A method, system, computer equipment, and storage medium for correcting nighttime light remote sensing data.

Abstract

This invention provides a method, system, computer equipment, and storage medium for correcting nighttime light remote sensing data, belonging to the field of urban environmental monitoring technology. The method includes: using a multi-source remote sensing sensor to statistically analyze the impermeable and non-impermeable layers in a target area, and calculating a multi-source data consistency index (CI); using the CI to correct the impermeable layer classification results of the target area; and based on the corrected impermeable layer classification results, counting the number N of impermeable layer pixels within the target area. b Number of pixels N in impermeable layer n , using N b and N n Calculate the proportion P of the impermeable layer within a unit pixel. b Using the nighttime light remote sensing brightness value NTL and the proportion of impermeable layer per unit pixel P b A calibration model was constructed to effectively reduce light spillover interference in impermeable areas, significantly improve the accuracy of impermeable layer data, and provide high-precision data support for urban expansion monitoring, light pollution assessment, and socio-economic activity analysis.

Description

Technical Field

[0001] This invention belongs to the field of urban environmental monitoring technology, specifically relating to a nighttime light remote sensing data correction method, system, computer equipment, and storage medium. Background Technology

[0002] With the acceleration of urbanization, nighttime light remote sensing data is playing an increasingly important role in urban environmental monitoring, urban expansion research, and light pollution assessment. Nighttime light intensity not only reflects urban economic activity and population distribution but is also closely related to urban energy consumption and environmental quality. However, nighttime light remote sensing data suffers from spillover effects, which severely impacts its accuracy and reliability, thereby affecting its refined application.

[0003] While some existing technologies have attempted to correct nighttime light remote sensing data, traditional correction methods often rely on complex models and a large amount of prior knowledge, making it difficult to achieve efficient and accurate correction in practical applications. Moreover, they are mostly focused on the correlation analysis between impermeable layers and urban expansion, while the application of impermeable layer data in the correction of nighttime light remote sensing spillover effects is still in the exploratory stage. Summary of the Invention

[0004] To address the aforementioned background issues, this invention establishes a physically meaningful nighttime light correction model by quantifying the correlation between the spatial distribution characteristics of impermeable layers and the intensity of nighttime light remote sensing spillover. This provides a nighttime light remote sensing data correction method, system computer equipment, and storage medium for urban environmental monitoring technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for correcting nighttime light remote sensing data, specifically including:

[0007] The surface space of the target area is divided into a uniform resolution pixel grid, and the total number N of the pixel grids in the target area is counted. total Extract N total The NTL value of the nighttime light remote sensing brightness corresponding to each pixel grid.

[0008] Obtain impermeable layer classification data of different resolutions in the target area, map the impermeable layer classification data of different resolutions to the same geographic coordinates, and align them one by one with the center of the pixel grid.

[0009] If different resolutions of impermeable layer classification data at the same pixel grid location all classify the same pixel as an impermeable layer, then the pixel is determined to be an impermeable layer; otherwise, the pixel is a non-impermeable layer.

[0010] Let N be the target region.total The total number of pixels identified as impermeable layers is N. agree , by N agree With N total Calculate the multi-source data consistency index (CI), and use the CI of the multi-source data consistency index of the target area to calibrate the classification results of the impermeable layer in the target area.

[0011] Based on the classification results of the impermeable layer in the target area after calibration, the number N of impermeable layer pixels in the target area is counted. b Number of pixels N in impermeable layer n Using N b and N n Calculate the impermeable layer ratio P per unit pixel b .

[0012] Using the nighttime light remote sensing brightness value NTL and the impermeable layer ratio P per unit pixel b A calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

[0013] Preferably, the N agree With N total The multi-source data consistency index (CI) is calculated using the following expression:

[0014]

[0015] Where, N total N represents the total number of cell grids within the target region. agree For N in the target area total The total number of pixel grids identified as impermeable layers.

[0016] Preferably, the step of calibrating the classification results of the impermeable layer of the target area by using the multi-source data consistency index (CI) of the target area specifically includes: calibrating by using weighted voting and visual comparison verification.

[0017] Preferably, the verification using weighted voting and visual comparison includes:

[0018] If the multi-source data consistency index (CI) for the target region is in the range of 0.5 ≤ CI < 0.8, the classification result is determined by weighted voting, as shown in the following formula:

[0019]

[0020] Among them, P best For the optimal impermeable layer classification result, k represents the impermeable or non-impermeable layer, n represents the impermeable layer classification data at different resolutions, and w i Let δ(P) be the weight of the i-th data source. i,k) is an indicator function, which takes the value 1 when the classification result of the i-th data source is k, and 0 otherwise;

[0021] If the multi-source data consistency index (CI) of the target area is less than 0.5, the classification results will be overlaid onto the high-resolution image for manual correction.

[0022] Preferably, the use of N b and N n Calculate the impermeable layer ratio P per unit pixel b The expressions include:

[0023]

[0024] Among them, P b N represents the proportion of the impermeable layer within a unit cell grid. b N represents the number of impermeable layer cell grids within the target area. n This represents the number of non-impermeable layer pixels within the target area.

[0025] Preferably, the expression of the correction model is as follows:

[0026] NTL ENH =NTL*P b ;

[0027] Among them, P b NTL represents the proportion of impermeable layer per unit pixel, and NTL is the nighttime light remote sensing brightness value. ENH This is the corrected nighttime light remote sensing value.

[0028] A nighttime light remote sensing data correction system, characterized in that it comprises:

[0029] The data processing module is used to divide the surface space of the target area into a uniform resolution pixel grid and count the total number N of the pixel grids within the target area. total Extract N total The nighttime light remote sensing brightness value (NTL) corresponding to each pixel grid is obtained; impermeable layer classification data of different resolutions in the target area are acquired, and the impermeable layer classification data of different resolutions are mapped to the same geographic coordinates and aligned with the center of the pixel grid one by one.

[0030] The data classification module is used to classify impermeable layer data of different resolutions at the same pixel grid location as impermeable layers. If all classifications for the same pixel classify it as an impermeable layer, then the pixel is determined to be an impermeable layer; otherwise, the pixel is a non-impermeable layer. Let N be the target area. total The total number of pixels identified as impermeable layers is N. agree , by N agree With N totalCalculate the multi-source data consistency index (CI), and use the CI of the multi-source data consistency index of the target area to calibrate the classification results of the impermeable layer in the target area.

[0031] The correction module is used to count the number N of impermeable layer pixels in the target area based on the classification results of the impermeable layer in the calibrated target area. b Number of pixels N in impermeable layer n Using N b and N n Calculate the impermeable layer ratio P per unit pixel b Using the nighttime light remote sensing brightness value NTL and the impermeable layer ratio P per unit pixel b A calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

[0032] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in the nighttime light remote sensing data correction method.

[0033] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any of the steps in the nighttime light remote sensing data correction method.

[0034] The nighttime light remote sensing data correction method provided by this invention has the following beneficial effects:

[0035] The classification results of the impermeable layer in the target area are calibrated using the multi-source data consistency index (CI), significantly improving the accuracy of the impermeable layer data and providing a reliable foundation for subsequent calibration. The constructed calibration model can effectively reduce light spillover interference in low impermeable layer areas and improve the correction effect of spatial autocorrelation effect in nighttime light remote sensing data. The calibrated nighttime light remote sensing data can more accurately reflect the boundaries of urban built-up areas, providing high-precision data support for urban expansion monitoring, light pollution assessment, and socio-economic activity analysis. Through automated calculation and calibration processes, manual intervention is reduced, data processing efficiency is improved, and it is suitable for large-scale urban environmental monitoring applications. Attached Figure Description

[0036] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a nighttime light remote sensing data correction method according to an embodiment of the present invention;

[0038] Figure 2 This is a graph showing the relationship between impermeable data and nighttime light remote sensing brightness values ​​according to an embodiment of the present invention; wherein, Figure 2 (a) is a distribution map of the proportion of impermeable layers in Zhejiang Province according to an embodiment of the present invention. Figure 2 (b) is the NDBI distribution map of Zhejiang Province. Figure 2 (c) is a scatter plot showing the correlation between the proportion of impermeable layers and the nighttime light value. Figure 2 (d) is a scatter plot showing the correlation between NDBI and nighttime light value;

[0039] Figure 3 This is a spatial comparison of the changes in luminescence and the proportion of impermeable layers in an embodiment of the present invention;

[0040] Figure 4 This is a proportional segmentation method according to an embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram of the nighttime light emission results based on the impermeability index correction in an embodiment of the present invention; wherein... Figure 5 (a) is the original NTL distribution map. Figure 5 (b) is the NTL result after correction based on the Pb exponent. Detailed Implementation

[0042] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0043] This invention provides a method for correcting nighttime light remote sensing data, specifically as follows: Figure 1 As shown, it includes:

[0044] S1. Divide the surface space of the target area into a uniform resolution pixel grid, and count the total number N of pixel grids in the target area. total Extract N total The nighttime light remote sensing brightness value (NTL) corresponding to each pixel grid is obtained; impermeable layer classification data of different resolutions in the target area are acquired, and the impermeable layer classification data of different resolutions are mapped to the same geographic coordinates and aligned with the center of the pixel grid one by one.

[0045] Multiple impermeable layer data sources with different resolutions (10m to 30m) are acquired and spatially overlaid and matched. The best impermeable layer data is determined by spatial comparison. The impermeable layer classification data of different resolutions are uniformly resampled to the same resolution and projected onto the same coordinate system so that the cell grids of the impermeable layer classification data of different resolutions are completely matched.

[0046] S2. If different resolutions of impermeable layer classification data at the same pixel grid location all classify the same pixel grid as an impermeable layer, then the pixel grid is determined to be an impermeable layer; otherwise, the pixel is a non-impermeable layer. Let N be the region within the target area. total The total number of pixel grids identified as impermeable layers is N. agree , by N agree With N total Calculate the multi-source data consistency index (CI), and use the CI of the multi-source data consistency index of the target area to calibrate the classification results of the impermeable layer in the target area.

[0047] Consistency analysis was performed on the classification results of multi-source impermeable layers for each pixel, and the Consistency Index (CI) was calculated:

[0048]

[0049] Where, N total N represents the total number of cell grids within the target area. agree For N in the target area total The total number of pixels identified as impermeable layers.

[0050] When the identification results from multiple data sources are consistent, it indicates that the area is an accurate land surface classification; when there are inconsistent classifications from multiple data sources, the results represented by the majority of data sources shall prevail; when there are significant discrepancies, the classification results are overlaid on Google imagery for visual comparison to determine the final impermeable layer classification.

[0051] When the consistency index (CI) of multi-source data is ≥0.8, it indicates that the classification results of impermeable layers in the region are highly consistent, and the classification results are directly adopted as the best impermeable layer data.

[0052] When the multi-source data consistency index is 0.5 ≤ CI < 0.8, the weighted voting method is used to determine the optimal impermeable layer classification.

[0053]

[0054] Among them, P best For the optimal impermeable layer classification result, k represents the impermeable or non-impermeable layer, n represents the impermeable layer classification data at different resolutions, and w i Let δ(P) be the weight of the i-th data source. i ,k) is an indicator function that takes the value 1 when the classification result of the i-th data source is k, and 0 otherwise.

[0055] For areas with significant discrepancies (such as CI < 0.5), the classification results are overlaid onto high-resolution imagery (such as Google Imagery) for visual comparison, and the classification results are manually corrected to ensure the accuracy of the impermeable layer data.

[0056] S3. Based on the classification results of the impermeable layer in the target area after calibration, count the number of impermeable layer pixels N in the target area. b Number of pixels N in impermeable layer n Using N b and N n Calculate the proportion P of the impermeable layer within a unit cell grid. b Using the nighttime light remote sensing brightness value NTL and the proportion of impermeable layer P within a unit pixel grid. b A calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

[0057] Based on the integration of multiple impermeable layer data, the proportion P of the impermeable layer within a 500m grid cell is calculated. The specific calculation formula is as follows:

[0058]

[0059] Where, N b N represents the number of impermeable layer pixels within a single pixel. n This refers to the number of pixels in non-impermeable layers (such as water bodies, urban green spaces, wastelands, etc.).

[0060] The nighttime light remote sensing data is corrected based on the impermeable layer ratio, and the original nighttime light remote sensing brightness value NTL for the corresponding grid is extracted. A correction model is constructed using the product of the impermeable layer ratio P and NTL to obtain the corrected nighttime light remote sensing value. The calculation formula is as follows:

[0061] NTL ENH =BTL*P b ;

[0062] Among them, P b NTL represents the proportion of impermeable layer per unit pixel, and NTL represents the nighttime light remote sensing brightness value. ENH This is the corrected nighttime light remote sensing value.

[0063] Based on the above formula, for low impermeable layer areas such as water bodies and vegetation in urban areas, spatial autocorrelation effect correction of nighttime light remote sensing data can be achieved by reducing the interference value of light overflow from adjacent high-brightness areas.

[0064] To verify the effectiveness of the above methods, Zhejiang Province will be used as an example for verification and explanation. Figure 2 The results of extracting impermeable surfaces at 500m resolution in Zhejiang Province and their relationship with the nighttime light remote sensing brightness value NTL are presented. Figure 2(a) intuitively reflects the spatial distribution of impermeable surfaces, mainly concentrated in the main urban area of ​​Hangzhou and the central and southeastern regions, with distribution characteristics similar to... Figure 1 The NTL values ​​of the nighttime light remote sensing data are basically consistent. The impervious surface density data proposed in this study not only characterize the spatial range of impervious surfaces, but also quantify their density levels. Areas with pixel values ​​close to 1 represent a high proportion of impervious surface coverage, while suburban areas have relatively lower values. Figure 2 (b) shows the results of the NDBI (Normalized Difference Built-up Index) extraction, although the overall distribution is similar to... Figure 2 Similar to (a), but the NDBI value distribution in the impermeable area is more discrete, and the performance is not as intuitive as the method in this study.

[0065] Figure 2 (c) shows the correlation between the impermeability ratio index (Pb) and the NTL (Net Neural Layer), with a correlation coefficient of 0.74, indicating that the nighttime light intensity mainly originates from the impermeable surface coverage area. However, spatial mismatches still exist locally; for example, some high NTL areas have almost no impermeable surfaces, while some high-density impermeable surface areas have low nighttime light values. Furthermore, Figure 2 (d) shows that the correlation between NDBI and NTL is weak, only 0.37, indicating that it does not accurately reflect the distribution and density of impermeable surfaces. Therefore, it can be deduced that the Pb index is better suited for nighttime light correction.

[0066] Figure 3 Images (a), (b), (c), and (d) respectively demonstrate the spatial distribution relationship between the nighttime light intensity (NTL) and the impermeable surface ratio (Pb) and clustering results in the main urban area of ​​Hangzhou. Figure 3 (d) indicates that the NTL value is generally positively correlated with the proportion of impervious surfaces. When a pixel corresponds to an impervious feature, the NTL value increases significantly, and vice versa, forming a high-nightlight-high-construction-land clustering pattern. However, anomalies exist in some water areas where, despite low Pb values, the NTL values ​​are high, exhibiting a high-nightlight-low-construction-land clustering phenomenon. This situation is mainly affected by the "nightlight spillover effect," where high-intensity nightlight from urban areas diffuses into adjacent water bodies, leading to artificially high NTL values ​​in water areas. Therefore, it is necessary to correct for the nightlight spillover effect based on the Pb index to improve the accuracy of nightlight data in representing the distribution characteristics of impervious surfaces.

[0067] according to Figure 4 The segmentation method will Figure 5 Perform data processing. Figure 5 The results show a comparison of the NTL (Net Light Level) values ​​of nighttime light remote sensing before and after Pb index correction. Figure 5 (a) is the original NTL distribution map, which shows that the nighttime light intensity in water bodies such as the Qiantang River is artificially high. Figure 5(b) shows the NTL results after correction based on the Pb index, which significantly corrects the nightlight spillover phenomenon in the Qiantang River area and more accurately reflects the true nightlight distribution.

[0068] By integrating multiple impermeable layer data sources, the optimal impermeable layer classification is determined, significantly improving the accuracy of impermeable layer data and providing a reliable foundation for subsequent correction. The correction model constructed based on the proportion of impermeable layers has physical significance and can effectively reduce light spillover interference in low impermeable layer areas, improve the spatial autocorrelation effect correction effect of nighttime light remote sensing data, and the corrected nighttime light remote sensing data can more accurately reflect the boundaries of urban built-up areas, providing high-precision data support for urban expansion monitoring, light pollution assessment, and socio-economic activity analysis. Through automated calculation and correction processes, manual intervention is reduced, data processing efficiency is improved, and it is suitable for large-scale urban environmental monitoring applications.

[0069] Based on the same inventive concept, the present invention also provides a nighttime light remote sensing correction system, comprising:

[0070] The data processing module is used to divide the surface space of the target area into a uniform resolution pixel grid and count the total number N of the pixel grids within the target area. total Extract N total The nighttime light remote sensing brightness value (NTL) corresponding to each pixel grid is obtained; impermeable layer classification data of different resolutions in the target area are obtained, and the impermeable layer classification data of different resolutions are mapped to the same geographic coordinates and aligned with the center of the pixel grid one by one.

[0071] The data classification module is used to classify impermeable layer data of different resolutions at the same pixel grid location as impermeable layers. If all classifications for the same pixel classify it as an impermeable layer, then the pixel is determined to be an impermeable layer; otherwise, the pixel is a non-impermeable layer. Let N be the target area. total The total number of pixels identified as impermeable layers is N. agree , by N agree With N total Calculate the multi-source data consistency index (CI), and use the multi-source data consistency index (CI) of the target area to verify the classification results of the impermeable layer in the target area.

[0072] The correction module is used to count the number N of impermeable layer pixels in the target area based on the classification results of the impermeable layer in the calibrated target area. b Number of pixels N in impermeable layer n Using N b and N n Calculate the impermeable layer ratio P per unit pixel b Using the nighttime light remote sensing brightness value NTL and the impermeable layer ratio P per unit pixel bA calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

[0073] This invention also provides a computer device, which, at the hardware level, includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the nighttime remote sensing correction method provided above.

[0074] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the nighttime light remote sensing correction method provided above.

[0075] Specific limitations regarding the nighttime light remote sensing correction system can be found in the limitations of the nighttime light remote sensing correction method described above, and will not be repeated here. Each module in the aforementioned nighttime light remote sensing correction system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0076] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the patent of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for correcting nighttime light remote sensing data, characterized in that, The method includes: The surface space of the target area is divided into a uniform resolution pixel grid, and the total number of pixel grids within the target area is counted. N total ;extract N total The nighttime light remote sensing brightness value corresponding to each pixel grid. NTL ; Obtain impermeable layer classification data of different resolutions in the target area, map the impermeable layer classification data of different resolutions to the same geographic coordinates, and align them one by one with the center of the pixel grid. If different resolutions of impermeable layer classification data at the same pixel grid position all classify the same pixel as an impermeable layer, then the pixel is determined to be an impermeable layer; otherwise, the pixel is a non-impermeable layer. Record the target area N total The total number of pixels identified as impermeable layers in the middle is N agree ,Depend on N agree and N total Calculate the multi-source data consistency index (CI), and use the multi-source data consistency index (CI) of the target area to verify the classification results of the impermeable layer in the target area. Based on the classification results of impermeable layers in the target area after calibration, the number of impermeable layer pixels is counted within each unit pixel. N b Number of pixels in impermeable layer N n ,use N b and N n Calculate the proportion of impermeable layer per unit pixel. P b The unit cell is composed of multiple cell grids; Using nighttime light remote sensing brightness values NTL The ratio of impermeable layer per unit pixel P b A calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

2. The method for correcting nighttime light remote sensing data according to claim 1, characterized in that, The N agree and N total The multi-source data consistency index (CI) is calculated using the following expression: in, N total The total number of cell grids within the target area. N agree Within the target area N total The total number of pixel grids identified as impermeable layers.

3. The method for correcting nighttime light remote sensing data according to claim 1, characterized in that, The method of calibrating the classification results of the impermeable layer in the target area by using the multi-source data consistency index (CI) of the target area specifically includes: calibrating by using weighted voting and visual comparison verification.

4. The nighttime light remote sensing data correction method according to claim 3, characterized in that, The verification process using weighted voting and visual comparison includes: If the multi-source data consistency index (CI) for the target region is in the range of 0.5 ≤ CI < 0.8, the classification result is determined by weighted voting, as shown in the following formula: ； in, For the best impermeable layer classification results, k It can be either an impermeable layer or a non-impermeable layer. n Classification data for impermeable layers at different resolutions. For the first i The weight of each data source, For indicator functions, when the first i The classification results of the data sources are k The value is 1 if it is true, and 0 otherwise. If the multi-source data consistency index (CI) of the target area is less than 0.5, the classification results will be overlaid onto the high-resolution image for manual correction.

5. The method for correcting nighttime light remote sensing data according to claim 1, characterized in that, The use of N b and N n Calculate the proportion of impermeable layer per unit pixel P b The expressions include: in, P b The proportion of the impermeable layer within a unit pixel grid. N b The number of impermeable layer cells within a unit cell grid. N n This represents the number of non-impermeable layer pixels within a single pixel grid.

6. The method for correcting nighttime light remote sensing data according to claim 1, characterized in that, The expression for the correction model is as follows: ； in, P b The proportion of impermeable layer within a unit pixel. NTL This represents the brightness value of nighttime light remote sensing. This is the corrected nighttime light remote sensing value.

7. A nighttime light remote sensing data correction system, characterized in that, include: The data processing module is used to divide the surface space of the target area into a uniform resolution pixel grid and count the total number of pixel grids within the target area. N total ;extract N total The nighttime light remote sensing brightness value corresponding to each pixel grid. NTL ; Obtain impermeable layer classification data of different resolutions in the target area, map the impermeable layer classification data of different resolutions to the same geographic coordinates, and align them one by one with the center of the pixel grid. The data classification module is used to classify impermeable layer data of different resolutions at the same pixel grid location as impermeable layers. If all classifications for the same pixel indicate that it is an impermeable layer, then the pixel is identified as an impermeable layer; otherwise, the pixel is identified as a non-impermeable layer. This is recorded within the target area. N total The total number of pixels identified as impermeable layers in the middle is N agree ,Depend on N agree and N total Calculate the multi-source data consistency index (CI), and use the multi-source data consistency index (CI) of the target area to verify the classification results of the impermeable layer in the target area. The correction module is used to count the number of impermeable layer pixels in each unit pixel based on the impermeable layer classification results of the target area after correction. N b Number of pixels in impermeable layer N n ,use N b and N n Calculate the proportion of impermeable layer per unit pixel. P b The unit cell is composed of multiple cell grids; Using nighttime light remote sensing brightness values NTL The ratio of impermeable layer per unit pixel P b A calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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