A frozen soil inversion method based on high-resolution five satellite full-spectrum data
By using full-spectrum data from the Gaofen-5 satellite and brightness temperature calculations based on the Planck function, combined with a top-level model of permafrost, the problems of low resolution in permafrost inversion and difficulty in monitoring were solved, enabling high-precision remote monitoring of permafrost distribution.
Patent Information
- Application Number
- CN202211669144.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-23
- Publication Date
- 2026-08-25
- Estimated Expiration
- 2042-12-23
AI Technical Summary
Existing permafrost inversion methods have low resolution, making it difficult to meet the high-precision monitoring needs of permafrost in high-altitude areas. Furthermore, traditional monitoring methods are limited by environmental and cost constraints.
Using full-spectrum data from the Gaofen-5 satellite and combining it with Planck function for brightness temperature calculation, high-resolution surface temperature grid data was obtained, and the permafrost top-level calculation model was used to identify permafrost types and distribution.
It has achieved high-resolution permafrost inversion, breaking through the environmental and cost limitations of traditional monitoring, and enabling large-area, synchronous, and remote monitoring of permafrost distribution in high-altitude areas.
Smart Images
Figure CN116087106B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of remote sensing technology, and in particular to a method for permafrost inversion based on full-spectrum data from the Gaofen-5 satellite. Background Technology
[0002] Permafrost refers to various soils and rocks containing ice when the temperature is equal to or below 0°C. The existence of permafrost and its freeze-thaw cycles have a significant impact on the safe operation of infrastructure in permafrost regions. In permafrost areas, the frost heave and thaw settlement of permafrost have a strong destructive effect on infrastructure such as transportation roads and water conservancy projects, easily leading to frost damage phenomena such as cracks, threatening the safe operation of infrastructure. Land surface temperature (LST) is an important influencing factor of surface physical processes and is one of the most important factors affecting permafrost distribution in permafrost research. However, ground observations of LST are limited in high-altitude and cold regions. With the development of thermal infrared remote sensing and the continuous maturation of LST inversion algorithms, satellite remote sensing has become an effective means of obtaining the spatiotemporal distribution of regional LST, thus making it possible to estimate permafrost distribution based on satellite remote sensing image data.
[0003] With the development of remote sensing technology, hyperspectral remote sensing can compensate for the limitations and deficiencies of quantitative applications of multispectral or panchromatic imaging remote sensing, and also brings opportunities to improve the spatial resolution of remote sensing LST products. The fifth satellite in the Gaofen series, "Gaofen-5" (hereinafter referred to as GF-5), is a high-resolution Earth observation satellite and the world's first full-spectrum hyperspectral satellite to achieve comprehensive observation of the atmosphere and land. Launched on May 9, 2018, GF-5 carries a Visual and Infrared Multispectral Imager (VIMI) with a wide spectral range, high spatial resolution, and high radiometric calibration accuracy. The GF-5 satellite's VIMI sensor has 12 channels, covering the spectral range from visible light and near-infrared to thermal infrared, including four thermal infrared channels with a spatial resolution of 40m. Because the GF-5 satellite's full-spectrum imager has multiple thermal infrared bands, it provides the possibility for high-resolution inversion of Earth's surface temperature. However, currently, most permafrost inversion methods are based on thermal infrared remote sensing satellite data, which has relatively low resolution and faces certain challenges and constraints in terms of security and confidentiality. Summary of the Invention
[0004] The technical problem addressed by this application is that existing permafrost inversion schemes cannot meet practical needs. This application provides a permafrost inversion method based on full-spectrum data from the Gaofen-5 satellite. The scheme provided in this application introduces the Planck function to calculate the brightness temperature of designated spectral bands sensitive to land surface temperature in the full-spectrum remote sensing image data collected by Gaofen-5 at different time points. This yields gridded land surface temperature data for different time points. The spatial resolution of the grid is 50m, the same as the spatial resolution of the Gaofen-5 full-spectrum camera imaging, which is significantly higher than the resolution of the widely used MODIS land surface temperature sequence data, achieving a more refined calculation breakthrough in the land surface temperature inversion process. Furthermore, it is not limited by the environmental constraints of on-site monitoring, effectively solving the limitations of traditional permafrost monitoring methods in high-altitude areas, such as harsh environments, expensive instruments, and point-scale monitoring. It enables large-area, synchronous, and remote inversion of perennial and seasonal permafrost in high-altitude areas of my country.
[0005] In a first aspect, embodiments of this application provide a method for permafrost inversion based on full-spectrum data from the Gaofen-5 satellite. This method includes: acquiring remote sensing image datasets of the full spectrum from the Gaofen-5 satellite at different time points; selecting remote sensing image data of a specified spectral band corresponding to each time point from the remote sensing image dataset; calculating the brightness temperature of the remote sensing image data of the specified spectral band according to a preset Planck function to obtain surface temperature grid data corresponding to each time point; obtaining surface temperature spatial distribution sequence data based on the surface temperature grid data; calculating the total energy of each grid in the remote sensing image data within a specified period in both the thawing and frozen states based on the surface temperature spatial distribution sequence data and a preset permafrost top-level calculation model; and determining the distribution information of permafrost based on the total energy of each grid.
[0006] Optionally, selecting remote sensing image data corresponding to a specified spectral band at each time point from the remote sensing image dataset includes: comparing and analyzing the remote sensing image data corresponding to each time point with the spectral band data of a specified sensor on a preset thermal infrared satellite, and selecting remote sensing image data corresponding to the specified sensor spectral band from the full-spectrum remote sensing image dataset.
[0007] Optionally, the surface temperature grid data corresponding to each time node is obtained by calculating the brightness temperature of the remote sensing image data of the specified spectral band according to a preset Planck function, including: determining the radiance value of the remote sensing image data of the specified spectral band at each time node; and calculating the surface temperature grid data corresponding to each time node according to the radiance value and the preset Planck function.
[0008] Optionally, obtaining the spatial distribution sequence data of surface temperature based on the surface temperature grid data includes: acquiring measured temperature data from meteorological stations; performing accuracy calibration and deviation analysis on the surface temperature grid data based on the measured temperature data to obtain a nonlinear regression function corresponding to the surface temperature grid data and the measured temperature; interpolating the linear regression function data of the surface temperature grid data based on preset surface temperature product sequence data to obtain the spatial distribution sequence data of surface temperature; wherein, the spatial distribution sequence data of surface temperature is the surface temperature spatial data within a specified time period corresponding to the Gaofen-5 satellite obtained by inverting remote sensing image data from the Gaofen-5 satellite.
[0009] Optionally, the measured temperature data is the temperature data at a height of 2m measured by a meteorological station in the area corresponding to the remote sensing image data.
[0010] Optionally, based on the spatial distribution sequence data of surface temperature and a preset permafrost top-level calculation model, the total energy of each grid in the remote sensing image data in the thawing and freezing states within a specified period is calculated, including: using the spatial distribution sequence data of surface temperature as input to the permafrost top-level calculation model to construct a permafrost rapid identification model with grids as units; and calculating the total energy of each grid in the thawing and freezing states within a specified period based on the permafrost rapid identification model.
[0011] Optionally, the distribution information of permafrost is determined based on the total energy of each grid, including: identifying each grid based on the total energy of the thawing state and the total energy of the freezing state corresponding to each grid; wherein, the identifier of each grid is used to characterize the permafrost type of the grid, the permafrost type including perennial permafrost, short-term permafrost and seasonal permafrost; and determining the permafrost distribution information corresponding to each permafrost type according to the identifier corresponding to each grid.
[0012] Optionally, each grid is identified based on the total energy of each grid, including: identifying grids whose total energy in the molten state is greater than the total energy in the frozen state as 0, and identifying grids whose total energy in the frozen state is greater than the total energy in the molten state as 1.
[0013] Optionally, the permafrost distribution information corresponding to each permafrost type is determined based on the identifier corresponding to each grid, including: spatially upscaling grids with the same identifier to obtain the permafrost distribution information corresponding to each permafrost type.
[0014] Secondly, this application provides a computer device, the computer device comprising:
[0015] Memory, used to store at least one instruction executed by a processor;
[0016] A processor is configured to execute instructions stored in memory to perform the method described in the first aspect.
[0017] Compared with the prior art, the solution provided in this application has at least the following beneficial effects:
[0018] 1. The scheme provided in this application introduces the Planck function to calculate the brightness temperature of the remote sensing image data of the specified spectral bands that are sensitive to land surface temperature in the full-spectrum remote sensing image data collected by Gaofen-5 at different time nodes, so as to obtain the land surface temperature grid data at different time nodes. The spatial resolution of the grid is 50m, which is the spatial resolution of the Gaofen-5 full-spectrum camera imaging. Compared with the resolution of MODIS land surface temperature sequence product data that is more widely used abroad, it has greatly improved the resolution and achieved a more refined calculation breakthrough in the land surface temperature inversion part.
[0019] 2. The solution provided in this application is not limited by the environment of on-site monitoring, and effectively solves the limitations of traditional permafrost monitoring methods in high-altitude areas, such as harsh environment, expensive instruments, and point-scale monitoring. It can realize the inversion of permafrost and seasonal permafrost in high-altitude areas of my country on a large scale, synchronously and remotely. Attached Figure Description
[0020] Figure 1 A flowchart illustrating a permafrost inversion method based on full-spectrum data from the Gaofen-5 satellite, provided as an embodiment of this application;
[0021] Figure 2 A schematic diagram of the process for another permafrost inversion method based on full-spectrum data from the Gaofen-5 satellite provided in this application embodiment;
[0022] Figure 3 A schematic diagram of a permafrost inversion result provided in an embodiment of this application;
[0023] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0024] The embodiments described in this application are only a part of the embodiments, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0025] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.
[0026] Figure 1 This paper presents a flowchart illustrating a permafrost inversion method based on full-spectrum data from the Gaofen-5 satellite, as provided in an embodiment of this application.
[0027] As an example, in Figure 1 In the scheme provided in this application embodiment, the permafrost inversion calculation based on the full spectrum data of Gaofen-5 satellite includes three processes: process 101, calculation of surface temperature grid data; process 102, calculation of surface temperature spatial distribution sequence data; and process 103, calculation of permafrost region from single grid to surface scale. Figure 1 As shown, the steps corresponding to each process are as follows:
[0028] I. Calculation of Surface Temperature Grid Data
[0029] Before calculating the surface temperature grid data, we first acquire full-spectrum remote sensing image data from the Gaofen-5 satellite, measured temperature data from meteorological stations, and existing surface temperature product sequence data. Then, we analyze the full-spectrum remote sensing image data from the Gaofen-5 satellite to select specific spectral remote sensing image data that are sensitive to surface temperature, and construct a nonlinear regression relationship. Finally, we calculate the brightness temperature data based on the radiance values of the specified spectral remote sensing image data.
[0030] II. Calculation of Spatial Distribution Sequence Data of Land Surface Temperature
[0031] Based on the surface temperature grid data obtained from the above calculations, spatiotemporal interpolation calculations of surface temperature grid data at different time points, as well as accuracy calibration and deviation analysis of temperature data at different time periods, are used to obtain surface temperature spatial distribution sequence data.
[0032] III. Calculation from Single Grid to Surface Scale in Permafrost Regions
[0033] Using spatial distribution sequence data of land surface temperature as input, a rapid identification model of permafrost with grid as the unit is constructed, and spatial upscaling calculations are carried out from the grid to the study area.
[0034] To facilitate understanding of the above, the following explanation is provided. Figure 1The process shown will be described in further detail below. The following, in conjunction with the accompanying drawings, provides a more detailed description of another permafrost inversion method based on full-spectrum data from the Gaofen-5 satellite, as provided in this application. The specific implementation of this method may include the following steps (method flow as follows): Figure 2 As shown):
[0035] Step 201: Obtain remote sensing image datasets of the full spectrum of Gaofen-5 satellite at different time points, and select remote sensing image data of the specified spectrum corresponding to each time point from the remote sensing image dataset.
[0036] The Gaofen-5 satellite (GF-5) carries a Visual and Infrared Multispectral Imager (VIMI), which provides full-spectrum remote sensing images across 12 spectral bands from visible to long-wavelength, particularly data in four subdivided long-wavelength infrared bands. The full-spectrum remote sensing image dataset provided in this application refers to the 12 spectral bands from visible to long-wavelength provided by the VIMI. Furthermore, the thawing or freezing states of different permafrost types vary at different times. For example, in summer, permafrost is frozen, while short-term and seasonal permafrost are thawing; in winter, all types are frozen. To determine permafrost regions and types, full-spectrum remote sensing image data from different time points are collected by the Gaofen-5 satellite, and the full-spectrum remote sensing image data corresponding to different time points are used as the dataset for determining permafrost regions and types. For example, different time points include one or more time points within the time interval corresponding to at least one of the seasons: spring, summer, autumn, and winter.
[0037] Furthermore, since it is a full-spectrum remote sensing image dataset, specifically including remote sensing image data from the VIMI full-spectrum imager on the Gaofen-5 satellite across 12 spectral bands from visible to long wavelengths, not all spectral bands can be used for calculating permafrost-related information. To determine permafrost regions and types, brightness temperature calculations based on remote sensing image data are required. Therefore, this embodiment of the application needs to select remote sensing image data sensitive to surface temperature from the full-spectrum remote sensing image dataset, such as remote sensing image data in the thermal infrared band. Additionally, since the full-spectrum remote sensing image dataset includes remote sensing image data corresponding to different time points, when selecting remote sensing image data for a specific spectral band from the full-spectrum dataset, it is necessary to compare and analyze the remote sensing image data corresponding to each time point with the spectral band data of a specified sensor on a preset thermal infrared satellite, and select the remote sensing image data corresponding to the specified sensor spectral band from the full-spectrum remote sensing image dataset. For example, analysis was conducted on the full-spectrum remote sensing image data collected by Gaofen-5 and compared with existing land surface temperature sequence product data. The radiance values of the 12 spectral bands of remote sensing image data collected by Gaofen-5 were analyzed one by one, and regression analysis was performed with measured temperature data from preset meteorological stations and existing land surface temperature sequence product data to screen out the remote sensing image data of spectral bands sensitive to land surface temperature (i.e., the remote sensing image data of the spectral bands specified above). As an example, the values of existing land surface temperature series product data are remote sensing image data collected by the Landsat TIRS thermal infrared satellite sensor. The full-spectrum remote sensing image data collected by the Gaofen-5 satellite for each time point is subjected to spectral band analysis, and compared and analyzed with the remote sensing image data collected by the Landsat TIRS thermal infrared satellite sensor. After multi-layer analysis, remote sensing image data with the same or similar spectral bands as the Landsat TIRS thermal infrared satellite sensor are selected from the full-spectrum remote sensing image data corresponding to the Gaofen-5 satellite, such as the remote sensing image data of bands 11-12 collected by VIMI.
[0038] Step 202: Calculate the brightness temperature of the remote sensing image data of the specified spectral band according to the preset Planck function to obtain the surface temperature grid data corresponding to each time node, and obtain the surface temperature spatial distribution sequence data based on the surface temperature grid data.
[0039] As an example, after selecting remote sensing image data corresponding to a specified spectral band for each time node from the full-spectrum remote sensing image dataset of the Gaofen-5 satellite, the radiance value of the remote sensing image data of the specified spectral band for each time node is determined, and the land surface temperature grid data corresponding to each time node is calculated based on the radiance value and a preset Planck function. For example, by introducing the Planck function, brightness temperature calculations are performed on the remote sensing image data of the specified spectral band corresponding to different time nodes to obtain land surface temperature grid data for different time nodes.
[0040] As another example, measured temperature data from weather stations is obtained. For instance, the measured temperature data is the temperature data at a height of 2m measured by a weather station within the area corresponding to the remote sensing image data. Then, based on the measured temperature data, the accuracy of the surface temperature grid data is calibrated and deviation analysis is performed to obtain the nonlinear regression function between the surface temperature grid data and the measured temperature. Based on a preset surface temperature product sequence data, the linear regression function of the surface temperature grid data is interpolated to obtain the surface temperature spatial distribution sequence data. The surface temperature spatial distribution sequence data is the surface temperature spatial data within a specified time period corresponding to the Gaofen-5 satellite, obtained by inverting remote sensing image data from the Gaofen-5 satellite. For example, spatiotemporal interpolation is performed on surface temperature grid data at different time points. Using 2m altitude temperature data measured by meteorological stations as a standard, accuracy calibration and bias analysis are conducted on temperature data for different time periods. For patches not fully covered by GF-5 satellite data, mature MODIS surface temperature product sequence data is used for supplementation. Interpolation is performed using the nonlinear regression relationship derived from synchronously covered GF-5 inverted surface temperature data and mature MODIS surface temperature product sequence data. After interpolation and bias processing analysis, a 16-day average spatial distribution sequence of surface temperature based on Gaofen-5 remote sensing image data is formed. In this embodiment, the spatial resolution of the grid is 50m, the spatial resolution of the Gaofen-5 full-spectrum camera imaging, which is significantly higher than the resolution of the widely used MODIS surface temperature sequence product data, achieving a more refined computational breakthrough in the surface temperature inversion part.
[0041] Step 203: Based on the spatial distribution sequence data of surface temperature and the preset permafrost top-level calculation model, calculate the total energy of each grid in the remote sensing image data in the thawing and freezing states within a specified period, and determine the distribution information of permafrost based on the total energy of each grid.
[0042] As an example, based on the spatial distribution sequence data of surface temperature and a preset permafrost top-level calculation model, the total energy of each grid in the remote sensing image data within a specified period is calculated in both the thawing and freezing states. This includes: using the spatial distribution sequence data of surface temperature as input to the permafrost top-level calculation model to construct a rapid permafrost identification model based on grid units; and calculating the total energy of each grid in both the thawing and freezing states within a specified period based on the rapid permafrost identification model. As another example, the distribution information of permafrost is determined based on the total energy of each grid, including: identifying each grid based on the total energy of the thawing and freezing states corresponding to each grid; wherein the identifier of each grid is used to characterize the permafrost type of that grid, and the permafrost types include perennial permafrost, short-term permafrost, and seasonal permafrost; and determining the permafrost distribution information corresponding to each permafrost type based on the identifier corresponding to each grid.
[0043] Optionally, each grid is identified based on the total energy of each grid, including: identifying grids whose total energy in the molten state is greater than the total energy in the frozen state as 0, and identifying grids whose total energy in the frozen state is greater than the total energy in the molten state as 1.
[0044] Optionally, determining the permafrost distribution information corresponding to each permafrost type based on the identifier corresponding to each grid includes: spatially upscaling grids with the same identifier to obtain the permafrost distribution information corresponding to each permafrost type. For example, Figure 3 The results of permafrost inversion based on the scheme provided in the embodiments of this application are shown.
[0045] The specific steps are as follows:
[0046] 1) Using the temperature retrieved from Gaofen-5 remote sensing image data as input, a top-level calculation model for permafrost is introduced to construct a large-scale rapid identification model for permafrost based on Gaofen-5 remote sensing image data; using measured temperature data from meteorological stations as a standard, parameter calibration and calculation are performed on the nonlinear regression relationship.
[0047] 2) Calculate the total energy of each grid cell (spatial resolution 50m) in both thawing and frozen states over a fixed period (at least 1 year). This identifies the permafrost type for each grid cell. Grid cells with a greater total energy in the thawing state than in the frozen state are marked as 0, and those with a greater total energy in the frozen state than in the thawing state are marked as 1. Grid cells with the same state are spatially upscaled to obtain the distribution range of permafrost over a large area in high-altitude regions, forming a thematic product on permafrost distribution.
[0048] The solution provided in this application introduces the Planck function to calculate the brightness temperature of designated spectral bands sensitive to land surface temperature in the full-spectrum remote sensing image data collected by Gaofen-5 at different time points. This yields land surface temperature grid data for different time points. The spatial resolution of the grid is 50m, the same as that of the Gaofen-5 full-spectrum camera imaging, which is significantly higher than the resolution of MODIS land surface temperature sequence data widely used abroad. This achieves a more refined calculation breakthrough in the land surface temperature inversion part. Furthermore, it is not limited by the environment of on-site monitoring, effectively solving the limitations of traditional permafrost monitoring methods in high-altitude areas, such as harsh environment, expensive instruments, and point-scale monitoring. It can realize the inversion of permafrost and seasonal permafrost in high-altitude areas of my country on a large scale, synchronously, and remotely.
[0049] See Figure 4 This application provides a computer device, the computer device comprising:
[0050] Memory 401 is used to store at least one instruction executed by a processor;
[0051] Processor 402 is used to execute instructions stored in memory. Figure 2 The method described.
[0052] Those skilled in the art will understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can be implemented as a completely hardware embodiment, a completely software embodiment, or a combination of...
[0053] The application can take the form of embodiments combining software and hardware aspects. Furthermore, it can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage, etc.) containing computer-usable program code.
[0054] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that the flowchart illustrations and / or block diagrams can be implemented by computer program instructions.
[0055] Or each flow and / or block in the flowchart and / or block diagram, and combinations of flow and / or 0 blocks in the flowchart and / or block diagram. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, produce a machine for implementing the flow... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device 5 to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented processing, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0058] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. A method for permafrost inversion based on full-spectrum data from the Gaofen-5 satellite, characterized in that, include: Step 201: Obtain remote sensing image datasets of the full spectrum of the Gaofen-5 satellite at different time points, and select remote sensing image data of the specified spectral band corresponding to each time point from the remote sensing image dataset, including: The remote sensing image data corresponding to each time point is compared and analyzed with the spectral data of a designated sensor on a preset thermal infrared satellite, and the remote sensing image data corresponding to the spectral band of the designated sensor is selected from the full-spectrum remote sensing image dataset. Step 202 involves calculating the brightness temperature of the remote sensing image data in the specified spectral band according to a preset Planck function to obtain the land surface temperature grid data corresponding to each time node, including: determining the radiance value of the remote sensing image data in the specified spectral band at each time node; calculating the land surface temperature grid data corresponding to each time node based on the radiance value and the preset Planck function; and obtaining the spatial distribution sequence data of land surface temperature based on the land surface temperature grid data, including: Acquire measured temperature data from meteorological stations, and based on the measured temperature data, perform accuracy calibration and deviation analysis on the surface temperature grid data to obtain a nonlinear regression function between the surface temperature grid data and the measured temperature; The spatial distribution sequence data of surface temperature is obtained by interpolating the linear regression function data of the surface temperature grid number based on the preset surface temperature product sequence data; wherein, the spatial distribution sequence data of surface temperature is the surface temperature spatial data within a specified time period corresponding to the Gaofen-5 satellite obtained by inverting the remote sensing image data of the Gaofen-5 satellite. The measured temperature data is the temperature data at a height of 2m measured by a meteorological station in the area corresponding to the remote sensing image data; For patches in space not fully covered by GF-5 satellite data, mature MODIS surface temperature product sequence data are used to supplement the coverage. Interpolation is performed using the nonlinear regression relationship obtained from the synchronously covered GF-5 inverted surface temperature data and the mature MODIS surface temperature product sequence data. The spatial resolution of the grid is 50m, which is the spatial resolution of the Gaofen-5 full-spectrum camera imaging. Step 203: Based on the spatial distribution sequence data of surface temperature and the preset permafrost top-level calculation model, calculate the total energy of each grid cell in the remote sensing image data within a specified period in both the thawing and freezing states. Determine the distribution information of the permafrost based on the total energy of each grid cell, including: Each grid is identified based on the sum of the thawing state energy and the sum of the freezing state energy corresponding to each grid; wherein, the identifier of each grid is used to characterize the permafrost type of the grid, and the permafrost type includes perennial permafrost, short-term permafrost and seasonal permafrost; The distribution information of permafrost for each type of permafrost is determined based on the identifier corresponding to each grid.
2. The method as described in claim 1, characterized in that, Based on the spatial distribution sequence data of surface temperature and the preset permafrost top-level calculation model, the total energy of each grid in the remote sensing image data within a specified period is calculated in both the thawing and freezing states, including: The spatial distribution sequence data of surface temperature is used as input to the top-level calculation model of permafrost to construct a rapid permafrost identification model with grid as the unit. Based on the rapid permafrost identification model, the total energy of each grid in the thawing and freezing states within a specified period is calculated.
3. The method as described in claim 1, characterized in that, Each grid is identified based on the total energy of all the grid cells, including: Grids where the total energy in the molten state is greater than the total energy in the frozen state are marked as 0, and grids where the total energy in the frozen state is greater than the total energy in the molten state are marked as 1.
4. The method as described in claim 3, characterized in that, Based on the identifier corresponding to each grid, the permafrost distribution information corresponding to each permafrost type is determined, including: Spatially upscaling grids with the same identifier yields permafrost distribution information for each permafrost type.