Ground feature reflectivity simulation method and device based on inherent spectral characteristics between wavebands

Abstract

The invention discloses a ground object reflectivity simulation method and device based on inter-band inherent spectral characteristics, and belongs to the technical field of remote sensing science. The method comprises the following steps: firstly, acquiring hyperspectral data and MOD09A1 data of an MODIS reflectivity product, and carrying out space matching with GMTED2010 data; secondly, preprocessing the data, including projection conversion, resampling, multi-layer integration, MODIS multispectral data simulation, spectral index calculation and sample making, and construction of a training data set and a prediction data set; performing hyper-parameter optimization based on a random forest model, and establishing a regression model between reflectivity of seven visible-near infrared bands of MOD09A1 data and reflectivity of a target band (such as 532nm); and finally, simulating the reflectivity of the unobserved wave band of the satellite by using the trained model. According to the method, the inherent spectral characteristics among the wavebands are creatively utilized, the problem that the reflectivity data of the unobserved wavebands of the satellite are missing is solved, and the simulation precision is high.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing science and technology, specifically relating to a method and apparatus for simulating the reflectance of ground objects based on inherent spectral characteristics between bands. Background Technology

[0002] Surface reflectance is a key parameter characterizing the Earth's surface energy balance, material cycle, and ecological environment status, playing a crucial role in environmental monitoring, disaster early warning, and climate change research. Active remote sensing technologies such as lidar rely on surface reflectance data in specific wavelength bands (e.g., 532nm and 1064nm), but existing satellite sensors typically cannot directly acquire observational data in these bands, severely hindering the development of related applications.

[0003] Currently, surface reflectance inversion methods mainly include atmospheric correction based on physical models and band relationship modeling based on statistical learning. For example, surface reflectance of observed bands can be inverted using atmospheric correction algorithms based on satellite data such as MODIS. However, for unobserved bands (such as the working bands of lidar), existing methods have significant shortcomings: (1) Although hyperspectral data (such as AVIRIS) can provide continuous spectral information, its coverage is limited and its update frequency is low, making it difficult to meet the needs of large-scale dynamic monitoring; (2) Traditional statistical models are mostly designed for specific bands and lack systematic mining of the inherent spectral characteristics between bands, resulting in insufficient simulation accuracy of reflectance in unobserved bands.

[0004] In recent years, machine learning technology has provided new ideas for modeling band relationships. However, existing research mainly focuses on reflectance inversion of observed bands, and there is still a lack of effective methods for simulating unobserved bands. Therefore, there is an urgent need for a high-precision reflectance simulation method that can fully utilize the advantages of multi-source data and mine spectral correlations between bands to fill the gap in reflectance data for unobserved satellite bands. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method and apparatus for simulating the reflectance of ground objects based on inherent spectral characteristics between bands. By utilizing the spectral correlation between adjacent bands of typical ground objects, a regression relationship is established between the reflectance of seven visible-near-infrared bands of MOD09A1 data and the reflectance of unobserved bands from satellites, thereby simulating the surface reflectance of these bands.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for simulating the reflectance of ground features based on inherent spectral characteristics between bands, the method comprising:

[0008] Step 1: Using hyperspectral data and MODIS reflectance product MOD09A1 data as a reference, obtain the corresponding GMTED2010 data in time and space.

[0009] Step 2: Preprocess the hyperspectral data and GMTED2010 data to create a training dataset;

[0010] Step 3: Perform preprocessing operations on MODIS reflectivity product MOD09A1 data and GMTED2010 data to create a prediction dataset;

[0011] Step 4: Input the training dataset into the random forest model for training, perform hyperparameter optimization, and obtain the ground reflectance simulation model. The ground reflectance simulation model is a regression model between the reflectance of the 7 visible-near infrared bands of MOD09A1 data and the reflectance of the unobserved bands of the target.

[0012] Step 5: Input the predicted dataset into the ground reflectance simulation model and output the reflectance data of the target band.

[0013] On the other hand, the present invention provides a ground object reflectance simulation device based on inherent spectral characteristics between bands, comprising:

[0014] The data acquisition module is used to acquire GMTED2010 data corresponding to hyperspectral data and MODIS reflectance product MOD09A1 data in time and space, respectively, based on hyperspectral data and MODIS reflectance product MOD09A1 data.

[0015] The training dataset module is used to preprocess hyperspectral data and GMTED2010 data to create the training dataset;

[0016] The Predictive Dataset module is used to preprocess MODIS reflectivity product MOD09A1 data and GMTED2010 data to create a predictive dataset.

[0017] The simulation model module is used to input the training dataset into the random forest model for training, perform hyperparameter optimization, and obtain a ground reflectance simulation model. The ground reflectance simulation model is a regression model between the reflectance of the seven visible-near infrared bands of the MOD09A1 data and the reflectance of the unobserved bands of the target.

[0018] The reflectance calculation module is used to input the predicted dataset into the ground reflectance simulation model and output the reflectance data of the target band.

[0019] Thirdly, the present invention provides an electronic device, comprising: one or more processors; and 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 aforementioned method for simulating ground reflectance based on inherent spectral characteristics between bands.

[0020] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for simulating ground reflectance based on inherent spectral characteristics between bands.

[0021] The beneficial effects of this invention are as follows:

[0022] Filling the gap in unobserved satellite reflectivity data, a high-precision regression model was established between the seven visible-near-infrared bands of MOD09A1 data and the target band (such as 532nm). This achievement enabled the first effective simulation of unobserved satellite reflectivity, solving the key data gap problem in applications such as lidar.

[0023] The simulation is highly accurate and reliable. Based on the random forest model and the optimization of spectral features between bands, the coefficient of determination R² between the simulation results and the actual observations reaches 0.9686, and the root mean square error (RMSE) is only 0.0200, which is significantly better than traditional statistical methods and meets the requirements of high-precision applications.

[0024] With outstanding spatiotemporal coverage capabilities, relying on the advantages of MODIS data's global coverage and 8-day time resolution, it can produce high-frequency updated reflectance datasets worldwide, breaking through the bottleneck of limited spatiotemporal coverage of airborne data.

[0025] With a high degree of automation and wide applicability, it adopts standardized preprocessing procedures and automated model training, which can quickly adapt to the needs of different regions and bands, providing flexible data support for applications such as surface monitoring and disaster early warning.

[0026] By leveraging multi-source data collaboratively, the innovative integration of the spectral detail advantage of hyperspectral data and the spatiotemporal coverage advantage of MODIS data, along with terrain data-assisted correction, significantly improves the accuracy of reflectivity simulation in complex environments. Attached Figure Description

[0027] Figure 1 This is a schematic diagram illustrating the principle of the ground reflectance simulation method based on the inherent spectral characteristics between bands in this invention.

[0028] Figure 2 This is a flowchart of the ground reflectance simulation method based on the inherent spectral characteristics between bands according to the present invention.

[0029] Figure 3This is a schematic diagram of the random forest model structure;

[0030] Figure 4 Accuracy map of 532nm band reflectance for ground object reflectance model simulation. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0032] like Figure 1 and Figure 2 The diagram shown is a flowchart of the ground cover reflectance simulation method based on inherent spectral characteristics between bands according to the present invention, including:

[0033] Step 1: Using hyperspectral data and MODIS reflectance product MOD09A1 data as a reference, obtain the corresponding GMTED2010 data in time and space.

[0034] The hyperspectral data used in this application was acquired by the AVIRIS airborne hyperspectral sensor and is currently freely available to the public. The download website is https: / / avirisng.jpl.nasa.gov / dataportal / . The data covers a spectrum from 376 to 2500 nm with a spectral resolution of 5 nm, comprising 425 bands. The reflectance corresponding to seven visible-near-infrared bands of the MOD09A1 data was obtained as input for model training, and the reflectance at 532 nm was used as the ground truth for model training. The MOD09A1 data used in this application is an 8-day composite product of surface reflectance collected by the MODIS sensor aboard the Terra satellite, containing seven visible-near-infrared bands (Band 1: 620–670 nm, Band 2: 841–876 nm, Band 3: 459–479 nm, Band 4: 545–565 nm, Band 5: 1230–1250 nm, Band 6: 1628–1652 nm, Band 7: 2105–2155 nm). The reflectance of these seven visible-near-infrared bands was obtained as input for model prediction.

[0035] The GMTED2010 data used in this application is global topographic elevation data from 2010. Complex terrain alters the radiative transfer process of solar radiation energy between the sun, the earth's surface, and the sensor, resulting in different reflectance values ​​for the same ground feature in the same image at different slopes and aspects. Using elevation values ​​as input parameters for ground feature reflectance simulation models allows for more accurate simulation of reflectance. Therefore, this application subsequently performs spatial matching of the GMTED2010 data with hyperspectral data and MOD09A1 data, selecting GMTED2010 data with the same latitude and longitude values ​​as the data to be matched, and using them as input for model training / prediction.

[0036] Step 2: Preprocess the hyperspectral data and GMTED2010 data to create a training dataset.

[0037] The preprocessing operations in this application include hyperspectral data projection transformation, GMTED2010 data resampling, multi-layer integration of hyperspectral data and GMTED2010 data, MODIS multispectral data simulation, and spectral index calculation.

[0038] The hyperspectral data projection conversion includes: The hyperspectral data is projected as UTM_Zone_11N, and the hyperspectral data projection is converted to GCS_WGS_1984 by software. The resampling technology is set to "BILINEAR", and the output pixel size is set to 0.00416 degrees (approximately 500 meters).

[0039] The GMTED2010 data resampling process includes: the spatial resolution of the GMTED2010 data is 250m, and the spatial resolution of the GMTED2010 data is resampled to 0.00416 degrees (approximately 500 meters) using software.

[0040] The multi-layer integration of hyperspectral and GMTED2010 data includes merging the bands of hyperspectral and GMTED2010 data into a new raster dataset, with each integrated image containing 46 bands.

[0041] The MODIS multispectral data simulation includes: MOD09A1 is multispectral data, with reflectance data recorded by the MODIS medium resolution imaging spectrometer across seven broad bands, while the hyperspectral data records reflectance at multiple adjacent wavelengths (intervals of 5 nm). Therefore, it is necessary to convert the hyperspectral reflectance into equivalent reflectance in the seven visible-near-infrared bands of the MOD09A1 data using a spectral response function.

[0042] The formula for converting hyperspectral reflectance to multispectral reflectance is as follows:

[0043] ,

[0044] In the formula, For the calculation of the first Reflectivity of a wide band (7 visible-near-infrared bands in MOD09A1 data), wavelength Reflectivity at that location For MOD09A1 data, the first A wide band at wavelength Spectral response at that location, This represents the lower limit of the wideband spectral range. This represents the upper limit of the wideband spectral range.

[0045] The spectral index calculation includes: calculating the following four spectral features for equivalent reflectance to help the model learn the variation patterns between the reflectance of the seven visible-near-infrared bands of MOD09A1 data and the reflectance of the unobserved satellite bands in different land cover types.

[0046] (a) The Normalized Difference Vegetation Index (NDVI) effectively reflects vegetation growth and coverage, and is helpful in distinguishing vegetation from non-vegetation information such as water bodies, built-up areas, bare land, and snow cover. Its calculation formula is:

[0047] ,

[0048] In the formula, For near-infrared reflectivity, The reflectance is in the infrared band, and Band2 and Band1 are the simulated equivalent reflectances, with center wavelengths of 858nm and 645nm, respectively.

[0049] (b) The Normalized Difference Water Index (NDWI) can be used to quickly, easily, and accurately extract water body information. Its calculation formula is:

[0050] ,

[0051] In the formula, For green band reflectivity, The reflectance is in the near-infrared band, and Band 4 and Band 2 are the simulated equivalent reflectances, with center wavelengths of 555nm and 858nm, respectively.

[0052] (c) The Bare Soil Index (BSI) enhances the brightness of bare soil, effectively distinguishing it from non-bare soil features such as vegetation, water bodies, built-up areas, and snow cover. Its calculation formula is as follows:

[0053] ,

[0054] In the formula, , , , The reflectance values ​​are for the mid-infrared band, infrared band, near-infrared band, and blue band, respectively, corresponding to Band 6, Band 1, Band 2, and Band 3 with simulated equivalent reflectance, and their center wavelengths are 1640nm, 645nm, 858nm, and 469nm, respectively.

[0055] (d) The Normalized Difference Barren Index (NDBI) can accurately reflect information on building land use; a higher NDBI value indicates a higher proportion of building land and higher building density. Its calculation formula is:

[0056] ,

[0057] In the formula, For mid-infrared reflectivity, The reflectance values ​​are for the near-infrared band, and Band 6 and Band 2 are the simulated equivalent reflectance values, with center wavelengths of 1640nm and 858nm, respectively.

[0058] Step 3: Perform preprocessing operations on the MOD09A1 and GMTED2010 data to create a prediction dataset.

[0059] The preprocessing operations in this application include MOD09A1 data preprocessing, GMTED2010 data resampling, multi-layer integration of MOD09A1 and GMTED2010 data, spectral index calculation, and sample preparation.

[0060] MOD09A1 data preprocessing includes: the MOD09A1 data downloaded from NASA is in HDF format, the original projection is "sine curve projection", the MOD09A1 data is converted from HDF format to GeoTIFF format using ENVI software, and the projection is converted to GCS_WGS_1984.

[0061] The GMTED2010 data resampling process includes: the spatial resolution of the GMTED2010 data is 250m, and the spatial resolution of the GMTED2010 data is resampled to 0.00416 degrees (approximately 500 meters) using software.

[0062] Multi-layer integration of MOD09A1 and GMTED2010 data includes merging the bands of MOD09A1 and GMTED2010 data into a new raster dataset, with each integrated image containing 8 bands.

[0063] Spectral index calculations include: normalized vegetation index (NDVI), normalized water index (NDWI), bare soil index (BSI), and normalized building index (NDBI) for reflectance calculations of MOD09A1 data.

[0064] Sample creation includes: The integrated data from multiple layers contains various information, requiring the setting of conditions to filter useful information and data cleaning to ensure data quality. The specific steps are as follows:

[0065] (a) Determining the effective value range;

[0066] MODIS itself is not a dedicated remote sensor for water color. Under the seawater type, the surface reflectance can be negative in all bands. Therefore, when making samples, it is necessary to filter each band of the MOD09A1 data and only keep the samples whose values ​​are within the normal range of surface reflectance (between 0 and 1).

[0067] (b) Determination of cloud cover;

[0068] Due to abundant water vapor supply, plentiful rainfall, and significant vertical climate in some areas, cloud cover exists year-round. However, cloud cover hinders satellite observation of the land surface, preventing the direct provision of accurate land surface reflectance. Therefore, when creating prediction samples, it is necessary to remove cloud-covered pixels from the MOD09A1 data based on its quality control bands.

[0069] Step 4: Construct a random forest model. Use the training dataset as input features to train the random forest model, perform hyperparameter optimization, and obtain a ground reflectance simulation model. The ground reflectance simulation model is a regression model between the reflectance of the seven visible-near infrared bands of MOD09A1 data and the reflectance of the unobserved bands of the target.

[0070] like Figure 3 As shown, the regression model here refers to the linear relationship between the reflectance of the seven visible-near-infrared bands of the MOD09A1 data and the reflectance of the unobserved bands of the target. In the model training phase, the hyperspectral data, after spectral convolution preprocessing, is equivalent to the reflectance of the seven visible-near-infrared bands of the MOD09A1 data, which is used as the input for model training. Then, the reflectance of the hyperspectral data at 532nm is obtained as the ground truth for model training. This model training process can be viewed as learning the linear relationship between the reflectance of the seven visible-near-infrared bands of the MOD09A1 data and the 532nm reflectance. In the prediction phase, the reflectance of the seven visible-near-infrared bands of the MOD09A1 data is input, and the 532nm reflectance can be calculated based on this linear relationship.

[0071] During model building, some parameters need to be set in advance. Hyperparameters are parameters that are pre-defined before model training begins, and their proper configuration plays a crucial role in the model's performance. Grid search is one of the most widely used hyperparameter optimization methods. This method predefines a list of candidate hyperparameter values ​​and performs a comprehensive exhaustive search of all possible parameter combinations. Then, cross-validation is used to evaluate the performance of each parameter combination, and finally, the optimal parameter combination is selected as the model's hyperparameters.

[0072] This application constructs a random forest model, using the training dataset as input. The hyperparameters are progressively tuned using a grid search method, and the optimal hyperparameter combination is found through five-fold cross-validation. The random forest model is then retrained using this optimal hyperparameter combination to establish the mapping relationship between bands. The model performance is evaluated on the validation set to ensure that the accuracy requirements are met. The hyperparameters of the random forest model are the number of decision trees (n_estimators), the maximum depth of the decision trees (max_depth), the minimum number of samples that a node can separate (min_samples_spli), and the minimum number of samples required for a leaf node (min_samples_leaf). The search range and optimal hyperparameters of the random forest model are shown in Table 1.

[0073] Table 1

[0074]

[0075] Step 5: Input the predicted dataset into the ground object reflectance simulation model, simulate the reflectance of the unobserved satellite bands, and output the reflectance data of the target bands.

[0076] A certain region was selected, and the reflectivity of the 532nm band was simulated based on a pre-trained ground reflectivity simulation model.

[0077] When simulating the reflectivity in the 532nm band, the reflectivity of the MODIS fourth band (wavelength range of 545–565nm), which is closest to the 532nm band, was used as the true value. Finally, the coefficient of determination R was calculated by comparing the reflectivity obtained from the model simulation with the reflectivity observed by the MODIS satellite. 2 Root mean square error (RMSE) and mean absolute error (MAE), such as Figure 4 As shown in the figure. The horizontal axis of each graph represents the reflectance observed by the MODIS satellite, and the vertical axis represents the reflectance simulated by the model. The scatter points in the graph represent data points. In the scale axis on the right side of the graph, the black at the bottom indicates a lower scatter point density, and the gray-white in the middle indicates a higher scatter point density. A 1:1 standard line (dashed line) and a fitted line (solid line) are also plotted in the graph to evaluate the accuracy of the model simulation. Most data points in the graph are distributed around the 1:1 standard line (dashed line), indicating that the model has high accuracy in predicting the 532nm reflectance.

[0078] In summary, this invention effectively solves the problem of missing reflectance data in unobserved satellite bands through innovative multi-source data fusion strategies and machine learning modeling methods, providing reliable technical support for remote sensing quantitative inversion and lidar applications. Experimental data fully verify the high accuracy, high stability, and good engineering applicability of this method.

[0079] On the other hand, the present invention provides a ground reflectance simulation device based on the inherent spectral characteristics between bands, the various modules of which can implement the various steps of the aforementioned method, specifically including:

[0080] The data acquisition module is used to acquire GMTED2010 data corresponding to hyperspectral data and MODIS reflectance product MOD09A1 data in time and space, respectively, based on hyperspectral data and MODIS reflectance product MOD09A1 data.

[0081] The training dataset module is used to preprocess hyperspectral data and GMTED2010 data to create the training dataset;

[0082] The Predictive Dataset module is used to preprocess MODIS reflectivity product MOD09A1 data and GMTED2010 data to create a predictive dataset.

[0083] The simulation model module is used to input the training dataset into the random forest model for training, perform hyperparameter optimization, and obtain a ground reflectance simulation model. The ground reflectance simulation model is a regression model between the reflectance of the seven visible-near infrared bands of the MOD09A1 data and the reflectance of the unobserved bands of the target.

[0084] The reflectance calculation module is used to input the predicted dataset into the ground reflectance simulation model and output the reflectance data of the target band.

[0085] Thirdly, the present invention provides an electronic device, comprising: one or more processors; and 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 aforementioned method for simulating ground reflectance based on inherent spectral characteristics between bands.

[0086] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for simulating ground reflectance based on inherent spectral characteristics between bands.

[0087] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simulating the reflectance of ground objects based on inherent spectral characteristics between bands, characterized in that, The method includes: Step 1: Using hyperspectral data and MODIS reflectance product MOD09A1 data as a reference, obtain the corresponding GMTED2010 data in time and space. Step 2: Preprocess the hyperspectral data and GMTED2010 data to create a training dataset; Step 3: Perform preprocessing operations on MODIS reflectivity product MOD09A1 data and GMTED2010 data to create a prediction dataset; Step 4: Input the training dataset into the random forest model for training, perform hyperparameter optimization, and obtain the ground reflectance simulation model. The ground reflectance simulation model is a regression model between the reflectance of the 7 visible-near infrared bands of MOD09A1 data and the reflectance of the unobserved bands of the target. Step 5: Input the predicted dataset into the ground reflectance simulation model and output the reflectance data of the target band.

2. The method for simulating ground reflectance based on inherent spectral characteristics between bands according to claim 1, characterized in that, In step 1, the hyperspectral data has a spectral coverage range of 376–2500 nm and a spectral resolution of 5 nm; the MOD09A1 data contains 7 visible-near-infrared bands; and the GMTED2010 data is topographic elevation data.

3. The method for simulating ground reflectance based on inherent spectral characteristics between bands according to claim 1, characterized in that, In step 2, the preprocessing operations include hyperspectral data projection conversion, GMTED2010 data resampling, multi-layer integration of hyperspectral data and GMTED2010 data, MODIS multispectral data simulation, and spectral index calculation.

4. The method for simulating ground reflectance based on inherent spectral characteristics between bands according to claim 3, characterized in that, The spectral index calculation includes the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), and Normalized Building Index (NDBI).

5. The method for simulating ground reflectance based on inherent spectral characteristics between bands according to claim 1, characterized in that, In step 3, the preprocessing operations include MOD09A1 data preprocessing, GMTED2010 data resampling, multi-layer integration of MOD09A1 and GMTED2010 data, spectral index calculation, and sample preparation.

6. The method for simulating ground reflectance based on inherent spectral characteristics between bands according to claim 1, characterized in that, Step 4 includes: Initialize the random forest regression model and define the set of hyperparameters to be optimized; Configure the parameter space range for grid search, including the number of decision trees and the maximum depth; A grid search optimization strategy using five-fold cross-validation is employed to determine the optimal hyperparameter combination. The random forest model is retrained using the optimal hyperparameter combination to establish the mapping relationship between bands; Evaluate the model performance on the validation set to ensure that the accuracy requirements are met.

7. The method for simulating ground reflectance based on inherent spectral characteristics between bands according to claim 5, characterized in that, The sample preparation includes screening each band of the MOD09A1 data, retaining only samples whose surface reflectance values ​​are between 0 and 1; and removing cloud-covered pixels from the MOD09A1 data based on the quality control bands of the MOD09A1 data.

8. A ground cover reflectance simulation device based on inherent spectral characteristics between bands, characterized in that, include: The data acquisition module is used to acquire GMTED2010 data corresponding to hyperspectral data and MODIS reflectance product MOD09A1 data in time and space, respectively, based on hyperspectral data and MODIS reflectance product MOD09A1 data. The training dataset module is used to preprocess hyperspectral data and GMTED2010 data to create the training dataset; The Predictive Dataset module is used to preprocess MODIS reflectance product MOD09A1 data and GMTED2010 data to create a predictive dataset. The simulation model module is used to input the training dataset into the random forest model for training, perform hyperparameter optimization, and obtain a ground reflectance simulation model. The ground reflectance simulation model is a regression model between the reflectance of the seven visible-near infrared bands of the MOD09A1 data and the reflectance of the unobserved bands of the target. The reflectance calculation module is used to input the predicted dataset into the ground reflectance simulation model and output the reflectance data of the target band.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the ground reflectance simulation method based on the inherent spectral characteristics between bands as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the ground reflectance simulation method based on the inherent spectral characteristics between bands as described in any one of claims 1-7.

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