Global Grid Research Articles

Cost-effective adaptations increase rice production while reducing pollution under climate change.

Rice is a major source of greenhouse gas (GHG) and nitrogen pollution. While best management practices have been developed to enhance the sustainability of rice production under current climates, their adaptability and efficacy under future climate scenarios remain uncertain. Here we evaluated 49 best management practices across global grid cells of rice-producing areas in terms of increasing rice production, reducing GHG emissions and minimizing nitrogen pollution under future climate conditions. Optimal climate adaptation measures were assigned to each grid cell. We show that implementing the proposed adaptation strategy could increase global rice production by 36% while reducing GHG emissions and nitrogen losses by 23% and 32%, respectively. This approach could lead to a global benefit of US$117 billion for food supply, resource saving, climate mitigation and environmental protection, with total implementation costs of US$13 billion. Establishing practical and cost-effective adaptation strategies is critical for the sustainable development of the global agricultural system in the face of climate challenges.

Recent Advances in DC-DC Converter for Vehicle-to-Grid Applications

In recent years, the global energy market is undergoing an unprecedented transition from traditional fossil fuels to clean energy. At present, China is actively promoting the optimization and upgrading of its energy structure, people will vigorously develop renewable energy sources such as wind power and solar energy. With the increasing number of new energy vehicles, the charging demand of a large number of electric vehicles has become an important issue in the construction of new power systems. In addition, as the global power grid becomes smarter, energy management becomes more diverse and interactive, with users becoming both consumers and providers of energy, the industry and academia are jointly promoting the green, efficient and intelligent development of the global energy system. In this paper, the vehicle-to-grid (V2G) function of domestic charging pile is realized by vehicle-borne bi-directional converters for V2G applications and the feasibility of this goal is analyzed, finally, the prospect and summary are put forward.

Precise hexagonal pixel modeling and an easy-sharing storage scheme for remote sensing images based on discrete global grid system

ABSTRACT Discrete Global Grid Systems (DGGSs) are an emerging Earth reference model that support the integration and analysis of remote sensing (RS) data. Grid modeling, sampling, quantization, and storage are the key points and difficulties of DGGS. The Icosahedral Snyder Equal-area Aperture-4 Hexagonal DGGS was introduced as the basic framework to improve the geospace sampling efficiency. We presented an approach for precise hexagonal pixel modeling and an easy-sharing storage scheme compatible with open-standard formats for RS images based on the DGGS. Firstly, the proposed interpolation is computed by overlaps between quadrilateral and hexagonal pixels. The hexagonal grids were then mapped onto the icosahedral surface, and a strict correspondence between hexagonal and rectangular pixels is established. The open-standard formats were used to accurately store the hexagonal attribute values and metadata. Finally, a multiscale hexagonal aggregation algorithm based on the dataset was designed. Experiments showed that the proposed modeling methodology had a higher accuracy and smaller errors. The hexagonal data can be stored as regular rectangles with a fixed pattern in any standard format. This storage scheme was more conducive to data processing and sharing compared to SMOS, which was expected to promote hexagonal DGGS in RS data organization, processing and sharing.

Accuracy Evaluation Method for Vector Data Based on Hexagonal Discrete Global Grid

With the continuous advancement of technology for obtaining geographic spatial data, the accumulated volume of such data has been increasing, thus imposing higher demands on the storage, organization, and management of such data. As a new form of data management, the Discrete Global Grid System (DGGS) provides standardized descriptions and the exchange of geographic information on a global scale, enabling the efficient storage and application of large-scale global spatial data. Constituting a traditional type of GIS spatial data, vector data have advantages such as clear positions, implicit attributes, and suitability for map output. The representation of vector data in the global discrete grid network based on an equal-area projection, such as the hexagonal grid, fundamentally solves problems such as data redundancy, geometric deformation, and data discontinuity that arise when representing multiple vector data in a gridded format. This paper proposes different gridding methods for various types of vector data, and a quantifiable accuracy evaluation system is established from the perspectives of geographical deviation, geometric features, and topological relationships, to evaluate the accuracy of the gridded vector data, covering all types of gridded vector data based on the hexagonal grid. The evaluation method is generally applicable to all hexagonal-grid-based gridded vector data, and can be generalized based on application scenarios, for evaluating the usability of hexagonal grid vector data.

Regional-Scale Equidistance Optimizing Method Considering the Equidistance Patterns of Discrete Global Grid Systems

The Discrete Global Grid System (DGGS) provides a foundational framework for the digital Earth, where uniform intercell distances are essential for accurate numerical simulations. However, due to the spherical topology, achieving strictly equidistant spherical grid cells is impractical. Most existing studies have focused on regional scales, which are constrained by data acquisition limitations and render global equidistant optimization algorithms economically infeasible. The equidistant characteristics of cells are influenced by map projections and often exhibit regional variations. In this paper, we analyze these equidistant characteristics and construct an equidistant pattern for an icosahedral hexagonal DGGS. By integrating this pattern into the icosahedral orientation method, we develop a regional-scale equidistant optimization method for DGGS. Experiments on river network extraction in the Yangtze River Basin demonstrate significant improvements: the equidistance of grid cells covering the region increased by over 34.2%, while the accuracy of river network extraction improved by 51.41%. Moreover, this method is extensible to other grid models, enhancing the broader applicability of DGGS.

Constrained Precipitation Extremes Reveal Unequal Future Socioeconomic Exposure

AbstractExtreme precipitation can lead to major flooding, impacting human health and safety. Thus, reliable projections of population and GDP exposure to future extreme precipitation are imperative. Here, we quantify future precipitation characteristics from robust emergent constraint relationships between historical and future monthly precipitation extremes (99th percentile) across 19 CMIP6 models (r2 > 0.7 in 74–84% of 0.5° global land grids), and narrow uncertainty by 37.0–39.5% (absolute reduction being 0.753–0.774 mm/day). The constrained grid‐averaged future 99th percentile extreme is 6.96 ± 0.0059, 7.03 ± 0.0061, 7.11 ± 0.0063, and 7.29 ± 0.0067 mm/day, under SSP126, SSP245, SSP370, and SSP585, respectively, which exceeds historical extremes substantially in terms of intensity (12.9–19.7%) and frequency (1.6–2.3 times more). Future population and GDP exposed to 99th percentile extreme precipitation grow quickly, and are projected to exceed 1 million people in 27–40 countries and 10 billion USD (2005 Purchasing‐Power Parity) in 48–77 countries. Growth of future population exposure is dominated by an increase in extreme precipitation frequency rather than a rise in population, especially in developed countries. GDP exposure is controlled by the coupled effects of rapid socio‐economic development and significant shifts in precipitation frequency. Using indices of socio‐economic vulnerability, government effectiveness and economic freedom, we identify the unequal situation that high‐risk countries with high exposure are commonly characterized by low GDP per capita and high sociopolitical instability.

PyIRTAM: A New Module of PyIRI for IRTAM Coefficients

AbstractA novel model called PyIRI was recently developed. It constructs the ionospheric electron density for the entire day and on the entire global grid in one computation, which has a very low computational overhead. PyIRI introduced a novel approach to the computation of the global and diurnal functions and their matrix multiplication with Consultative Committee on International Radio (CCIR) coefficients or the International Union of Radio Science (URSI) coefficients, that enabled this global approach for the density specification. Since the International Reference Ionosphere‐based Real‐Time Assimilative Model (IRTAM) produces coefficients in a similar format as CCIR/URSI coefficients, the PyIRI computational approach was extended to work with IRTAM coefficients. This technical note describes the PyIRTAM software and provides usage examples. The PyIRTAM tool is made publicly available through PyPI and GitHub.

Drivers of global irrigation expansion: the role of discrete global grid choice

Abstract. Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude–latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify the main drivers of historical global irrigation expansion using, among other data, outputs from the global dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJml). Irrigation is critical for food security amidst growing populations, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and the prediction of future trends. We compare predictive accuracy, simulated irrigation patterns, and identification of irrigation drivers between the two grid systems. Using the ISEA3H geodesic grid system increases the predictive accuracy by up to 28 % compared to the longitude–latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. Gross domestic product (GDP) per capita also shows some influence. We conclude that the geodesic discrete global grid system significantly affects predicted irrigation patterns and identification of drivers and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

Using a discrete global grid system for a scalable, interoperable, and reproducible system of land-use mapping

ABSTRACT An accurate and timely land-use map of an area is a valuable geospatial dataset. Making one requires assembling heterogeneous geospatial input data. This becomes computationally expensive as the number of inputs grow or the combinational classification logic becomes more convoluted. We present as a solution an analytical framework based on a DGGS. This provides a spatial data structure that: facilitates horizontal scaling; improves geospatial data interoperability; and makes classified geospatial data easier to reproduce. We demonstrate a benchmark of land-use assignment with a DGGS against the vector and raster geospatial data models. Significant performance benefits are apparent over the vector case, while performance is equivalent to raster. Yet a DGGS has other compelling benefits that makes it more convenient to use in the domain of land-use assignment than the raster data model.

Construction of inherent geometric pattern for equal-area hexagonal discrete global grid systems

ABSTRACT Discrete Global Grid Systems (DGGSs) are hierarchical frameworks that provide seamless global coverage and enable the efficient processing and analysis of heterogeneous geospatial data. As cell resolution becomes finer, similarities in cell attributes, such as cell size and shape, increase. Understanding these shared characteristics enhances our knowledge of DGGSs structures, improving their reliability and accuracy across a range of applications. However, existing methods for quantifying these characteristics are limited, often emphasizing overall analysis and visualization rather than detailed assessment. This paper introduces a geometric pattern quantification method for equal-area hexagonal DGGSs by calculating inter-level attribute similarities to identify a reference level, segmenting the spatial distribution image at this reference level, and fitting the boundaries of these sub-images to create iso-feature lines. The results show that this method improves the accuracy and efficiency of cell attribute calculations, with enhancements of at least 5.6 times for study regions and 13.8 times for sample points at lower levels. This pattern provides a robust basis for grid selection and optimization in regional applications, promoting the broader adoption of DGGSs across various fields.

Recent progress in atmospheric modeling over the Andes – part I: review of atmospheric processes

The Andes is the longest mountain range in the world, stretching from tropical South America to austral Patagonia (12°N-55°S). Along with the climate differences associated with latitude, the Andean region also features contrasting slopes and elevations, reaching altitudes of more than 4,000 m. a.s.l., in a relatively narrow crosswise section, and hosts diverse ecosystems and human settlements. This complex landscape poses a great challenge to weather and climate simulations. The interaction of the topography with the large-scale atmospheric motions controls meteorological phenomena at scales of a few kilometers, often inadequately represented in global (grid spacing ∼200–50 km) and regional (∼50–25 km) climate simulations previously studied for the Andes. These simulations typically exhibit large biases in precipitation, wind and near-surface temperature over the Andes, and they are not suited to represent strong gradients associated with the regional processes. In recent years (∼2010–2024), a number of modeling studies, including convection permitting simulations, have contributed to our understanding of the characteristics and distribution of a variety of systems and processes along the Andes, including orographic precipitation, precipitation hotspots, mountain circulations, gravity waves, among others. This is Part I of a two-part review about atmospheric modeling over the Andes. In Part I we review the current strengths and limitations of numerical modeling in simulating key atmospheric-orographic processes for the weather and climate of the Andean region, including low-level jets, downslope winds, gravity waves, and orographic precipitation, among others. In Part II, we review how climate models simulate surface-atmosphere interactions and hydroclimate processes in the Andes Cordillera to offer information on projections for land-cover/land-use change or climate change. With a focus on the hydroclimate, we also address some of the main challenges in numerical modeling for the region.

The impact of renewable energy auctions on global grid greening: A comparative analysis across economies

Renewable energy (RE) auctions have emerged as an essential strategy for countries who want to diversify their energy mix due to the climate change challenges. While earlier research predominantly establishes the fact that bidding positively influences RE capacity, they mostly fail to capture the mixed success that depends on the economic and corporate environment. This study aims to complement it by assessing the effects of RE auctions on the uptake of renewable energy in countries characterized by fluctuations in economic conditions or different business climates. This paper analyzes the factors in bid efficacy with a dataset of 98 countries from 2000 to 2020 through cluster analysis of the corporate climate quality index and DID analysis of sequential policy endogenously. The paper also shows that RE auctions are, on average, a successful policy instrument in growing the share of renewable energy capacity. However, their effectiveness strongly correlates with the context of the country. For countries with stable corporate environments, the average treatment effect is stronger, while for those experiencing financial or structural unpredictability, the issue of RE auctions should be better twiddled. These close results emphasize the need for the elaboration of specific policies concerning auctions that would take into consideration the features of a particular country's economy and legislation. Therefore, to sum it up, RE auctions are a possible instrument for augmenting the worldwide RE generation capacity. However, their potential is not enormous, and the effects are not equitable among all countries. The government should use outcome data to understand the conditions necessary for the long-term success of RE auctions.

Distribution-Based Approach for Efficient Storage and Indexing of Massive Infrared Hyperspectral Sounding Data

Hyperspectral infrared atmospheric sounding data, characterized by their high vertical resolution, play a crucial role in capturing three-dimensional atmospheric spatial information. The hyperspectral infrared atmospheric detectors HIRAS/HIRAS-II, mounted on the FY3D/EF satellite, have established an initial global coverage network for atmospheric sounding. The collaborative observation approach involving multiple satellites will improve both the coverage and responsiveness of data acquisition, thereby enhancing the overall quality and reliability of the data. In response to the increasing number of channels, the rapid growth of data volume, and the specific requirements of multi-satellite joint observation applications with infrared hyperspectral sounding data, this paper introduces an efficient storage and indexing method for infrared hyperspectral sounding data within a distributed architecture for the first time. The proposed approach, built on the Kubernetes cloud platform, utilizes the Google S2 discrete grid spatial indexing algorithm to establish a grid-based hierarchical model for unified metadata-embedded documents. Additionally, it optimizes the rowkey design using the BPDS model, thereby enabling the distributed storage of data in HBase. The experimental results demonstrate that the query efficiency of the Google S2 grid-based embedded document model is superior to that of the traditional flat model, achieving a query time that is only 35.6% of the latter for a dataset of 5 million records. Additionally, this method exhibits better data distribution characteristics within the global grid compared to the H3 algorithm. Leveraging the BPDS model, the HBase distributed storage system adeptly balances the node load and counteracts the detrimental effects caused by the accumulation of time-series remote sensing images. This architecture significantly enhances both storage and query efficiency, thus laying a robust foundation for forthcoming distributed computing.

Impact of Tonga volcanic eruption on the ionosphere based on ground-based GNSS and COSMIC occultation data

The eruption of large volcanoes may potentially disturb the upper atmosphere, causing disturbances in ionospheric plasma and triggering irregular changes in the Total Electron Content (TEC). The GNSS ionospheric inversion method has been used to analyze ionospheric anomalies caused by the volcano, while it was limited to the spatial distribution of ground-based GNSS. The joint observation with space-based GNSS can further improve the spatio-temporal distribution of ionospheric monitoring. The Tonga volcano erupted on January 15th, 2022, is the largest volcanic eruption since the beginning of the 21st century and generated intense acoustic, atmospheric gravity waves and ionospheric disturbance. In this paper, we used the GIM products, ground-based GNSS station data around the volcanic eruption point and COSMIC occultation data to analyze the short-term TEC change. The experiments showed that: the average TEC of GIM grid data on eruption day dropped by 7.5 TECu compared to the last day which was substantially larger than other three days. The TEC extracted from nearby tracking stations was smaller than GIM and the difference was inversely proportional to the distance between the station and the volcanic eruption site. The average daily ionospheric TEC obtained from the nearest TONG station was the biggest, as much as 3.44 TECu lower. The peak electron density of COSMIC occultation data gradually diminished and then recovered with TEC about 7.32 TECu lower than GIM products. The experments showed that there was a good consistency in the TEC obtained from space-based and ground-based GNSS, the Tonga volcano produced a huge TEC change over 10 TECu or even bigger. As a global grid product, GIM underestimated ionospheric changes due to its limited spatial resolution, which needs further improvement.

The Impact of Portuguese Community Pharmacies on Influenza Vaccination Accessibility and Coverage

Abstract Background Vaccination reduces influenza infections. Non-adherence often stems from external barriers like distance to vaccination sites. To counteract the projected coverage decrease for 2023/24, Portugal’s Government expanded free Influenza vaccination to those ≥ 60 and to community pharmacies (CP), enhancing proximity and availability. This study aimed to assess the impact of CP inclusion on the distance to vaccination sites and on Influenza vaccine coverage in the 2023/24 campaign. Methods Analytical study, evaluating the average distance (km) from population to the nearest vaccination site per municipality using GPS coordinates from National Health Service (NHS) sites and CP. The Global Human Settlement resident population grid was derived from Eurostat 2011 Census, Corine Land Cover Refined 2006 and European Settlement Map 2016. Scenarios with and without CP were compared. The difference (%) in distances between 2022/23 (NHS sites) and 2023/24 (NHS sites and CP) and the coverage for the top and bottom 10% of municipalities with the highest and lowest distance reductions were calculated. Results In the 2023/24 Influenza vaccination campaign, 844 NHS sites and 2487 CP participated. Excluding CP, the average distance to the nearest NHS site was 2.4 km, decreasing to 1.2 km with CP inclusion (-50.5%). As of March 31, 2024, influenza vaccine coverage for those aged ≥65 was 72.1%, decreasing 0.1 percentage points (pp) compared to 2022/23 (72.2%). For the top 10% municipalities with the highest distance reduction (up to 66.5%), coverage increased by 1.3 pp compared to 2022/23. Conversely, the bottom 10% (reduction under 10.3%) experienced a decrease of 4.5 pp. Conclusions The inclusion of pharmacies has effectively reduced access barriers, filled geographical gaps, and improved proximity to vaccination. This strategy appears to have a positive impact on influenza vaccination coverage, particularly amid growing hesitancy, with potential for broader public health strategies. Key messages • The inclusion of community pharmacies in the 2023/24 Influenza vaccination campaign reduced the average distance to the nearest vaccination site by 50.5%, from 2.4 km to 1.2 km. • In 2023/24, for the top 10% municipalities with the highest distance reduction (up to 66.5%), coverage increased 1.3 pp compared to 2022/23.

A real-time phase transition modeling of supercritical steam cycle and load variation rate enhancement of thermal power plants under deep peak shaving

The ambitious green revolution to renewable energy sources in global power grids necessitates massive integration of solar and wind energy, which involves intermittent and unpredictable challenges. Thermal power plants are crucial in stabilizing the grid and addressing these challenges through flexibility reformation including deep peak shaving and frequent load variations since the unsteady state energy transfer and thermal dynamics during combustion and heat transformation in thermodynamic processes vary significantly. These conditions lead to issues such as furnace instability and latent heat of phase transition. This study introduces a novel approach to modeling phase transitions of supercritical steam cycle, and investigatesthe length, position, temperature, and energy transfer of the working medium and components under normal and low operational states. Conducting and analyzing the thermal feasible region associating the security of components and working medium this study establishes a control strategy for dynamic heat transfer to reduce component degradation effects andenhance load variation rate under flexible operations. Simulation model of a supercritical power unit based on the proposed method demonstrates an accuracy of 97.94%. Results from the optimal approach in maximizing load variation rate show the effectiveness and achieve 1.2%p.e./min most under transition process.

Constructing Efficient Mesh-Based Global Grid Systems with Reduced Distortions

Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial capabilities. Current DGGSs employ a simple, low-resolution polyhedral approximation of the Earth for efficient operations, but require a projection between the Earth’s surface and the polyhedral faces. Equal-area DGGSs are desirable for their low distortion, but they fall short of this promise due to the inefficiency of equal-area projections. On the other hand, efficiency-first DGGSs need to better address distortion. We introduce a novel mesh-based DGGS (MBD) which generalizes efficient operations over watertight triangular meshes with spherical topology. Unlike traditional approaches that rely on Platonic or Catalan solids, our mesh-based method leverages high-resolution spherical meshes to offer greater flexibility and accuracy. MBD allows high-resolution polyhedra (HRP) to be used as the base polyhedron of a DGGS, significantly reducing distortion. To address the operational challenges, we introduce a new hash encoding method and an efficient barycentric indexing method (BIM). MBD extends Atlas of Connectivity Maps to the BIM to provide efficient spatial and hierarchical traversal. We introduce several new base polyhedra with lower areal and angular distortion, and we experimentally validate their properties and demonstrate their efficiency. Our experimentation shows that we achieve constant-time operations for high-resolution MBD, and we recommend polyhedra to be used as the base polyhedron for low-distortion DGGSs, compact faces, and efficient operations.

HierGP: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics

Application domains such as environmental health science, climate science, and geosciences—where the relationship between humans and the environment is studied—are constantly evolving and require innovative approaches in geospatial data analysis. Recent technological advancements have led to the proliferation of high-granularity geospatial data, enabling such domains but posing major challenges in managing vast datasets that have high spatiotemporal similarities. We introduce the Hierarchical Grid Partitioning (HierGP) framework to address this issue. Unlike conventional discrete global grid systems, HierGP dynamically adapts to the data’s inherent characteristics. At the core of our framework is the Map Point Reduction (MPR) algorithm, designed to aggregate and then collapse data points based on user-defined similarity criteria. This effectively reduces data volume while preserving essential information. The reduction process is particularly effective in handling environmental data from extensive geographical regions. We structure the data into a multilevel hierarchy from which a reduced representative dataset can be extracted. We compare the performance of HierGP against several state-of-the-art geospatial indexing algorithms and demonstrate that HierGP outperforms the existing approaches in terms of runtime, memory footprint, and scalability. We illustrate the benefits of the HierGP approach using two representative applications: analysis of over 289 million location samples from a registry of participants and efficient extraction of environmental data from large polygons. While the application demonstration in this work has focused on environmental health, the methodology of the HierGP framework can be extended to explore diverse geospatial analytics domains.

Novel hybrid data-driven models for enhanced renewable energy prediction

Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system.

Spatio-temporal grid-based storage method for aerospace operation and control data

Abstract The current aerospace operation and control data system has problems such as difficult integration organization and difficult data retrieval in terms of mission planning efficiency, which limits the development of satellite operation and control systems. This paper is oriented to the organization and management of aerospace operation and control data, according to the nature of the current big data indexing model. Based on the principle of earth dissection, we aim to design a grid that can be indexed. First of all, the Earth is dissected to form a hierarchical and uniformly distributed global geographic grid, and then the formed grid is encoded to store satellite attribute information and orbit data for mission planning. Finally, through the data retrieval experiment, the retrieval time is within 100 milliseconds under ten million pieces of data, which proves that the spatial-temporal grid model has the characteristics of efficient and fast retrieval. It can provide the basic information for satellite mission planning to further improve the efficiency of satellite mission planning and management and the efficiency and quality of satellite mission planning.