Earth Observation Satellites Research Articles

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

AbstractThe task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi‐global coverage, are non‐intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short‐term numerical weather and sub‐seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.

Analysis of proliferated low Earth orbit constellation resilience against solar weather radiation effects

Proliferated low Earth orbit constellations are becoming more common both in reality and in planning. The resilience of these constellations to the radiation effects of adverse solar weather is of interest given that some of them pass over polar regions. This paper stochastically simulates solar weather events over the course of solar cycle 25 in order to quantify radiation effects on a model of Planet Lab’s Flock constellation under various design and programmatic decisions. The Flock constellation is a highly proliferated, commercial, and polar low Earth orbit earth observation architecture. Lifecycle and radiation failures are simulated along with a reconstitution system in order to quantify architectural performance as a function of constellation population. This time-based measure of performance is then used to quantify the constellation resilience and allow for programmatic and design variables to be altered in order to observe the effects on the resilience of proliferated constellations against adverse solar weather radiation effects. A sensitivity analysis is performed in order to assess which decisions most improve or degrade resilience. Broadly, the results show that commercial off the shelf components and programmatic decisions such as launch and manufacturing schedules may improve constellation resilience against adverse solar weather effects. More specifically, the results also show that commercial off the shelf components with even basic shielding levels provide high resilience to cumulative total ionizing dose effects and single event upset radiation effects. However, any further reductions to shielding would have significant impacts on single event upset rates whereas total ionizing dose would remain negligible. Furthermore, manufacturing time for Flock satellites is sufficient to maintain a resilient reconstitution strategy. Finally, results show that in order to improve the resilience afforded by a reconstitution strategy, improvements to manufacturing and launch times should be made in parallel, rather than one or the other in isolation.

Design and implementation of variable coding and modulation for LEO high‐resolution Earth observation satellite

SummaryAs LEO (Low Earth Orbit) high‐resolution Earth observation satellite needs to transmit a large amount of remote sensing data from the satellite to the ground station, higher downlink capacity still poses a severe challenge to LEO. Therefore, variable coding and modulation (VCM) has become an important scheme to optimize the performance of satellite downlink. In this paper, the downlink VCM system of China Earth observation satellite is studied for the first time. By evaluating the free space loss of the satellite downlink path, a VCM‐based satellite downlink system is designed, and a DVB‐S2 BCH‐LDPC encoded variable bit rate high‐order MAPSK modulator is implemented. The maximum channel bit rate can reach 3.5 Gbps. Then, the experimental results of Gaofen‐7 satellite, which was launched in November 2019, are carried out which showed that our design method effectively meets the design requirements, and the performance is verified.

Very low thrust station-keeping for low Earth orbiting satellites

Traditional station-keeping for Earth observation satellites with chemical thrusters generally involves maneuvers every couple months that are able to change significantly the semi-major axis and the inclination. These strategies do not scale down to very low thrust level (a few hundreds of μN) electrical thrusters. This paper presents both in-plane and out-of-plane strategies that spread corrections over very long arcs and discretize them to tiny maneuvers every couple orbits, taking into account mission-constraints on maneuvers locations. These strategies scale up to medium thrust strategies, filling the gap between propulsion technologies. The out-of-plane strategy although features a new no-deadband property and controls the full orbital momentum. All strategies allow control very close to the reference (a few hundreds meters in osculating parameters) and very low cost.

A review of multi-class change detection for satellite remote sensing imagery

ABSTRACT Change Detection (CD) provides a research basis for environmental monitoring, urban expansion and reconstruction as well as disaster assessment, by identifying the changes of ground objects in different time periods. Traditional CD focused on the Binary Change Detection (BCD), focusing solely on the change and no-change regions. Due to the dynamic progress of earth observation satellite techniques, the spatial resolution of remote sensing images continues to increase, Multi-class Change Detection (MCD) which can reflect more detailed land change has become a hot research direction in the field of CD. Although many scholars have reviewed change detection at present, most of the work still focuses on BCD. This paper focuses on the recent progress in MCD, which includes five major aspects: challenges, datasets, methods, applications and future research direction. Specifically, the background of MCD is first introduced. Then, the major difficulties and challenges in MCD are discussed and delineated. The benchmark datasets for MCD are described, and the available open datasets are listed. Moreover, MCD is further divided into three categories and the specific techniques are described, respectively. Subsequently, the common applications of MCD are described. Finally, the relevant literature in the main journals of remote sensing in the past five years are analyzed and the development and future research direction of MCD are discussed. This review will help researchers understand this field and provide a reference for the subsequent development of MCD. Our collections of MCD benchmark datasets are available at: https://zenodo.org/record/6809804#.YsfvxXZByUk

Algorithm theoretical basis for ozone and sulfur dioxide retrievals from DSCOVR EPIC

Abstract. On board the Deep Space Climate Observatory (DSCOVR), the first Earth-observing satellite at the L1 point (the first Lagrangian point in the Earth–Sun system), the Earth Polychromatic Imaging Camera (EPIC) continuously observes the entire sunlit face of the Earth. EPIC measures the solar backscattered and reflected radiances in 10 discrete spectral channels, four of which are in the ultraviolet (UV) range. These UV bands are selected primarily for total ozone (O3) and aerosol retrievals based on heritage algorithms developed for the series of Total Ozone Mapping Spectrometers (TOMS). These UV measurements also provide sensitive detection of sulfur dioxide (SO2) and volcanic ash, both of which may be episodically injected into the atmosphere during explosive volcanic eruptions. This paper presents the theoretical basis and mathematical procedures for the direct vertical column fitting (DVCF) algorithm used for retrieving total vertical columns of O3 and SO2 from DSCOVR EPIC. This paper describes algorithm advances, including an improved O3 profile representation that enables profile adjustments from multiple spectral measurements and the spatial optimal estimation (SOE) scheme that reduces O3 artifacts resulting from EPIC's band-to-band misregistrations. Furthermore, this paper discusses detailed error analyses and presents intercomparisons with correlative data to validate O3 and SO2 retrievals from EPIC.

Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning

One of the goals of the scientific community is to equip the discipline of spatial planning with efficient tools to handle huge amounts of data. In this sense, unsupervised machine learning techniques (UMLT) can help overcome this obstacle to further the study of spatial dynamics. New machine-learning-based technologies make it possible to simulate the development of urban spatial dynamics and how they may interact with ecosystem services provided by nature. Modeling information derived from various land cover datasets, satellite earth observation and open resources such as Volunteered Geographic Information (VGI) represent a key structural step for geospatial support for land use planning. Sustainability is certainly one of the paradigms on which planning and the study of past, present and future spatial dynamics must be based. Topics such as Urban Ecosystem Services have assumed such importance that they have become a prerogative on which to guide the administration in the difficult process of transformation, taking place not only in the urban context, but also in the peri-urban one. In this paper, we present an approach aimed at analyzing the performance of clustering methods to define a standardized system for spatial planning analysis and the study of associated dynamics. The methodology built ad hoc in this research was tested in the spatial context of the city of L’Aquila (Abruzzo, Italy) to identify the urbanized and non-urbanized area with a standardized and automatic method.

Scheduling single-satellite observation and transmission tasks by using hybrid Actor-Critic reinforcement learning

Earth observation satellites(EOS) generate a large amount of observation data in intensive observation scenarios, while the ability of data storage on the EOS is limited. It makes integrating satellite observation and data transmission tasks for EOS imperative. This paper establishes a time-continuous model for the single EOS integrated scheduling problem, which considers data transmission and observation simultaneously. A hybrid Actor-Critic reinforcement learning method is adopted to solve the EOS integrated scheduling problem for a more efficient solution in intensive observation scenarios. Furthermore, the algorithm can flexibly determine the start and end time of the data transmission task. Experimental results show that the hybrid Actor-Critic reinforcement learning method deals with a large scale of problems with high efficiency and good results.

Analysis of upper atmospheric effects on material per onboard atomic oxygen monitor system of SLATS

JAXA has proposed an innovative idea for satellites in Low Earth Orbit (LEO). The Super-Low Altitude Test Satellite (SLATS), also known as TSUBAME, is the first Earth observation satellite to occupy a Super-Low Orbit (S-LEO) or Very Low Earth Orbit (VLEO), below 300 km. The purposes of SLATS are 1) testing the maintenance of the satellite’s altitude with its ion engine against high atmospheric drag at a super-low altitude, 2) acquiring data on atmospheric density and atomic oxygen (AO), and 3) testing optical Earth observation. SLATS was successfully launched on 23 December 2017. SLATS was then altitude-controlled for 636 days to 271.7 km using chemical thrusters, aerodynamic drag, and ion engine propulsion. SLATS finally maintained its orbit of 167.4 km for 7 days and finished its operation on 1 October 2019. All the SLATS and Atomic oxygen MOnitor (AMO) data was acquired during these operations. The AMO is one of the mission sensors that monitor AO and its effects on spacecraft materials. The data from the AMO contributes to the choice of materials in future S-LEO satellite design. The data obtained by the AMO are valuable in that they provide considerable knowledge on AO fluence and its effects on space materials. A precise atmospheric density model and atmospheric composition model are indispensable for predicting the trajectory or re-entry of debris in orbit. Atmospheric models such as NRLMSISE-00, JB 2008, and DTM2013 have been developed, but few studies compare these models and the actual atmospheric environment in LEO. The average atmospheric density obtained from SLATS is lower than the value predicted by the atmospheric models (NRLMSISE-00, JB 2008, and DTM 2013). Understanding the model’s accuracy will contribute to the orbit control of future S-LEO satellites and the orbit prediction and control of debris in LEO.

Geometric accuracy assessment and a framework for automatic sub-pixel registration of WFI images from CBERS-4, CBERS-4A, and Amazonia-1 satellites over Brazil

Brazil faces the challenge of conciliating food production with natural vegetation preservation, and the government has been investing in Earth Observation satellites to assist in this task. In this context, images from the Wide-Field Imager (WFI) sensor onboard the China-Brazil Earth Resources Satellite (CBERS-4), CBERS-4A, and Amazonia-1 have been used in remote sensing applications in Brazil. However, the geometric accuracy of the WFI data has not yet been documented in the literature, as it was done for other satellites/sensors such as TERRA/MODIS, Landsat-8/OLI, and Sentinel-2/MSI. This work presents the first study to assess the geometric accuracy of WFI images along different spectral channels, from multi-temporal images, and taking Sentinel-2/MSI images as a reference. Surface reflectance WFI images, Level-4 (orthorectified) with spatial resolution of 55 m (CBERS-4A) and 64 m (CBERS-4 and Amazonia-1) were analysed. Thousands of control points (CPs) were used to quantify the geometric accuracy of more than 300 WFI images and to construct multiple mapping functions to correct the displacements found. The NIR band was taken as a reference in the inter-channel (band-to-band) registration evaluation. In this case, all tiles had an RMSE average of fewer than 0.44 pixels. For the multi-temporal case, the CBERS-4A/WFI had the highest average RMSE (0.87 ± 0.34 pixels). Considering the Sentinel-2/MSI as a reference, there were observed shifts up to 1.45 pixels for CBERS-4/WFI and Amazonia-1/WFI and 2.5 pixels for CBERS-4A/WFI. Given the displacements found, image registration is needed for further time series analysis of WFI images for several applications that requiring precise sub-pixel registration (i.e., land use cover change). As a result, we propose a framework for the automatic registration of WFI images to properly foster the use of WFI images. This framework substantially reduced the misregistrations, decreasing the RMSE average to less than 0.29 pixels for the WFI images from the three satellites. The use of the 2nd degree polynomial as a mapping function proved to be appropriate for registering the WFI data.

Open-weather: Speculative-feminist propositions for planetary images in an era of climate crisis

This article explores how speculative feminist practices can contribute to the collection and production of images of a planet in climate crisis. Inspired by feminist geographers and the speculative pragmatism of Didier Debaise and Isabelle Stengers, we suggest how speculative and feminist thinking unsettles the mediated optics of contemporary satellite earth observation. In a speculative feminist framework, earth images may transcend their dominant roles as scientific tools and cultural allegories and become tactical devices for imagining and acting otherwise. To illustrate this, we draw empirically from the work of the feminist artistic project open-weather to make three propositions: first, envisioning a fractal earth; second, writing experience into remotely-sensed images; and third, recording the more-than-meteorological weather. Images and imaginaries of earth are not only co-constitutive of the material conditions and worlds that produce them; in a speculative feminist framework, these images and imaginaries are also accountable to these worlds.

Estimating Time of Urbanization with Moderate-Resolution Sensors

The continuous recording of images of the Earth’s surface through Earth observation satellites has enabled the study of large-scale dynamical process, such as urbanization. Mapping the urban land cover at a given time is a first-order problem that has been largely addressed, and for which several acceptable solutions exist. A second-order problem is the analysis of change through time, which has been generally addressed through change vector or change matrix analyses, but they tend to neglect the spatial dimensions in favor of the temporal one. In this study, we propose the estimation of the time of urbanization across space as a useful indicator of the spatiotemporal structure of the urbanization process. A map of the time of urbanization can be considered a third-order product, as it compresses time series of urban land cover in one image that can effectively help identify areas of accelerated development and the time when they occurred. We apply linear spectral unmixing to quarterly composites of Landsat 8 imagery over the metropolitan corridor in central Mexico, from which a time series of urban percentage coverage is generated for the period between 2013 and 2021. Time filters were then applied to the time series and the time of urbanization was detected as the time when an increase of 25 percent of urbanization or larger first occurred. The accuracy assessment of the urbanization time showed a good correspondence with reference points that were randomly selected in the area of study.

A Methodology for National Scale Coastal Landcover Mapping in New Zealand

Satellite earth observation data has become fundamental in efforts to map coastal change at large geographic scales. Research has generally focussed on extracting the instantaneous waterline position from time-series of satellite images to interpret long-term trends. The use of this proxy can, however, be uncertain because the waterline is sensitive to marine conditions and beach gradient. In addition, the technique disregards potentially useful data stored in surrounding pixels. In this paper, we describe a pixel-based technique to analyse coastal change. A hybrid rule-based and machine learning methodology was developed using a combination of Sentinel multispectral and Synthetic Aperture Radar composite imagery. The approach was then used to provide the first national-scale pixel-based landcover classification for the open coast of New Zealand. Nine landcover types were identified including vegetation, rock, and sedimentary classes that are common on beaches (dark sand, light sand, and gravel). Accuracy was assessed at national scale (overall accuracy: 86%) and was greater than 90% when normalised for class area. Using a combination of optical and Synthetic Aperture Radar data improved overall accuracy by 14% and enhanced the separation of coastal sedimentary classes. Comparison against a previous classification approach of sandy coasts indicated improvements of 30% in accuracy. The outputs and code are freely available and open-source providing a new framework for per-pixel coastal landcover mapping for all regions where public earth observation data is available.

A Heuristic Construction Neural Network Method for the Time-Dependent Agile Earth Observation Satellite Scheduling Problem

The agile earth observation satellite scheduling problem (AEOSSP), as a time-dependent and arduous combinatorial optimization problem, has been intensively studied in the past decades. Many studies have proposed non-iterative heuristic construction algorithms and iterative meta-heuristic algorithms to solve this problem. However, the heuristic construction algorithms spend a relatively shorter time at the expense of solution quality, while the iterative meta-heuristic algorithms accomplish a high-quality solution with a lot of time. To overcome the shortcomings of these approaches and efficiently utilize the historical scheduling information and task characteristics, this paper introduces a new neural network model based on the deep reinforcement learning and heuristic algorithm (DRL-HA) to the AEOSSP and proposes an innovative non-iterative heuristic algorithm. The DRL-HA is composed of a heuristic construction neural network (HCNN) model and a task arrangement algorithm (TAA), where the HCNN aims to generate the task planning sequence and the TAA generates the final feasible scheduling order of tasks. In this study, the DRL-HA is examined with other heuristic algorithms by a series of experiments. The results demonstrate that the DRL-HA outperforms competitors and HCNN possesses outstanding generalization ability for different scenario sizes and task distributions. Furthermore, HCNN, when used for generating initial solutions of meta-heuristic algorithms, can achieve improved profits and accelerate interactions. Therefore, the DRL-HA algorithm is verified to be an effective method for solving AEOSSP. In this way, the high-profit and high-timeliness of agile satellite scheduling can be guaranteed, and the solution of AEOSSP is further explored and improved.

Bayesian Optimization-Based Tasking Method for an Ozone Observation Satellite with Maneuverable Sensors

This paper presents a novel method to guide an Earth observation satellite equipped with maneuverable sensors for remote sensing using the measurement of the total column ozone (TCO) as the application. The resulting task planning can interpret the measurements in real time and balance the exploration and exploitation of the area of interest intelligently. A Bayesian optimization (BO)-based method is developed to achieve this goal. For comparison, by the nadir-pointing strategy, the satellite always points to and measures the nadir points; whereas by the offset-pointing strategy, the satellite is guided by the BO-based tasking method to measure the location with either larger TCO values or higher model uncertainties. As demonstrated by the simulation results, the proposed tasking method leads to both better prediction accuracy and better precision of the TCO distribution. This reveals that the proposed observation strategy can enhance the efficiency and accuracy of the remote sensing satellite. A comparison with a deterministic offset-pointing method is also presented to demonstrate that the BO-based tasking method is indeed superior.

Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation Data

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning-based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.

On-Orbit Radiometric Calibration of Hyperspectral Sensors on Board Micro-Nano Satellite Constellation Based on RadCalNet Data

The stability and accuracy of the on-orbit radiometric calibration of hyperspectral sensors are prerequisites for the quantitative application of satellite hyperspectral data. The Zhuhai-1 micro-nano satellite constellation is composed of eight hyperspectral satellite missions. The Orbita Hyperspectral Sensor (OHS) on board each satellite has a gradient filter spectroscopic design. When observing the Earth, eight integration stages can be set for each band according to different lighting conditions. Due to high manufacturing costs, OHSs are not equipped with on-board calibration devices. Therefore, it is very difficult to accurately calibrate OHSs for all of the integration stages. On the other hand, it is extremely important to ensure radiometric consistency between different OHSs within the Zhuhai-1 micro-nano satellite constellation. To carry out the rapid radiometric calibration of the Zhuhai-1 constellation, an on-orbit radiometric calibration model considering all of the integration stages related to hyperspectral sensors was built based on the BOA reflectance and atmosphere parameters published by the Committee on Earth Observation Satellites (CEOS) radiometric calibration network (RadCalNet). The RadCalNet product was used to derive the TOA radiance base in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer (RT) model. In this paper, we analyzed the radiometric stability of the same sensor and the consistency of different calibration results regarding four RadCalNet sites, and the on-orbit radiometric performance evaluation of OHSs was also carried out. The data retrieved from OHSs regarding hyperspectral surface reflectance were preliminarily validated using site-synchronous surface reflectance measurements.

Sakarya Nehri’nin İnhisar (Bilecik) Çevresindeki 6 Yıllık Arazi Kullanımı Değişiminin Uzaktan Algılama Tabanında İncelenmesi

İnhisar (Bilecik) is located in the center-east of the study area, which is bounded by Söğüt (Bilecik) in the southwest and Yenipazar (Bilecik) in the northeast. Sakarya River, located in the center of the study area, provides continuity in the East-West direction for approximately 40 km and is fed by drains from both the north and the south. Marble enterprises with economic value are widely observed in the region. In addition, it is very important for the natural environment to protect areas such as İnhisar waterfall and Harmankaya Canyon Nature Park in the region from harm that may be caused by human activities. The main purpose of the study is to examine the areas with natural structures that are important for the environment on a regional scale with data provided by earth observation satellites and to define the time-dependent changes of these areas. Landsat-8 satellite images, a 12.5m-resolution digital elevation model and geology map constitute the main data of the study. The results of the change analysis conducted on the Landsat images classified with the random forest algorithm are discussed on the geology and topography maps. When the 6-year change analysis findings are interpreted, it is suggested that the mining activities, which intensified especially around Gölpazarı, should not approach the Yenipazar-İnhisar line in the east.

Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations

Evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. ET remains one of the most problematic components of the water cycle to be determined due to the heterogeneity of the landscape and the complexity of driving factors. The satellite-based earth observation is expected to provide ET information at large-scales. However, accurate global ET information, with spatially and temporally continuous coverage at moderate-to-high resolution, is still scarce. In this paper, a combined model, called ETMonitor, with multi-process parameterizations, was improved and applied to estimate the global ET, mainly using the biophysical and hydrological parameters/variables retrieved from satellite observations. The ETMonitor model was improved in several aspects in this study to generate the global ET datasets during 2000–2019 at daily/1-km resolution, including: 1) adopting high temporal resolution surface water cover and snow/ice cover as input, to simulate the impact of their seasonal change on ET variation; 2) parameterizing the impact of soil moisture on plant transpiration and soil evaporation using high resolution soil moisture, which was downscaled to 1-km resolution from the coarse resolution data retrieved from microwave remote sensing observation; 3) involving a better soil heat flux estimation to reduce its impact on the uncertainty of estimated ET; 4) being calibrated based on global ground flux observations to achieve better accuracy. The estimated daily ET was validated based on the global in situ observations at site scale across various ecosystems, with overall high correlation (0.75), low bias (0.08 mm d-1), and low root mean square error (0.93 mm d-1). It had good ability to partition total ET to plant transpiration and soil evaporation indicated by the good agreement with the ground isotope measurements in a growing season at one site in the northwest China. The estimated global ET was cross-validated by comparing with other existing ET products, and it showed the global ET estimated by ETMonitor could capture the expected global ET patterns both in space and in time. It also indicated the superiority of the ET product by ETMonitor in the following aspects: capability in capturing the seasonal dynamics of waterbody evaporation and sublimation; better performance in capturing the spatial variation of ET in the irrigated cropland regions and mountain regions with complex terrain than other global ET products, e.g., the GLEAM and MOD16 ET products; capability of ET component partitioning at high spatial (1-km) and high temporal (daily) resolutions with good accuracy. The estimated plant transpiration, soil evaporation, canopy rainfall interception loss, and water body evaporation and snow sublimation accounted for 61.54 % (±0.44 %), 19.08 % (±0.54 %), 13.54 % (±0.49 %), and 5.84 % (±0.24 %), respectively, of the total ET on global average. The ETMonitor global ET dataset could provide important information on studies of global terrestrial water and energy cycles and climate change studies, and water resources management at global and regional scales.

Unrelated parallel machine scheduling with multiple time windows: An application to earth observation satellite scheduling

In this paper, we investigate a variant of the unrelated parallel machine scheduling problem with multiple time windows that is widely employed in the scheduling of earth observation satellites. Two mixed-integer linear programming formulations are proposed based on the concept of non-immediate precedence and the deep analysis of the relationship of time windows. To facilitate the problem solving, we develop a bidirectional rolling horizon preprocessing algorithm to reduce the problem size. Based on detailed overlapping analysis, more inequalities are obtained to tighten the formulations. Furthermore, the dominance among derived inequalities is analyzed, and only nondominated inequalities are appended to limit the size of the problem. The proposed formulation and algorithm can optimally solve instances of up to 800 jobs and 20 machines within 20 min. Moreover, to verify the practicality of our method, we also conducted numerous experiments on realistic instances of satellite scheduling. The experimental results prove that our algorithm is effective for obtaining optimal solutions and can be applied in practice.