Surface Reflectance Data Fusion Method and System Based on Multi-Factor Weight Optimization

Abstract

The present invention discloses a method and system for fusing surface reflectance data based on multi-factor weight optimization, which relates to the technical field of remote sensing image processing. This method performs radiometric calibration, geometric correction, and atmospheric correction on the data of MODIS and Landsat 8 OLI sensors, and uses the Spectral Band Adjustment Factor (SBAF) technology to achieve spectral consistency, ensuring that the spectral responses of data from different sensors are consistent. At the same time, the present invention calculates and comprehensively considers multi-factor weights such as cloud, rainfall, and aerosol, constructs a comprehensive weight factor screening function, and selects the pixels with the least comprehensive influence for data fusion, thereby generating surface reflectance data with high consistency and accuracy. This method effectively solves the problems of spectral response differences and remote sensing data fusion deviations caused by cloud, rainfall, and aerosol, and significantly improves the accuracy and reliability of the fused data.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing image processing, and particularly relates to a surface reflectance data fusion method and system based on multi-factor weight optimization. Background Technique

[0002] In recent years, remote sensing optical surface reflectance data has been widely used in fields such as climate change research, agricultural management, environmental monitoring, and disaster prevention and mitigation. Accurate remote sensing optical surface reflectance data is of great significance for studying surface morphological changes, ecosystem dynamics, water resource management, and weather forecasting. With the continuous development of remote sensing satellite imaging technology, it has gradually become efficient and convenient to obtain large-scale reflectance data. While the demand for remote sensing optical surface reflectance data is increasing, higher requirements are also put forward for the parameter settings of newly developed remote sensing satellite imaging systems. High-quality surface reflectance data is also of great significance for the imaging parameter settings of remote sensing satellites.

[0003] Traditional methods for obtaining remote sensing optical surface reflectance data mainly rely on data from a single sensor. However, this method has significant limitations. Data from a single sensor often has biases and cannot comprehensively reflect the true situation of the surface. In addition, cloud cover is a major challenge in remote sensing data acquisition. Clouds will block satellite observations, resulting in the absence of surface reflectance data. These problems limit the application of traditional remote sensing data in high temporal resolution and continuous monitoring.

[0004] To make up for the problem of data loss, researchers usually use a simple splicing method to fill in the missing data. However, this method also has obvious disadvantages. The splicing method is prone to introducing errors, especially when there are differences in observation conditions between different data sources. In addition, the splicing method cannot effectively handle the dynamic changes of surface reflectance, especially in periods and regions with drastic climate changes.

[0005] Based on this, the multi-source heterogeneous data fusion method has gradually received attention. By combining data from multiple sensors and performing band-pass adjustment, more comprehensive and accurate surface reflectance data can be generated. However, this method also has deficiencies. When generating surface reflectance data using the current multi-source data fusion method, firstly, the differences between spectral response functions (RSRs) are usually ignored. Different sensors have different spectral response characteristics, which can lead to systematic biases in the observed surface reflectance data, affecting the accuracy and consistency of the data, resulting in the fused data being unable to truly reflect the actual situation of the surface, and reducing the reliability of the data in scientific research and applications; secondly, the combined effects of factors such as aerosols, clouds, and rain are often not fully considered, and the atmosphere and clouds may have an important impact on the reliability of remote sensing data, causing missing values and errors in the remote sensing data. If this impact cannot be quantified and taken into account, it will affect the data accuracy of the finally produced surface reflectance data. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes a surface reflectance data fusion method and system based on multi-factor weight optimization. The method uses the Spectral Band Adjustment Factor (SBAF) technology to adjust the spectrum and the multi-factor weight fusion technology to comprehensively consider the impacts of clouds, rain, and aerosols on imaging, improving the quality and reliability of the finally fused data, significantly improving the effect of optical multi-source remote sensing data fusion, and meeting the requirements of scientific research and practical applications.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] On the one hand, the present invention provides a surface reflectance data fusion method based on multi-factor weight optimization, and the method includes the following steps:

[0009] Step 1: Perform radiometric calibration and geometric correction on MODIS and Landsat8 OLI multi-spectral data respectively to generate top-of-atmosphere reflectance data, and generate surface reflectance data of different spectral bands after atmospheric correction processing;

[0010] Step 2: Use the spectral band pass adjustment factor technology to adjust the surface reflectance data of Landsat8 OLI to be consistent with the spectral band range of the surface reflectance data of MODIS;

[0011] Step 3: Calculate the cloud weight factor using the HOT-VBR index method, calculate the rainfall weight factor using the MODRA algorithm, calculate the aerosol weight factor of each pixel using the dark pixel method, and calculate the normalized time weight factor by normalizing the time of all data and normalize the cloud weight factor, rainfall weight factor, and aerosol weight factor;

[0012] Step 4: Calculate the comprehensive weight factor by combining the normalized cloud weight factor, rainfall weight factor, aerosol weight factor, and time weight factor. Screen the comprehensive weight factor through the comprehensive weight factor screening function, and use the pixels with the least influence of the comprehensive weight factor for data fusion.

[0013] On the other hand, the present invention provides a surface reflectance data fusion system based on multi-factor weight optimization, including:

[0014] A data preprocessing unit for performing radiometric calibration and geometric correction on MODIS and Landsat8 OLI multispectral data respectively to generate top-of-atmosphere reflectance data, and generating surface reflectance data of different spectral bands after atmospheric correction processing;

[0015] A data adjustment unit for using the spectral bandpass adjustment factor technology to adjust the surface reflectance data of Landsat8 OLI to be consistent with the spectral band range of the surface reflectance data of MODIS;

[0016] A weight factor calculation unit for calculating the cloud weight factor using the HOT-VBR index method, calculating the rainfall weight factor using the MODRA algorithm, calculating the aerosol weight factor of each pixel using the dark pixel method, calculating the time weight factor and normalizing it;

[0017] A comprehensive weight factor screening unit for combining the normalized cloud weight factor, rainfall weight factor, aerosol weight factor, and time weight factor to calculate the comprehensive weight factor, screening the comprehensive weight factor through the comprehensive weight factor screening function, and using the pixels with the least influence of the comprehensive weight factor for data fusion.

[0018] In the third aspect, the present invention provides an electronic device, including: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the foregoing surface reflectance data fusion method based on multi-factor weight optimization.

[0019] In the fourth aspect, the present invention provides a computer-readable storage medium, on which executable instructions are stored, and when the instructions are executed by a processor, the processor can implement the foregoing surface reflectance data fusion method based on multi-factor weight optimization.

[0020] The beneficial effects of the present invention are as follows:

[0021] (1) The present invention adopts the Spectral Band Adjustment Factor (SBAF) technology to ensure the spectral response consistency of different sensors, fundamentally solving the data deviation problem caused by spectral response differences, and making the fused data highly consistent and accurate spectrally.

[0022] (2) The multi-factor weight fusion technology of the present invention can select the optimal data for fusion by comprehensively considering factors such as clouds, rain, and aerosols, significantly reducing the negative impact of atmospheric and meteorological factors on data quality, and ensuring the high reliability of the data.

[0023] (3) Through the improved data fusion algorithm, the present invention can screen and fuse the optimal data sources from the complexity of multi-source data, improving the accuracy and application effect of the data, and meeting the needs of high-precision scientific research and practical applications. Description of the Drawings

[0024] Figure 1 It is a flow chart of the surface reflectance data fusion method based on multi-factor weight optimization of the present invention;

[0025] Figure 2 It is a schematic diagram of the principle of the SBAF technology of the present invention;

[0026] Figure 3 It is a schematic diagram of the concept of the fusion technology based on multi-factor weights of the present invention;

[0027] Figure 4 It is a schematic diagram of an example of the surface reflectance data of the present invention. Detailed Embodiments

[0028] The present invention will be further described below in conjunction with the drawings and embodiments.

[0029] The present invention provides a surface reflectance data fusion method and system based on multi-factor weight optimization, and the main technologies adopted include:

[0030] (1) Adjust the spectrum using the Spectral Band Adjustment Factor (SBAF) technology: The spectral responses of different satellite sensors are different, which is a hard problem faced in the fusion of different remote sensing data. In view of the difference in the spectral response functions of different sensors, the present invention introduces the Spectral Band Adjustment Factor (SBAF) technology. The SBAF technology can effectively correct the spectral response differences between different sensors, making the observation data of each sensor consistent spectrally. This adjustment process ensures that when multi-source data fusion is carried out, the spectral information of each data source matches each other, thereby improving the accuracy and consistency of the fused data.

[0031] (2) Adopt the multi-factor weight fusion technology and comprehensively consider the impacts of clouds, rain, and aerosols on imaging: To better handle the impacts of atmospheric and meteorological factors such as clouds, rain, and aerosols on remote sensing imaging, the present invention adopts a multi-factor weight fusion technology. This technology performs weighted processing on different meteorological and atmospheric factors, comprehensively considers their impacts on imaging quality, and selects the data with the least impact for fusion. This method can not only effectively reduce the negative impacts of clouds, rain, and aerosols on remote sensing data but also improve the quality and reliability of the final fused data.

[0032] Such as Figure 1 shown, it is a flowchart of a method for fusing surface reflectance data based on multi-factor weight optimization according to the present invention, including the following steps:

[0033] Step 1. Data preprocessing: Perform radiometric calibration and geometric correction on MODIS and Landsat8 OLI multi-spectral data respectively to generate top-of-atmosphere reflectance data, and generate surface reflectance data of different spectral bands through atmospheric correction processing;

[0034] Perform radiometric calibration, geometric correction, and atmospheric correction on MODIS and Landsat8 OLI data from 2019 to 2023, and resample the spatial resolution to 0.01° to obtain calibrated remote sensing surface reflectance data , where i represents the sensor code, i = 1, 2, i = 1 represents the MODIS sensor, and i = 2 represents the Landsat 8 OLI sensor; j represents the year of the data, j = 1, 2, 3, 4, 5 represent 2019, 2020, 2021, 2022, and 2023 respectively. k represents the month of the data, with a value range of 1 - 12, b represents the band of the data source, with a value range of 1, 2,... 8, and m and n are the coordinates of the pixel;

[0035] Step 2. Use the spectral bandpass adjustment factor technology (SBAF) to adjust the surface reflectance data of Landsat8OLI to be consistent with the spectral band range of the surface reflectance data of MODIS;

[0036] Such as Figure 2 shown, use SBAF to adjust the surface reflectance data of each band of Landsat 8 OLI to be the same as that of MODIS, and the formula is:

[0037] ,

[0038] In the formula, represents the adjusted surface reflectance data of Landsat 8 OLI, represents the spectral bandpass adjustment factor of each band;

[0039] Among them, The simulated reflectance through the MODIS and landsat 8 OLI sensors , is calculated by the ratio:

[0040] ,

[0041] In the formula, λ represents the wavelength; the simulated reflectances of the two sensors are obtained by using the sample hyperspectral data of the Hyperion sensor and the relative spectral response curves of the sensors themselves. The specific method is as follows:

[0042] Obtain the hyperspectral full-band average spectral response curve from the sample data in the Hyperion sensor Obtain the relative spectral response curves of the landsat 8 OLI and MODIS sensors ;

[0043] Calculate the simulated reflectance of the Landsat8 OLI sensor , and the calculation formula is as follows:

[0044] ,

[0045] Calculate the simulated reflectance of the MODIS sensor :

[0046] ,

[0047] After the surface reflectance data of Landsat 8 OLI is adjusted, the MODIS data does not need to be adjusted, that is:

[0048] .

[0049] Step 3: Calculate the cloud weight factor using the HOT-VBR index method, calculate the rainfall weight factor using the MODRA algorithm, calculate the aerosol weight factor of each pixel using the dark pixel method, and calculate and normalize the time weight factor; including:

[0050] Step 3.1: Calculation of the cloud weight factor based on the HOT-VBR index;

[0051] Cloud weight factor The calculation uses the HOT index (Haze optimized transformation) and the VBR (visible band ratio) index method, and the calculation formula is as follows:

[0052] ,

[0053] ,

[0054] Among them, utilizes the surface reflectance data after adjustment corresponding to two bands, namely the first band and the third band and the calculated HOT index data, is the VBR index data calculated using the surface reflectance data after adjustment corresponding to three bands, namely the first band, the second band and the third band;

[0055] Use the two indices to comprehensively calculate the cloud weight factor :

[0056] .

[0057] Step 3.2, Calculation of the rainfall weight factor based on the MODRA index;

[0058] The rainfall weight factor The MODRA algorithm mainly uses the thresholds in the red band and the near-infrared band.

[0059] MODRA calculates the rainfall weight factor The specific algorithm varies according to different thresholds, as follows:

[0060] ,

[0061] Among them, refers to the surface reflectance data of the adjusted eighth band, refers to the surface reflectance data of the adjusted third band.

[0062] Step 3.3, Calculation of the aerosol weight factor based on the dark pixel method;

[0063] The dark pixel method for AOD inversion used to calculate the aerosol factor is also known as the dense vegetation method. It utilizes the characteristic that dense vegetation has low reflectance in the red and blue bands and is a widely used AOD inversion algorithm at present. The so-called dark pixels refer to areas with low reflectance in remote sensing images, such as trees and shrubs. Research shows that the 2.1μm band is not affected by emission radiation, its apparent reflectance is approximately equal to the surface reflectance, and it is transparent to most aerosol types. Therefore, it can be used to detect dark targets. By analyzing the spectral data obtained by satellites and airplanes, it is found that there is an obvious linear relationship between the red (0.66μm), blue (0.47μm) bands and the 2.1μm band:

[0064] ,

[0065] ,

[0066] Among them, and are the surface reflectances in the red and blue bands respectively, represents the apparent reflectance in the 2.1-μm band. Therefore, the surface reflectances in the red and blue bands can be obtained from the apparent reflectance in the mid-infrared band, and then the surface contribution is removed from the apparent reflectances in the red and blue bands to obtain the atmospheric parameters, construct a look-up table, and further obtain the AOD. The spectral parameters use the red band, blue band, and mid-infrared band. The AOD values inverted by the dark pixel method range from 0 to 2. The closer the value is to 0, the more transparent the atmosphere is and the less polluted it is. The closer the value is to 2, the higher the atmospheric turbidity and the more serious the pollution. The specific calculation process is as follows:

[0067] First, obtain the TOA reflectance of the data , and obtain the simulated surface reflectance by using the linear relationship between the red and blue bands and the short-wave infrared :

[0068] ,

[0069] ,

[0070] Among them, is the short-wave infrared band data of the TOA data, is the red band data of the simulated surface reflectance, is the blue band data of the simulated surface reflectance;

[0071] Then, remove the surface contribution from the apparent reflectances in the red and blue bands to obtain the atmospheric parameters, construct a look-up table, and further obtain the aerosol weight factor :

[0072] .

[0073] Among them, represents the TOA reflectance data of the 3rd band, represents the TOA reflectance data of the 5th band, and the function f(·) represents the process of removing the surface contribution from the apparent reflectances in the red and blue bands to obtain the atmospheric parameters, construct a look-up table, and obtain the aerosol optical thickness.

[0074] Step 3.4, normalization of the time weight factor;

[0075] By normalizing the time of all the surface reflectance data according to the time distance, the time weight factor is formed:

[0076] Calculate the maximum and minimum ranges of the time weight factor:

[0077] ,

[0078] Among them, and represent the minimum and maximum values of the time weight factor respectively.

[0079] Normalized time weight factor:

[0080] ,

[0081] Among them, represents the normalized time weight factor, and its range is between [0, 1].

[0082] Step 4: Calculate the comprehensive weight factor by combining the normalized cloud weight factor, rainfall weight factor, aerosol weight factor, and time weight factor, and screen the comprehensive weight factor through the comprehensive weight factor screening function, and use the pixel with the smallest influence of the comprehensive weight factor for data fusion;

[0083] As Figure 3 shown, in order to obtain the final fusion data , use the comprehensive weight factor screening function to screen to make optimized within the range of [i, j]. That is:

[0084] ,

[0085] Among them, represents the final fusion data, and the matrix F is represents the comprehensive weight factor screening function, which is a standard basis matrix with one element being 1 and the other elements being zero. Its calculation method is that when the comprehensive weight factor is within (x is within the range of i, y is within the range of j) and obtains the maximum value, then is equal to 1, and the other are equal to 0, that is:

[0086] ,

[0087] Among them, the comprehensive weight factor is:

[0088] ,

[0089] Among them, represents the normalized cloud weight factor, represents the normalized rainfall weight factor, represents the normalized aerosol weight factor, Let \(\omega\) represent the normalized time weight factor, and \(w_1\), \(w_2\), \(w_3\), \(w_4\) represent the weights corresponding to the normalized cloud weight factor, the normalized rainfall weight factor, the normalized aerosol weight factor, and the normalized time weight factor respectively. According to requirements, they are respectively determined as: \(w_1 = 0.4\), \(w_2 = 0.1\), \(w_3 = 0.2\), \(w_4 = 0.3\).

[0090] As Figure 4 shown This is the schematic diagram of the global surface reflectance data finally obtained under the above conditions.

[0091] On the other hand, the present invention provides a surface reflectance data fusion system based on multi-factor weight optimization. Each unit included therein can implement each step of the foregoing method. Specifically, the system includes:

[0092] A data preprocessing unit, which is used to perform radiometric calibration and geometric correction processing on MODIS and Landsat8 OLI multi-spectral data respectively to generate top-of-atmosphere reflectance data, and generate surface reflectance data of different spectral bands through atmospheric correction processing;

[0093] A data adjustment unit, which is used to adjust the surface reflectance data of Landsat8 OLI to be consistent with the spectral band range of the surface reflectance data of MODIS by using the spectral bandpass adjustment factor technology;

[0094] A weight factor calculation unit, which is used to calculate the cloud weight factor by using the HOT-VBR index method, calculate the rainfall weight factor by using the MODRA algorithm, calculate the aerosol weight factor of each pixel by using the dark pixel method, calculate the time weight factor and normalize it;

[0095] A comprehensive weight factor screening unit, which is used to calculate the comprehensive weight factor by combining the normalized cloud weight factor, rainfall weight factor, aerosol weight factor, and time weight factor, screen the comprehensive weight factor through the comprehensive weight factor screening function, and use the pixel with the least influence of the comprehensive weight factor for data fusion.

[0096] In the third aspect, the present invention provides an electronic device, including: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the foregoing surface reflectance data fusion method based on multi-factor weight optimization.

[0097] In the fourth aspect, the present invention provides a computer-readable storage medium, on which executable instructions are stored. When the instructions are executed by a processor, the processor can implement the foregoing surface reflectance data fusion method based on multi-factor weight optimization.

[0098] The specific embodiments described above further elaborate on the objective, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc., made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A surface reflectance data fusion method based on multi-factor weight optimization, characterized in that: The method comprises the following steps: Step 1: Perform radiometric calibration and geometric correction on MODIS and Landsat8 OLI multispectral data to generate top of atmosphere reflectance data, and generate surface reflectance data of different spectral bands after atmospheric correction; Step 2: Use the spectral bandpass adjustment factor technology to adjust the surface reflectance data of Landsat8 OLI to be consistent with the spectral range of the surface reflectance data of MODIS; Step 3: Calculate the cloud weight factor using the HOT-VBR index method, calculate the rainfall weight factor using the MODRA algorithm, calculate the aerosol weight factor of each pixel using the dark pixel method, calculate the time weight factor and normalize it; Step 4: Calculate the comprehensive weight factor by combining the normalized cloud weight factor, rainfall weight factor, aerosol weight factor, and time weight factor, and select the comprehensive weight factor through the comprehensive weight factor screening function. , data fusion is performed based on the screened comprehensive weight factors: ， in, Represents the final fused data, matrix represents the comprehensive weight factor screening function, which is a standard basis matrix with one element being 1 and other elements being zero. Indicates the adjusted Landsat8 OLI surface reflectance data. The superscript i indicates the sensor code, which takes the value of 1 or 2. i=1 indicates the MODIS sensor, and i=2 indicates the Landsat 8OLI sensor. j indicates the year of the data, k indicates the month of the data, b indicates the band of the data source, which takes the value of 1, 2, ... 8, and m and n are the coordinates of the pixel. When the comprehensive weight factor exist When the maximum value is obtained, the element is equal to 1, the other elements is equal to 0: ， Among them, i∈x, j∈y.

2. The surface reflectance data fusion method based on multi-factor weight optimization according to claim 1 is characterized in that: The second step comprises: The spectral bandpass adjustment factor technology is used to adjust the surface reflectance data of Landsat8 OLI to be consistent with the spectral range of the surface reflectance data of MODIS: ， In the formula, the superscript i represents the sensor code, which takes the value of 1 or 2, i=1 represents the MODIS sensor, and i=2 represents the Landsat 8 OLI sensor; j represents the year of the data, k represents the month of the data, b represents the band of the data source, which takes the value of 1, 2, ... 8, m and n are the coordinates of the pixel, Represents the adjusted Landsat 8 OLI surface reflectance data, Represents the Landsat 8 OLI surface reflectance data before adjustment, Represents the spectral bandpass adjustment factor for each band.

3. The surface reflectance data fusion method based on multi-factor weight optimization according to claim 1 is characterized in that: The step three comprises: The cloud weight factor calculation formula is as follows: ， ， ， in, represents the cloud weight factor, Indicates the adjusted Landsat8 OLI surface reflectance data corresponding to the first and third bands and The calculated HOT index data, It is the VBR index data calculated using the adjusted Landsat8 OLI surface reflectance data corresponding to the three bands: Band 1, Band 2, and Band 3.

4. The surface reflectance data fusion method based on multi-factor weight optimization according to claim 1 is characterized in that: The step three comprises: The MODRA algorithm calculates the rainfall weight factor using thresholds in the red and near-infrared bands. : ， in, Refers to the adjusted Landsat8 OLI surface reflectance data of Band 8, Refers to the adjusted Band 3 Landsat8 OLI surface reflectance data.

5. The surface reflectance data fusion method based on multi-factor weight optimization according to claim 1 is characterized in that: The step three comprises: Aerosol weight factor Calculate according to the following formula: ， in, Indicates the TOA reflectivity data of the third band, represents the TOA reflectance data of band 5, and the f(·) function represents the removal of the surface contribution from the apparent reflectance of the red and blue bands; The time of all surface reflectance data Normalize according to the time distance to form a time weight factor , and normalize it: ， in, is the normalized time weight factor, and Respectively represent the minimum and maximum values ​​of the time weight factor.

6. The surface reflectance data fusion method based on multi-factor weight optimization according to claim 1 is characterized in that: The comprehensive weight factor for: ， In the formula, represents the normalized cloud weight factor, represents the normalized rainfall weight factor, represents the normalized aerosol weight factor, represents the normalized time weight factor, w1, w2, w3, and w4 represent the weights corresponding to the normalized cloud weight factor, the normalized rainfall weight factor, the normalized aerosol weight factor, and the normalized time weight factor, respectively.

7. A surface reflectance data fusion system based on multi-factor weight optimization, characterized in that: include: The data preprocessing unit is used to perform radiometric calibration and geometric correction on MODIS and Landsat8 OLI multispectral data, generate top of atmosphere reflectance data, and generate surface reflectance data of different spectral bands after atmospheric correction; A data adjustment unit is used to adjust the surface reflectance data of Landsat8 OLI to be consistent with the spectral range of the surface reflectance data of MODIS by using the spectral band adjustment factor technology; A weight factor calculation unit is used to calculate the cloud weight factor using the HOT-VBR index method, calculate the rainfall weight factor using the MODRA algorithm, calculate the aerosol weight factor of each pixel using the dark pixel method, calculate the time weight factor and normalize it; The comprehensive weight factor screening unit is used to calculate the comprehensive weight factor by combining the normalized cloud weight factor, rainfall weight factor, aerosol weight factor, and time weight factor, and to screen the comprehensive weight factor through the comprehensive weight factor screening function. , data fusion is performed based on the screened comprehensive weight factors: ， in, Represents the final fused data, matrix represents the comprehensive weight factor screening function, which is a standard basis matrix with one element being 1 and other elements being zero. Indicates the adjusted Landsat8 OLI surface reflectance data. The superscript i indicates the sensor code, which takes the value of 1 or 2. i=1 indicates the MODIS sensor, and i=2 indicates the Landsat 8OLI sensor. j indicates the year of the data, k indicates the month of the data, b indicates the band of the data source, which takes the value of 1, 2, ... 8, and m and n are the coordinates of the pixel. When the comprehensive weight factor exist When the maximum value is obtained, the element is equal to 1, the other elements is equal to 0: ， Among them, i∈x, j∈y.

8. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; Wherein, when one or more programs are executed by the one or more processors, the one or more processors implement the surface reflectance data fusion method based on multi-factor weight optimization as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, and when the instructions are executed by the processor, the processor can implement the surface reflectance data fusion method based on multi-factor weight optimization as described in any one of claims 1-6.

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