scale image datasets. In this case study, we report on an automated analysis of over 800 satellite images that tracked vehicular traffic as a proxy for activity levels at the core North Korean nuclear facilities, as well as international trade at border crossings between China and North Korea. Geospatial artificial intelligence and automated image analysis improves the efficiency of collecting analytical metrics, yielding results more quickly and of higher quality.

Characteristics and trends of hillside urbanization in China from 2007 to 2017

China's rapid urbanization has resulted in a dramatic loss in cultivated land that threatens future food security. Local and central government have successively launched a series of Low-slope Hilly Regions Comprehensive Development and Utilization (LHRCDU) pilot projects (i.e., the rudiment of China's hillside urbanization) since 2006 to balance the contradiction between urban growth and flat farmland protection. However, the process, quantity and effect of hillside urbanization over China remain unknown. In this study, we conducted an investigation of hillside urbanization (mainly refers to the urban land expansion on hillsides) over China from 2007 to 2017 by combining earth observation satellite data with spatial analysis techniques. Our results revealed that 12.32% of China's urban expansion area came from hillside urbanization, in which Guizhou, Yunnan, Shanxi and Fujian had large areas of hillside urbanization area (accounting for 25.63% of the total hillside urbanization area in China), while Guizhou, Tibet, Yunnan, Chongqing, Hong Kong and Shanxi had higher hillside urbanization rates (>30%). China's hillside urbanization rate also showed a steady upward trend, and 20% of hillside urbanization was high environmental cost (HEC), occupying 1535.94 km2 of hillside forest. Our findings provide timely and transparent monitoring on China's hillside urbanization, and indicate that hillside urbanization can relieve the pressure of urban development on flat farmland but needs to be regulated for ecological safety.

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.

Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting

Recent decades have witnessed a tremendous growth in the number of Earth observation satellites (EOSs), which presents a huge challenge for mission planning. For the EOSs with optical sensors particularly, the observation mission is significantly influenced by the uncertainty of cloud coverage, which has been identified as the most dominant factor for the invalidation of remote sensing images. To overcome this uncertainty, uncertainty programming methods, namely, chance constraint programming (CCP), stochastic expectation model, and robust optimization, are put forth. Despite their success, these approaches are limited in that they simplified the complex cloud coverage uncertainty, which may be different from the true cloud conditions, and they did not take the true cloud information into consideration. Motivated by these recent trends toward Big Data of satellite cloud images and machine learning for spatiotemporal prediction, this article explores a dynamic replanning scheme for multiple EOSs based on cloud forecasting. Specifically, we propose a new approach mainly in the following three steps: first, proactive scheduling based on a CCP is implemented and uploaded via ground control; second, cloud forecasting can be continuously conducted relying on the predictive recurrent neural network and the latest satellite cloud image; and third, mission replanning can be conducted according to the initial schedule and relatively accurate cloud information. Simulation results show that the cloud forecasting method is effective, and the replanning approach presents highly efficient and accurate scheduling results.

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

The last decades have witnessed a rapid increase of Earth observation satellites (EOSs), leading to the increasing complexity of EOSs scheduling. On account of the widespread applications of large region observation, this paper aims to address the EOSs observation scheduling problem for large region targets. A rapid coverage calculation method employing a projection reference plane and a polygon clipping technique is first developed. We then formulate a nonlinear integer programming model for the scheduling problem, where the objective function is calculated based on the developed coverage calculation method. A greedy initialization-based resampling particle swarm optimization (GI-RPSO) algorithm is proposed to solve the model. The adopted greedy initialization strategy and particle resampling method contribute to generating efficient and effective solutions during the evolution process. In the end, extensive experiments are conducted to illustrate the effectiveness and reliability of the proposed method. Compared to the traditional particle swarm optimization and the widely used greedy algorithm, the proposed GI-RPSO can improve the scheduling result by 5.42% and 15.86%, respectively.

Edge Computing-Based SAT-Video Coding for Remote Sensing

This paper proposes an edge computing-based video coding implementation on an Earth observation satellite (SAT-video coding), which can encode video using limited resources and the power of mini/microsatellites. SAT-video coding proposes a hardware-related quantization (Q) function, hardware reduction of the motion estimation (ME) method, and simplified entropy coding (EC), which reduces the computation complexity. The hardware-related Q reduces hardware resource and power consumption by 72% and 55%, respectively, compared with traditional Q implementation. The hardware reduction of ME reduces resource use compared with regular ME implementation (59 % of lookup tables [LUTs] and 79% of Registers). The total number of LUTs used for the simplified EC function is also much lower than other EC hardware implementations. The SAT-video encoder IP uses fewer hardware resources, and the power consumption is estimated at 0.0894 W at a high working frequency (125 MHz). The SAT-video encoding speed is 18.95 frames per second for 2560×2560 video. Therefore, the proposed SAT-video coding is an edge computation suitable for micro/minisatellites. The coding efficiency records the highest compression ratio at 33.8, with a peak signal-to-noise ratio of 34.46 dB. With the important task of designing edge computing based on satellite video encoding, these are adequate values for remote sensing video.

Characterization of the PAZ X-Band SAR Using the HITCHHIKER Ground Receiver

With the start of the Spanish PAZ system, another earth observation satellite has become available to the scientific community. Following the launch of the PAZ science phase, we performed a number of ground experiments using different modes of the satellites SAR instrument, measuring the transmitted radar signal as well as its ground reflections using the HITCHHIKER receiver. The data acquired in these experiments is analyzed and used in collaboration with the PAZ calibration team at INTA to characterize the PAZ instrument and thus verify the system calibration. Further we obtained bistatic radar images from the data of the HITCHHIKER ground receiver.

Satellite Observation and Data-Transmission Scheduling using Imitation Learning based on Mixed Integer Linear Programming

The Earth observation satellites (EOSs) scheduling problem is generally considered as a complex combinatorial optimization problem due to various technical constraints. It is significant to develop efficient computational frameworks to solve this problem. In this paper, an intelligent EOSs scheduling framework is developed using imitation learning based on mixed integer linear programming (MILP). The scheduling framework is composed of two processes: pre-processing, modeling and solving process. In the pre-processing process, an analytical method to generate the available time windows of an EOS is derived after considering the effects of Earth's <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$J_{2}$</tex-math></inline-formula> perturbation on the elliptic orbit. Based on the pre-processing results, this problem is formulated as an MILP model in the modeling process. In the solving process, a smart algorithm is proposed based on imitation learning for branch-and-bound to accelerate the solving process. Compared with normal imitation learning, a data selection method works in our algorithm to avoid potential misleading for learning. Besides, an iterative view is also adopted to improve the performance of the trained strategy. In the end, several real-world EOSs scheduling scenarios are investigated to demonstrate the reliability and high-efficiency of this framework.

Dual-Population Artificial Bee Colony Algorithm for Joint Observation Satellite Mission Planning Problem

Earth observation satellites provide services for users by taking images. The rapid increase in the number of satellites and missions makes mission planning more difficult. In order to solve this problem, this paper adopts a multi-population evolutionary algorithm. First, a mixed-integer programming model of the joint observation satellites mission planning problem (JOSMPP) is constructed. After that, a dual-population artificial bee colony algorithm (DPABC) and a heuristic task scheduling algorithm are proposed. Two learning-based nectar generation methods are used to guide the direction of population optimization. The search population is used to lead the nectar search at the stage of employed bees and supplement population is used to improve search performance. According to the performance of these two populations, candidate populations are generated to realize the search of onlooker bees. After each generation of population search, the composition of the two populations is adjusted according to the performance. Finally, the experimental results show that the DPABC algorithm has more advantages than multiple state-of-the-art algorithms to solve the joint mission planning problem of earth observation satellites.

Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire data sets based on Earth observation satellites, our data set combines 2D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, population density) aligned over 2D regions, providing a feature-rich data set for machine learning. To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This data set can be used as a benchmark for developing wildfire propagation models based on remote sensing data for a lead time of one day.

Orbit Design for a Satellite Swarm-Based Motion Induced Synthetic Aperture Radiometer (MISAR) in Low-Earth Orbit for Earth Observation Applications

Soil Moisture and Ocean Salinity mapping by Earth observation satellites has contributed significantly toward a better understanding of the Earth system, such as its hydrosphere or climate. Nevertheless, an increased spatial resolution below 10 km with a radiometric resolution in the range of 2 K–3 K of radiometric data could yield a more complete picture of global hydrological processes and climate change. Operational radiometers, such as SMOS, have already approached prohibitive sizes for spacecraft due to the required large antenna apertures. Therefore, radiometer concepts based on a large number of satellites flying in close proximity (swarms) have been proposed as a possible solution. This article investigates the orbit mechanics of placing a satellite swarm-based motion induced synthetic aperture radiometer (MISAR) in low Earth orbit for Earth observation applications. The aperture synthesis antenna array is formed by a large number of individual antennas on autonomously controlled nanosatellites (deputies) and a correlator antenna in the Y-configuration carried by a chief satellite. The proposed design methodology is based on the optimization of satellite positions within a plane and the subsequent translation of coordinates into initial conditions for general circular orbits (GCOs). This enables a more computationally efficient orbit optimization and ensures the time invariance of the antenna array response. Based on this methodology, simulations have been performed with swarms consisting of up to 96 satellites. Simulations show that the spatial resolution of an aperture synthesis radiometer can be increased to less than 10 km for applications where the requirements on radiometric sensitivity are more relaxed ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta T\sim 3$ </tex-math></inline-formula> K).