Constellation Of Earth Observation Satellites Research Articles

Deterministic Multistage Constellation Reconfiguration Using Integer Programming and Sequential Decision-Making Methods

This paper addresses the problem of reconfiguring Earth observation satellite constellation systems through multiple stages. The Multistage Constellation Reconfiguration Problem (MCRP) aims to maximize the total observation rewards obtained by covering a set of targets of interest through the active manipulation of the orbits and relative phasing of constituent satellites. This paper considers deterministic problem settings in which the targets of interest are known a priori. We propose a novel integer linear programming formulation for MCRP, capable of obtaining provably optimal solutions. To overcome computational intractability due to the combinatorial explosion in solving large-scale instances, we introduce two computationally efficient sequential decision-making methods based on the principles of a myopic policy and a rolling horizon procedure. The computational experiments demonstrate that the devised sequential decision-making approaches yield high-quality solutions with improved computational efficiency over the baseline MCRP. Finally, a case study using Hurricane Harvey data showcases the advantages of multistage constellation reconfiguration over single-stage and no-reconfiguration scenarios.

Agile earth observation satellite constellations scheduling for large area target imaging using heuristic search

Demand for imaging of large area targets by space-based assets such as constellations of Earth Observation Satellites (EOS) is on the rise for different applications. The area targets coverage cannot be achieved by a single satellite in a single observation opportunity, as the region for imaging spreads wider than the coverage swaths of imaging sensors. Hence, the imaging satellites are scheduled in a cooperative way to achieve maximum coverage of the area in a given planning time horizon. The scheduling algorithms also aim to maximize or minimize some specific objectives of interest while simultaneously complying with all resource constraints for an efficient use of precious space resources. As the current generation satellites are highly agile, there are many ways of choosing an imaging location in the target area for each single observation opportunity of a satellite. Even though the possible imaging region in continuous space is discretized into a finite number of strips for each observation, the huge combinatorial search space formed with all satellites’ imaging opportunities makes the problem intractable. Thus, the area target imaging scheduling is a complex combinatorial NP-hard optimization problem. Many exact and heuristic methods were evolved to provide a solution to this scheduling problem. Even though the exact methods solve the problem to optimality, they are limited to smaller problem instances because of the high computational complexities involved in those methods. More focus is seen in the literature on heuristic approaches as they guarantee scheduling performance with feasible solutions within an acceptable computational complexity. This paper presents our efficient heuristic approach proposed for the scheduling problem for imaging a large area target using a constellation of multiple satellites. Our approach is an extension of a widely used greedy approach in combination with insights from dynamic programming. In this paper, first we addressed the scheduling problem by describing it in detail and formulating it into a non-linear integer programming problem, considering the multi-objectives of achieving maximum coverage and minimum imaging resolution. The solution to the problem comprises two phases, namely the area decomposition phase and the scheduling phase. In the area decomposition phase, the area target is divided into strips dynamically for each observation opportunity of a satellite. Then exact observation period of each strip is computed using a simplified semi-analytical computational method. In the scheduling phase, we applied our heuristic search strategy, namely Forward and Backward Heuristic Search (FBHS) to obtain a near optimal solution within an acceptable computational time. Through the extensive simulations conducted under various scenarios, the effectiveness of the proposed method is verified in comparison with the baseline greedy approach.

Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data

Abstract. The constellation of Earth-observing satellites has now produced atmospheric greenhouse gas concentration estimates covering a period of several years. Their global coverage is providing additional information on the global carbon cycle. These products can be combined with complex inversion systems to infer the magnitude of carbon sources and sinks around the globe. Multiple factors, including the atmospheric transport model and satellite product aggregation method, can impact such flux estimates. Analysis of variance (ANOVA) is a well-established statistical framework for estimating common signals while partitioning variability across factors in the analysis of experiments. Functional ANOVA extends this approach with a statistical model that incorporates spatiotemporal correlation for each ANOVA component. The approach is illustrated on inversion experiments with different satellite retrieval aggregation methods and identifies consistent significant patterns in flux increments that span large spatial scales. Functional ANOVA identifies these patterns while accounting for the uncertainty at small spatial scales that is attributed to differences in the aggregation method. Functional ANOVA is also applied to a recent flux model intercomparison project (MIP), and the relative magnitudes of inversion system effects and data source (satellite versus in situ) are similar but exhibit slightly different importance for fluxes over different continents. In all examples, the unexplained residual variability is locally sizable in magnitude but with limited spatial and temporal correlation. These common behaviors across flux inversion experiments demonstrate the diagnostic capability for functional ANOVA to simultaneously distinguish the spatiotemporal coherence of carbon cycle processes and algorithmic factors.

Unifying radiative transfer models in computer graphics and remote sensing, Part II: A differentiable, polarimetric forward model and validation

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 2 of this work, we demonstrate that Monte Carlo forward models in computer graphics are capable of sufficient accuracy for remote sensing by extending Mitsuba 3, a forward and inverse modeling framework recently developed in the computer graphics community, to simulate simple atmosphere-ocean systems and show that our framework is capable of achieving error on par with codes currently used by the remote sensing community on benchmark results.

Unifying radiative transfer models in computer graphics and remote sensing, Part I: A survey

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 1 of this work, we examine the evolution of radiative transfer models in computer graphics and highlight recent advancements that have the potential to push forward models in remote sensing beyond their current periphery of realism.

A Double Auction Mechanism for Task Scheduling of An EOS Constellation

This study investigates the task scheduling problem for an earth observation satellite (EOS) constellation. The EOS constellation needs to collaboratively complete a set of observation tasks, but each EOS should satisfy the observation order specification described by a task graph due to its motion constraints. The goal is to maximize the total number of observation tasks by the constellation, for which repeated observations are counted only for once. Firstly, the task scheduling problem is formulated as an exact potential game and the goal of the task scheduling problem is converted to find a Nash equilibrium (NE). Secondly, a double auction strategy is proposed to solve for an NE iteratively and a sieving scheme is presented for the buyer to build its candidate set. It is proved that our algorithm converges to an NE in finite steps.

Ground Track Control Using Differential Drag for Small Earth Observation Satellite Constellations

This paper presents a framework for the acquisition and the maintenance of ground track configurations of small Earth observation satellite constellations. Specifically, the relative ground track spacings at the equator are controlled using differential drag to achieve the desired Earth coverage pattern without a propulsion system. The proposed framework is based on a divide-and-conquer approach to solving the optimal differential drag constellation management problem and comprises five processes for solving the related subproblems. The proposed framework can be used to manage end-to-end applications, from establishing the initial acquisition plan for the target ground track configuration, to the closed-loop command generation for the ground track acquisition and maintenance. Using the proposed framework, optimal acquisition plans with the minimum time and minimum altitude losses can be identified in a systematic way. The usefulness of the proposed method is demonstrated based on simulations incorporating the uncertainties in drag prediction.

Towards a novel approach for Sentinel-3 synergistic OLCI/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation

Sentinel-3 is an Earth observation satellite constellation launched by the European Space Agency. Each satellite carries two optical multispectral instruments: the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). OLCI and SLSTR sensors produce images covering the visible and infrared spectrum that can be collocated in order to generate synergistic products. In Earth observation, a particular weakness of optical sensors is their high sensitivity to clouds and their shadows. An incorrect cloud and cloud shadow detection leads to mistakes in both land and ocean retrievals of biophysical parameters. In order to exploit both OLCI and SLSTR capabilities, image co-registration at ground level is needed. However, applying such collocation of the images results in cloud location mismatches due to the different viewing angles of OLCI and SLSTR, which complicates the synergistic cloud detection. This study seeks to provide a solution to correctly obtain the projected clouds based on the estimation of cloud top heights in order to better collocate clouds between sensors and detect their shadows. The study presents a forward and backward method to estimate the real nadir position of a cloud on the satellite image starting from an existing cloud mask, as well as the corresponding cloud projections on the surface depending on the solar and sensor viewing angles. The estimation of cloud top heights is based on differences in the cloud projections from SLSTR nadir and oblique views. Experimental results show that the stereo cloud matching based on maximum cross-correlation between SLSTR nadir and oblique spectra was the most robust method to match SLSTR clouds for both nadir and oblique views as compared to spectral distance and spectral angle minimization. We test the method over several images around the world, leading to higher overall accuracy (OA) as compared to Sentinel-3 official products, both in detecting SLSTR clouds and OLCI cloud shadows (SLSTR nadir OA = 93.6%, SLSTR oblique OA = 88.7%, OLCI cloud shadow OA = 93.9% for the stereo matcher, against 82.2%, 81.3% and 90.5%, respectively, for the official Sentinel-3 products). This study also provides a starting point in the development of a cloud screening approach for the upcoming Fluorescence Explorer (FLEX) satellite mission, expected to fly in tandem with Sentinel-3.

AI4GEO: A DATA INTELLIGENCE PLATFORM FOR 3D GEOSPATIAL MAPPING

Abstract. The availability of 3D Geospatial information is a key issue for many expanding sectors such as autonomous vehicles, business intelligence and urban planning. Its production is now possible thanks to the abundance of available data (Earth observation satellite constellations, insitu data, …) but manual interventions are still needed to guarantee a high level of quality, which prevents mass production. New artificial intelligence and big data technologies adapted to 3D imagery can help to remove these obstacles. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information and new added-value services. This paper will first introduce AI4GEO initiative, context and overall objectives. It will then present the current status of the project and in particular it will focus on the innovative platform put in place to handle big 3D datasets for analytics needs and it will present the first results of 3D semantic segmentations and associated perspectives.

TEMPORAL REPETITION DETECTION FOR GROUND VISIBILITY ASSESSMENT

Abstract. Assessing ground visibility is a crucial step in automatic satellite image analysis. Nevertheless, several recent Earth observation satellite constellations lack specially designed spectral bands and use a frame camera, precluding spectrum-based and parallax-based cloud detection methods. An alternative approach is to detect the parts of each image where the ground is visible. This can be done by comparing locally pairs of registered images in a temporal series: matching regions are necessarily cloud free. Indeed, the ground has persistent patterns that can be observed repetitively in the time series while the appearance of clouds changes at each date. To detect reliably the “visible” ground, we propose here an a contrario local image matching method coupled with an efficient greedy algorithm.

Development of the Chinese Space-Based Radiometric Benchmark Mission LIBRA

Climate observations and their applications require measurements with high stability and low uncertainty in order to detect and assess climate variability and trends. The difficulty with space-based observations is that it is generally not possible to trace them to standard calibration references when in orbit. In order to overcome this problem, it has been proposed to deploy space-based radiometric reference systems which intercalibrate measurements from multiple satellite platforms. Such reference systems have been strongly recommended by international expert teams. This paper describes the Chinese Space-based Radiometric Benchmark (CSRB) project which has been under development since 2014. The goal of CSRB is to launch a reference-type satellite named LIBRA in around 2025. We present the roadmap for CSRB as well as requirements and specifications for LIBRA. Key technologies of the system include miniature phase-change cells providing fixed-temperature points, a cryogenic absolute radiometer, and a spontaneous parametric down-conversion detector. LIBRA will offer measurements with SI traceability for the outgoing radiation from the Earth and the incoming radiation from the Sun with high spectral resolution. The system will be realized with four payloads, i.e., the Infrared Spectrometer (IRS), the Earth-Moon Imaging Spectrometer (EMIS), the Total Solar Irradiance (TSI), and the Solar spectral Irradiance Traceable to Quantum benchmark (SITQ). An on-orbit mode for radiometric calibration traceability and a balloon-based demonstration system for LIBRA are introduced as well in the last part of this paper. As a complementary project to the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and the Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS), LIBRA is expected to join the Earth observation satellite constellation and intends to contribute to space-based climate studies via publicly available data.

Main Detrainment Height of Deep Convection Systems over the Tibetan Plateau and Its Southern Slope

Deep convection systems (DCSs) can rapidly lift water vapor and other pollutants from the lower troposphere to the upper troposphere and lower stratosphere. The main detrainment height determines the level to which the air parcel is lifted. We analyzed the main detrainment height over the Tibetan Plateau and its southern slope based on the CloudSat Cloud Profiling Radar 2B_GEOPROF dataset and the Aura Microwave Limb Sounder Level 2 cloud ice product onboard the A-train constellation of Earth-observing satellites. It was found that the DCSs over the Tibetan Plateau and its southern slope have a higher main detrainment height (about 10–16 km) than other regions in the same latitude. The mean main detrainment heights are 12.9 and 13.3 km over the Tibetan Plateau and its southern slope, respectively. The cloud ice water path decreases by 16.8% after excluding the influences of DCSs, and the height with the maximum increase in cloud ice water content is located at 178 hPa (about 13 km). The main detrainment height and outflow horizontal range are higher and larger over the central and eastern Tibetan Plateau, the west of the southern slope, and the southeastern edge of the Tibetan Plateau than that over the northwestern Tibetan Plateau. The main detrainment height and outflow horizontal range are lower and broader at nighttime than during daytime.

A branch and price algorithm for EOS constellation imaging and downloading integrated scheduling problem

Earth observation satellite (EOS) constellation imaging and downloading integrated scheduling (EOSCIDIS) is a challenging optimization problem with several engineering applications. Given a large number of imaging requests and a constellation consisting of a group of EOSs, a workable schedule must be developed for each EOS to allow for imaging and downloading in the most efficient manner given a certain scheduling horizon. No studies have proposed exact algorithms for the EOSCIDIS problem, although it has considerable potential for applications. In this paper, we developed a branch and price (B&P) algorithm for the EOSCIDIS problem. First, we decomposed the problem by extracting the sub-structure of the problem, and the decomposition resulted in a master problem and multiple pricing problems. Solving the linear relaxed master problem with column generation (CG) method provides a very tight upper bounds for the primal problem. We then embedded the CG process into a branch and bound (B&B) framework to find optimal integer solutions. In order to accelerate the convergence of the algorithm, we developed a heuristic utilizing the generated columns to construct feasible integer solutions to prune B&P tree branches earlier. We tested the B&P algorithm on highly-simulated instances that are constructed according to the Chinese Gaofen-1 satellite and 3 types of random instances which correspond to short, medium and long term scheduling respectively. Both results show that our method could find high-quality solutions with optimality gap less than 10% for most instances using reasonable computational costs .

Design and Analysis of the Satellite Structure for the Development of the Earth-Observation Satellites

In general, the design concepts of earth-observation satellites are established depending on the purposes of utilization such as commercial imagery business, public services, scientific research. Nowadays, The lightweight satellite structure is considered as an effective method for developing the earth-observation satellite. This paper introduces a design concept of the lightweight satellite structure for the constellation of earth-observation satellites. The modular design of the satellite structure is applied to save manpower and shorten the AIT process, in addition, a propulsion module is adopted to allow a hydrazine propulsion system to be installed on the satellite. The finite element method is used for the structural analysis of the satellite. The axial and lateral frequency requirements of satellite structure were verified by mode analysis. also, the margin of safety of satellite structure parts were satisfied with design requirements. As a result, the structural integrity of the suggested satellite structure is verified by mode analysis and static analysis.

Analysis of Communication Strategies for Earth Observation Satellite Constellations

Earth observation satellite constellations are arising as a new paradigm with important advantages in comparison with traditional monolithic systems. In particular, the concept of Segmented Architecture proposes to transform these constellations into a distributed and connected system in order to exploit resource sharing. However, this imposes severe technological challenges such as packetized space networking communications. In general, and due to the limited availability of data transmission opportunities, these communications can be modeled as delay and disruption tolerant networks (DTN). In this article, we propose to evaluate a particular case study of several flight-formation satellites intended for the observation of the Earth. To this end, we consider both the availability of Earth-to-space links and their combination with cooperative inter-satellite links. In the latter, different routing schemes such as Contact Graph Routing (CGR) and its extension Multi-Graph CGR (MG-CGR) are analyzed and compared

Satellite-based estimate of global aerosol–cloud radiative forcing by marine warm clouds

Changes in aerosol concentrations affect cloud albedo and Earth’s radiative balance. Aerosol radiative forcing from pre-industrial time to the present due to the effect of atmospheric aerosol levels on the micro- and macrophysics of clouds bears the largest uncertainty among external influences on climate change. Of all cloud forms, low-level marine clouds exert the largest impact on the planet’s albedo. For example, a 6% increase in the albedo of global marine stratiform clouds could offset the warming that would result from a doubling of atmospheric CO_2 concentrations. Marine warm cloud properties are thought to depend on aerosol levels and large-scale dynamic or thermodynamic states. Here we present a comprehensive analysis of multiple measurements from the A-Train constellation of Earth-observing satellites, to quantify the radiative forcing exerted by aerosols interacting with marine clouds. Specifically, we analyse observations of co-located aerosols and clouds over the world’s oceans for the period August 2006–April 2011, comprising over 7.3 million CloudSat single-layer marine warm cloud pixels. We find that thermodynamic conditions—that is, tropospheric stability and humidity in the free troposphere—and the state of precipitation act together to govern the cloud liquid water responses to the presence of aerosols and the strength of aerosol–cloud radiative forcing.

Landsat continuity: Issues and opportunities for land cover monitoring

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.

Respite for NASA's troubled data system

washington NASA remains optimistic that its troubled computer system for handling data from a planned constellation of Earth-observation satellites will be up and running for the scheduled launch of the first major satellite, AM1, next June.